ALBERT

Description: The time synchronization protocol is indispensable in various applications of wireless sensor networks, such as scheduling, monitoring, and tracking. Numerous protocols and algorithms have been proposed in recent decades, and many of them provide micro-scale resolutions. However, designing and implementing a time synchronization protocol in a practical wireless network is very challenging compared to implementation in a wired network; this is because its performance can be deteriorated significantly by many factors, including hardware quality, message delay jitter, ambient environment, and network topology. In this study, we measure the performance of the Flooding Time Synchronization Protocol (FTSP) and Gradient Time Synchronization Protocol (GTSP) in terms of practical network conditions, such as message delay jitter, synchronization period, network topology, and packet loss. This study provides insights into the operation and optimization of time synchronization protocols. In addition, the performance evaluation identifies that FTSP is highly affected by message delay jitter due to error accumulation over multi-hops. We demonstrate that the proposed extended version of the FTSP (E-FTSP) alleviates the effect of message delay jitter and enhances the overall performance of FTSP in terms of error, time, and other factors.

Description: The Internet of Things (IoT) will feature pervasive sensing and control capabilities via the massive deployment of machine-type communication devices in order to greatly improve daily life. However, machine-type communications can be illegally used (e.g., by criminals or terrorists) which is difficult to monitor, and thus presents new security challenges. The information exchanged in machine-type communications is usually transmitted in short packets. Thus, this paper investigates a legitimate surveillance system via proactive eavesdropping at finite blocklength regime. Under the finite blocklength regime, we analyze the channel coding rate of the eavesdropping link and the suspicious link. We find that the legitimate monitor can still eavesdrop the information sent by the suspicious transmitter as the blocklength decreases, even when the eavesdropping is failed under the Shannon capacity regime. Moreover, we define a metric called the effective eavesdropping rate and study the monotonicity. From the analysis of monotonicity, the existence of a maximum effective eavesdropping rate for a moderate or even high signal-to-noise (SNR) is verified. Finally, numerical results are provided and discussed. In the simulation, we also find that the maximum effective eavesdropping rate slowly increases with the blocklength.

Description: GNSS receiver data crowdsourcing is of interest for multiple applications, e.g., weather monitoring. The bottleneck in this technology is the quality of the GNSS receivers. Therefore, we lay out in an introductory manner the steps to estimate the performance of an arbitrary GNSS receiver via the measurement errors related to its instrumentation. Specifically, we do not need to know the position of the receiver antenna, which allows also for the assessment of smartphone GNSS receivers having integrated antennas. Moreover, the method is independent of atmospheric errors so that no ionospheric or tropospheric correction services provided by base stations are needed. Error models for performance evaluation can be calculated from receiver RINEX (receiver independent exchange format)data using only ephemeris corrections. For the results, we present the quality of different receiver grades through parametrized error models that are likely to be helpful in stochastic modeling, e.g., for Kalman filters, and in assessing GNSS receiver qualities for crowdsourcing applications. Currently, the typical positioning precision for the latest smartphone receivers is around the decimeter level, while for a professional-grade receiver, it is within a few millimeters.

Description: The power transmission lines are the link between power plants and the points of consumption, through substations. Most importantly, the assessment of damaged aerial power lines and rusted conductors is of extreme importance for public safety; hence, power lines and associated components must be periodically inspected to ensure a continuous supply and to identify any fault and defect. To achieve these objectives, recently, Unmanned Aerial Vehicles (UAVs) have been widely used; in fact, they provide a safe way to bring sensors close to the power transmission lines and their associated components without halting the equipment during the inspection, and reducing operational cost and risk. In this work, a drone, equipped with multi-modal sensors, captures images in the visible and infrared domain and transmits them to the ground station. We used state-of-the-art computer vision methods to highlight expected faults (i.e., hot spots) or damaged components of the electrical infrastructure (i.e., damaged insulators). Infrared imaging, which is invariant to large scale and illumination changes in the real operating environment, supported the identification of faults in power transmission lines; while a neural network is adapted and trained to detect and classify insulators from an optical video stream. We demonstrate our approach on data captured by a drone in Parma, Italy.

Description: The number of connected sensors and devices is expected to increase to billions in the near future. However, centralised cloud-computing data centres present various challenges to meet the requirements inherent to Internet of Things (IoT) workloads, such as low latency, high throughput and bandwidth constraints. Edge computing is becoming the standard computing paradigm for latency-sensitive real-time IoT workloads, since it addresses the aforementioned limitations related to centralised cloud-computing models. Such a paradigm relies on bringing computation close to the source of data, which presents serious operational challenges for large-scale cloud-computing providers. In this work, we present an architecture composed of low-cost Single-Board-Computer clusters near to data sources, and centralised cloud-computing data centres. The proposed cost-efficient model may be employed as an alternative to fog computing to meet real-time IoT workload requirements while keeping scalability. We include an extensive empirical analysis to assess the suitability of single-board-computer clusters as cost-effective edge-computing micro data centres. Additionally, we compare the proposed architecture with traditional cloudlet and cloud architectures, and evaluate them through extensive simulation. We finally show that acquisition costs can be drastically reduced while keeping performance levels in data-intensive IoT use cases.

Description: One of the most significant challenges in Internet of Things (IoT) environments is the protection of privacy. Failing to guarantee the privacy of sensitive data collected and shared over IoT infrastructures is a critical barrier that delays the wide penetration of IoT technologies in several user-centric application domains. Location information is the most common dynamic information monitored and lies among the most sensitive ones from a privacy perspective. This article introduces a novel mechanism that aims to protect the privacy of location information across Data Centric Sensor Networks (DCSNs) that monitor the location of mobile objects in IoT systems. The respective data dissemination protocols proposed enhance the security of DCSNs rendering them less vulnerable to intruders interested in obtaining the location information monitored. In this respect, a dynamic clustering algorithm is that clusters the DCSN nodes not only based on the network topology, but also considering the current location of the objects monitored. The proposed techniques do not focus on the prevention of attacks, but on enhancing the privacy of sensitive location information once IoT nodes have been compromised. They have been extensively assessed via series of experiments conducted over the IoT infrastructure of FIT IoT-LAB and the respective evaluation results indicate that the dynamic clustering algorithm proposed significantly outperforms existing solutions focusing on enhancing the privacy of location information in IoT.

Description: There is strong demand for real-time suspicious tracking across multiple cameras in intelligent video surveillance for public areas, such as universities, airports and factories. Most criminal events show that the nature of suspicious behavior are carried out by un-known people who try to hide themselves as much as possible. Previous learning-based studies collected a large volume data set to train a learning model to detect humans across multiple cameras but failed to recognize newcomers. There are also several feature-based studies aimed to identify humans within-camera tracking. It would be very difficult for those methods to get necessary feature information in multi-camera scenarios and scenes. It is the purpose of this study to design and implement a suspicious tracking mechanism across multiple cameras based on correlation filters, called suspicious tracking across multiple cameras based on correlation filters (STAM-CCF). By leveraging the geographical information of cameras and YOLO object detection framework, STAM-CCF adjusts human identification and prevents errors caused by information loss in case of object occlusion and overlapping for within-camera tracking cases. STAM-CCF also introduces a camera correlation model and a two-stage gait recognition strategy to deal with problems of re-identification across multiple cameras. Experimental results show that the proposed method performs well with highly acceptable accuracy. The evidences also show that the proposed STAM-CCF method can continuously recognize suspicious behavior within-camera tracking and re-identify it successfully across multiple cameras.

Description: The paper proposes a sensors platform to control a barrier that is installed for vehicles entrance. This platform is automatized by image-based license plate recognition of the vehicle. However, in situations where standardized license plates are not used, such image-based recognition becomes non-trivial and challenging due to the variations in license plate background, fonts and deformations. The proposed method first detects the approaching vehicle via ultrasonic sensors and, at the same time, captures its image via a camera installed along with the barrier. From this image, the license plate is automatically extracted and further processed to segment the license plate characters. Finally, these characters are recognized with the help of a standard optical character recognition (OCR) pipeline. The evaluation of the proposed system shows an accuracy of 98% for license plates extraction, 96% for character segmentation and 93% for character recognition.

Description: Wireless sensor networks are widely used in many fields. Nodes in the network are typically powered by batteries. Because the energy consumption of wireless communication is related to the transmission distance, the energy consumption of nodes in different locations is different, resulting in uneven energy distribution of nodes. In some special applications, all nodes are required to work at the same time, and the uneven energy distribution makes the effective working time of the system subject to the node with the largest energy consumption. The commonly used clustering protocol can play a role in balancing energy consumption, but it does not achieve optimal energy consumption. This paper proposes to use the power supply line to connect the nodes to fully balance the energy. The connection scheme with the shortest power line length is also proposed. On the basis of energy balance, the method of transmitting data with the best hop count is proposed, which fully reduces the power consumption of the data transmission. The simulation results show that the proposed method can effectively reduce the energy consumption and prolong the lifetime of wireless sensor networks.

Description: High frequency guided-waves offer a trade-off between the high sensitivity of local bulk ultrasonic thickness measurements and the large area scanning of lower frequency guided-waves, so it has been a growing interest for corrosion inspection with the dispersive SH1 mode. However, according to the dispersive curve, it is hard to generate the pure SH1 mode since the non-dispersive SH0 mode will be excited simultaneously. Thus, this paper investigates a transducer design method to generate a pure SH1 guided-wave, where the dual periodic-permanent-magnet electromagnetic acoustic transducers (PPM EMATs) are placed on exactly opposite positions either side of the plate symmetrically. The suppression effect for SH0 and the enhancement effect for SH1 of the dual PPM EMATs are mainly discussed by theoretical analysis and simulation analysis, and the influence of positioning errors of PPM EMATs placed on opposite sides of the plate on its performances are analyzed. Employing the proposed dual PPM EMATs, some experiments are performed to verify the reliability of finite element simulation. The results indicate that the dual PPM EMATs can suppress the SH0 mode and generate the pure SH1 mode effectively. Moreover, the longitudinal and lateral positioning errors can affect the dual PPM EMATs performances significantly.

Description: In this paper, we report a wireless gas sensor based on surface acoustic waves (SAW). For room temperature detection of oxygen gas, a novel nanostructured ZnxFeyO gas-sensitive film was deposited on the surface of a SAW resonator by an oblique magnetron co-sputtering method. The measurements of X-ray diffraction (XRD) and a scanning electron microscope (SEM) showed that the crystal phase composition and the microstructures of ZnxFeyO films were significantly affected by the content of Fe. The experimental results showed that the sensors had a good response to O2 at room temperature. The max frequency shift of the sensors reached 258 kHz as the O2 partial pressure was 20%. Moreover, X-ray photoelectron spectroscopy (XPS) was performed to analyze the role of Fe in the sensitization process of the ZnxFeyO film. In addition, the internal relationship between the Fe content of the film and the sensitivity of the sensor was presented and discussed. The research indicates that the nanostructured ZnxFeyO film has a good potential for room temperature O2 gas detection applications.

Description: According to the survey on various health centres, smart log-based multi access physical monitoring system determines the health conditions of humans and their associated problems present in their lifestyle. At present, deficiency in significant nutrients leads to deterioration of organs, which creates various health problems, particularly for infants, children, and adults. Due to the importance of a multi access physical monitoring system, children and adolescents’ physical activities should be continuously monitored for eliminating difficulties in their life using a smart environment system. Nowadays, in real-time necessity on multi access physical monitoring systems, information requirements and the effective diagnosis of health condition is the challenging task in practice. In this research, wearable smart-log patch with Internet of Things (IoT) sensors has been designed and developed with multimedia technology. Further, the data computation in that smart-log patch has been analysed using edge computing on Bayesian deep learning network (EC-BDLN), which helps to infer and identify various physical data collected from the humans in an accurate manner to monitor their physical activities. Then, the efficiency of this wearable IoT system with multimedia technology is evaluated using experimental results and discussed in terms of accuracy, efficiency, mean residual error, delay, and less energy consumption. This state-of-the-art smart-log patch is considered as one of evolutionary research in health checking of multi access physical monitoring systems with multimedia technology.

Description: Smart grids incorporating internet-of-things are emerging solutions to provide a reliable, sustainable and efficient electricity supply, and electric vehicle drivers can access efficient charging services in the smart grid. However, traditional electric vehicle charging systems are vulnerable to distributed denial of service and privileged insider attacks when the central charging server is attacked. The blockchain-based charging systems have been proposed to resolve these problems. In 2018, Huang et al. proposed the electric vehicle charging system using lightning network and smart contract. However, their system has an inefficient charging mechanism and does not guarantee security of key. We propose a secure charging system for electric vehicles based on blockchain to resolve these security flaws. Our charging system ensures the security of key, secure mutual authentication, anonymity, and perfect forward secrecy, and also provides efficient charging. We demonstrate that our proposed system provides secure mutual authentication using Burrows–Abadi–Needham logic and prevents replay and man-in-the-middle attacks using automated validation of internet security protocols and applications simulation tool. Furthermore, we compare computation and communication costs with previous schemes. Therefore, the proposed charging system efficiently applies to practical charging systems for electric vehicles.

Description: Composite indicators are becoming more relevant for evaluating the performance of water companies from a holistic perspective. Some of them are related with economic aspects, and others focus on social and environmental features. Consequently, a multidimensional evaluation is necessary for handling the great amount of information provided by multiple single indicators of a different nature. This paper presents a two-phase approach to evaluate the sustainability of water companies. First, a partial composite indicator for each dimension (social, environmental, economic) is obtained using multi-criteria decision making (MCDM). Then, a global indicator is obtained, in terms of the values reached in the previous stage for every partial indicator, by means an optimization problem rooted in data envelopment analysis (DEA). Our proposal offers the possibility of analyzing the performance of each water company under each dimension that characterizes the concept of sustainability, as well as a joint assessment including all the dimensions, facilitating the decision-making process. We apply it to evaluate the sustainability of 163 Portuguese water companies. The results show the strengths and weaknesses of each unit and serve as a guideline to decision-makers on the aspects for improving the performance of water utilities.

Description: This study investigates the preferences of Italian home-owners when choosing a new domestic heating system. The focus is on understanding the influence on consumer choice of a potential label certifying the effect of the heating system on the greenhouse effect. To this end, we designed a survey including a discrete choice experiment and administered it to residents in north-eastern Italy. Our findings reveal that, on average, respondents pay particular attention to the green effect of their purchase. The carbon dioxide reduction label was considered second in terms of importance after cost. Further analysis found that our sample presents three clusters of customers, with intra-cluster homogeneous preferences. The cluster analysis showed that while the initial system costs are considered to varying degrees by the whole sample, the carbon dioxide reduction label was considered important by 79% of respondents (members of clusters 1 and 2). To achieve greater results in reducing the greenhouse effect of the domestic heating sector, a combination of policies should be used simultaneously to achieve greater effectiveness. Our simulations support the hypothesis that policymakers should achieve greater results in terms of reducing the domestic greenhouse gas emissions by applying a combined policy that leverages the importance citizens accord to the different characteristics of a heating system. From our results, the application of a ‘low carbon dioxide ( C O 2 ) emissions’ label will amplify the effect of a subsidy that reduces the initial system costs.

Description: Underwater deployed sensors nodes are energy-constrained. Therefore, energy efficiency becomes crucial in underwater wireless sensor networks (U-WSNs). The adverse channel corrupts the packets and challenges their reliability. To handle these challenges, two routing schemes are introduced in this paper. They are effective energy and reliable delivery (EERD) and cooperative effective energy and reliable delivery (CoEERD). In EERD, the packets follow single-path routing and the best forwarder node is selected using a weight function such that packets are transferred via the reliable paths with low energy usage. Packet transfer via a single route in EERD has, however, compromised reliability as the undersea links bear harshness and unpredictability. Therefore, the CoEERD scheme adds cooperative routing to EERD, in which a relay node is introduced between a source–destination pair. The destination requests the relay when the packets it gets from the source are corrupted beyond a threshold value. Selection of weight function is unique and considers many factors to ensure low energy usage with reliability while considering nodes for data transfer. This also helps in selecting a single relay node rather than many relays in the conventional cooperative routing model. Based on simulation results, the EERD and CoEERD protocols have improved performance in energy usage, reliable packet transfer and delay.

Description: This paper presents a numerical investigation of thermal response of mortar panels, incorporating macro-encapsulated paraffin in different forms. Two types of macro capsules were fabricated and tested in this study using an instrumented hot plate device. The experimental results show that macro encapsulated paraffin reduced the temperature and increased time lag in the mortar panels due to the latent heat capacity of paraffin. Finite element models adopting the effective heat capacity method to model phase change effects were able to capture the overall thermal response of panels incorporated with paraffin well. Then, a parametric study was conducted using the validated finite element (FE) modelling technique to investigate the effects of different forms of macro capsules, the quantity of paraffin and the position of macro capsules. It was found that the tube and sphere macro capsules showed similar thermal responses, while the plate shaped capsules may cause a non-uniform temperature distribution in mortar panels. The quantity and position of paraffin have significant effects on the thermal response of the mortal panels. A higher paraffin content results in a significantly longer temperature lag and a lower temperature during the phase transition of paraffin. Furthermore, placing the paraffin away from the heating face can cause a longer temperature lag on the other face, which is desirable for building façade applications.

Description: Single room ventilation units with heat recovery is one of the ventilation solutions that have been used in renovated residential buildings in Estonia. In multi-story buildings, especially in a cold climate, the performance of units is affected by the stack effect and wind-induced pressure differences between the indoor and the outdoor air. Renovation of the building envelope improves air tightness and the impact of the pressure conditions is amplified. The aim of this study was to predict the air pressure conditions in typical renovated multi-story apartment buildings and to analyze the performance of room-based ventilation units. The field measurements of air pressure differences in a renovated 5-story apartment building during the winter season were conducted and the results were used to simulate whole-year pressure conditions with IDA-ICE software. Performance of two types of single room ventilation units were measured in the laboratory and their suitability as ventilation renovation solutions was assessed with simulations. The results show that one unit stopped its operation as a heat recovery ventilator. In order to ensure satisfactory indoor climate and heat recovery using wall mounted units the pressure difference values were determined and proposed for correct design.

Description: Traditional plug-flow anaerobic reactors (PFRs) are characterized by lacking a mixing system and operating at high total solid concentrations, which limits their applicability for several kinds of manures. This paper studies the performance of a novel modified PFR for the treatment of pig manure, characterized by having an internal sludge mixing system by biogas recirculation in the range of 0.270–0.336 m3 m−3 h−1. The influence on the methane yield of four operating parameters (recirculation rate, hydraulic retention time, organic loading rate, and total solids) was evaluated by running four modified PFRs at the pilot scale in mesophilic conditions. While the previous biodegradability of organic matter by biochemical methane potential tests were between 31% and 47% with a methane yield between 125 and 184 LCH4 kgVS−1, the PFRs showed a suitable performance with organic matter degradation between 25% and 51% and a methane yield of up to 374 LCH4 kgVS−1. Operational problems such as solid stratification, foaming, or scum generation were avoided.

Description: In the eddy current braking system of high-speed maglev, the peak braking force and the critical speed are key factors determining the performance of eddy current braking force. In this paper, the analytical formula of eddy current braking force is derived by a subdomain method considering the skin effect of the induction plate, and, subsequently, the characteristics of peak braking force and critical speed are analyzed. The analytical model is set up in a 2D Cartesian coordinate system. The Poisson equations in each subdomain are listed by treating the vector magnetic potential as a variable. By combining the boundary conditions between two adjacent subdomains, the expressions of eddy current density and magnetic density in the induction plate are obtained. Then, the analytical formula of the eddy current braking force is obtained by the Ampere force formula. The results of finite-element analysis confirm the validity of the analytical calculation. The methods of improving the performance of eddy current braking force under high speed are proposed by parametric analysis of peak braking force and critical speed, which provides guidance for the design of the eddy current braking system in high-speed maglev.

Description: This paper proposes an input voltage disturbance suppression control strategy for the unidirectional matrix converter (UMC) with a new modulation scheme enhancing the stability. In the new scheme, the modulation index is directly, rather than reversely, proportional to the instantaneous amplitude of input filter capacitor voltages. Contrary to traditional schemes, the stability of the UMC with this new scheme is even better with the increase of the transferred active power, which is particularly suitable for applications with sinusoidal and balanced input conditions. As to the disturbed input conditions, the new scheme could introduce low-frequency harmonics into output currents. To address this issue, a feedback control strategy of output current amplitude is further proposed to eliminate the additional harmonics. Stability analysis of a UMC with the proposed modulation scheme and feedback control strategy is presented. Experimental results have verified the validity of the proposed control solution.

Description: The interest in pellets utilization for households heating has been growing significantly in the last several years. However, the pellets need to meet certain quality requirements, including the mechanical durability (DU) index. In the winter seasons, the pellets are very often stored in unheated in-door systems or are transported by trucks over long distances. As a result, the pellets are exposed to external weather factors, including very low temperatures (even freezing ones), which can have a negative impact on the quality parameters of the fuel. There are several parameters affecting mechanical durability, but little is known about the influence of a very low temperature on the pellet properties. The aim of this research was to analyze the influence of freezing temperature storage on the mechanical durability of commercial pellets made of different biomass. The research was carried out in accordance with the international standard for solid biofuels PN-EN ISO 17831-1:2016-02. The samples were investigated under three different conditions: after normal storage conditions (20 °C), after frozen storage conditions (−28 °C) and after the defrosting of the pellets. The results revealed that the freezing process and subsequent defrosting of the pellets only causes a small drop in their mechanical durability in comparison to the normal storage conditions. The highest mechanical durability was established for digestate pellet and pine sawdust pellet, at 99.0 ± 0.1% and 98.7 ± 0.1% respectively (p 〈 0.05). The greatest change of mechanical durability was observed after the defrosting process of pellets, which in the initial stage and at the normal storage temperature were characterized by low mechanical durability. The pellets made of sunflower husk (DU = 87.4%) and coal/straw blend (DU = 96.2%) were distinguished by the highest change in their mechanical durability (ΔDU = 1.7%, p 〈 0.05). Based on the obtained results, it was concluded that the storage of pellets at freezing temperature does not significantly affect their mechanical durability. However, if the mechanical durability decreases, this result is related to pellets with low initial mechanical durability.

Description: The present study scrutinized the impacts of terrain-induced turbulence on wind turbine blades, examining measurement data regarding wind conditions and the strains of wind turbine blades. Furthermore, we performed a high-resolution large-eddy simulation (LES) and identified the three-dimensional airflow structures of terrain-induced turbulence. Based on the LES results, we defined the Uchida-Kawashima Scale_1 (the U-K scale_1), which is a turbulence evaluation index, and clarified the existence of the terrain-induced turbulence quantitatively. The threshold value of the U-K scale_1 was determined as 0.2, and this index was confirmed to not be dependent on the inflow profile, the influence of the horizontal grid resolution, and the influence of the computed azimuth. In addition, we defined the Uchida-Kawashima Scale_2 (the U-K scale_2), which is a fatigue damage evaluation index based on the measurement data and the design value obtained by DNV GL’s Bladed. DNV GL (Det Norske Veritas Germanischer Lloyed) is a third party certification body in Norway, and Bladed has been the industry standard aero-elastic wind turbine modeling software. Using the U-K scale_2, the following results were revealed: the U-K scale_2 was 0.86 〈 1.0 (within the designed value) in the case of northerly wind, and the U-K scale_2 was 1.60 〉 1.0 (exceeding the designed value) in the case of easterly wind. As a result, it was revealed that the blades of the target wind turbine were directly and strongly affected by terrain-induced turbulence when easterly winds occurred.

Description: This paper proposes a harmonic compensation control with disturbance rejection function for a standalone inverter. Due to the LC type three-phase three-leg inverter is connected to nonlinear loads, low-order harmonic components appears in the inverter output current. These harmonic current generate harmonic voltage drops when flowing through the filter inductor and the feeder impedance, which causes the output voltage of the inverter distorted. In order to compensate harmonics and produce sinusoidal voltage without additional compensation devices, virtual harmonic impedance method can be added to the fundamental voltage control. Due to the compensation effect of virtual harmonic impedance are very sensitive to the fluctuation of filter inductance. Therefore, inductance variation, as a disturbance in physical system, should be considered. In this paper, linear active disturbance rejection control (LADRC) is proposed in the fundamental voltage control loop to reduce the sensitivity of virtual harmonic impedance and decouple the model. Compared with traditional dual-loop PI control, the proposed strategy has faster dynamic response in control performance and fewer acquisition modules in engineering applications. The whole design process of virtual harmonic impedance and stability analyses of this strategy are provided. The simulation and experiment results show the good performance of the proposed strategy.

Description: This work explores the possibility to adopt in organic Rankine cycle (ORC) plants mixtures of water (acting as solvent) plus an organic compound (acting as solute) as the working fluid. Initially an evaluation of the thermodynamic properties of the mixtures is performed, in order to assess their properties, and to point out the molar fractions which entail a near-azeotropic behaviour. Four species from three different classes of chemical compounds are investigated: 2,2,2-trifluoroethanol and n-butanol for alcohols, where the first is fluorinated, acetonitrile for nitrile class and 2-methylpyrazine as a heterocyclic aromatic compound. Simultaneously, the thermal stability of the pure substances considered as the possible solute for the mixtures is experimentally investigated in order to estimate the temperature applicability range. The ORC plant performance, from a low-enthalpy geothermal heat source (hot water stream from 100 to 200 °C), adopting the selected mixtures as the working fluid is finally evaluated, and the analysis includes a preliminary discussion on the turbine design; results are compared with respect to the reference case of a hypothetical plant adopting water as the working fluid.

Description: In this paper, the optimal site and size selection of wind turbines (WTs) is presented considering the maximum allowable capacity constraint with the objective of loss reduction and voltage profile improvement of distribution grids based on particle swarm optimization (PSO as a multi-objective problem using weighted coefficients method. The optimal site, size, and power factor of the WTs are determined using PSO. The proposed method is implemented on 84- and 32-bus standard grids. In this study, PSO algorithm is applied to determine the size, site, and power factor of WTs considering their maximum size constraint (with constraint, variant size) and also not considering their maximum size constraint (without constraint, constant size). The simulation results showed that the PSO is effective to find the site, size, and power factor of WTs optimally in the single and multi-objective problem. The results of this method showed that the power loss is reduced more and voltage profile improved more considering WTs maximum allowable size versus not considering this constraint. Additionally, the multi-objective results showed that there is a compromise between the objectives in the multi-objective WTs site selection and the multi-objective problem solution is a more realistic and accurate approach in comparison with the single-objective problem solution.

Description: Aiming to maximize the transmission efficiency of inductively coupled power transmission (ICPT) system with the designed output power, a frequency locking method for an ICPT system based on LCC/S compensation topology is proposed in this paper. Firstly, the relationship between compensation component Lf1 and output power was deduced by the lossless model, and the initial value of Lf1 was obtained. Then, considering the system loss, the designed output power and frequency were input into the frequency locking program, and Lf1 and other compensation parameters were dynamically tracked. At the same time, the transmission efficiency of the system was calculated, and the frequency that achieved maximum efficiency was automatically locked when the system met the requirements of the designed output power. Finally, based on the method, the output characteristics of the system were verified by experiments.

Description: Wind Energy Conversion System (WECS) plays an inevitable role across the world. WECS consist of many components and equipment’s such as turbines, hub assembly, yaw mechanism, electrical machines; power electronics based power conditioning units, protection devices, rotor, blades, main shaft, gear-box, mainframe, transmission systems and etc. These machinery and devices technologies have been developed on gradually and steadily. The electrical machine used to convert mechanical rotational energy into electrical energy is the core of any WECS. Many electrical machines (generator) has been used in WECS, among the generators the Permanent Magnet Synchronous Generators (PMSGs) have gained special focus, been connected with wind farms to become the most desirable due to its enhanced efficiency in power conversion from wind energy turbine. This article provides a review of literatures and highlights the updates, progresses, and revolutionary trends observed in WECS-based PMSGs. The study also compares the geared and direct-driven conversion systems. Further, the classifications of electrical machines that are utilized in WECS are also discussed. The literature review covers the analysis of design aspects by taking various topologies of PMSGs into consideration. In the final sections, the PMSGs are reviewed and compared for further investigations. This review article predominantly emphasizes the conceptual framework that shed insights on the research challenges present in conducting the proposed works such as analysis, suitability, design, and control of PMSGs for WECS.

Description: Calcium looping is a promising technology to capture CO2 from the process of coal-fired power generation and gasification of coal/biomass for hydrogen production. The decay of CO2 capture activities of calcium-based sorbents is one of the main problems holding back the development of the technology. Taking carbide slag as a main raw material and Ca12Al14O33 as a support, highly active CO2 sorbents were prepared using the hydrothermal template method in this work. The effects of support ratio, cycle number, and reaction conditions were evaluated. The results show that Ca12Al14O33 generated effectively improves the cyclic stability of CO2 capture by synthetic sorbents. When the Al2O3 addition is 5%, or the Ca12Al14O33 content is 10%, the synthetic sorbent possesses the highest cyclic CO2 capture performance. Under harsh calcination conditions, the CO2 capture capacity of the synthetic sorbent after 30 cycles is 0.29 g/g, which is 80% higher than that of carbide slag. The superiority of the synthetic sorbent on the CO2 capture kinetics mainly reflects at the diffusion-controlled stage. The cumulative pore volume of the synthetic sorbent within the range of 10–100 nm is 2.4 times as high as that of calcined carbide slag. The structure of the synthetic sorbent reduces the CO2 diffusion resistance, and thus leads to better CO2 capture performance and reaction rate.

Description: Renewable energy sources can help the countries to achieve some of the Sustainable Development Goals (SDGs) provided from the recent 2030 Agenda, allowing for clean, secure, reliable and affordable energy. Biomass technology is a relevant renewable energy to contribute to reach a clean and affordable energy production system with important emissions reduction of greenhouse gases (GHG). An innovative technological application of biomass energy consisting of a burner coupled with an external fired gas turbine (EFGT) has been developed for the production of electricity. This paper shows the results of the plant modelling by Aspen Plus environment and preliminary experimental tests; the validation of the proposed model allows for the main parameters to be defined that regulate the energy production plant supplied by woodchips.

Description: Several window functions are currently applied to improve the performance of the discrete Fourier transform (DFT) harmonic detection method. These window functions exhibit poor accuracy in measuring the harmonic contents of a signal with high-order and weak-amplitude components when the power frequency fluctuates within a small range. In this paper, a minimum side-lobe optimization window function that is aimed at overcoming the abovementioned issue is proposed. Moreover, an improved DFT harmonic detection algorithm based on the six-term minimum side-lobe optimization window and four-spectrum-line interpolation method is proposed. In this context, the minimum side-lobe optimization window is obtained by optimizing the conventional cosine window function according to the optimization rules, and the characteristics of the new proposed window are provided to analyze its performance. Then, the proposed optimization window function is employed to improve the DFT harmonic detection algorithm based on the six-term minimum side-lobe optimization window and four-spectrum-line interpolation method. The proposed technique is used to detect harmonics of an electricity gird in which the six-term minimum side-lobe optimization window is utilized to eliminate the influence of spectrum leakage caused by nonsynchronous sampling of signal processing. The four-spectrum-line interpolation method is employed to eliminate or mitigate the fence effect caused by the inherent measurement error of the DFT method. Simulation experiments in two complex conditions and an experiment test are carried out to validate the improved performance of the proposed window. Results reveal that the six-term minimum side-lode optimization window has the smallest peak side lobe when compared with existing windows, which can effectively reduce the interaction influence of spectrum leakage, improve the measurement accuracy of the DFT harmonic detection method, and meet the standard requirement of harmonic measurement in complex situations.

Description: Apart from electric vehicles, most internal combustion (IC) engines are powered while burning petroleum-based fossil or alternative fuels after mixing with inducted air. Thereby the operations of mixing and combustion evolve in a turbulent flow environment created during the intake phase and then intensified by the piston motion and influenced by the shape of combustion chamber. In particular, the swirl and turbulence levels existing immediately before and during combustion affect the evolution of these processes and determine engine performance, noise and pollutant emissions. Both the turbulence characteristics and the bulk flow pattern in the cylinder are strongly affected by the inlet port and valve design. In the present paper, large eddy simulation (LES) is appraised and applied to studying the turbulent fluid flow around the intake valve of a single cylinder IC-engine as represented by the so called magnetic resonance velocimetry (MRV) flow bench configuration with a relatively large Reynolds number of 45,000. To avoid an intense mesh refinement near the wall, various subgrid scale models for LES; namely the Smagorinsky, wall adapting local eddy (WALE) model, SIGMA, and dynamic one equation models, are employed in combination with an appropriate wall function. For comparison purposes, the standard RANS (Reynolds-averaged Navier–Stokes) k- ε model is also used. In terms of a global mean error index for the velocity results obtained from all the models, at first it turns out that all the subgrid models show similar predictive capability except the Smagorinsky model, while the standard k- ε model experiences a higher normalized mean absolute error (nMAE) of velocity once compared with MRV data. Secondly, based on the cost-accuracy criteria, the WALE model is used with a fine mesh of ≈39 millions control volumes, the averaged velocity results showed excellent agreement between LES and MRV measurements, revealing the high prediction capability of the suggested LES tool for valve flows. Thirdly, the turbulent flow across the valve curtain clearly featured a back flow resulting in a high speed intake jet in the middle. Comprehensive LES data are generated to carry out statistical analysis in terms of (1) evolution of the turbulent morphology across the valve passage relying on the flow anisotropy map, (2) integral turbulent scales along the intake-charge stream, (3) turbulent flow properties such as turbulent kinetic energy, turbulent velocity and its intensity within the most critical zone in intake-port and along the port length, it further transpires that the most turbulence are generated across the valve passage and these are responsible for the in-cylinder turbulence.

Description: Electronic load (e-load) is essential equipment for power converter performance test, where a designated load profile is executed. Electronic load is usually implemented with the analog controller for fast tracking of the load profile reference. In this paper, a low-power low-cost electronic load is proposed. MOSFETs (metal-oxide-semiconductor field-effect transistors) are used as the power consumption devices, which are regulated to the active region as controlled current-sink. In order to achieve fast transient response using the low-cost digital signal controller (DSC) PWM peripherals, the interleaving PWM method is proposed to achieve active current ripple mitigation. To obtain the system open-loop gain for current-sink operation, an offline digital system identification method, followed by model reduction, is proposed by applying Pseudo-Random Binary Sequence (PRBS) excitation. Pole-zero cancelation method is used in the control system design and later implemented in a DSC. The prototype is built and tested, in which meaningful testing scenarios under constant current-sink mode, pulse current sink mode, and double line-frequency current mode are verified. The experimental results indicate that the proposed e-load can sink pre-programmed current profile with well-attenuated ripple for static and dynamic load testing, and is applicable to fully digitalized power testing equipment.

Description: A centrifugal carbon dioxide compressor is a kind of general machine with extensive applications. The geometry of the side chambers of the compressors can be determined by studying the rotor-stator cavity with centripetal through-flow. In this paper, numerical simulations were conducted to predict the characteristics of flow and heat transfer. Three different speeds of rotation and two axial gap widths were considered. The correlations of the core swirl ratios were determined by fitting the results for two axial gap widths. The amounts of the moment coefficients of the disk were predicted. In order to better analyze the temperature field, the radial distributions of the local heat transfer coefficient were numerically investigated. According to the simulation results, the average Nusselt number was found to be dominated by the turbulent flow parameter. It also seemed to be proportional to the moment coefficient at a fixed circumferential Reynolds number.

Description: The pattern of micro-electricity production of simple two-chamber microbial fuel cells (MFC) was monitored in this study. Piggery wastewater and anaerobic sludge served as fuel and inocula for the MFC, respectively. The output power, including voltage and current generation, of triplicate MFCs was measured using an on-line monitoring system. The maximum voltage obtained among the triplicates was 0.663 V. We also found that removal efficiency of chemical oxygen demand (COD) and biochemical oxygen demand (BOD) in the piggery wastewater was 94.99 and 98.63%, respectively. Moreover, analytical results of Fast Fourier Transform (FFT) demonstrated that the output current comprised alternating current (AC) and direct current (DC) components, ranging from mA to μA.

Description: When the offshore wind energy industry attempts to develop in cold regions, ice load becomes the main technological challenge for offshore wind turbine foundation design. Dynamic ice loads acting on wind turbine foundations should be calculated in a reasonable way. The scope of this study is to present a numerical model that considers the non-simultaneous ice crushing failure acting on the vertical structure of a wind turbine’s foundation. The local ice crushing force at the contact surface between the ice sheet and structure is calculated. The boundary of the ice sheet is updated at each time step based on the indentation length of the ice sheet according to its structure. Ice loads are validated against two model tests with three different structure models developed by other researchers. The time series of the ice forces derived from the simulation and model tests are compared. The proposed numerical model can capture the main trends of ice–wind turbine foundation interaction. The simulation results agree well with measured data from the model tests in terms of maximum ice force, which is a key factor for wind turbine design. The proposed model will be helpful for assisting the initial design of wind turbine foundations in cold regions.

Description: The optimum nitrogen concentration for media supplementation and strain dominance are aspects of key importance to the industrial production of ethanol with a view to reducing costs and increasing yields. In this work, these two factors were investigated for four ethanologenic Saccharomyces cerevisiae strains (CLQCA-INT-001, CLQCA-INT-005, CLQCA-10-099, and UCLM 325), selected from the screening of 150 isolates, mostly from Ecuadorian yeast biodiversity. The effect of nitrogen concentration was assessed in terms of cellular growth, glucose consumption and ethanol production, and the yeast strains’ dominance was evaluated in continuous co-fermentation with cellular recycling by mitochondrial DNA analyses. Among the four selected yeast strains under study, CLQCA-INT-005 presented the highest glucose consumption at a nitrogen supplement concentration as low as 0.4 g·L−1, attaining an ethanol yield of up to 96.72% in 24 h. The same yeast strain was found to be highly competitive, showing a dominance of 80% after four cycles of fermentation in co-culture. Thus, CLQCA-INT-005 may be deemed as a very promising candidate to be used both at pilot-plant scale and at industrial scale cellulosic ethanol production.

Description: In the past decade, the condensation of internal air humidity in automotive headlamps has become more prevalent than ever due to the increased usage of a new light source—LEDs. LEDs emit far less heat than previously-used halogen lamps, which makes them far more susceptible to fogging. This fogging occurs when the internal parts of the headlamp fall to a temperature below the dew point. The front glass is most vulnerable to condensation due to its direct exposure to ambient conditions. Headlamp fogging leads to a decrease in performance and the possibility of malfunctions, which has an impact not only on the functional aspect of the product’s use but also on traffic safety. There are currently several technical solutions available which can determine the effectivity of ventilation systems applied for headlamp defogging. Another approach to this problem may be to use a numerical simulation. This paper proposes a CFD (computational fluid dynamics) simulation with a slightly simplified 3D model of an actual headlamp, which allows simulation of all the phenomena closely connected with fluid flow and phase change. The results were validated by real experiments on a special fogging–defogging test rig. This paper compares three different simulations and their compliance with real experiments.

Description: The depletion and usage of fossil fuels causes environmental issues and alternative fuels and technologies are urgently required. Therefore, thermal arc water vapor plasma for a fast and robust waste/biomass treatment is an alternative to the syngas method. Waste cooking oil (WCO) can be used as an alternative potential feedstock for syngas production. The goal of this experimental study was to conduct experiments gasifying waste cooking oil to syngas. The WCO was characterized in order to examine its properties and composition in the conversion process. The WCO gasification system was quantified in terms of the produced gas concentration, the H2/CO ratio, the lower heating value (LHV), the carbon conversion efficiency (CCE), the energy conversion efficiency (ECE), the specific energy requirements (SER), and the tar content in the syngas. The best gasification process efficiency was obtained at the gasifying agent-to-feedstock (S/WCO) ratio of 2.33. At this ratio, the highest concentration of hydrogen and carbon monoxide, the H2/CO ratio, the LHV, the CCE, the ECE, the SER, and the tar content were 47.9%, 22.42%, 2.14, 12.7 MJ/Nm3, 41.3% 85.42%, 196.2 kJ/mol (or 1.8 kWh/kg), and 0.18 g/Nm3, respectively. As a general conclusion, it can be stated that the thermal arc-plasma method used in this study can be effectively used for waste cooking oil gasification to high quality syngas with a rather low content of tars.

Description: This paper presents a mathematical model of measuring blood flow based on electromagnetic induction for predicting the rate of arterial stenosis. Firstly, an electrode sensor was used to collect the induced potential differences from human skin surface in a uniform magnetic field. Then, the inversion matrix was constructed by the weight function theory and finite element method. Next, the blood flow volume inversion model was constructed by combining the induction potential differences and inversion matrix. Finally, the rate of arterial stenosis was predicted based on mathematical relationship between blood flow and the area of arterial stenosis. To verify the accuracy of the model, a uniform magnetic field distribution of Helmholtz coil and a 3D geometric model of the ulnar artery of the forearm with different rates of stenosis were established in COMSOL, a finite element analysis software. Simulation results showed that the inversion model had high accuracy in the measurement of blood flow and the prediction of rate of stenosis, and is of great significance for the early diagnosis of arterial stenosis and other vessel diseases.

Description: Magnetic Induction Tomography (MIT) is a non-invasive imaging technique that has been widely applied for imaging materials with high electrical conductivity contrasts. Steel production is among an increasing number of applications that require a contactless method for monitoring the casting process due to the high temperature of hot steel. In this paper, an MIT technique is proposed for detecting defects and deformations in the external surfaces of metal, which has the potential to be used to monitor the external surface of hot steel during the continuous casting process. The Total Variation (TV) reconstruction algorithm was developed to image the conductivity distributions. Nonetheless, the reconstructed image of the deformed square metal obtained using the TV algorithm directly does not yield resonable images of the surface deformation. However, differential images obtained by subtracting the image of a perfect square metal with no deformations from the image obtained for a deformed square metal does provide accurate and repeatable deformation information. It is possible to obtain a more precise image of surface deformation by thresholding the differential image. This TV-based threshold-differencing method has been analysed and verified from both simulation and experimental tests. The simulation results reported that 0.92 % of the image region can be detected, and the experimental results indicated a 0.57 % detectability. Use of the proposed method was demonstareted in a MIT device which was used in continuous casting set up. The paper shows results from computer simulation, lab based cold tests, and real life data from continoeus cating demonstating the effectiveness of the proposed method.

Description: Gas mixture quantification is essential for the recording and reproducing odors, because an odor consists of multiple chemical compounds. Gas mixture quantification using field asymmetric ion mobility spectrometry (FAIMS) was studied. Acetone, ethanol, and diethyl ether were selected as components of a ternary gas mixture sample as representatives of the ketone, alcohol, and ether chemical classes, respectively. One hundred and twenty-five points with different concentrations were measured. The results were evaluated by error hypersurface, variance, and the coefficient of variation. The error hypersurface showed that it is possible to reach the target composition by following the error-hypersurface gradient. Successful convergence was achieved with the gradient descent method in a simulation based on the measurement data. This result verified the feasibility of the quantification of a gas mixture using FAIMS.

Description: In this study, a novel strain gauge arrangement and error reduction techniques were proposed to minimize crosstalk reading and simultaneously increase sensitivity on a decoupled six-axis force–moment (F/M) sensor. The calibration process that comprises the least squares method and error reduction techniques was implemented to obtain a robust decoupling matrix. A decoupling matrix is very crucial for minimizing error and crosstalk. A novel strain gauge arrangement that comprised double parallel strain gauges in the decoupled six-axis force–moment sensor was implemented to obtain high sensitivity. The experimental results revealed that the maximum calibration error, F/M sensor measurement error, and crosstalk readings were reduced to 3.91%, 1.78%, and 4.78%, respectively.

Description: Conventional eddy current testing (ECT) using a pickup coil probe is widely employed for the detection of structural cracks. However, the inspection of conventional ECT for steel structures is difficult because of the magnetic noise caused by the nonuniform permeability of steel. To combat this challenge, we have developed a small magnetic sensor probe with a dual-channel tunneling magnetoresistance sensor that is capable of reducing magnetic noise. Applying this probe to a complicated component of steel structures—such as the welds joining a U-shaped rib and deck plate together—requires the reduction of signal fluctuation caused by the distance (liftoff) variations between the sensor probe and the subject. In this study, the fundamental crack signal and the liftoff signal were investigated with the dual-channel sensor. The results showed that the liftoff signals could be reduced and differentiated from the crack signals by the differential parameters of the dual-channel sensor. In addition, we proposed an extraction technique for the crack signal using the Lissajous curve of the differential parameters. The extraction technique could be applied to the inspections not only for flat plates but also for welded angles to detect cracks without the influence of the liftoff signal.

Description: The objective of this paper is to study the ability of polymer optical fiber (POF) to be inserted in a knitted fabric and to measure both pressure and friction when walking. Firstly, POF, marketed and in development, have been compared in terms of the required mechanical properties for the insertion of the fiber directly into a knitted fabric on an industrial scale, i.e. elongation, bending rigidity, and minimum bending radius before plastic deformation. Secondly, the chosen optical fiber was inserted inside several types of knitted fabric and was shown to be sensitive to friction and compression. The knitted structure with the highest sensitivity has been chosen for sock prototype manufacturing. Finally, a feasibility study with an instrumented sock showed that it is possible to detect the different phases of walking in terms of compression and friction.

Description: Dielectric grating-based sensors are usually based on the guided mode resonance (GMR) obtained using a thin planar waveguide layer (PWL) adjacent to a thin subwavelength grating layer. In this work, we present a detailed investigation of thick subwavelength dielectric grating structures that exhibit reflection resonances above a certain thickness without the need for the waveguide layer, showing great potential for applications in biosensing and tunable filtering. Analytic and numerical results are thoroughly discussed, as well as an experimental demonstration of the structure as a chemical sensor in the SWIR (short wave infrared) spectral range (1200–1800 nm). In comparison to the GMR structure with PWL, the thick grating structure has several unique properties: (i) It gives higher sensitivity when the spaces are filled, with the analyte peaking at certain space values due to an increase in the interaction volume between the analyte and the evanescent optical field between the grating lines; (ii) the TM (transverse magnetic) resonance, in certain cases, provides a better figure of merit; (iii) the sensitivity increases as the grating height increases; (iv) the prediction of the resonance locations based on the effective medium approximation does not give satisfactory results when the grating height is larger than a certain value, and the invalidity becomes more severe as the period increases; (v) a sudden increase in the Q-factor of the resonance occurs at a specific height value accompanied by the high local field enhancement (~103) characteristic of a nano-antenna type pattern. Rigorous numerical simulations of the field distribution are presented to explain the different observed phenomena.

Description: In 2016, the IEEE task group ah (TGah) released a new standard called IEEE 802.11ah, and industrial Internet of Things (IoT) is one of its typical use cases. The restricted access window (RAW) is one of the core MAC mechanisms of IEEE 802.11ah, which aims to address the collision problem in the dense wireless networks. However, in each RAW period, stations still need to contend for the channel by Distributed Coordination Function and Enhanced Distributed Channel Access (DCF/EDCA), which cannot meet the real-time requirements of most industrial applications. In this paper, we propose a channel-aware contention window adaption (CA-CWA) algorithm. The algorithm dynamically adapts the contention window based on the channel status with an external interference discrimination ability, and improves the real-time performance of the IEEE 802.11ah. To validate the real-time performance of CA-CWA, we compared CA-CWA with two other backoff algorithms with an NS-3 simulator. The results illustrate that CA-CWA has better performance than the other two algorithms in terms of packet loss rate and average delay. Compared with the other two algorithms, CA-CWA is able to support industrial applications with higher deadline constraints under the same channel conditions in IEEE 802.11ah.

Description: Existing correlations between features extracted from Electroencephalography (EEG) signals and emotional aspects have motivated the development of a diversity of EEG-based affect detection methods. Both intra-subject and inter-subject approaches have been used in this context. Intra-subject approaches generally suffer from the small sample problem, and require the collection of exhaustive data for each new user before the detection system is usable. On the contrary, inter-subject models do not account for the personality and physiological influence of how the individual is feeling and expressing emotions. In this paper, we analyze both modeling approaches, using three public repositories. The results show that the subject’s influence on the EEG signals is substantially higher than that of the emotion and hence it is necessary to account for the subject’s influence on the EEG signals. To do this, we propose a data transformation that seamlessly integrates individual traits into an inter-subject approach, improving classification results.

Description: Depth cameras play a vital role in three-dimensional (3D) shape reconstruction, machine vision, augmented/virtual reality and other visual information-related fields. However, a single depth camera cannot obtain complete information about an object by itself due to the limitation of the camera’s field of view. Multiple depth cameras can solve this problem by acquiring depth information from different viewpoints. In order to do so, they need to be calibrated to be able to accurately obtain the complete 3D information. However, traditional chessboard-based planar targets are not well suited for calibrating the relative orientations between multiple depth cameras, because the coordinates of different depth cameras need to be unified into a single coordinate system, and the multiple camera systems with a specific angle have a very small overlapping field of view. In this paper, we propose a 3D target-based multiple depth camera calibration method. Each plane of the 3D target is used to calibrate an independent depth camera. All planes of the 3D target are unified into a single coordinate system, which means the feature points on the calibration plane are also in one unified coordinate system. Using this 3D target, multiple depth cameras can be calibrated simultaneously. In this paper, a method of precise calibration using lidar is proposed. This method is not only applicable to the 3D target designed for the purposes of this paper, but it can also be applied to all 3D calibration objects consisting of planar chessboards. This method can significantly reduce the calibration error compared with traditional camera calibration methods. In addition, in order to reduce the influence of the infrared transmitter of the depth camera and improve its calibration accuracy, the calibration process of the depth camera is optimized. A series of calibration experiments were carried out, and the experimental results demonstrated the reliability and effectiveness of the proposed method.

Description: Two types of urea biosensors were integrated with a wireless measurement system and microfluidic measurement system. The two biosensors used were (i) a magnetic beads (MBs)-urease/graphene oxide (GO)/titanium dioxide (TiO2)-based biosensor and (ii) an MBs-urease/GO/ nickel oxide (NiO)-based biosensor, respectively. The wireless measurement system work exhibited the feasibility for the remote detection of urea, but it will require refinement and modification to improve stability and precision. The microchannel fluidic system showed the measurement reliability. The sensing properties of urea biosensors at different flow rates were investigated. From the measurement results, the decay of average sensitivity may be attributed to the induced vortex-induced vibrations (VIV) at the high flow rate. In the aspect of wireless monitoring, the average sensitivity of the urea biosensor based on MBs-urease/GO/NiO was 4.780 mV/(mg/dl) and with the linearity of 0.938. In the aspect of measurement under dynamic conditions, the average sensitivity of the urea biosensor based on MBs-urease/GO/NiO were 5.582 mV/(mg/dl) and with the linearity of 0.959. Both measurements performed NiO was better than TiO2 according to the comparisons.

Description: Radio frequency (RF) based wireless power transfer provides an attractive solution to extend the lifetime of power-constrained wireless sensor networks. Through harvesting RF energy from surrounding environments or dedicated energy sources, low-power wireless devices can be self-sustaining and environment-friendly. These features make the RF energy harvesting wireless communication (RF-EHWC) technique attractive to a wide range of applications. The objective of this article is to investigate the latest research activities on the practical RF-EHWC design. The distribution of RF energy in the real environment, the hardware design of RF-EHWC devices and the practical issues in the implementation of RF-EHWC networks are discussed. At the end of this article, we introduce several interesting applications that exploit the RF-EHWC technology to provide smart healthcare services for animals, wirelessly charge the wearable devices, and implement 5G-assisted RF-EHWC.

Description: Complex environments pose great challenges for autonomous mobile robot navigation. In this study, we address the problem of autonomous navigation in 3D environments with staircases and slopes. An integrated system for safe mobile robot navigation in 3D complex environments is presented and both the perception and navigation capabilities are incorporated into the modular and reusable framework. Firstly, to distinguish the slope from the staircase in the environment, the robot builds a 3D OctoMap of the environment with a novel Simultaneously Localization and Mapping (SLAM) framework using the information of wheel odometry, a 2D laser scanner, and an RGB-D camera. Then, we introduce the traversable map, which is generated by the multi-layer 2D maps extracted from the 3D OctoMap. This traversable map serves as the input for autonomous navigation when the robot faces slopes and staircases. Moreover, to enable robust robot navigation in 3D environments, a novel camera re-localization method based on regression forest towards stable 3D localization is incorporated into this framework. In addition, we utilize a variable step size Rapidly-exploring Random Tree (RRT) method which can adjust the exploring step size automatically without tuning this parameter manually according to the environment, so that the navigation efficiency is improved. The experiments are conducted in different kinds of environments and the output results demonstrate that the proposed system enables the robot to navigate efficiently and robustly in complex 3D environments.

Description: In this work, we isolate individual wurtzite InAs nanowires and fabricate electrical contacts at both ends, exploiting the single nanostructures as building blocks to realize two different architectures of conductometric sensors: (a) the nanowire is drop-casted onto—supported by—a SiO2/Si substrate, and (b) the nanowire is suspended at approximately 250 nm from the substrate. We test the source-drain current upon changes in the concentration of humidity, ethanol, and NO2, using synthetic air as a gas carrier, moving a step forward towards mimicking operational environmental conditions. The supported architecture shows higher response in the mid humidity range (50% relative humidity), with shorter response and recovery times and lower detection limit with respect to the suspended nanowire. These experimental pieces of evidence indicate a minor role of the InAs/SiO2 contact area; hence, there is no need for suspended nanostructures to improve the sensing performance. Moreover, the sensing capability of single InAs nanowires for detection of NO2 and ethanol in the ambient atmosphere is reported and discussed.

Description: Displacement is crucial for structural health monitoring, although it is very challenging to measure under field conditions. Most existing displacement measurement methods are costly, labor-intensive, and insufficiently accurate for measuring small dynamic displacements. Computer vision (CV)-based methods incorporate optical devices with advanced image processing algorithms to accurately, cost-effectively, and remotely measure structural displacement with easy installation. However, non-target-based CV methods are still limited by insufficient feature points, incorrect feature point detection, occlusion, and drift induced by tracking error accumulation. This paper presents a reference frame-based Deepflow algorithm integrated with masking and signal filtering for non-target-based displacement measurements. The proposed method allows the user to select points of interest for images with a low gradient for displacement tracking and directly calculate displacement without drift accumulated by measurement error. The proposed method is experimentally validated on a cantilevered beam under ambient and occluded test conditions. The accuracy of the proposed method is compared with that of a reference laser displacement sensor for validation. The significant advantage of the proposed method is its flexibility in extracting structural displacement in any region on structures that do not have distinct natural features.

Description: Convenient indoor positioning has become an urgent need due to the improvement it offers to quality of life, which inspires researchers to focus on device-free indoor location. In areas covered with Wi-Fi, people in different locations will to varying degrees have an impact on the transmission of channel state information (CSI) of Wi-Fi signals. Because space is divided into several small regions, the idea of classification is used to locate. Therefore, a novel localization algorithm is put forward in this paper based on Deep Neural Networks (DNN) and a multi-model integration strategy. The approach consists of three stages. First, the local outlier factor (LOF), the anomaly detection algorithm, is used to correct the abnormal data. Second, in the training phase, 3 DNN models are trained to classify the region fingerprints by taking advantage of the processed CSI data from 3 antennas. Third, in the testing phase, a model fusion method named group method of data handling (GMDH) is adopted to integrate 3 predicted results of multiple models and give the final position result. The test-bed experiment was conducted in an empty corridor, and final positioning accuracy reached at least 97%.

Description: Recently, the importance of individual pitch control (IPC) capability in wind turbine systems has been emphasized to achieve the desired power performance and mitigate the aerodynamic imbalance load for the mechanical integrity. Compared to collective pitch control (CPC), which assigns identical pitch angles for all employed blades, IPC is capable of generating other various sets of pitch angles to manipulate the aerodynamic load. Thus, the mechanical elements of wind turbine systems may take advantages from this variation, which allows wind turbines to have lighter designs and longer lifetimes. One of the essential mechanical components in the wind turbine is a primary bearing supporting the blades–rotor–shaft unit, which has not been fully investigated yet among the structural elements in the wind turbine system. In this regard, this research focuses on predicting the bearing life span of a NACA64-A17 two-blade 5-MW wind turbine system for the domains of allowable individual pitch angles by IPC. In particular, under the effect of various wind speeds, a bearing life span was determined based on the average value of load cases—satisfying both appropriate power level and the allowable domain of pitch control angles, which were possibly conveyed by IPC—and the result was compared with the bearing life predicted based on the domain of pitch angles, as generated by the CPC strategy. Consequently, in the ranges of high wind speeds, it was found that the average applied load to the bearing is reduced under the domain of the IPC-based pitch angle, resulting in possibly increasing the life span of the bearing. With the presented results, it is hoped that this work will provide important insights for those that majorly concern designing the primary bearing of the IPC-based wind turbine system.

Description: The selective catalytic reduction (SCR) methodology is notably recognized as the widely applied strategy for NOX control in exhaust after-treatment technologies. In real SCR systems, complex unsteady turbulent multi-phase flow phenomena including poly-dispersed AdBlue® spray evolve with a wide ranging relative velocity between the droplet phase and carrier gas phase. This results from an AdBlue® spray that is injected into a mixing pipe which is cross-flowing by a hot exhaust gas. To reduce the complexity while gaining early information on the injected droplet size and velocity needed for a minimum deposition and optimal conversion, a single droplet with a specified diameter is addressed to mimic a spray featuring the same Sauter Mean Diameter. For that purpose, effects of turbulent hot cross-flow on thermal decomposition processes of a single AdBlue® droplet are numerically investigated. Thereby, a single AdBlue® droplet is injected into a hot cross-flowing stream within a mixing pipe in which it may experience phase change processes including interaction with the pipe wall along with liquid wall–film and possible solid deposit formation. First of all, the prediction capability of the multi-component evaporation model and thermal decomposition is evaluated against the detailed simulation results for standing droplet case for which experimental data is not available. Next, exploiting Large Eddy Simulation features the effect of hot turbulent co- and cross-flowing streams on the dynamic droplet characteristics and on the droplet/wall interaction is analyzed for various droplet diameters and operating conditions. This impact is highlighted in terms of droplet evaporation time, decomposition efficiency, droplet trajectories and wall–film formation. It turns out that smaller AdBlue® droplet diameter, higher gas temperature and relative velocity lead to shorter droplet life time as the droplet evaporates faster. Under such conditions, possible droplet/wall interaction processes on the pipe wall or at the entrance front of the monolith may be avoided. Since the ammonia (NH3) gas generated by urea decomposition is intended to reduce NOX emission in the SCR system, it is apparent for the prediction of high NOX removal performance that UWS injector system which allows to realize such operating conditions is favorable to support high conversion efficiency of urea into NH3.

Description: In today’s connected world, there is a tendency of connectivity even in the sectors which conventionally have been not so connected in the past, such as power systems substations. Substations have seen considerable digitalization of the grid hence, providing much more available insights than before. This has all been possible due to connectivity, digitalization and automation of the power grids. Interestingly, this also means that anybody can access such critical infrastructures from a remote location and gone are the days of physical barriers. The power of connectivity and control makes it a much more challenging task to protect critical industrial control systems. This capability comes at a price, in this case, increasing the risk of potential cyber threats to substations. With all such potential risks, it is important that they can be traced back and attributed to any potential threats to their roots. It is extremely important for a forensic investigation to get credible evidence of any cyber-attack as required by the Daubert standard. Hence, to be able to identify and capture digital artifacts as a result of different attacks, in this paper, the authors have implemented and improvised a forensic testbed by implementing a sandboxing technique in the context of real time-hardware-in-the-loop setup. Newer experiments have been added by emulating the cyber-attacks on WAMPAC applications, and collecting and analyzing captured artifacts. Further, using sandboxing for the first time in such a setup has proven helpful.

Description: Understanding the rheological behavior of cement slurries is important in cement and petroleum industries. In this paper, we study the fully developed flow of a cement slurry inside a wellbore. The slurry is modeled as a non-linear fluid, where a constitutive relation for the viscous stress tensor based on a modified form of the second grade (Rivlin–Ericksen) fluid is used;we also propose a diffusion flux vector for the concentration of particles. The one-dimensional forms of the governing equations and the boundary conditions are made dimensionless and solved numerically. A parametric study is performed to present the effect of various dimensionless numbers on the velocity and the volume fraction profiles.

Description: Due to the recent rise in the use of lower-limb exoskeletons as an alternative for gait rehabilitation, gait phase detection has become an increasingly important feature in the control of these devices. In addition, highly functional, low-cost recovery devices are needed in developing countries, since limited budgets are allocated specifically for biomedical advances. To achieve this goal, this paper presents two gait phase partitioning algorithms that use motion data from a single inertial measurement unit (IMU) placed on the foot instep. For these data, sagittal angular velocity and linear acceleration signals were extracted from nine healthy subjects and nine pathological subjects. Pressure patterns from force sensitive resistors (FSR) instrumented on a custom insole were used as reference values. The performance of a threshold-based (TB) algorithm and a hidden Markov model (HMM) based algorithm, trained by means of subject-specific and standardized parameters approaches, were compared during treadmill walking tasks in terms of timing errors and the goodness index. The findings indicate that HMM outperforms TB for this hardware configuration. In addition, the HMM-based classifier trained by an intra-subject approach showed excellent reliability for the evaluation of mean time, i.e., its intra-class correlation coefficient (ICC) was greater than 0.75 . In conclusion, the HMM-based method proposed here can be implemented for gait phase recognition, such as to evaluate gait variability in patients and to control robotic orthoses for lower-limb rehabilitation.

Description: Traditional compensation methods based on temperature-related parameters are not effective for complex total reflection prism laser gyro (TRPLG) bias variation. Because the high frequency oscillator voltage (UHFO) fundamentally affects the TRPLG bias, and the UHFO has a stronger correlation with the TRPLG bias when compared with the temperature, an introduction of UHFO into the TRPLG bias compensation can be evaluated. In consideration of the limitations of least squares (LS) regression and multivariate stepwise regression, we proposed a compensation method for TRPLG bias based on iterative re-weighted least squares support vector machine (IR-LSSVM) and compared with LS regression, stepwise regression, and LSSVM algorithm in large temperature cycling experiments. When temperature, slope of temperature variation, and UHFO were selected as inputs, the IR-LSSVM based on myriad weight function improved the TRPLG bias stability by 61.19% to reach the maximum and eliminated TRPLG bias drift. In addition, the UHFO proved to be the most important parameter in the process of TRPLG bias compensation; accordingly, it can alleviate the shortcomings of traditional compensation based on temperature-related parameters and can greatly improve the TRPLG bias stability.

Description: A convolutional neural network (CNN) algorithm was developed to retrieve the land surface temperature (LST) from Advanced Microwave Scanning Radiometer 2 (AMSR2) data in China. Reference data were selected using the Moderate Resolution Imaging Spectroradiometer (MODIS) LST product to overcome the problem related to the need for synchronous ground observation data. The AMSR2 brightness temperature (TB) data and MODIS surface temperature data were randomly divided into training and test datasets, and a CNN was constructed to simulate passive microwave radiation transmission to invert the surface temperature. The twelve V/H channel combinations (7.3, 10.65, 18.7, 23.8, 36.5, 89 GHz) resulted in the most stable and accurate CNN retrieval model. Vertical polarizations performed better than horizontal polarizations; however, because CNNs rely heavily on large amounts of data, the combination of vertical and horizontal polarizations performed better than a single polarization. The retrievals in different regions indicated that the CNN accuracy was highest over large bare land areas. A comparison of the retrieval results with ground measurement data from meteorological stations yielded R2 = 0.987, RMSE = 2.69 K, and an average relative error of 2.57 K, which indicated that the accuracy of the CNN LST retrieval algorithm was high and the retrieval results can be applied to long-term LST sequence analysis in China.

Description: This paper investigates mobile relaying in wireless powered communication networks (WPCN), where an unmanned aerial vehicle (UAV) is employed to help information delivery from multiple sources to destination with communication channels severely blocked. The sources are low-power without energy supply. To support information transmission, the UAV acts as a hybrid access point (AP) to provide wireless power transfer (WPT) and information reception for sources. We set the issue of system throughput maximization as the optimization problem. On the one hand, the system is subject to the information causality constraint due to the dependent processes of information reception and transmission for the UAV. On the other hand, the sources are constrained by a so-called neutrality constraints due to the dependent processes of energy harvesting and energy consumption. In addition, we take account of the access delay issue of all ground nodes. Specifically, two paradigms of delay-tolerant case and delay-sensitive case are presented. However, the formulated problem including optimizations for time slot scheduling, power allocation and UAV trajectory is non-convex and thus is difficult to obtain its optimal solution. To tackle this problem, we apply the successive convex approximation (SCA) technique and propose an iterative algorithm by which a suboptimal solution can be achieved. Simulation results validate our proposed design, and show that the obtained suboptimal solution is high-quality, as compared to benchmark scheme.

Description: A piezoelectric fiber patch (PFP) is a transducer type that is suitable for guided-wave-based structural health monitoring (SHM) due to its light, thin, and flexible characteristics. In our previous work, a PFP-based transducer design for selective excitation of the zero-order shear horizontal wave mode (SH0) was introduced (shear horizontal PFP (SHPFP)). In this work, two modified SH0 wave PFP transducer designs are proposed: the rounded corner design and the dual design. The degree of improvement is determined by a numerical simulation and the dual design—the design with the most promise—is experimentally realized. Laser Vibrometry measured the generated wave field, confirming the results from the simulation. The new designs can generate an almost pure SH0 wave. The dual design has a very strong directivity that is useful for several guided-wave-based SHM applications. The conclusions on the design’s properties as a transmitter are also valid for its properties as a sensor due to the reciprocity of piezoelectric transducers.

Description: In recent years, due to the favorable climate conditions of Iran, the cultivation of rapeseed has increased significantly. The aim of this study was to investigate the possibility of biodiesel production from one genotype of rapeseed (TERI (OE) R-983). An ultrasonic approach was used in order to intensify the reaction. Response surface methodology (RSM) was applied to identify the optimum conditions of the process. The results of this research showed that the conversion of biodiesel was found to be 87.175% under the optimized conditions of a 4.63:1 molar ratio (methanol to oil), 56.50% amplitude, and 0.4 s pulses for a reaction time of 5.22 min. Increasing the operating conditions, such as the molar ratio from 4:1 to 5.5:1, amplitude from 50% to 72.5%, reaction time from 3 min to 7 min, and pulse from 0.4 s to 1 s, increased the FAME (fatty acid methyl esters) yield by approximately 4.5%, 2.3%, 1.2%, and 0.5%, respectively. The properties of the TERI (OE) R-983 methyl ester met the requirements of the biodiesel standard (ASTM D6751), indicating the potential of the produced biodiesel as an alternative fuel.

Description: The heat exchanger in a gas instantaneous water heater is a thermal device used for heat transfer from the high-temperature flue gas to the low-temperature water. The fireside corrosion, due to the reaction of acidic condensate formed on the heat exchanger surfaces and its metallic material, is one of the major hazards for gas instantaneous water heaters. This paper focuses on identifying and quantifying the fireside corrosion on the surface of heat exchangers in gas-fired instantaneous water heaters. Durability tests lasting for 2000 cycles were undertaken for five gas-fired instantaneous water heaters, which were different in terms of the heat input and coating of heat exchangers. The corrosion deposits on the surface of the heat exchangers were surveyed by several methods. The results show that the corrosion deposit grew as the test duration increased. The fins of the heat exchanger with a lead coating had been corroded and copper was exposed. Cu4(OH)6SO4 was the main corrosion product of heat exchangers without a lead coating, whereas PbSO4 was the main corrosion product of heat exchangers with a lead coating. The experiments demonstrate that the corrosion rate decreased with the increase of the heat input. The experiments also show that the thermal efficiency of gas instantaneous water heaters decreased by 2.4% to 6% at the end of the test duration.

Description: Combustion condition monitoring is a fundamental and critical issue that needs to be addressed in the wide-load operation of coal-fired boilers. In this paper, an unsupervised classification framework based on the convolutional auto-encoder (CAE), the principal component analysis (PCA), and the hidden Markov model (HMM) is proposed to monitor the combustion condition with the uniformly spaced flame images, which are collected from the furnace combustion monitoring system. First, CAE is adopted to extract the features from the flame images, which obtain the sparse representations in the images. Then, PCA is applied to project the feature vectors into the orthogonal space for robustness and computation efficiency. Finally, a HMM is built to calculate the corresponding optimal states by learning the temporal behaviors in the compressed representations. A coal combustion adjustment experiment was conducted in a 660 MW opposed-firing boiler, and the sequential 14,400 flame images with three different combustion states were obtained to evaluate the effectiveness of the proposed approach. We tested six different compression dimensions of the latent variable z in the CAE model and ensured that the appropriate compress parameter was 1024. The proposed framework is compared with five other methods: the CAE + Gaussian mixture model (GMM), CAE + Kmean, the CAE + fuzzy c-mean method, CAE + HMM, and the traditional handcraft feature extraction method (TH) + HMM. The results show that the proposed framework has the highest classification accuracy (95.25% for the training samples and 97.36% for the testing samples) and has the best performance in recognizing the semi-stable state (85.67% for the training samples and 77.60% for the testing samples), indicating that the proposed framework is capable of identifying the combustion condition, changing when the combustion deteriorates as the coal feed rate falls.

Description: Confronting an energy crisis, the government of Ghana enacted a power factor correction policy in 1995. The policy imposes a penalty on large-scale electricity users, namely, special load tariff (SLT) customers of the Electricity Company of Ghana (ECG), whose power factor is below 90%. This paper investigates the impact of this policy on these firms’ power factor improvement by using panel data from 183 SLT customers from 1994 to 1997 and from 2012. To avoid potential endogeneity, this paper adopts a regression discontinuity design (RDD) with the power factor of the firms in the previous year as a running variable, with its cutoff set at the penalty threshold. The result shows that these large-scale electricity users who face the penalty because their power factor falls just short of the threshold are more likely to improve their power factor in the subsequent year, implying that the power factor correction policy implemented by Ghana’s government is effective.

Description: The phase-out of nuclear energy in Germany will take place in 2022. A site for final disposal of high-level radioactive waste (HLRW) has not yet been chosen, but a site selection process was restarted by the Site Selection Act in 2017. This Act was based on a recommendation by a commission which also advised to follow up the development of deep borehole disposal (DBD) as a possible option for final disposal of HLRW. This paper describes briefly the status of DBD in Germany and if this option is to be pursued in Germany. Although DBD has some merits, it can only be a real option if supported by research and development. The technical equipment for larger boreholes of the required size will only be developed if there is funding and a feasibility test. Furthermore, any DBD concept must be detailed further, and some requirements of the Act must be reconsidered. Therefore, the support of DBD will likely remain at a low level if there are no political changes.

Description: Life cycle assessment (LCA) is an established method to assess the various environmental impacts associated with all the stages of a building. The goal of this project was to calculate the environmental releases for a whole office building and investigate the contribution in terms of environmental impact for different parts of the building, as well as the impact from different stages of the life cycle. The construction process was followed up during production and the contractors provided real-time data on the input required in terms of building products, transport, machinery, energy use, etc. The results are presented for five environmental impact categories and, as expected, materials that constitute the main mass of the building and the energy used during operation contribute the largest share of environmental impact. It is usually difficult to evaluate the environmental impact of the materials in technical installations due to the lack of data. However, in this study, the data were provided by the contractors directly involved in the construction and can, therefore, be considered highly reliable. The results show that materials for installations have a significant environmental impact for four of the environmental impact categories studied, which is a noteworthy finding.

Description: Smart cities can effectively improve the quality of urban life. Intelligent Transportation System (ITS) is an important part of smart cities. The accurate and real-time prediction of traffic flow plays an important role in ITSs. To improve the prediction accuracy, we propose a novel traffic flow prediction method, called AutoEncoder Long Short-Term Memory (AE-LSTM) prediction method. In our method, the AutoEncoder is used to obtain the internal relationship of traffic flow by extracting the characteristics of upstream and downstream traffic flow data. Moreover, the Long Short-Term Memory (LSTM) network utilizes the acquired characteristic data and the historical data to predict complex linear traffic flow data. The experimental results show that the AE-LSTM method had higher prediction accuracy. Specifically, the Mean Relative Error (MRE) of the AE-LSTM was reduced by 0.01 compared with the previous prediction methods. In addition, AE-LSTM method also had good stability. For different stations and different dates, the prediction error and fluctuation of the AE-LSTM method was small. Furthermore, the average MRE of AE-LSTM prediction results was 0.06 for six different days.

Description: Radio frequency identification is one of the key techniques for Internet of Things, which has been widely adopted in many applications for identification. However, there exist various security and privacy issues in radio frequency identification (RFID) systems. Particularly, one of the most serious threats is to clone tags for the goal of counterfeiting goods, which causes great loss and danger to customers. To solve these issues, lots of authentication protocols are proposed based on physical unclonable functions that can ensure an anti-counterfeiting feature. However, most of the existing schemes require secret parameters to be stored in tags, which are vulnerable to physical attacks that can further lead to the breach of forward secrecy. Furthermore, as far as we know, none of the existing schemes are able to solve the security and privacy problems with good scalability. Since many existing schemes rely on exhaustive searches of the backend server to validate a tag and they are not scalable for applications with a large scale database. Hence, in this paper, we propose a lightweight RFID mutual authentication protocol with physically unclonable functions (PUFs). The performance analysis shows that our proposed scheme can ensure security and privacy efficiently in a scalable way.

Description: Sensors provide the foundation of many smart applications and cyber–physical systems by measuring and processing information upon which applications can make intelligent decisions or inform their users. Inertial measurement unit (IMU) sensors—and accelerometers and gyroscopes in particular—are readily available on contemporary smartphones and wearable devices. They have been widely adopted in the area of activity recognition, with fall detection and step counting applications being prominent examples in this field. However, these sensors may also incidentally reveal sensitive information in a way that is not easily envisioned upfront by developers. Far worse, the leakage of sensitive information to third parties, such as recommender systems or targeted advertising applications, may cause privacy concerns for unsuspecting end-users. In this paper, we explore the elicitation of age and gender information from gait traces obtained from IMU sensors, and systematically compare different feature engineering and machine learning algorithms, including both traditional and deep learning methods. We describe in detail the prediction methods that our team used in the OU-ISIR Wearable Sensor-based Gait Challenge: Age and Gender (GAG 2019) at the 12th IAPR International Conference on Biometrics. In these two competitions, our team obtained the best solutions amongst all international participants, and this for both the age and gender predictions. Our research shows that it is feasible to predict age and gender with a reasonable accuracy on gait traces of just a few seconds. Furthermore, it illustrates the need to put in place adequate measures in order to mitigate unintended information leakage by abusing sensors as an unanticipated side channel for sensitive information or private traits.

Description: In rehabilitation procedures related to the lower limbs, gait monitoring is an important source of information for the therapist. However, many of the approaches proposed in the literature require the use of uncomfortable and invasive devices. In this work, an instrumented tip is developed and detailed, which can be connected to any crutch. The instrumented tip provides objective data of the crutch motion, which, combined with patient movement data, might be used to monitor the daily activities or assess the recovery status of the patient. For that purpose, the tip integrates a two-axis inclinometer, a tri-axial gyroscope, and a force sensor to measure the force exerted on the crutch. In addition, a novel algorithm to estimate the pitch angle of the crutch is developed. The proposed approach is tested experimentally, obtaining acceptable accuracies and demonstrating the validity of the proposed lightweight, portable solution for gait monitoring.

Description: The distribution of total electron content (TEC) in the ionosphere is irregular and complex, and it is hard to model accurately. The polynomial (POLY) model is used extensively for regional ionosphere modeling in two-dimensional space. However, in the active period of the ionosphere, the POLY model is difficult to reflect the distribution and variation of TEC. Aiming at the limitation of the regional POLY model, this paper proposes a new ionosphere modeling method with combining the support vector machine (SVM) regression model and the POLY model. Firstly, the POLY model is established using observations of regional continuously operating reference stations (CORS). Then the SVM regression model is trained to compensate the model error of POLY, and the TEC SVM-P model is obtained by the combination of the POLY and the SVM. The fitting accuracies of the models are verified with the root mean square errors (RMSEs) and static single-frequency precise point positioning (PPP) experiments. The results show that the RMSE of the SVM-P is 0.980 TECU (TEC unit), which produces an improvement of 17.3% compared with the POLY model (1.185 TECU). Using SVM-P models, the positioning accuracies of single-frequency PPP are improved over 40% compared with those using POLY models. The SVM-P is also compared with the back-propagation neural network combined with POLY (BPNN-P), and its performance is also better than BPNN-P (1.070 TECU).

Description: In the last years, several attempts to combine the Internet of Things (IoT) and social networking have been made. In the meantime, things involved in IoT are becoming increasingly sophisticated and intelligent, showing a behavior that tends to look like the one of users in social networks. Therefore, it is not out of place to talk about profiles of things and about information and topics exchanged among them. In such a context, constructing topic-driven virtual communities starting from the real ones operating in a Multi-IoT scenario is an extremely challenging issue. This paper aims at providing some contributions in this setting. First of all, it presents the concept of profile of a thing. Then, it introduces the concept of topic-guided virtual IoT. Finally, it illustrates two approaches (one supervised and one unsupervised) to constructing topic-guided virtual IoTs in a Multi-IoT scenario.

Description: As an important development direction of star sensor technology, the All-Time star sensor technology can expand the application of star sensors to flight platforms inside the atmosphere. Due to intense atmospheric background radiation during the daytime, the commonly used star sensors operating in the visible wavelength range are significantly limited in their ability to detect stars, and hence the All-Time star sensor technology which is based on the shortwave infrared (SWIR) imaging system has become an effective research direction. All-Time star sensor detection capability is significantly affected by observation conditions and, therefore, an optimized selection of optical parameters, which mainly includes the field of view (FOV) and the detection wavelength band, can effectively improve the detection performance of All-Time star sensors under harsh observation conditions. This paper uses the model simulation method to analyze and optimize the optical parameters under various observation conditions in a high-altitude environment. A main parameter among those discussed is the analysis of detection band optimization based on the SWIR band. Due to the huge cost constraints of high-altitude experiments, we conducted experiments near the ground to verify the effectiveness of the detection band selection and the correctness of the SWIR star sensor detection model, which thereby proved that the optimization of the optical parameters for high altitudes was effective and could be used as a reference.

Description: Device-free human gesture recognition (HGR) using commercial off the shelf (COTS) Wi-Fi devices has gained attention with recent advances in wireless technology. HGR recognizes the human activity performed, by capturing the reflections of Wi-Fi signals from moving humans and storing them as raw channel state information (CSI) traces. Existing work on HGR applies noise reduction and transformation to pre-process the raw CSI traces. However, these methods fail to capture the non-Gaussian information in the raw CSI data due to its limitation to deal with linear signal representation alone. The proposed higher order statistics-based recognition (HOS-Re) model extracts higher order statistical (HOS) features from raw CSI traces and selects a robust feature subset for the recognition task. HOS-Re addresses the limitations in the existing methods, by extracting third order cumulant features that maximizes the recognition accuracy. Subsequently, feature selection methods derived from information theory construct a robust and highly informative feature subset, fed as input to the multilevel support vector machine (SVM) classifier in order to measure the performance. The proposed methodology is validated using a public database SignFi, consisting of 276 gestures with 8280 gesture instances, out of which 5520 are from the laboratory and 2760 from the home environment using a 10 × 5 cross-validation. HOS-Re achieved an average recognition accuracy of 97.84%, 98.26% and 96.34% for the lab, home and lab + home environment respectively. The average recognition accuracy for 150 sign gestures with 7500 instances, collected from five different users was 96.23% in the laboratory environment.

Description: Local administrations demand real-time and continuous pollution monitoring in sewer networks. Spectroscopy is a non-destructive technique that can be used to continuously monitor quality in sewers. Covering a wide range of wavelengths can be useful for improving pollution characterization in wastewater. Cost-effective and in-sewer spectrophotometers would contribute to accomplishing discharge requirements. Nevertheless, most available spectrometers are based on incandescent lamps, which makes it unfeasible to place them in a sewerage network for real-time monitoring. This research work shows an innovative calibration procedure that allows (Light-Emitting Diode) LED technology to be used as a replacement for traditional incandescent lamps in the development of spectrophotometry equipment. This involves firstly obtaining transmittance values similar to those provided by incandescent lamps, without using any optical components. Secondly, this calibration process enables an increase in the range of wavelengths available (working range) through a better use of the LED’s spectral width, resulting in a significant reduction in the number of LEDs required. Thirdly, this method allows important reductions in costs, dimensions and consumptions to be achieved, making its implementation in a wide variety of environments possible.

Description: Cloud providers such as Amazon Web Services (AWS) stand out as useful platforms to teach distributed computing concepts as well as the development of Cloud-native scalable application architectures on real-world infrastructures. Instructors can benefit from high-level tools to track the progress of students during their learning paths on the Cloud, and this information can be disclosed via educational dashboards for students to understand their progress through the practical activities. To this aim, this paper introduces CloudTrail-Tracker, an open-source platform to obtain enhanced usage analytics from a shared AWS account. The tool provides the instructor with a visual dashboard that depicts the aggregated usage of resources by all the students during a certain time frame and the specific use of AWS for a specific student. To facilitate self-regulation of students, the dashboard also depicts the percentage of progress for each lab session and the pending actions by the student. The dashboard has been integrated in four Cloud subjects that use different learning methodologies (from face-to-face to online learning) and the students positively highlight the usefulness of the tool for Cloud instruction in AWS. This automated procurement of evidences of student activity on the Cloud results in close to real-time learning analytics useful both for semi-automated assessment and student self-awareness of their own training progress.

Description: Considering the manufacturing of automotive components, there exists a dilemma around the substitution of traditional cast iron (CI) with lighter metals. Currently, aluminum alloys, being lighter compared to traditional materials, are considered as a more environmentally friendly solution. However, the energy required for the extraction of the primary materials and manufacturing of components is usually not taken into account in this debate. In this study, an extensive literature review was performed to estimate the overall energy required for the manufacturing of an engine cylinder block using (a) cast iron and (b) aluminum alloys. Moreover, data from over 100 automotive companies, ranging from mining companies to consultancy firms, were collected in order to support the soundness of this investigation. The environmental impact of the manufacturing of engine blocks made of these materials is presented with respect to the energy burden; the “cradle-to-grave approach” was implemented to take into account the energy input of each stage of the component life cycle starting from the resource extraction and reaching to the end-of-life processing stage. Our results indicate that, although aluminum components contribute toward reduced fuel consumption during their use phase, the vehicle distance needed to be covered in order to compensate for the up-front energy consumption related to the primary material production and manufacturing phases is very high. Thus, the substitution of traditional materials with lightweight ones in the automotive industry should be very thoughtfully evaluated.

Description: The design and operation of a custom-built LIDAR-compatible, four-channel Raman spectrometer integrated to a 532 nm pulsed laser is presented. The multichannel design allowed for simultaneous collection of Raman photons at two spectral regions identified as highly sensitive to changes in water temperature. For each of these spectral bands, the signals having polarization parallel to (∥) and perpendicular to (⟂), the excitation polarization were collected. Four independent temperature markers were calculated from the Raman signals: two-colour(∥), two-colour(⟂), depolarization(A) and depolarization(B). A total of sixteen datasets were analysed for one ultrapure (Milli-Q) and three samples of natural water. Temperature accuracies of ±0.4 °C–±0.8 °C were achieved using the two-colour(∥) marker. When multiple linear regression models were constructed (linear combination) utilizing all simultaneously acquired temperature markers, improved accuracies of ±0.3 °C–±0.7 °C were achieved.

Description: To prevent serious shaft deflection disasters under asymmetric mining conditions, it is urgent to solve the problem of designing shaft protection rock pillar (SPRP) sizes in thick soil and thin rock strata. In this paper, based on the parallel mining model and the perpendicular mining model, a dynamic prediction model that can describe the horizontal movement of the shaft was established by the probability integration method and the Knothe time function. Next, according to the measured data of the shaft deflection in the Guotun Coal Mine, a back analysis was used to calculate the prediction parameters that were suitable for the deep soil strata. Based on the mining model, the variation law of the horizontal deflection displacement of the shaft and SPRP size was obtained. The results showed that the final displacements of the shaft under the two ideal mining models were equal, while the parallel mining model was superior to the perpendicular mining model at the initial stage of mining. The horizontal displacement of the shaft head had a nonlinear negative correlation with the SPRP, and the SPRP size in thick soil and thin rock strata calculated by the parallel mining model was more reasonable. For the Guotun Coal Mine, when the soil movement angle was 57.8% of the actual value, the horizontal displacement of the main shaft head was reduced by 87%. The results have important theoretical and practical value in preventing shaft deflection in thick soil and thin rock strata.

Description: The authors would like to add the following note in the Affiliation Section of their paper published in Energies [...]

Description: In this study, p-type amorphous silicon oxide (a-SiOx) films are deposited using a radio-frequency inductively-coupled plasma chemical vapor deposition system. Effects of the CO2 gas flow rate on film properties and crystalline silicon heterojunction (HJ) solar cell performance are investigated. The experimental results show that the band gap of the a-SiOx film can reach 2.1 eV at CO2 flow rate of 10 standard cubic centimeters per minute (sccm), but the conductivity of the film deteriorates. In the device simulation, the transparent conducting oxide and contact resistance are not taken into account. The electrodes are assumed to be perfectly conductive and transparent. The simulation result shows that there is a tradeoff between the increase in the band gap and the reduction in conductivity at increasing CO2 flow rate, and the balance occurs at the flow rate of six sccm, corresponding to a band gap of 1.95 eV, an oxygen content of 34%, and a conductivity of 3.3 S/cm. The best simulated conversion efficiency is 25.58%, with an open-circuit voltage of 741 mV, a short-circuit current density of 42.3 mA/cm2, and a fill factor of 0.816%.

Description: The loading-to-flow diagram is a widely used classical method for the preliminary design of radial turbines. This study improves this method to optimize the design of radial turbines in the early design phase under variable operating conditions. The guide vane outlet flow angle is a key factor affecting the off-design performance of the radial turbine. To optimize the off-design performance of radial turbines in the early design phase, we propose a hypothesis that uses the ratio of the mean velocity of the fluid relative to the rotor passage with respect to the circumferential velocity of the rotor as an indicator to indirectly and qualitatively estimate the rotor loss, as it plays a key role in the off-design efficiency. Theoretical analysis of rotor loss characteristics under different types of variable operating conditions shows that a smaller design value of guide vane outlet flow angle results in a better off-design performance in the case of a reduced mass flow. In contrast, radial turbines with a larger design value of guide vane outlet flow angle can obtain a better off-design performance with increased mass flow. The above findings were validated with a mean-line model method. Furthermore, this study discusses the optimization of the design value of guide vane outlet flow angle based on the matching of rotor loss characteristics with specified variable operating conditions. It provides important guidance for the design optimization of multistage radial turbines with variable operating conditions in compressed air energy storage (CAES) systems.

Description: This paper explores the alternative roles hydrogen can play in the future European Union (EU) energy system, within the transition towards a carbon-neutral EU economy by 2050, following the latest policy developments after the COP21 agreement in Paris in 2015. Hydrogen could serve as an end-use fuel, a feedstock to produce carbon-neutral hydrocarbons and a carrier of chemical storage of electricity. We apply a model-based energy system analysis to assess the advantages and drawbacks of these three roles of hydrogen in a decarbonized energy system. To this end, the paper quantifies projections of the energy system using an enhanced version of the PRIMES energy system model, up to 2050, to explore the best elements of each role under various assumptions about deployment and maturity of hydrogen-related technologies. Hydrogen is an enabler of sectoral integration of supply and demand of energy, and hence an important pillar in the carbon-neutral energy system. The results show that the energy system has benefits both in terms of CO2 emission reductions and total system costs if hydrogen technology reaches high technology readiness levels and economies of scale. Reaching maturity requires a significant investment, which depends on the positive anticipation of market development. The choice of policy options facilitating visibility by investors is the focus of the modelling in this paper.

Description: Understanding the details of micro-scale wind fields is important in the development of wind energy. Research has proven that coupling Numerical Weather Prediction (NWP) and Computational Fluid Dynamics (CFD) models is a better approach for micro-scale wind field simulation. The main purpose of this work is to improve the NWP/CFD model performance in two parts: (i) developing a new coupling method that is more suitable for complex terrain between the NWP and CFD models, and (ii) applying a data assimilation system in the CFD model. Regarding part (i), in order to solve the problem of great topographical difference at the domain boundaries between the two models, Cressman interpolation is utilized to impose the NWP model wind on the CFD model boundaries. In part (ii), an assimilation method, nudging, to apply assimilation of observations into the CFD model is explored. Based on the Cressman interpolation coupling method, a preliminary implementation of data assimilation is performed. The results show that the NWP/CFD model with the improved coupling method may capture the details of micro-scale wind fields more accurately. Using data assimilation, the NWP/CFD model performance may be further improved by cooperating observation data.

Description: This article describes an aging study of a foam-vacuum insulation panel (VIP) composite insulation board installed on a test wall in a natural exposure test facility through a 30-month period. Silica-based VIPs with a polymeric barrier film were used in this study. The study results showed the effectiveness of a VIP-based insulation to reduce the heat gains and losses through a wall compared to regular rigid foam insulation of the same thickness. However, the long-term performance monitoring indicated a gradual decline in the thermal performance of the foam-VIP composite. In addition, one-dimensional numerical models were created to simulate the in situ behavior of the foam-VIP composite. One model utilized constant thermal conductivities of the test wall components and another utilized temperature-dependent thermal conductivities; the latter used measurements of conductivity over temperatures ranging from −15 to 55 °C. The results of the simulations emphasized the need to use both temperature and time-dependent material properties for accurately predicting the long-term performance of VIP-based insulation systems.

Description: Land use changes have significantly altered the natural environment in which humans live. In urban areas, diminishing air quality poses a large threat to human health. In order to investigate the relationship between land use/cover change (LUCC) and air pollutants of Wuyishan City between 2014–2017, an integrated approach was used by combining remote sensing techniques with a landscape ecology methods. Annual, seasonal, and weekly mean values of air pollutant (SO2, NO2, CO, PM10, O3, PM2.5, black carbon) concentration and atmospheric visibility were calculated to develop a Pearson correlation between LUCC and air pollutants concentration. Results showed an increase in forested areas (1.79%) and water areas (15.89%), with a simultaneous reduction in cultivated land (6.47%), bare land (72.61%), and built-up land (16.03%) from 2014 to 2017. The transition matrix of land use types revealed that (i) forest expansion took place mainly at the expense of cultivated land (13.94%) and bare land (27.48%); and (ii) water area expansion took place mainly at the expense of cultivated land (1.29%) and forests (0.21%). In 2017, the proportion of days with AQI level I (94.52%) was higher than that in 2014 (88.77%). Additionally, the annual average visibility in 2017 (37.42 km) was higher than 2014 (27.46 km). The concentration of SO2, CO, O3, and black carbon was positively correlated with the cultivated land. The concentration of SO2, CO, and black carbon negatively correlated with the increase of forests. PM10, and PM2.5 is negatively correlated with the water area. Visibility was found to be positively correlated with forested area, and negatively correlated with cultivated land. The findings from this study represent a valuable gain in understanding of policies aimed at improving, safeguarding, and monitoring air quality. These results can be used to inform land-use planning decisions in a comprehensive way and could be a valuable tool for LUCC rational management strategies.

Description: A comparative study regarding the performance of real-scale oil-free centrifugal chillers having the flooded evaporator or falling film evaporator was conducted in this study. The nominal capacity for the test chillers was around 200~230 USRT (US refrigeration ton) (703~809 kW). The compressors of the two chillers were identical and R-134a was used as the working fluid. Both evaporators employed the same enhanced tubes (GEWA-B) to fulfill phase change. Tests were conducted in full, 75%, 50%, and 25% loading. Test results indicate that both chillers contained a comparable system performance with an integrated part-load value of around 8.62~8.63. The overall heat transfer coefficient for the flooded evaporator was appreciably higher (20~40%) than the falling film evaporator. This is because the falling film flowrate was below the threshold value and the heat transfer was dominated by evaporation mode. Yet, the heat transfer performance for the falling film evaporator was further jeopardized due to starvation of the film flowrate (partial dry-out), especially in the middle or bottom of the tube bundle. This phenomenon became even more pronounced at partial loading (25%), whereas the flooded evaporator did not reveal such a performance dip at partial loading.

Description: This study examines the effect of mixing on the performance of anaerobic digestion of cow manure in Chinese dome digesters (CDDs) at ambient temperatures (27–32 °C) in comparison with impeller mixed digesters (STRs) and unmixed digesters (UMDs) at the laboratory scale. The CDD is a type of household digester used in rural and pre-urban areas of developing countries for cooking. They are mixed by hydraulic variation during gas production and gas use. Six digesters (two of each type) were operated at two different influent total solids (TS) concentration, at a hydraulic retention time (HRT) of 30 days for 319 days. The STRs were mixed at 55 rpm, 10 min/hour; the unmixed digesters were not mixed, and the Chinese dome digesters were mixed once a day releasing the stored biogas under pressure. The reactors exhibited different specific biogas production and treatment efficiencies at steady state conditions. The STR 1 exhibited the highest methane (CH4) production and treatment efficiency (volatile solid (VS) reduction), followed by STR 2. The CDDs performed better (10% more methane) than the UMDs, but less (approx. 8%) compared to STRs. The mixing regime via hydraulic variation in the CDD was limited despite a higher volumetric biogas rate and therefore requires optimization.

Description: The paper presents a new concept of a vertical axis wind turbine. The idea is focused on small wind turbines, and therefore, the dominating quality is safety. Another important necessary feature is efficient operation at small winds. This implies an application of the drag driven solution such as the Savonius rotor. The presented concept is aimed at reducing the rotor size and the cost of implementation. A new wind turbine solution, its efficiency, and functionality are described. The results of numerical simulations being a proof of the concept are reported. The simulations were followed by wind tunnel tests. Finally several prototypes were built and investigated for a longer period of time. The new wind turbine concept has undergone various testing and implementation efforts, making this idea matured, well proven and documented. A new feature, namely, the wind turbine size reduction at strong winds, or in other words, an increase in the wind turbine size at low winds is the reason why it is difficult to compare this turbine with other turbines on the market. The power output depends not only on the turbine efficiency but also on its varying size.

Description: The shift from fossil fuel to more renewable electricity generation will require the broader implementation of Demand Side Response (DSR) into the grid. Utility processes in industry are suited for this, having a large thermal time constant or buffer, and large electricity consumption. A widespread utility system in industry is an induced draft evaporative cooling tower. Considering the safety aspect, such a process needs to maintain cooling water temperature within predefined safe boundaries. Therefore, in this paper, two modelling methods for the prediction of the basin temperature of an induced draft evaporative cooling tower are proposed. Both a white box and a black box methodology are presented, based on the physical principles of fluid dynamics and adaptive neuro-fuzzy interference system (ANFIS) modelling, respectively. By analysing the accuracy of both models with a focus to cooling tower fan state changes, i.e., DSR purposes, it is shown that the white box model performs best. Fostering the idea of using such a system for DSR purposes, the concept of design for flexibility is also touched upon, discussing the thermal mass. Pre-cooling, where the temperature of the cooling water basin is lowered before a fan switch off period, was simulated with the white box model. It was shown that beneficial pre-cooling (to lower the temperature peak) is limited in time.

Description: I would like to take the occasion of writing this newsletter to draw a sort of summary of our publishing activities at Energies and of the results and perspectives [...]

Description: Microalgae is considered as an excellent potential renewable source of fuel in many forms including powder or slurry. A high percentage of emulsified water in the fuel is reported to reduce diesel engines’ emissions such as NOx, but that will compromise the engine output power. Using microalgae powder as an additive to enhance the emulsified water fuel heating value is the main objective of this work. Diesel engine combustion, vibration, performance and emissions were evaluated for pure cottonseed biodiesel (CS-B100), emulsified water 20% (vol.) in cottonseed biodiesel (CSB-E20) and emulsified water 20% (vol.) containing Fresh Water Microalgae Chlorella Vulgaris (FWM-CV) in cottonseed biodiesel (CSB-ME20). The emulsified water fuels showed a reduction in in-cylinder pressure, vibration, brake power, torque, exhaust gas temperature, CO2 and NOx, while BSFC and O2 were higher than the pure biodiesel (CS-B100). CSB-ME20 produced higher power and torque than CSB-E20 due to the presence of microalgae in the fuel that increased the energy content of the fuel.

Description: The low energy adaptive clustering hierarchy (LEACH) is the classical hierarchical topology control algorithm and still widely used today in wireless sensor networks. There are some problems in LEACH such us an unreasonable selection and uneven distribution of cluster heads, not considering the residual energy of nodes, the cluster head is overloaded and there is a high energy consumption of data transmission. In this paper, a topology control algorithm with double cluster heads and multi-hop based on affinity propagation clustering (APDC-M) was proposed. In APDC-M, firstly, a cluster head election algorithm with double choices based on the affinity propagation clustering algorithm (AP) and reference node strategy (APDC) was proposed. APDC is responsible to elect the fusion cluster head for node clustering. Secondly, a forwarding cluster head is elected within each cluster. The forwarding cluster head is responsible for the data transmission in order to reduce the energy consumption of a single cluster head. Lastly, the forwarding cluster heads complete the data transmission from a cluster to the base station by the shortest path inter-cluster multi-hop mode. The comparison simulation results show that APDC-M can make the cluster head election more reasonable and the cluster head distribution more uniform, and effectively reduce the energy consumption of the cluster head nodes when transmitting data, thus prolonging the network life.

Description: Ceramic-coated materials used in different engineering sectors are the focus of world-wide interest and have generated a need for inspection techniques that detect very small structural anomalies. Non-destructive testing is increasingly being used to evaluate coating thickness and to test for coating flaws. The main pros of non-destructive testing is that the tested object remains intact and available for continued use afterward. This paper reports on an integrated, non-destructive testing approach that combines infrared thermography and acousto-ultrasonics to evaluate advanced aerospace sandwich structure materials with the aim of exploring any potential for detecting defects of more than one type. Combined, these two techniques successfully detected fabrication defects, including inclusions and material loss.

Description: Germany has experienced rapid growth in onshore wind capacities over the past two decades. Substantial capacities of offshore wind turbines have been added since 2013. On a local, highly-resolved level, this analysis evaluated if differences in wind speed forecast errors exist for offshore and onshore locations regarding magnitude and variation. A model based on the Extra Trees algorithm is proposed and found to be a viable method to transform local wind speeds and capacities into aggregated wind energy feed-in. This model was used to analyze if offshore and onshore wind power expansion lead to different distributions of day-ahead wind energy forecast errors in Germany. The Extra Trees model results indicate that offshore wind capacity expansion entails an energy forecast error distribution with more frequent medium to high deviations, stemming from larger and more variable wind speed deviations of offshore locations combined with greater geographical concentration of offshore wind turbines and their exposure to high-wind oceanic conditions. The energy forecast error distribution of onshore expansion, however, shows heavier tails and consequently more frequent extreme deviations. The analysis suggests that this can be rooted in the simultaneous over- or underestimation of wind speeds at many onshore locations.

Description: The intermittency of solar energy resources has brought a big challenge for the optimization and planning of a future smart grid. To reduce the intermittency, an accurate prediction of photovoltaic (PV) power generation is very important. Therefore, this paper proposes a new forecasting method based on the recurrent neural network (RNN). At first, the entire solar power time series data is divided into inter-day data and intra-day data. Then, we apply RNN to discover the nonlinear features and invariant structures exhibited in the adjacent days and intra-day data. After that, a new point prediction model is proposed, only by taking the previous PV power data as input without weather information. The forecasting horizons are set from 15 to 90 min. The proposed forecasting method is tested by using real solar power in Flanders, Belgium. The classical persistence method (Persistence), back propagation neural network (BPNN), radial basis function (RBF) neural network and support vector machine (SVM), and long short-term memory (LSTM) networks are adopted as benchmarks. Extensive results show that the proposed forecasting method exhibits a good forecasting quality on very short-term forecasting, which demonstrates the feasibility and effectiveness of the proposed forecasting model.