ALBERT

Description: In this work, we propose a convenient method for evaluating levels of angular jerk in augmented reality (AR) and virtual reality (VR). Jerk is a rarely analyzed metric in usability studies, although it can be measured and calculated easily with most head-worn displays and can yield highly relevant information to designers. Here, we developed and implemented a system capable of calculating and analyzing jerk in real-time based on orientation data from an off-the-shelf head-worn display. An experiment was then carried out to determine whether the presence of AR user interface annotations results in changes to users’ angular head jerk when conducting a time-pressured visual search task. Analysis of the data indicates that a decrease in jerk is significantly associated with the use of AR augmentations. As noted in the limitations section, however, the conclusions drawn from this work should be limited, as this analysis method is novel in the VR/AR space and because of methodological limitations that limited the reliability of the jerk data. The work presented herein considerably facilitates the use of jerk as a quick component measure of usability and serves as an initial point off which future research involving jerk in VR and AR can be performed.

Description: Recombination of charge carriers can affect the response of ionization detectors used for the dosimetry of radiotherapy fields. In this work, we present a method for correcting online the recombination effects in arrays of ionization chambers irradiated with time-varying dose rates. The method is based on the characterization of the dose rate/recombination response of the detector, and the measurement of the instant ionization current in the detector, rather than the integrated charge. The proposed method was investigated with simulations of the response of different air and liquid ionization chambers in situations where recombination can be large. In addition, we experimentally investigated the application of the method with an in-house-developed liquid-filled ionization chamber. The proposed online correction method can compensate for recombination losses and seems feasible to implement in the software of ionization arrays/detectors used for the dosimetry of radiotherapy fields.

Description: Smart factories have made great progress with the development of various ICT technologies, such as IoT, big data, and artificial intelligence. The recent development of smart factory technology has shown results in automation and data acquisition and processing. However, it still has incomplete points to be converted to advanced technology, including intelligence. For intelligentization, there is a need to propose a new research method in addition to the previous methodologies. Considering the specificity of the factory, the data structure and methodology of the Semantic Web can be effective. Therefore, in this study, a smart factory was designed by the convergence of monitoring technology, autonomous control technology, and semantic web technologies. Based on the proposed methodology, a methodology for the autonomous control of a smart factory on a digital twin was designed.

Description: High dynamic performance of a deep-sea electric oil-filled joint actuator is an important premise to guarantee the working performance of an electric underwater manipulator. However, the unfavorable factors (i.e., extremely high water pressure, near freezing temperature) brought by the deep-sea working environment seriously affect the characteristic and dynamic performance of the electric oil-filled joint actuator, which mainly includes oil stirring viscos loss, output shaft dynamic seal loss, and core loss. In this paper, a novel observer-based robust control method named prescribed performance non-singular fast-terminal sliding-mode control (PP-NFTSMC-ESO) was synthesized for improving the dynamic performance of a deep-sea electric oil-filled joint actuator. The extended state observer (ESO) was employed to observe the unmeasured joint velocity signal and estimate the lumped uncertainties, while the prescribed performance function (PPF) was applied to constrain the instantaneous and steady-state performance of the trajectory-tracking error. The robust NFTSMC control method was then established by integrating the function of ESO and PPF through backstepping methodology. The stability of the proposed PP-NFTSMC-ESO strategy was analyzed and proved by the Lyapunov’s stability theory. It was proven that under the proposed controller, all the closed-loop signals are bounded and the trajectory tracking errors will converge to a small neighborhood of the origin with appropriate design parameters. The effectiveness of the proposed control scheme was illustrated by comparative simulation studies.

Description: Historically, researchers and practitioners have often failed to consider all the areas, factors, and implications of a process within an integrated manufacturing model. Thus, the aim of this research was to propose a holistic approach to manufacturing processes in order to assess their status and performance to improve target indicators such as product quality. For this purpose, a conceptual model is designed by identifying areas, flows, and indicators that are relevant to the assessment of a manufacturing system. Moreover, using the conceptual model, manufacturing systems can be modeled considering all related flows and decision-making options in the respective areas of production, maintenance, and quality. As a result, this model serves as the basis for the integral management and control of manufacturing systems in digital twin models for the regulation of process stability and quality with maintenance strategies. Thus, an assessment based on the conceptual model improves the knowledge level of all elements involved in the manufacturing of a product according to the desired quality specifications. The continuous monitoring of all areas and flows together with the optimal strategies in the quality and maintenance areas can enable companies to increase their profitability and customer service level. In this context, the discussion section lists key decision aspects for the assessment and improvement of manufacturing systems, while also providing a methodological sequence to evaluate and improve manufacturing systems. In conclusion, the conceptual approach allows better decision making, ensuring continuous optimization along the manufacturing asset lifecycle and providing a unique selling proposition for equipment producers and service engineering suppliers, as well as for production and assembly companies.

Description: In the current trend of consumption, electricity consumption will become a very high cost for the end-users. Consumers acquire energy from suppliers who use short, medium, and long-term forecasts to place bids in the power market. This study offers a detailed analysis of relevant literature and proposes a deep learning methodology for forecasting industrial electric usage for the next 24 h. The hourly load curves forecasted are from a large furniture factory. The hourly data for one year is split into training (80%) and testing (20%). The algorithms use the previous two weeks of hourly consumption and exogenous variables as input in the deep neural networks. The best results prove that deep recurrent neural networks can retain long-term dependencies in high volatility time series. Gated recurrent units (GRU) obtained the lowest mean absolute percentage error of 4.82% for the testing period. The GRU improves the forecast by 6.23% compared to the second-best algorithm implemented, a combination of GRU and Long short-term memory (LSTM). From a practical perspective, deep learning methods can automate the forecasting processes and optimize the operation of power systems.

Description: Six degree-of-freedom (6-DOF) robotic manipulators have been increasingly adopted in various applications in industries due to various advantages, such as large operation space, more degrees of freedom, low cost, easy placement, and convenient programming. However, the robotic manipulator has the problem of insufficient stiffness due to the series structures, which will cause motion errors of the manipulator end. In this paper, taking a 6-DOF robotic manipulator as an example, forward and inverse kinematics models are established, and a new modeling method for the joint angle and space stiffness of the end of the manipulator is proposed, which can establish the composite stiffness model of joint link stiffness and joint stiffness. An error compensation model is subsequently established. The experimental results indicate that the proposed error compensation method can effectively reduce the end motion error of the robotic manipulator, and hence, the working performance and accuracy of the manipulator can be improved. The proposed research is helpful for extending the application of robotic manipulators in precision machining and measurement.

Description: Driven by the supply chain, suppliers, manufacturers and warehouses are working more closely together for improving service quality. However, tremendous cost may incur in the supply chain if transportation is not planned properly and efficiently, which frustrates enterprises in the intense market. In this paper, we present a model of vehicle routing problem in complex supply chain (VRPCSC) and propose an intelligent algorithm called hybrid chaotic discrete bat algorithm with variable neighborhood search for minimizing the purchase cost of materials, processing cost, and delivery cost along the path from suppliers, to manufacturers and warehouses in the vehicle routing problem. Based on the principles of bat algorithm, a discrete chaotic initialization strategy (DCIS) and a variable neighborhood search (VNS) are adopted to enhance the convergence capacity. Finally, two sets of experiments are conducted, which show that the proposed algorithm can solve the VRPCSC effectively.

Description: It is essential to measure and monitor the intravenous (IV) infusion rate of inpatients. Medical staff monitor the IV infusion rate continually to ensure that a constant value is maintained. Developing an IV infusion monitoring system can help alleviate the workload of medical staff. Therefore, this paper proposes a method based on optical sensors, in conjunction with the Internet of Things, to overcome the challenges of IV therapy. To increase the sensitivity of the system, both optical sensors and load cells are used to compensate for their disadvantages. Our system yielded accuracies of 87.9% and 89.9% under horizontal and vertical motion conditions, respectively. The proposed system can send notifications to nursing stations regarding the residual IV fluid, current infusion rate, and expected end time, which can be monitored remotely by medical staff. Furthermore, this system can help prevent possible medical accidents during fluid treatment.

Description: Microplastics (MPs), and specifically microfibres (MPFs), are ubiquitous in water bodies, including wastewater and drinking water. In this work, a thorough literature review on the occurrence and removal of MPs, and specifically MPFs in WWTPs and DWTPs, has been carried out. When the water is treated, an average microfiber removal efficiency over 70% is achieved in WWTPs and DWTPs. These high percentages are still inefficient for avoiding the presence of a large number of microfibres in treated wastewater and also in tap water. RSF, DAF, oxidation ditch and CAS processes have been described as the most efficient treatments for eliminating MPFs from wastewater treatment. It is remarkable the wide range of the data reported on this topic; for example, treated wastewater contains between not detected and 347 MPFs/L, whereas tap water contains between not detected and 168 MPFs/L. Microfibres constitute more than half of the MPs found in treated wastewater and sewage sludge, whereas in DWTP effluents the percentage of MPFs is around 32%. Nevertheless, the relative amount of MPFs reported in tap water is notably higher (71%). Microfibres from WWTPs are discharged to the environment, being a source of MP pollution. Additionally, MPs released by DWTPs directly enter the drinking water lines, which constitute a direct route for MP human consumption, so that it has been estimated that an adult may ingest an average value of 7500 MPFs per year only via tap water. Thus, this review provides an update on the performance of WWTPs and DWTPs in removing MPs from water, which is an issue of great interest.