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  • Articles  (53,906)
  • Latest Papers from Table of Contents or Articles in Press  (53,906)
  • Sensors  (19,790)
  • 15954
  • 1
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    Real-Time 2-D Lidar Odometry Based on ICP (2021)
    Molecular Diversity Preservation International
    Details
    Publication Date: 2021-10-29
    Description: This study presents a 2-D lidar odometry based on an ICP (iterative closest point) variant used in a simple and straightforward platform that achieves real-time and low-drift performance. With a designated multi-scale feature extraction procedure, the lidar cloud information can be utilized at multiple levels and the speed of data association can be accelerated according to the multi-scale data structure, thereby achieving robust feature extraction and fast scan-matching algorithms. First, on a large scale, the lidar point cloud data are classified according to the curvature into two parts: smooth collection and rough collection. Then, on a small scale, noise and unstable points in the smooth or rough collection are filtered, and edge points and corner points are extracted. Then, the proposed tangent-vector-pairs based on edge and corner points are applied to evaluate the rotation term, which is significant for producing a stable solution in motion estimation. We compare our performance with two excellent open-source SLAM algorithms, Cartographer and Hector SLAM, using collected and open-access datasets in structured indoor environments. The results indicate that our method can achieve better accuracy.
    Electronic ISSN: 1424-8220
    Topics: Chemistry and Pharmacology , Electrical Engineering, Measurement and Control Technology
    Published by Molecular Diversity Preservation International
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  • 2
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    A Vision-Based Approach for the Analysis of Core Characteristics of Volcanic Ash (2021)
    Molecular Diversity Preservation International
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    Publication Date: 2021-10-29
    Description: Volcanic ash fall-out represents a serious hazard for air and road traffic. The forecasting models used to predict its time–space evolution require information about the core characteristics of volcanic particles, such as their granulometry. Typically, such information is gained by the spot direct observation of the ash collected at the ground or by using expensive instrumentation. In this paper, a vision-based methodology aimed at the estimation of ash granulometry is presented. A dedicated image processing paradigm was developed and implemented in LabVIEW™. The methodology was validated experimentally using digital reference images resembling different operating conditions. The outcome of the assessment procedure was very encouraging, showing an accuracy of the image processing algorithm of 1.76%.
    Electronic ISSN: 1424-8220
    Topics: Chemistry and Pharmacology , Electrical Engineering, Measurement and Control Technology
    Published by Molecular Diversity Preservation International
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  • 3
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    FaTEMa: A Framework for Multi-Layer Fault Tolerance in IoT Systems (2021)
    Molecular Diversity Preservation International
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    Publication Date: 2021-10-29
    Description: Fault tolerance in IoT systems is challenging to overcome due to its complexity, dynamicity, and heterogeneity. IoT systems are typically designed and constructed in layers. Every layer has its requirements and fault tolerance strategies. However, errors in one layer can propagate and cause effects on others. Thus, it is impractical to consider a centralized fault tolerance approach for an entire system. Consequently, it is vital to consider multiple layers in order to enable collaboration and information exchange when addressing fault tolerance. The purpose of this study is to propose a multi-layer fault tolerance approach, granting interconnection among IoT system layers, allowing information exchange and collaboration in order to attain the property of dependability. Therefore, we define an event-driven framework called FaTEMa (Fault Tolerance Event Manager) that creates a dedicated fault-related communication channel in order to propagate events across the levels of the system. The implemented framework assist with error detection and continued service. Additionally, it offers extension points to support heterogeneous communication protocols and evolve new capabilities. Our empirical results show that introducing FaTEMa provided improvements to the error detection and error resolution time, consequently improving system availability. In addition, the use of Fatema provided a reliability improvement and a reduction in the number of failures produced.
    Electronic ISSN: 1424-8220
    Topics: Chemistry and Pharmacology , Electrical Engineering, Measurement and Control Technology
    Published by Molecular Diversity Preservation International
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  • 4
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    System Identification of Conveyor Belt Microwave Drying Process of Polymer Foams Using Electrical Capacitance Tomography (2021)
    Molecular Diversity Preservation International
    Details
    Publication Date: 2021-10-28
    Description: The microwave drying process has a wide application in industry, including drying polymer foams after the impregnation process for sealings in the construction industry. The objective of the drying process is to reach a certain moisture in the foam by adjusting the power levels of the microwave sources. A moisture controller can be designed to achieve this goal; however, a process model is required to design model-based controllers. Since complex physics governs the microwave drying process, system identification tools are employed in this paper to exploit the process input and output information and find a simplified yet accurate model of the process. The moisture content of the foam that is the process output is measured using a designed electrical capacitance tomography (ECT) sensor. The ECT sensor estimates the 2D permittivity distribution of moving foams, which correlates with the foam moisture. Experiments are conducted to collect the ECT measurements while giving different inputs to the microwave sources. A state-space model is estimated using one of the collected datasets and is validated using the other datasets. The comparison between the model response and the actual measurements shows that the model is accurate enough to design a controller for the microwave drying process.
    Electronic ISSN: 1424-8220
    Topics: Chemistry and Pharmacology , Electrical Engineering, Measurement and Control Technology
    Published by Molecular Diversity Preservation International
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  • 5
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    Pressure-Sensitive Nano-Sheet for Optical Pressure Measurement (2021)
    Molecular Diversity Preservation International
    Details
    Publication Date: 2021-10-28
    Description: Pressure-Sensitive Paint (PSP) is a powerful measurement technique to obtain pressure distribution on a model of interest by measuring the emission intensity of the PSP coating with a camera. Since a PSP coating is prepared by applying a solution containing an organic solvent, generally, by sprayer, the properties such as the pressure- and the temperature-sensitivity depends on the skill of the person applying it. This fabrication process is one of the barriers to use of the PSP technique because of the legal restrictions on the use of organic solvents. Thus, a sticker-like PSP coating is useful because it does not require the use of organic solvent and the applying skill. In this study, we have fabricated freestanding Pressure-Sensitive Nano-Sheet (PSNS) by a sacrificial layer process using a spin-coating method. We employed Pt(II) meso-tetra(pentafluorophenyl)porphine (PtTFPP) as a pressure-sensitive dye and poly(1-trimethylsilyl-propyne) (PTMSP) and poly(L-lactic acid) (PLLA) as a polymer binder; thus, the PSNS samples based on PTMSP and PLLA were prepared. The pressure- and the temperature-sensitivity, the lifetime of the luminescence, and the quantum yield of the fabricated PSNS have been investigated. The pressure-sensitivity of PTMSP-based PSNS is higher than that of PLLA-based PSNS. Conversely, the quantum yield of PLLA-based PSNS is higher than that of PTMSP-based PSNS.
    Electronic ISSN: 1424-8220
    Topics: Chemistry and Pharmacology , Electrical Engineering, Measurement and Control Technology
    Published by Molecular Diversity Preservation International
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  • 6
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    Isolating the Unique and Generic Movement Characteristics of Highly Trained Runners (2021)
    Molecular Diversity Preservation International
    Details
    Publication Date: 2021-10-28
    Description: Human movement patterns were shown to be as unique to individuals as their fingerprints. However, some movement characteristics are more important than other characteristics for machine learning algorithms to distinguish between individuals. Here, we explored the idea that movement patterns contain unique characteristics that differentiate between individuals and generic characteristics that do not differentiate between individuals. Layer-wise relevance propagation was applied to an artificial neural network that was trained to recognize 20 male triathletes based on their respective movement patterns to derive characteristics of high/low importance for human recognition. The similarity between movement patterns that were defined exclusively through characteristics of high/low importance was then evaluated for all participants in a pairwise fashion. We found that movement patterns of triathletes overlapped minimally when they were defined by variables that were very important for a neural network to distinguish between individuals. The movement patterns overlapped substantially when defined through less important characteristics. We concluded that the unique movement characteristics of elite runners were predominantly sagittal plane movements of the spine and lower extremities during mid-stance and mid-swing, while the generic movement characteristics were sagittal plane movements of the spine during early and late stance.
    Electronic ISSN: 1424-8220
    Topics: Chemistry and Pharmacology , Electrical Engineering, Measurement and Control Technology
    Published by Molecular Diversity Preservation International
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  • 7
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    Prediction of Tool Forces in Manual Grinding Using Consumer-Grade Sensors and Machine Learning (2021)
    Molecular Diversity Preservation International
    Details
    Publication Date: 2021-10-28
    Description: Tool forces are a decisive parameter for manual grinding with hand-held power tools, which can be used to determine the productivity, quality of the work result, vibration exposition, and tool lifetime. One approach to tool force determination is the prediction of tool forces via measured operating parameters of a hand-held power tool. The problem is that the accuracy of tool force prediction with consumer-grade sensors remains unclear in manual grinding. Therefore, the accuracy of tool force prediction using Gaussian process regression is examined in a study for two hand-held angle grinders in four different applications in three directions using measurement data from an inertial measurement unit, a current sensor, and a voltage sensor. The prediction of the grinding normal force (rMAE = 11.44% and r = 0.84) and the grinding tangential force (rMAE = 18.21% and r = 0.82) for three tested applications, as well as the radial force for the application cutting with a cut-off wheel (rMAE = 19.67% and r = 0.80) is shown to be feasible. The prediction of the guiding force (rMAE = 87.02% and r = 0.37) for three tested applications is only possible to a limited extent. This study supports data acquisition and evaluation of hand-held power tools using consumer-grade sensors, such as an inertial measurement unit, in real-world applications, resulting in new potentials for product use and product development.
    Electronic ISSN: 1424-8220
    Topics: Chemistry and Pharmacology , Electrical Engineering, Measurement and Control Technology
    Published by Molecular Diversity Preservation International
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  • 8
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    LiDAR Positioning Algorithm Based on ICP and Artificial Landmarks Assistance (2021)
    Molecular Diversity Preservation International
    Details
    Publication Date: 2021-10-28
    Description: As one of the automated guided vehicle (AGV) positioning methods, the LiDAR positioning method, based on artificial landmarks, has been widely used in warehousing logistics industries in recent years. However, the traditional LiDAR positioning method based on artificial landmarks mainly depends on the three-point positioning method, the performance of which is limited due to landmarks’ layout and detection requirements. This paper proposes a LiDAR positioning algorithm based on iterative closest point (ICP) and artificial landmarks assistance. It provides improvements based on the traditional ICP algorithm. The result of positioning provided by the landmarks is used as the initial iteration ICP value. The combination of the ICP algorithm and landmarks enables the positioning algorithm to maintain a certain positioning precision when landmark detection is disturbed. By comparing the proposed algorithm with the positioning scheme developed by SICK in Germany, we prove that the combination of the ICP algorithm and landmarks can effectively improve the robustness under the premise of ensuring precision.
    Electronic ISSN: 1424-8220
    Topics: Chemistry and Pharmacology , Electrical Engineering, Measurement and Control Technology
    Published by Molecular Diversity Preservation International
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  • 9
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    Long-Term Ship Position Prediction Using Automatic Identification System (AIS) Data and End-to-End Deep Learning (2021)
    Molecular Diversity Preservation International
    Details
    Publication Date: 2021-10-28
    Description: The establishment of maritime safety and security is an important concern. Ship position prediction for maritime situational awareness (MSA), as a critical aspect of maritime safety and security, requires a longer time interval than collision avoidance and maritime traffic monitoring. However, previous studies focused mainly on shorter time-interval predictions ranging from 30 min to 10 h. A longer time-interval ship position prediction is required not only for MSA, but also for efficient allocation of ships by shipping companies in accordance with global freight demand. This study used an end-to-end tracking method that inputs the previous position of a vessel to a trained deep learning model to predict its next position with an average 24-h interval. An AIS dataset with a long-time-interval distribution in a nine-year timespan for capesize bulk carriers worldwide was used. In the first experiment, a deep learning model of the Indian Ocean was examined. Subsequently, the model performance was compared for six different oceans and six primary maritime chokepoints to investigate the influence of each area. In the third experiment, a sample location within the Malacca Strait area was selected, and the number of ships was counted daily. The results indicate that the ship position can be predicted accurately with an average time interval of 24 h using deep learning systems with AIS data.
    Electronic ISSN: 1424-8220
    Topics: Chemistry and Pharmacology , Electrical Engineering, Measurement and Control Technology
    Published by Molecular Diversity Preservation International
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  • 10
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    A High-Density EEG Study Investigating VR Film Editing and Cognitive Event Segmentation Theory (2021)
    Molecular Diversity Preservation International
    Details
    Publication Date: 2021-10-28
    Description: This paper introduces a cognitive psychological experiment that was conducted to analyze how traditional film editing methods and the application of cognitive event segmentation theory perform in virtual reality (VR). Thirty volunteers were recruited and asked to watch a series of short VR videos designed in three dimensions: time, action (characters), and space. Electroencephalograms (EEG) were recorded simultaneously during their participation. Subjective results show that any of the editing methods used would lead to an increased load and reduced immersion. Furthermore, the cognition of event segmentation theory also plays an instructive role in VR editing, with differences mainly focusing on frontal, parietal, and central regions. On this basis, visual evoked potential (VEP) analysis was performed, and the standardized low-resolution brain electromagnetic tomography algorithm (sLORETA) traceability method was used to analyze the data. The results of the VEP analysis suggest that shearing usually elicits a late event-related potential component, while the sources of VEP are mainly the frontal and parietal lobes. The insights derived from this work can be used as guidance for VR content creation, allowing VR image editing to reveal greater richness and unique beauty.
    Electronic ISSN: 1424-8220
    Topics: Chemistry and Pharmacology , Electrical Engineering, Measurement and Control Technology
    Published by Molecular Diversity Preservation International
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