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Sensors, Volume 22, Issue 1 (January-1 2022) – 408 articles

Cover Story (view full-size image): A reliable estimation of the traffic state in a network is essential, as it is the input of any traffic management strategy. However, the problem of estimating the traffic state alongside combining input data from multiple sensors is complex (e.g., noise, heterogeneity) and involves challenging data fusion. We organized an experiment campaign in an urban area in Zurich, Switzerland, to assess the accuracy of traditional and novel sensors and to propose a fusion methodology. The work focuses on capturing traffic states regarding traffic flows and travel times. Finally, we propose an estimation baseline, multiple linear regression (MLR) (5% data sample), that is compared to a final MLR model fusing the 5% sample with loop detector and traffic signal data. Results compared with ground-truth data demonstrate the effectiveness of the proposed methodology. View this paper
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18 pages, 2061 KiB  
Article
SensorHub: Multimodal Sensing in Real-Life Enables Home-Based Studies
by Jonas Chromik, Kristina Kirsten, Arne Herdick, Arpita Mallikarjuna Kappattanavar and Bert Arnrich
Sensors 2022, 22(1), 408; https://doi.org/10.3390/s22010408 - 5 Jan 2022
Cited by 6 | Viewed by 5636
Abstract
Observational studies are an important tool for determining whether the findings from controlled experiments can be transferred into scenarios that are closer to subjects’ real-life circumstances. A rigorous approach to observational studies involves collecting data from different sensors to comprehensively capture the situation [...] Read more.
Observational studies are an important tool for determining whether the findings from controlled experiments can be transferred into scenarios that are closer to subjects’ real-life circumstances. A rigorous approach to observational studies involves collecting data from different sensors to comprehensively capture the situation of the subject. However, this leads to technical difficulties especially if the sensors are from different manufacturers, as multiple data collection tools have to run simultaneously. We present SensorHub, a system that can collect data from various wearable devices from different manufacturers, such as inertial measurement units, portable electrocardiographs, portable electroencephalographs, portable photoplethysmographs, and sensors for electrodermal activity. Additionally, our tool offers the possibility to include ecological momentary assessments (EMAs) in studies. Hence, SensorHub enables multimodal sensor data collection under real-world conditions and allows direct user feedback to be collected through questionnaires, enabling studies at home. In a first study with 11 participants, we successfully used SensorHub to record multiple signals with different devices and collected additional information with the help of EMAs. In addition, we evaluated SensorHub’s technical capabilities in several trials with up to 21 participants recording simultaneously using multiple sensors with sampling frequencies as high as 1000 Hz. We could show that although there is a theoretical limitation to the transmissible data rate, in practice this limitation is not an issue and data loss is rare. We conclude that with modern communication protocols and with the increasingly powerful smartphones and wearables, a system like our SensorHub establishes an interoperability framework to adequately combine consumer-grade sensing hardware which enables observational studies in real life. Full article
(This article belongs to the Special Issue Application of Wearables in Digital Medicine)
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9 pages, 4541 KiB  
Article
Identification of Corrosion Minerals Using Shortwave Infrared Hyperspectral Imaging
by Thomas De Kerf, Georgios Pipintakos, Zohreh Zahiri, Steve Vanlanduit and Paul Scheunders
Sensors 2022, 22(1), 407; https://doi.org/10.3390/s22010407 - 5 Jan 2022
Cited by 16 | Viewed by 5068
Abstract
In this study, we propose a new method to identify corrosion minerals in carbon steel using hyperspectral imaging (HSI) in the shortwave infrared range (900–1700 nm). Seven samples were artificially corroded using a neutral salt spray test and examined using a hyperspectral camera. [...] Read more.
In this study, we propose a new method to identify corrosion minerals in carbon steel using hyperspectral imaging (HSI) in the shortwave infrared range (900–1700 nm). Seven samples were artificially corroded using a neutral salt spray test and examined using a hyperspectral camera. A normalized cross-correlation algorithm is used to identify four different corrosion minerals (goethite, magnetite, lepidocrocite and hematite), using reference spectra. A Fourier Transform Infrared spectrometer (FTIR) analysis of the scraped corrosion powders was used as a ground truth to validate the results obtained by the hyperspectral camera. This comparison shows that the HSI technique effectively detects the dominant mineral present in the samples. In addition, HSI can also accurately predict the changes in mineral composition that occur over time. Full article
(This article belongs to the Section Chemical Sensors)
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19 pages, 4485 KiB  
Article
Towards Interpretable Machine Learning for Automated Damage Detection Based on Ultrasonic Guided Waves
by Christopher Schnur, Payman Goodarzi, Yevgeniya Lugovtsova, Jannis Bulling, Jens Prager, Kilian Tschöke, Jochen Moll, Andreas Schütze and Tizian Schneider
Sensors 2022, 22(1), 406; https://doi.org/10.3390/s22010406 - 5 Jan 2022
Cited by 9 | Viewed by 3860
Abstract
Data-driven analysis for damage assessment has a large potential in structural health monitoring (SHM) systems, where sensors are permanently attached to the structure, enabling continuous and frequent measurements. In this contribution, we propose a machine learning (ML) approach for automated damage detection, based [...] Read more.
Data-driven analysis for damage assessment has a large potential in structural health monitoring (SHM) systems, where sensors are permanently attached to the structure, enabling continuous and frequent measurements. In this contribution, we propose a machine learning (ML) approach for automated damage detection, based on an ML toolbox for industrial condition monitoring. The toolbox combines multiple complementary algorithms for feature extraction and selection and automatically chooses the best combination of methods for the dataset at hand. Here, this toolbox is applied to a guided wave-based SHM dataset for varying temperatures and damage locations, which is freely available on the Open Guided Waves platform. A classification rate of 96.2% is achieved, demonstrating reliable and automated damage detection. Moreover, the ability of the ML model to identify a damaged structure at untrained damage locations and temperatures is demonstrated. Full article
(This article belongs to the Special Issue Smart Sensors for Damage Detection)
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12 pages, 2936 KiB  
Article
Inline 3D Volumetric Measurement of Moisture Content in Rice Using Regression-Based ML of RF Tomographic Imaging
by Abd Alazeez Almaleeh, Ammar Zakaria, Latifah Munirah Kamarudin, Mohd Hafiz Fazalul Rahiman, David Lorater Ndzi and Ismahadi Ismail
Sensors 2022, 22(1), 405; https://doi.org/10.3390/s22010405 - 5 Jan 2022
Cited by 3 | Viewed by 2402
Abstract
The moisture content of stored rice is dependent on the surrounding and environmental factors which in turn affect the quality and economic value of the grains. Therefore, the moisture content of grains needs to be measured frequently to ensure that optimum conditions that [...] Read more.
The moisture content of stored rice is dependent on the surrounding and environmental factors which in turn affect the quality and economic value of the grains. Therefore, the moisture content of grains needs to be measured frequently to ensure that optimum conditions that preserve their quality are maintained. The current state of the art for moisture measurement of rice in a silo is based on grab sampling or relies on single rod sensors placed randomly into the grain. The sensors that are currently used are very localized and are, therefore, unable to provide continuous measurement of the moisture distribution in the silo. To the authors’ knowledge, there is no commercially available 3D volumetric measurement system for rice moisture content in a silo. Hence, this paper presents results of work carried out using low-cost wireless devices that can be placed around the silo to measure changes in the moisture content of rice. This paper proposes a novel technique based on radio frequency tomographic imaging using low-cost wireless devices and regression-based machine learning to provide contactless non-destructive 3D volumetric moisture content distribution in stored rice grain. This proposed technique can detect multiple levels of localized moisture distributions in the silo with accuracies greater than or equal to 83.7%, depending on the size and shape of the sample under test. Unlike other approaches proposed in open literature or employed in the sector, the proposed system can be deployed to provide continuous monitoring of the moisture distribution in silos. Full article
(This article belongs to the Section Remote Sensors)
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17 pages, 5783 KiB  
Article
Proactive Guidance for Accurate UAV Landing on a Dynamic Platform: A Visual–Inertial Approach
by Ching-Wei Chang, Li-Yu Lo, Hiu Ching Cheung, Yurong Feng, An-Shik Yang, Chih-Yung Wen and Weifeng Zhou
Sensors 2022, 22(1), 404; https://doi.org/10.3390/s22010404 - 5 Jan 2022
Cited by 25 | Viewed by 3956
Abstract
This work aimed to develop an autonomous system for unmanned aerial vehicles (UAVs) to land on moving platforms such as an automobile or a marine vessel, providing a promising solution for a long-endurance flight operation, a large mission coverage range, and a convenient [...] Read more.
This work aimed to develop an autonomous system for unmanned aerial vehicles (UAVs) to land on moving platforms such as an automobile or a marine vessel, providing a promising solution for a long-endurance flight operation, a large mission coverage range, and a convenient recharging ground station. Unlike most state-of-the-art UAV landing frameworks that rely on UAV onboard computers and sensors, the proposed system fully depends on the computation unit situated on the ground vehicle/marine vessel to serve as a landing guidance system. Such a novel configuration can therefore lighten the burden of the UAV, and the computation power of the ground vehicle/marine vessel can be enhanced. In particular, we exploit a sensor fusion-based algorithm for the guidance system to perform UAV localization, whilst a control method based upon trajectory optimization is integrated. Indoor and outdoor experiments are conducted, and the results show that precise autonomous landing on a 43 cm × 43 cm platform can be performed. Full article
(This article belongs to the Topic Autonomy for Enabling the Next Generation of UAVs)
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20 pages, 10570 KiB  
Article
Motion Capture Sensor-Based Emotion Recognition Using a Bi-Modular Sequential Neural Network
by Yajurv Bhatia, ASM Hossain Bari, Gee-Sern Jison Hsu and Marina Gavrilova
Sensors 2022, 22(1), 403; https://doi.org/10.3390/s22010403 - 5 Jan 2022
Cited by 13 | Viewed by 3562
Abstract
Motion capture sensor-based gait emotion recognition is an emerging sub-domain of human emotion recognition. Its applications span a variety of fields including smart home design, border security, robotics, virtual reality, and gaming. In recent years, several deep learning-based approaches have been successful in [...] Read more.
Motion capture sensor-based gait emotion recognition is an emerging sub-domain of human emotion recognition. Its applications span a variety of fields including smart home design, border security, robotics, virtual reality, and gaming. In recent years, several deep learning-based approaches have been successful in solving the Gait Emotion Recognition (GER) problem. However, a vast majority of such methods rely on Deep Neural Networks (DNNs) with a significant number of model parameters, which lead to model overfitting as well as increased inference time. This paper contributes to the domain of knowledge by proposing a new lightweight bi-modular architecture with handcrafted features that is trained using a RMSprop optimizer and stratified data shuffling. The method is highly effective in correctly inferring human emotions from gait, achieving a micro-mean average precision of 0.97 on the Edinburgh Locomotive Mocap Dataset. It outperforms all recent deep-learning methods, while having the lowest inference time of 16.3 milliseconds per gait sample. This research study is beneficial to applications spanning various fields, such as emotionally aware assistive robotics, adaptive therapy and rehabilitation, and surveillance. Full article
(This article belongs to the Special Issue Section “Sensor Networks”: 10th Anniversary)
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21 pages, 6990 KiB  
Article
WiPg: Contactless Action Recognition Using Ambient Wi-Fi Signals
by Zhanjun Hao, Juan Niu, Xiaochao Dang and Zhiqiang Qiao
Sensors 2022, 22(1), 402; https://doi.org/10.3390/s22010402 - 5 Jan 2022
Cited by 3 | Viewed by 2488
Abstract
Motion recognition has a wide range of applications at present. Recently, motion recognition by analyzing the channel state information (CSI) in Wi-Fi packets has been favored by more and more scholars. Because CSI collected in the wireless signal environment of human activity usually [...] Read more.
Motion recognition has a wide range of applications at present. Recently, motion recognition by analyzing the channel state information (CSI) in Wi-Fi packets has been favored by more and more scholars. Because CSI collected in the wireless signal environment of human activity usually carries a large amount of human-related information, the motion-recognition model trained for a specific person usually does not work well in predicting another person’s motion. To deal with the difference, we propose a personnel-independent action-recognition model called WiPg, which is built by convolutional neural network (CNN) and generative adversarial network (GAN). According to CSI data of 14 yoga movements of 10 experimenters with different body types, model training and testing were carried out, and the recognition results, independent of bod type, were obtained. The experimental results show that the average correct rate of WiPg can reach 92.7% for recognition of the 14 yoga poses, and WiPg realizes “cross-personnel” movement recognition with excellent recognition performance. Full article
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17 pages, 620 KiB  
Article
QoS Aware and Fault Tolerance Based Software-Defined Vehicular Networks Using Cloud-Fog Computing
by Sidra Abid Syed, Munaf Rashid, Samreen Hussain, Fahad Azim, Hira Zahid, Asif Umer, Abdul Waheed, Mahdi Zareei and Cesar Vargas-Rosales
Sensors 2022, 22(1), 401; https://doi.org/10.3390/s22010401 - 5 Jan 2022
Cited by 11 | Viewed by 3097
Abstract
Software-defined network (SDN) and vehicular ad-hoc network (VANET) combined provided a software-defined vehicular network (SDVN). To increase the quality of service (QoS) of vehicle communication and to make the overall process efficient, researchers are working on VANET communication systems. Current research work has [...] Read more.
Software-defined network (SDN) and vehicular ad-hoc network (VANET) combined provided a software-defined vehicular network (SDVN). To increase the quality of service (QoS) of vehicle communication and to make the overall process efficient, researchers are working on VANET communication systems. Current research work has made many strides, but due to the following limitations, it needs further investigation and research: Cloud computing is used for messages/tasks execution instead of fog computing, which increases response time. Furthermore, a fault tolerance mechanism is used to reduce the tasks/messages failure ratio. We proposed QoS aware and fault tolerance-based software-defined V vehicular networks using Cloud-fog computing (QAFT-SDVN) to address the above issues. We provided heuristic algorithms to solve the above limitations. The proposed model gets vehicle messages through SDN nodes which are placed on fog nodes. SDN controllers receive messages from nearby SDN units and prioritize the messages in two different ways. One is the message nature way, while the other one is deadline and size way of messages prioritization. SDN controller categorized in safety and non-safety messages and forward to the destination. After sending messages to their destination, we check their acknowledgment; if the destination receives the messages, then no action is taken; otherwise, we use a fault tolerance mechanism. We send the messages again. The proposed model is implemented in CloudSIm and iFogSim, and compared with the latest models. The results show that our proposed model decreased response time by 50% of the safety and non-safety messages by using fog nodes for the SDN controller. Furthermore, we reduced the execution time of the safety and non-safety messages by up to 4%. Similarly, compared with the latest model, we reduced the task failure ratio by 20%, 15%, 23.3%, and 22.5%. Full article
(This article belongs to the Section Vehicular Sensing)
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18 pages, 2982 KiB  
Article
Implementing Machine Learning Algorithms to Classify Postures and Forecast Motions When Using a Dynamic Chair
by Ghazal Farhani, Yue Zhou, Patrick Danielson and Ana Luisa Trejos
Sensors 2022, 22(1), 400; https://doi.org/10.3390/s22010400 - 5 Jan 2022
Cited by 5 | Viewed by 2841
Abstract
Many modern jobs require long periods of sitting on a chair that may result in serious health complications. Dynamic chairs are proposed as alternatives to the traditional sitting chairs; however, previous studies have suggested that most users are not aware of their postures [...] Read more.
Many modern jobs require long periods of sitting on a chair that may result in serious health complications. Dynamic chairs are proposed as alternatives to the traditional sitting chairs; however, previous studies have suggested that most users are not aware of their postures and do not take advantage of the increased range of motion offered by the dynamic chairs. Building a system that identifies users’ postures in real time, as well as forecasts the next few postures, can bring awareness to the sitting behavior of each user. In this study, machine learning algorithms have been implemented to automatically classify users’ postures and forecast their next motions. The random forest, gradient decision tree, and support vector machine algorithms were used to classify postures. The evaluation of the trained classifiers indicated that they could successfully identify users’ postures with an accuracy above 90%. The algorithm can provide users with an accurate report of their sitting habits. A 1D-convolutional-LSTM network has also been implemented to forecast users’ future postures based on their previous motions, the model can forecast a user’s motions with high accuracy (97%). The ability of the algorithm to forecast future postures could be used to suggest alternative postures as needed. Full article
(This article belongs to the Section Biomedical Sensors)
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13 pages, 2194 KiB  
Article
Current-Induced Spin Photocurrent in GaAs at Room Temperature
by Yang Zhang, Yu Liu, Xiao-Lan Xue, Xiao-Lin Zeng, Jing Wu, Li-Wei Shi and Yong-Hai Chen
Sensors 2022, 22(1), 399; https://doi.org/10.3390/s22010399 - 5 Jan 2022
Viewed by 2034
Abstract
Circularly polarized photocurrent, observed in p-doped bulk GaAs, varies nonlinearly with the applied bias voltage at room temperature. It has been explored that this phenomenon arises from the current-induced spin polarization in GaAs. In addition, we found that the current-induced spin polarization direction [...] Read more.
Circularly polarized photocurrent, observed in p-doped bulk GaAs, varies nonlinearly with the applied bias voltage at room temperature. It has been explored that this phenomenon arises from the current-induced spin polarization in GaAs. In addition, we found that the current-induced spin polarization direction of p-doped bulk GaAs grown in the (001) direction lies in the sample plane and is perpendicular to the applied electric field, which is the same as that in GaAs quantum well. This research indicates that circularly polarized photocurrent is a new optical approach to investigate the current-induced spin polarization at room temperature. Full article
(This article belongs to the Special Issue Nanogenerators and Self-Powered Optical Sensors)
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10 pages, 5271 KiB  
Article
Issues with Modeling a Tunnel Communication Channel through a Plasma Sheath
by Anna V. Bogatskaya, Andrey E. Schegolev, Nikolay V. Klenov, Evgeniy M. Lobov, Maxim V. Tereshonok and Alexander M. Popov
Sensors 2022, 22(1), 398; https://doi.org/10.3390/s22010398 - 5 Jan 2022
Cited by 9 | Viewed by 2307
Abstract
We consider two of the most relevant problems that arise when modeling the properties of a tunnel radio communication channel through a plasma layer. First, we studied the case of the oblique incidence of electromagnetic waves on a layer of ionized gas for [...] Read more.
We consider two of the most relevant problems that arise when modeling the properties of a tunnel radio communication channel through a plasma layer. First, we studied the case of the oblique incidence of electromagnetic waves on a layer of ionized gas for two wave polarizations. The resonator parameters that provide signal reception at a wide solid angle were found. We also took into account the unavoidable presence of a protective layer between the plasma and the resonator, as well as the conducting elements of the antenna system in the dielectric itself. This provides the first complete simulation for a tunnel communication channel. Noise immunity and communication range studies were conducted for a prospective spacecraft radio line. Full article
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19 pages, 6992 KiB  
Article
Effect of Proximity, Burden, and Position on the Power Quality Accuracy Performance of Rogowski Coils
by Alessandro Mingotti, Federica Costa, Lorenzo Peretto and Roberto Tinarelli
Sensors 2022, 22(1), 397; https://doi.org/10.3390/s22010397 - 5 Jan 2022
Cited by 4 | Viewed by 2581
Abstract
Power quality evaluation is the process of assessing the actual power network parameters with respect to the ideal conditions. However, several new assets and devices among the grid include mining the voltage and current quality. For example, the power converters needed for renewable [...] Read more.
Power quality evaluation is the process of assessing the actual power network parameters with respect to the ideal conditions. However, several new assets and devices among the grid include mining the voltage and current quality. For example, the power converters needed for renewable energy sources’ connection to the grid, electric vehicles, etc., are some of the main sources of disturbances that inject high-frequency components into the grid. Consequently, instrument transformers (ITs) should be capable of measuring distorted currents and voltages with the same level of accuracy guaranteed for the ideal frequency (50–60 Hz). This is not a simple task if one considers that several other influence quantities endlessly act on the ITs. To this purpose, considering the lack of a standard, this work presents a measurement setup and specific tests for testing a commonly used type of low-power current transformer, the Rogowski coil (RC). In particular, the accuracy performance (ratio error and phase displacement) of the RCs was evaluated when measuring distorted signals while other influence quantities affected the RCs. Such quantities included positioning, burden, and magnetic field. The results indicate which quantities (or combination of them) have the greatest effect on the RC’s accuracy performance. Full article
(This article belongs to the Special Issue Sensors for Measurements and Diagnostic in Electrical Power Systems)
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21 pages, 42156 KiB  
Article
MIMO Radar Parallel Simulation System Based on CPU/GPU Architecture
by Gaogao Liu, Wenbo Yang, Peng Li, Guodong Qin, Jingjing Cai, Youming Wang, Shuai Wang, Ning Yue and Dongjie Huang
Sensors 2022, 22(1), 396; https://doi.org/10.3390/s22010396 - 5 Jan 2022
Cited by 8 | Viewed by 3794
Abstract
The data volume and computation task of MIMO radar is huge; a very high-speed computation is necessary for its real-time processing. In this paper, we mainly study the time division MIMO radar signal processing flow, propose an improved MIMO radar signal processing algorithm, [...] Read more.
The data volume and computation task of MIMO radar is huge; a very high-speed computation is necessary for its real-time processing. In this paper, we mainly study the time division MIMO radar signal processing flow, propose an improved MIMO radar signal processing algorithm, raising the MIMO radar algorithm processing speed combined with the previous algorithms, and, on this basis, a parallel simulation system for the MIMO radar based on the CPU/GPU architecture is proposed. The outer layer of the framework is coarse-grained with OpenMP for acceleration on the CPU, and the inner layer of fine-grained data processing is accelerated on the GPU. Its performance is significantly faster than the serial computing equipment, and satisfactory acceleration effects have been achieved in the CPU/GPU architecture simulation. The experimental results show that the MIMO radar parallel simulation system with CPU/GPU architecture greatly improves the computing power of the CPU-based method. Compared with the serial sequential CPU method, GPU simulation achieves a speedup of 130 times. In addition, the MIMO radar signal processing parallel simulation system based on the CPU/GPU architecture has a performance improvement of 13%, compared to the GPU-only method. Full article
(This article belongs to the Section Radar Sensors)
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12 pages, 2315 KiB  
Article
Heat Stroke Prevention in Hot Specific Occupational Environment Enhanced by Supervised Machine Learning with Personalized Vital Signs
by Takunori Shimazaki, Daisuke Anzai, Kenta Watanabe, Atsushi Nakajima, Mitsuhiro Fukuda and Shingo Ata
Sensors 2022, 22(1), 395; https://doi.org/10.3390/s22010395 - 5 Jan 2022
Cited by 8 | Viewed by 3001
Abstract
Recently, wet-bulb globe temperature (WBGT) has attracted a lot of attention as a useful index for measuring heat strokes even when core body temperature cannot be available for the prevention. However, because the WBGT is only valid in the vicinity of the WBGT [...] Read more.
Recently, wet-bulb globe temperature (WBGT) has attracted a lot of attention as a useful index for measuring heat strokes even when core body temperature cannot be available for the prevention. However, because the WBGT is only valid in the vicinity of the WBGT meter, the actual ambient heat could be different even in the same room owing to ventilation, clothes, and body size, especially in hot specific occupational environments. To realize reliable heat stroke prevention in hot working places, we proposed a new personalized vital sign index, which is combined with several types of vital data, including the personalized heat strain temperature (pHST) index based on the temperature/humidity measurement to adjust the WBGT at the individual level. In this study, a wearable device was equipped with the proposed pHST meter, a heart rate monitor, and an accelerometer. Additionally, supervised machine learning based on the proposed personalized vital index was introduced to improve the prevention accuracy. Our developed system with the proposed vital sign index achieved a prevention accuracy of 85.2% in a hot occupational experiment in the summer season, where the true positive rate and true negative rate were 96.3% and 83.7%, respectively. Full article
(This article belongs to the Section Wearables)
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39 pages, 703 KiB  
Review
Establishing A Sustainable Low-Cost Air Quality Monitoring Setup: A Survey of the State-of-the-Art
by Mannam Veera Narayana, Devendra Jalihal and S. M. Shiva Nagendra
Sensors 2022, 22(1), 394; https://doi.org/10.3390/s22010394 - 5 Jan 2022
Cited by 49 | Viewed by 6788
Abstract
Low-cost sensors (LCS) are becoming popular for air quality monitoring (AQM). They promise high spatial and temporal resolutions at low-cost. In addition, citizen science applications such as personal exposure monitoring can be implemented effortlessly. However, the reliability of the data is questionable due [...] Read more.
Low-cost sensors (LCS) are becoming popular for air quality monitoring (AQM). They promise high spatial and temporal resolutions at low-cost. In addition, citizen science applications such as personal exposure monitoring can be implemented effortlessly. However, the reliability of the data is questionable due to various error sources involved in the LCS measurement. Furthermore, sensor performance drift over time is another issue. Hence, the adoption of LCS by regulatory agencies is still evolving. Several studies have been conducted to improve the performance of low-cost sensors. This article summarizes the existing studies on the state-of-the-art of LCS for AQM. We conceptualize a step by step procedure to establish a sustainable AQM setup with LCS that can produce reliable data. The selection of sensors, calibration and evaluation, hardware setup, evaluation metrics and inferences, and end user-specific applications are various stages in the LCS-based AQM setup we propose. We present a critical analysis at every step of the AQM setup to obtain reliable data from the low-cost measurement. Finally, we conclude this study with future scope to improve the availability of air quality data. Full article
(This article belongs to the Section Environmental Sensing)
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19 pages, 3641 KiB  
Article
Structural Damage Identification Based on Transmissibility in Time Domain
by Yunfeng Zou, Xuandong Lu, Jinsong Yang, Tiantian Wang and Xuhui He
Sensors 2022, 22(1), 393; https://doi.org/10.3390/s22010393 - 5 Jan 2022
Cited by 5 | Viewed by 2272
Abstract
Structural damage identification technology is of great significance to improve the reliability and safety of civil structures and has attracted much attention in the study of structural health monitoring. In this paper, a novel structural damage identification method based on transmissibility in the [...] Read more.
Structural damage identification technology is of great significance to improve the reliability and safety of civil structures and has attracted much attention in the study of structural health monitoring. In this paper, a novel structural damage identification method based on transmissibility in the time domain is proposed. The method takes the discrepancy of transmissibility of structure response in the time domain before and after damage as the basis of finite element model updating. The damage is located and quantified through iteration by minimizing the difference between the measurements at gauge locations and the reconstruction response extrapolated by the finite element model. Taking advantage of the response reconstruction method based on empirical mode decomposition, damage information can be obtained in the absence of prior knowledge on excitation. Moreover, this method directly collects time-domain data for identification without modal identification and frequent time–frequency conversion, which can greatly improve efficiency on the premise of ensuring accuracy. A numerical example is used to demonstrate the overall damage identification method, and the study of measurement noise shows that the method has strong robustness. Finally, the present work investigates the method through a simply supported overhanging beam. The experiments collect the vibration strain signals of the beam via resistance strain gauges. The comparison between identification results and theoretical values shows the effectiveness and accuracy of the method. Full article
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20 pages, 8548 KiB  
Article
Electrical Discharges in Oil-Lubricated Rolling Contacts and Their Detection Using Electrostatic Sensing Technique
by Kamran Esmaeili, Ling Wang, Terry J. Harvey, Neil M. White and Walter Holweger
Sensors 2022, 22(1), 392; https://doi.org/10.3390/s22010392 - 5 Jan 2022
Cited by 14 | Viewed by 2742
Abstract
The reliability of rolling element bearings has been substantially undermined by the presence of parasitic and stray currents. Electrical discharges can occur between the raceway and the rolling elements and it has been previously shown that these discharges at relatively high current density [...] Read more.
The reliability of rolling element bearings has been substantially undermined by the presence of parasitic and stray currents. Electrical discharges can occur between the raceway and the rolling elements and it has been previously shown that these discharges at relatively high current density levels can result in fluting and corrugation damages. Recent publications have shown that for a bearing operating at specific mechanical conditions (load, temperature, speed, and slip), electrical discharges at low current densities (<1 mA/mm2) may substantially reduce bearing life due to the formation of white etching cracks (WECs) in bearing components, often in junction with lubricants. To date, limited studies have been conducted to understand the electrical discharges at relatively low current densities (<1 mA/mm2), partially due to the lack of robust techniques for in-situ quantification of discharges. This study, using voltage measurement and electrostatic sensors, investigates discharges in an oil-lubricated steel-steel rolling contact on a TE74 twin-roller machine under a wide range of electrical and mechanical conditions. The results show that the discharges events between the rollers are influenced by temperature, load, and speed due to changes in the lubricant film thickness and contact area, and the sensors are effective in detecting, characterizing and quantifying the discharges. Hence, these sensors can be effectively used to study the influence of discharges on WEC formation. Full article
(This article belongs to the Section State-of-the-Art Sensors Technologies)
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18 pages, 5111 KiB  
Article
Constrained ESKF for UAV Positioning in Indoor Corridor Environment Based on IMU and WiFi
by Zhonghan Li and Yongbo Zhang
Sensors 2022, 22(1), 391; https://doi.org/10.3390/s22010391 - 5 Jan 2022
Cited by 15 | Viewed by 2793
Abstract
The indoor autonomous navigation of unmanned aerial vehicles (UAVs) is the current research hotspot. Unlike the outdoor broad environment, the indoor environment is unknown and complicated. Global Navigation Satellite System (GNSS) signals are easily blocked and reflected because of complex indoor spatial features, [...] Read more.
The indoor autonomous navigation of unmanned aerial vehicles (UAVs) is the current research hotspot. Unlike the outdoor broad environment, the indoor environment is unknown and complicated. Global Navigation Satellite System (GNSS) signals are easily blocked and reflected because of complex indoor spatial features, which make it impossible to achieve positioning and navigation indoors relying on GNSS. This article proposes a set of indoor corridor environment positioning methods based on the integration of WiFi and IMU. The zone partition-based Weighted K Nearest Neighbors (WKNN) algorithm is used to achieve higher WiFi-based positioning accuracy. On the basis of the Error-State Kalman Filter (ESKF) algorithm, WiFi-based and IMU-based methods are fused together and realize higher positioning accuracy. The probability-based optimization method is used for further accuracy improvement. After data fusion, the positioning accuracy increased by 51.09% compared to the IMU-based algorithm and by 66.16% compared to the WiFi-based algorithm. After optimization, the positioning accuracy increased by 20.9% compared to the ESKF-based data fusion algorithm. All of the above results prove that methods based on WiFi and IMU (low-cost sensors) are very capable of obtaining high indoor positioning accuracy. Full article
(This article belongs to the Topic Autonomy for Enabling the Next Generation of UAVs)
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11 pages, 5151 KiB  
Article
Negative Effects of Annealed Seed Layer on the Performance of ZnO-Nanorods Based Nitric Oxide Gas Sensor
by Pragya Singh, Firman Mangasa Simanjuntak, Li-Lun Hu, Tseung-Yuen Tseng, Hsiao-Wen Zan and Jinn P. Chu
Sensors 2022, 22(1), 390; https://doi.org/10.3390/s22010390 - 5 Jan 2022
Cited by 6 | Viewed by 2409
Abstract
Nitric oxide (NO) is a toxic gas, which is dangerous for human health and causes many respiratory infections, poisoning, and lung damage. In this work, we have successfully grown ZnO nanorod film on annealed ZnO seed layer in different ambient temperatures, and the [...] Read more.
Nitric oxide (NO) is a toxic gas, which is dangerous for human health and causes many respiratory infections, poisoning, and lung damage. In this work, we have successfully grown ZnO nanorod film on annealed ZnO seed layer in different ambient temperatures, and the morphology of the nanorods sensing layer that affects the gas sensing response to nitric oxide (NO) gas were investigated. To acknowledge the effect of annealing treatment, the devices were fabricated with annealed seed layers in air and argon ambient at 300 °C and 500 °C for 1 h. To simulate a vertical device structure, a silver nanowire electrode covered in ZnO nanorod film was placed onto the hydrothermal grown ZnO nanorod film. We found that annealing treatment changes the seed layer’s grain size and defect concentration and is responsible for this phenomenon. The I–V and gas sensing characteristics were dependent on the oxygen defects concentration and porosity of nanorods to react with the target gas. The resulting as-deposited ZnO seed layer shows better sensing response than that annealed in an air and argon environment due to the nanorod morphology and variation in oxygen defect concentration. At room temperature, the devices show good sensing response to NO concentration of 10 ppb and up to 100 ppb. Shortly, these results can be beneficial in the NO breath detection for patients with chronic inflammatory airway disease, such as asthma. Full article
(This article belongs to the Section Chemical Sensors)
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17 pages, 2812 KiB  
Article
Design and Validation of an Augmented Reality Teaching System for Primary Logic Programming Education
by Chi-Yi Tsai and Yu-Cheng Lai
Sensors 2022, 22(1), 389; https://doi.org/10.3390/s22010389 - 5 Jan 2022
Cited by 13 | Viewed by 2897
Abstract
Programming is a skill that requires high levels of logical thinking and problem-solving abilities. According to the Curriculum Guidelines for the 12-Year Basic Education currently implemented in Taiwan, programming has been included in the mandatory courses of middle and high schools. Nevertheless, the [...] Read more.
Programming is a skill that requires high levels of logical thinking and problem-solving abilities. According to the Curriculum Guidelines for the 12-Year Basic Education currently implemented in Taiwan, programming has been included in the mandatory courses of middle and high schools. Nevertheless, the guidelines simply recommend that elementary schools conduct fundamental instructions in related fields during alternative learning periods. This may result in the problem of a rough transition in programming learning for middle school freshmen. To alleviate this problem, this study proposes an augmented reality (AR) logic programming teaching system that combines AR technologies and game-based teaching material designs on the basis of the fundamental concepts for seventh-grade structured programming. This system can serve as an articulation curriculum for logic programming in primary education. Thus, students are able to develop basic programming logic concepts through AR technologies by performing simple command programming. This study conducted an experiment using the factor-based quasi-experimental research design and questionnaire survey method, with 42 fifth and sixth graders enrolled as the experimental subjects. The statistical analysis showed the following results: In terms of learning effectiveness, both AR-based and traditional learning groups displayed a significant performance. However, of the two groups, the former achieved more significant effectiveness in the posttest results. Regarding learning motivation, according to the evaluation results of the Attention, Relevance, Confidence, and Satisfaction (ARCS) motivation model, the AR-based learning group manifested significantly higher levels of learning motivation than the traditional learning group, with particularly significant differences observed in the dimension of Attention. Therefore, the experimental results validate that the proposed AR-based logic programming teaching system has significant positive effects on enhancing students’ learning effectiveness and motivation. Full article
(This article belongs to the Special Issue Computational Intelligence and Intelligent Contents (CIIC))
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18 pages, 810 KiB  
Article
Bayesian Nonparametric Modeling for Predicting Dynamic Dependencies in Multiple Object Tracking
by Bahman Moraffah and Antonia Papandreou-Suppappola
Sensors 2022, 22(1), 388; https://doi.org/10.3390/s22010388 - 5 Jan 2022
Cited by 3 | Viewed by 1973
Abstract
The paper considers the problem of tracking an unknown and time-varying number of unlabeled moving objects using multiple unordered measurements with unknown association to the objects. The proposed tracking approach integrates Bayesian nonparametric modeling with Markov chain Monte Carlo methods to estimate the [...] Read more.
The paper considers the problem of tracking an unknown and time-varying number of unlabeled moving objects using multiple unordered measurements with unknown association to the objects. The proposed tracking approach integrates Bayesian nonparametric modeling with Markov chain Monte Carlo methods to estimate the parameters of each object when present in the tracking scene. In particular, we adopt the dependent Dirichlet process (DDP) to learn the multiple object state prior by exploiting inherent dynamic dependencies in the state transition using the dynamic clustering property of the DDP. Using the DDP to draw the mixing measures, Dirichlet process mixtures are used to learn and assign each measurement to its associated object identity. The Bayesian posterior to estimate the target trajectories is efficiently implemented using a Gibbs sampler inference scheme. A second tracking approach is proposed that replaces the DDP with the dependent Pitman–Yor process in order to allow for a higher flexibility in clustering. The improved tracking performance of the new approaches is demonstrated by comparison to the generalized labeled multi-Bernoulli filter. Full article
(This article belongs to the Special Issue Decentralized Systems for Secure and Interoperable IoT Applications)
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19 pages, 812 KiB  
Article
Multisensor Data Fusion for Localization of Pollution Sources in Wastewater Networks
by Krystian Chachuła, Tomasz Michał Słojewski and Robert Nowak
Sensors 2022, 22(1), 387; https://doi.org/10.3390/s22010387 - 5 Jan 2022
Cited by 5 | Viewed by 1799
Abstract
Illegal discharges of pollutants into sewage networks are a growing problem in large European cities. Such events often require restarting wastewater treatment plants, which cost up to a hundred thousand Euros. A system for localization and quantification of pollutants in utility networks could [...] Read more.
Illegal discharges of pollutants into sewage networks are a growing problem in large European cities. Such events often require restarting wastewater treatment plants, which cost up to a hundred thousand Euros. A system for localization and quantification of pollutants in utility networks could discourage such behavior and indicate a culprit if it happens. We propose an enhanced algorithm for multisensor data fusion for the detection, localization, and quantification of pollutants in wastewater networks. The algorithm processes data from multiple heterogeneous sensors in real-time, producing current estimates of network state and alarms if one or many sensors detect pollutants. Our algorithm models the network as a directed acyclic graph, uses adaptive peak detection, estimates the amount of specific compounds, and tracks the pollutant using a Kalman filter. We performed numerical experiments for several real and artificial sewage networks, and measured the quality of discharge event reconstruction. We report the correctness and performance of our system. We also propose a method to assess the importance of specific sensor locations. The experiments show that the algorithm’s success rate is equal to sensor coverage of the network. Moreover, the median distance between nodes pointed out by the fusion algorithm and nodes where the discharge was introduced equals zero when more than half of the network nodes contain sensors. The system can process around 5000 measurements per second, using 1 MiB of memory per 4600 measurements plus a constant of 97 MiB, and it can process 20 tracks per second, using 1.3 MiB of memory per 100 tracks. Full article
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32 pages, 1948 KiB  
Systematic Review
Caveats and Recommendations to Assess the Validity and Reliability of Cycling Power Meters: A Systematic Scoping Review
by Anthony Bouillod, Georges Soto-Romero, Frederic Grappe, William Bertucci, Emmanuel Brunet and Johan Cassirame
Sensors 2022, 22(1), 386; https://doi.org/10.3390/s22010386 - 5 Jan 2022
Cited by 6 | Viewed by 5749
Abstract
A large number of power meters have become commercially available during the last decades to provide power output (PO) measurement. Some of these power meters were evaluated for validity in the literature. This study aimed to perform a review of the available literature [...] Read more.
A large number of power meters have become commercially available during the last decades to provide power output (PO) measurement. Some of these power meters were evaluated for validity in the literature. This study aimed to perform a review of the available literature on the validity of cycling power meters. PubMed, SPORTDiscus, and Google Scholar have been explored with PRISMA methodology. A total of 74 studies have been extracted for the reviewing process. Validity is a general quality of the measurement determined by the assessment of different metrological properties: Accuracy, sensitivity, repeatability, reproducibility, and robustness. Accuracy was most often studied from the metrological property (74 studies). Reproducibility was the second most studied (40 studies) property. Finally, repeatability, sensitivity, and robustness were considerably less studied with only 7, 5, and 5 studies, respectively. The SRM power meter is the most used as a gold standard in the studies. Moreover, the number of participants was very different among them, from 0 (when using a calibration rig) to 56 participants. The PO tested was up to 1700 W, whereas the pedalling cadence ranged between 40 and 180 rpm, including submaximal and maximal exercises. Other exercise conditions were tested, such as torque, position, temperature, and vibrations. This review provides some caveats and recommendations when testing the validity of a cycling power meter, including all of the metrological properties (accuracy, sensitivity, repeatability, reproducibility, and robustness) and some exercise conditions (PO range, sprint, pedalling cadence, torque, position, participant, temperature, vibration, and field test). Full article
(This article belongs to the Section Physical Sensors)
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16 pages, 1803 KiB  
Article
Kinematic Analysis of 360° Turning in Stroke Survivors Using Wearable Motion Sensors
by Masoud Abdollahi, Pranav Madhav Kuber, Michael Shiraishi, Rahul Soangra and Ehsan Rashedi
Sensors 2022, 22(1), 385; https://doi.org/10.3390/s22010385 - 5 Jan 2022
Cited by 15 | Viewed by 2865
Abstract
Background: A stroke often bequeaths surviving patients with impaired neuromusculoskeletal systems subjecting them to increased risk of injury (e.g., due to falls) even during activities of daily living. The risk of injuries to such individuals can be related to alterations in their movement. [...] Read more.
Background: A stroke often bequeaths surviving patients with impaired neuromusculoskeletal systems subjecting them to increased risk of injury (e.g., due to falls) even during activities of daily living. The risk of injuries to such individuals can be related to alterations in their movement. Using inertial sensors to record the digital biomarkers during turning could reveal the relevant turning alterations. Objectives: In this study, movement alterations in stroke survivors (SS) were studied and compared to healthy individuals (HI) in the entire turning task due to its requirement of synergistic application of multiple bodily systems. Methods: The motion of 28 participants (14 SS, 14 HI) during turning was captured using a set of four Inertial Measurement Units, placed on their sternum, sacrum, and both shanks. The motion signals were segmented using the temporal and spatial segmentation of the data from the leading and trailing shanks. Several kinematic parameters, including the range of motion and angular velocity of the four body segments, turning time, the number of cycles involved in the turning task, and portion of the stance phase while turning, were extracted for each participant. Results: The results of temporal processing of the data and comparison between the SS and HI showed that SS had more cycles involved in turning, turn duration, stance phase, range of motion in flexion–extension, and lateral bending for sternum and sacrum (p-value < 0.035). However, HI exhibited larger angular velocity in flexion–extension for all four segments. The results of the spatial processing, in agreement with the prior method, showed no difference between the range of motion in flexion–extension of both shanks (p-value > 0.08). However, it revealed that the angular velocity of the shanks of leading and trailing legs in the direction of turn was more extensive in the HI (p-value < 0.01). Conclusions: The changes in upper/lower body segments of SS could be adequately identified and quantified by IMU sensors. The identified kinematic changes in SS, such as the lower flexion–extension angular velocity of the four body segments and larger lateral bending range of motion in sternum and sacrum compared to HI in turning, could be due to the lack of proper core stability and effect of turning on vestibular system of the participants. This research could facilitate the development of a targeted and efficient rehabilitation program focusing on the affected aspects of turning movement for the stroke community. Full article
(This article belongs to the Special Issue Multi-Sensor Systems for Object Tracking)
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17 pages, 10162 KiB  
Article
On-Demand Charging Management Model and Its Optimization for Wireless Renewable Sensor Networks
by Sandrine Mukase, Kewen Xia, Abubakar Umar and Eunice Oluwabunmi Owoola
Sensors 2022, 22(1), 384; https://doi.org/10.3390/s22010384 - 5 Jan 2022
Cited by 4 | Viewed by 1651
Abstract
Nowadays, wireless energy transfer (WET) is a new strategy that has the potential to essentially resolve energy and lifespan issues in a wireless sensor network (WSN). We investigate the process of a wireless energy transfer-based wireless sensor network via a wireless mobile charging [...] Read more.
Nowadays, wireless energy transfer (WET) is a new strategy that has the potential to essentially resolve energy and lifespan issues in a wireless sensor network (WSN). We investigate the process of a wireless energy transfer-based wireless sensor network via a wireless mobile charging device (WMCD) and develop a periodic charging scheme to keep the network operative. This paper aims to reduce the overall system energy consumption and total distance traveled, and increase the ratio of charging device vacation time. We propose an energy renewable management system based on particle swarm optimization (ERMS-PSO) to achieve energy savings based on an investigation of the total energy consumption. In this new strategy, we introduce two sets of energies called emin (minimum energy level) and ethresh (threshold energy level). When the first node reaches the emin, it will inform the base station, which will calculate all nodes that fall under ethresh and send a WMCD to charge them in one cycle. These settled energy levels help to manage when a sensor node needs to be charged before reaching the general minimum energy in the node and will help the network to operate for a long time without failing. In contrast to previous schemes in which the wireless mobile charging device visited and charged all nodes for each cycle, in our strategy, the charging device should visit only a few nodes that use more energy than others. Mathematical outcomes demonstrate that our proposed strategy can considerably reduce the total energy consumption and distance traveled by the charging device and increase its vacation time ratio while retaining performance, and ERMS-PSO is more practical for real-world networks because it can keep the network operational with less complexity than other schemes. Full article
(This article belongs to the Section Sensor Networks)
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23 pages, 2445 KiB  
Article
Discovering Stick-Slip-Resistant Servo Control Algorithm Using Genetic Programming
by Andrzej Bożek
Sensors 2022, 22(1), 383; https://doi.org/10.3390/s22010383 - 5 Jan 2022
Cited by 2 | Viewed by 2024
Abstract
The stick-slip is one of negative phenomena caused by friction in servo systems. It is a consequence of complicated nonlinear friction characteristics, especially the so-called Stribeck effect. Much research has been done on control algorithms suppressing the stick-slip, but no simple solution has [...] Read more.
The stick-slip is one of negative phenomena caused by friction in servo systems. It is a consequence of complicated nonlinear friction characteristics, especially the so-called Stribeck effect. Much research has been done on control algorithms suppressing the stick-slip, but no simple solution has been found. In this work, a new approach is proposed based on genetic programming. The genetic programming is a machine learning technique constructing symbolic representation of programs or expressions by evolutionary process. In this way, the servo control algorithm optimally suppressing the stick-slip is discovered. The GP training is conducted on a simulated servo system, as the experiments would last too long in real-time. The feedback for the control algorithm is based on the sensors of position, velocity and acceleration. Variants with full and reduced sensor sets are considered. Ideal and quantized position measurements are also analyzed. The results reveal that the genetic programming can successfully discover a control algorithm effectively suppressing the stick-slip. However, it is not an easy task and relatively large size of population and a big number of generations are required. Real measurement results in worse control quality. Acceleration feedback has no apparent impact on the algorithms performance, while velocity feedback is important. Full article
(This article belongs to the Collection Sensors and Data Processing in Robotics)
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16 pages, 8420 KiB  
Article
Deterioration Level Estimation Based on Convolutional Neural Network Using Confidence-Aware Attention Mechanism for Infrastructure Inspection
by Naoki Ogawa, Keisuke Maeda, Takahiro Ogawa and Miki Haseyama
Sensors 2022, 22(1), 382; https://doi.org/10.3390/s22010382 - 5 Jan 2022
Cited by 5 | Viewed by 1994
Abstract
This paper presents deterioration level estimation based on convolutional neural networks using a confidence-aware attention mechanism for infrastructure inspection. Spatial attention mechanisms try to highlight the important regions in feature maps for estimation by using an attention map. The attention mechanism using an [...] Read more.
This paper presents deterioration level estimation based on convolutional neural networks using a confidence-aware attention mechanism for infrastructure inspection. Spatial attention mechanisms try to highlight the important regions in feature maps for estimation by using an attention map. The attention mechanism using an effective attention map can improve feature maps. However, the conventional attention mechanisms have a problem as they fail to highlight important regions for estimation when an ineffective attention map is mistakenly used. To solve the above problem, this paper introduces the confidence-aware attention mechanism that reduces the effect of ineffective attention maps by considering the confidence corresponding to the attention map. The confidence is calculated from the entropy of the estimated class probabilities when generating the attention map. Because the proposed method can effectively utilize the attention map by considering the confidence, it can focus more on the important regions in the final estimation. This is the most significant contribution of this paper. The experimental results using images from actual infrastructure inspections confirm the performance improvement of the proposed method in estimating the deterioration level. Full article
(This article belongs to the Section Sensor Networks)
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13 pages, 5087 KiB  
Article
On Theoretical and Numerical Aspects of Bifurcations and Hysteresis Effects in Kinetic Energy Harvesters
by Grzegorz Litak, Jerzy Margielewicz, Damian Gąska, Andrzej Rysak and Carlo Trigona
Sensors 2022, 22(1), 381; https://doi.org/10.3390/s22010381 - 5 Jan 2022
Cited by 8 | Viewed by 1938
Abstract
The piezoelectric energy-harvesting system with double-well characteristics and hysteresis in the restoring force is studied. The proposed system consists of a bistable oscillator based on a cantilever beam structure. The elastic force potential is modified by magnets. The hysteresis is an additional effect [...] Read more.
The piezoelectric energy-harvesting system with double-well characteristics and hysteresis in the restoring force is studied. The proposed system consists of a bistable oscillator based on a cantilever beam structure. The elastic force potential is modified by magnets. The hysteresis is an additional effect of the composite beam considered in this system, and it effects the modal solution with specific mass distribution. Consequently, the modal response is a compromise between two overlapping, competing shapes. The simulation results show evolution in the single potential well solution, and bifurcations into double-well solutions with the hysteretic effect. The maximal Lyapunov exponent indicated the appearance of chaotic solutions. Inclusion of the shape branch overlap parameter reduces the distance between the external potential barriers and leads to a large-amplitude solution and simultaneously higher voltage output with smaller excitation force. The overlap parameter works in the other direction: the larger the overlap value, the smaller the voltage output. Presumably, the successful jump though the potential barrier is accompanied by an additional switch between the corresponding shapes. Full article
(This article belongs to the Special Issue Vibration Energy Harvesting for Wireless Sensors)
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17 pages, 7614 KiB  
Article
Classification of the Sidewalk Condition Using Self-Supervised Transfer Learning for Wheelchair Safety Driving
by Ha-Yeong Yoon, Jung-Hwa Kim and Jin-Woo Jeong
Sensors 2022, 22(1), 380; https://doi.org/10.3390/s22010380 - 5 Jan 2022
Cited by 10 | Viewed by 2657
Abstract
The demand for wheelchairs has increased recently as the population of the elderly and patients with disorders increases. However, society still pays less attention to infrastructure that can threaten the wheelchair user, such as sidewalks with cracks/potholes. Although various studies have been proposed [...] Read more.
The demand for wheelchairs has increased recently as the population of the elderly and patients with disorders increases. However, society still pays less attention to infrastructure that can threaten the wheelchair user, such as sidewalks with cracks/potholes. Although various studies have been proposed to recognize such challenges, they mainly depend on RGB images or IMU sensors, which are sensitive to outdoor conditions such as low illumination, bad weather, and unavoidable vibrations, resulting in unsatisfactory and unstable performance. In this paper, we introduce a novel system based on various convolutional neural networks (CNNs) to automatically classify the condition of sidewalks using images captured with depth and infrared modalities. Moreover, we compare the performance of training CNNs from scratch and the transfer learning approach, where the weights learned from the natural image domain (e.g., ImageNet) are fine-tuned to the depth and infrared image domain. In particular, we propose applying the ResNet-152 model pre-trained with self-supervised learning during transfer learning to leverage better image representations. Performance evaluation on the classification of the sidewalk condition was conducted with 100% and 10% of training data. The experimental results validate the effectiveness and feasibility of the proposed approach and bring future research directions. Full article
(This article belongs to the Special Issue Integration of Advanced Sensors in Assistive Robotic Technology)
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12 pages, 2200 KiB  
Communication
Diagnosing Extrusion Process Based on Displacement Signal and Simple Decision Tree Classifier
by Grzegorz Piecuch and Rafał Żyła
Sensors 2022, 22(1), 379; https://doi.org/10.3390/s22010379 - 5 Jan 2022
Cited by 1 | Viewed by 1905
Abstract
The article presents an extensive analysis of the literature related to the diagnosis of the extrusion process and proposes a new, unique method. This method is based on the observation of the punch displacement signal in relation to the die, and then approximation [...] Read more.
The article presents an extensive analysis of the literature related to the diagnosis of the extrusion process and proposes a new, unique method. This method is based on the observation of the punch displacement signal in relation to the die, and then approximation of this signal using a polynomial. It is difficult to find in the literature even an attempt to solve the problem of diagnosing the extrusion process by means of a simple distance measurement. The dominant feature is the use of strain gauges, force sensors or even accelerometers. However, the authors managed to use the displacement signal, and it was considered a key element of the method presented in the article. The aim of the authors was to propose an effective method, simple to implement and not requiring high computing power, with the possibility of acting and making decisions in real time. At the input of the classifier, authors provided the determined polynomial coefficients and the SSE (Sum of Squared Errors) value. Based on the SSE values only, the decision tree algorithm performed anomaly detection with an accuracy of 98.36%. With regard to the duration of the experiment (single extrusion process), the decision was made after 0.44 s, which is on average 26.7% of the extrusion experiment duration. The article describes in detail the method and the results achieved. Full article
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