-
FPGA-Microprocessor Based Sensor for Faults Detection in Induction Motors Using Time-Frequency and Machine Learning Methods Sensors (IF 3.9) Pub Date : 2024-04-22 Roque Alfredo Osornio-Rios, Isaias Cueva-Perez, Alvaro Ivan Alvarado-Hernandez, Larisa Dunai, Israel Zamudio-Ramirez, Jose Alfonso Antonino-Daviu
Induction motors (IM) play a fundamental role in the industrial sector because they are robust, efficient, and low-cost machines. Changes in the environment, installation errors, or modifications to working conditions can generate faults in induction motors. The trend on IM fault detection is focused on the design techniques and sensors capable of evaluating multiple faults with various signals using
-
Gain and Bandwidth Enhancement of 3D-Printed Short Backfire Antennas Using Rim Flaring and Iris Matching Sensors (IF 3.9) Pub Date : 2024-04-22 Yewande Mariam Aragbaiye, Dustin Isleifson
In this article, we present new design techniques to improve the gain and impedance bandwidth of short backfire antennas. For the gain enhancement procedure, our approach was to flare the rim of the antenna, which simultaneously led to an increase in the impedance bandwidth of the antenna. Parametric studies were carried out to obtain the optimal flaring angle. The peak realized gain was obtained as
-
Recent Advances in Intelligent Algorithms for Fault Detection and Diagnosis Sensors (IF 3.9) Pub Date : 2024-04-22 Paolo Mercorelli
Fault-finding diagnostics is a model-driven approach that identifies a system’s malfunctioning portion. It uses residual generators to identify faults, and various methods like isolation techniques and structural analysis are used. However, diagnostic equipment doesn’t measure the remaining signal-to-noise ratio. Residual selection identifies fault-detecting generators. Fault detective diagnostic (FDD)
-
Prototype Learning for Medical Time Series Classification via Human–Machine Collaboration Sensors (IF 3.9) Pub Date : 2024-04-22 Jia Xie, Zhu Wang, Zhiwen Yu, Yasan Ding, Bin Guo
Deep neural networks must address the dual challenge of delivering high-accuracy predictions and providing user-friendly explanations. While deep models are widely used in the field of time series modeling, deciphering the core principles that govern the models’ outputs remains a significant challenge. This is crucial for fostering the development of trusted models and facilitating domain expert validation
-
Adaptive Cruise Control Based on Safe Deep Reinforcement Learning Sensors (IF 3.9) Pub Date : 2024-04-22 Rui Zhao, Kui Wang, Wenbo Che, Yun Li, Yuze Fan, Fei Gao
Adaptive cruise control (ACC) enables efficient, safe, and intelligent vehicle control by autonomously adjusting speed and ensuring a safe following distance from the vehicle in front. This paper proposes a novel adaptive cruise system, namely the Safety-First Reinforcement Learning Adaptive Cruise Control (SFRL-ACC). This system aims to leverage the model-free nature and high real-time inference efficiency
-
Convolutional Neural Network-Based Pattern Recognition of Partial Discharge in High-Speed Electric-Multiple-Unit Cable Termination Sensors (IF 3.9) Pub Date : 2024-04-22 Chuanming Sun, Guangning Wu, Guixiang Pan, Tingyu Zhang, Jiali Li, Shibo Jiao, Yong-Chao Liu, Kui Chen, Kai Liu, Dongli Xin, Guoqiang Gao
Partial discharge detection is considered a crucial technique for evaluating insulation performance and identifying defect types in cable terminals of high-speed electric multiple units (EMUs). In this study, terminal samples exhibiting four typical defects were prepared from high-speed EMUs. A cable discharge testing system, utilizing high-frequency current sensing, was developed to collect discharge
-
Fault Diagnosis of Hydraulic Components Based on Multi-Sensor Information Fusion Using Improved TSO-CNN-BiLSTM Sensors (IF 3.9) Pub Date : 2024-04-22 Da Zhang, Kun Zheng, Fuqi Liu, Beili Li
In order to realize the accurate and reliable fault diagnosis of hydraulic systems, a diagnostic model based on improved tuna swarm optimization (ITSO), optimized convolutional neural networks (CNNs), and bi-directional long short-term memory (BiLSTM) networks is proposed. Firstly, sensor selection is implemented using the random forest algorithm to select useful signals from six kinds of physical
-
Using a Slit to Suppress Optical Aberrations in Laser Triangulation Sensors Sensors (IF 3.9) Pub Date : 2024-04-22 Steven Pigeon, Benjamin Lapointe-Pinel
In this paper, we present a laser triangulation sensor to measure the distance between the sensor and an object without contact using a diffraction slit rather than a traditional lens. We show that by replacing the lens with a slit, we can exploit the resulting diffraction pattern to have finer and yet simpler image analysis, yielding better estimation of the distance to the object. To test our hypothesis
-
Towards a Distributed Digital Twin Framework for Predictive Maintenance in Industrial Internet of Things (IIoT) Sensors (IF 3.9) Pub Date : 2024-04-22 Ibrahim Abdullahi, Stefano Longo, Mohammad Samie
This study uses a wind turbine case study as a subdomain of Industrial Internet of Things (IIoT) to showcase an architecture for implementing a distributed digital twin in which all important aspects of a predictive maintenance solution in a DT use a fog computing paradigm, and the typical predictive maintenance DT is improved to offer better asset utilization and management through real-time condition
-
Reengineering Indoor Air Quality Monitoring Systems to Improve End-User Experience Sensors (IF 3.9) Pub Date : 2024-04-22 Radu Nicolae Pietraru, Adriana Olteanu, Ioana-Raluca Adochiei, Felix-Constantin Adochiei
This paper presents an indoor air quality (IAQ) monitoring system designed for a better end-user experience. The monitoring system consists of elements, from the monitoring sensor to the monitoring interface, designed and implemented by the research team, especially for the proposed monitoring system. The monitoring solution is intended for users who live in houses without automatic ventilation systems
-
Innovative Solutions for Worn Fingerprints: A Comparative Analysis of Traditional Fingerprint Impression and 3D Printing Sensors (IF 3.9) Pub Date : 2024-04-20 Wenhui Mao, Yadong Zhao, Petro Pavlenko, Yihan Chen, Xuezhi Shi
Fingerprint recognition systems have achieved widespread integration into various technological devices, including cell phones, computers, door locks, and time attendance machines. Nevertheless, individuals with worn fingerprints encounter challenges when attempting to unlock original fingerprint systems, which results in disruptions to their daily activities. This study explores two distinct methods
-
Maximizing the Reliability and Precision of Measures of Prefrontal Cortical Oxygenation Using Frequency-Domain Near-Infrared Spectroscopy Sensors (IF 3.9) Pub Date : 2024-04-20 Elizabeth K. S. Fletcher, Joel S. Burma, Raelyn M. Javra, Kenzie B. Friesen, Carolyn A. Emery, Jeff F. Dunn, Jonathan D. Smirl
Frequency-domain near-infrared spectroscopy (FD-NIRS) has been used for non-invasive assessment of cortical oxygenation since the late 1990s. However, there is limited research demonstrating clinical validity and general reproducibility. To address this limitation, recording duration for adequate validity and within- and between-day reproducibility of prefrontal cortical oxygenation was evaluated.
-
Automatic Detection of the Running Surface of Railway Tracks Based on Laser Profilometer Data and Supervised Machine Learning Sensors (IF 3.9) Pub Date : 2024-04-20 Florian Mauz, Remo Wigger, Alexandru-Elisiu Gota, Michal Kuffa
The measurement of the longitudinal rail profile is relevant to the condition monitoring of the rail infrastructure. The running surface is recognizable as a shiny metallic area on top of the rail head. The detection of the running surface is crucial for vehicle-based rail profile measurements, as well as for defect detection. This paper presents a methodology for the automatic detection of the running
-
Characterization of Running Intensity in Canadian Football Based on Tactical Position Sensors (IF 3.9) Pub Date : 2024-04-21 Abdullah Zafar, Samuel Guay, Sophie-Andrée Vinet, Amélie Apinis-Deshaies, Raphaëlle Creniault, Géraldine Martens, François Prince, Louis De Beaumont
This study aimed to use a data-driven approach to identify individualized speed thresholds to characterize running demands and athlete workload during games and practices in skill and linemen football players. Data were recorded from wearable sensors over 28 sessions from 30 male Canadian varsity football athletes, resulting in a total of 287 performances analyzed, including 137 games and 150 practices
-
Exploring Human–Exoskeleton Interaction Dynamics: An In-Depth Analysis of Knee Flexion–Extension Performance across Varied Robot Assistance–Resistance Configurations Sensors (IF 3.9) Pub Date : 2024-04-21 Denis Mosconi, Yecid Moreno, Adriano Siqueira
Knee rehabilitation therapy after trauma or neuromotor diseases is fundamental to restore the joint functions as best as possible, exoskeleton robots being an important resource in this context, since they optimize therapy by applying tailored forces to assist or resist movements, contributing to improved patient outcomes and treatment efficiency. One of the points that must be taken into account when
-
A g\({_m}\)/I\({_D}\)-Based Low-Power LNA for Ka-Band Applications Sensors (IF 3.9) Pub Date : 2024-04-21 David Galante-Sempere, Jeffrey Torres-Clarke, Javier del Pino, Sunil Lalchand Khemchandani
This article presents the design of a low-power low noise amplifier (LNA) implemented in 45 nm silicon-on-insulator (SOI) technology using the gm/ID methodology. The Ka-band LNA achieves a very low power consumption of only 1.98 mW andis the first time the gm/ID approach is applied at such a high frequency. The circuit is suitable for Ka-band applications with a central frequency of 28 GHz, as the
-
Smart Sensors and Smart Data for Precision Agriculture: A Review Sensors (IF 3.9) Pub Date : 2024-04-21 Abdellatif Soussi, Enrico Zero, Roberto Sacile, Daniele Trinchero, Marco Fossa
Precision agriculture, driven by the convergence of smart sensors and advanced technologies, has emerged as a transformative force in modern farming practices. The present review synthesizes insights from a multitude of research papers, exploring the dynamic landscape of precision agriculture. The main focus is on the integration of smart sensors, coupled with technologies such as the Internet of Things
-
Understanding the Nonlinear Response of SiPMs Sensors (IF 3.9) Pub Date : 2024-04-21 Víctor Moya-Zamanillo, Jaime Rosado
A systematic study of the nonlinear response of Silicon Photomultipliers (SiPMs) was conducted through Monte Carlo (MC) simulations. The MC code was validated against experimental data for two different SiPMs. Nonlinearity mainly depends on the balance between the photon rate and the pixel recovery time. Additionally, nonlinearity has been found to depend on the light pulse shape, the correlated noise
-
Implementing Gait Kinematic Trajectory Forecasting Models on an Embedded System Sensors (IF 3.9) Pub Date : 2024-04-21 Madina Shayne, Leonardo A. Molina, Bin Hu, Taylor Chomiak
Smart algorithms for gait kinematic motion prediction in wearable assistive devices including prostheses, bionics, and exoskeletons can ensure safer and more effective device functionality. Although embedded systems can support the use of smart algorithms, there are important limitations associated with computational load. This poses a tangible barrier for models with increased complexity that demand
-
Vehicle-Type Recognition Method for Images Based on Improved Faster R-CNN Model Sensors (IF 3.9) Pub Date : 2024-04-21 Tong Bai, Jiasai Luo, Sen Zhou, Yi Lu, Yuanfa Wang
The rapid increase in the number of vehicles has led to increasing traffic congestion, traffic accidents, and motor vehicle crime rates. The management of various parking lots has also become increasingly challenging. Vehicle-type recognition technology can reduce the workload of humans in vehicle management operations. Therefore, the application of image technology for vehicle-type recognition is
-
Robust Cooperative Fault-Tolerant Control for Uncertain Multi-Agent Systems Subject to Actuator Faults Sensors (IF 3.9) Pub Date : 2024-04-21 Jiantao Shi, Xiang Chen, Shuangqing Xing, Anning Liu, Chuang Chen
This article investigates the robust cooperative fault-tolerant control problem of multi-agent systems subject to mismatched uncertainties and actuator faults. During the design process of the intermediate variable estimator, there is no need to satisfy fault estimation matching conditions, and this overcomes a crucial constraint of traditional observers and estimators. The feedback term of the designed
-
Machine Tool Wear Prediction Technology Based on Multi-Sensor Information Fusion Sensors (IF 3.9) Pub Date : 2024-04-21 Kang Wang, Aimin Wang, Long Wu, Guangjun Xie
The intelligent monitoring of cutting tools used in the manufacturing industry is steadily becoming more convenient. To accurately predict the state of tools and tool breakages, this study proposes a tool wear prediction technique based on multi-sensor information fusion. First, the vibrational, current, and cutting force signals transmitted during the machining process were collected, and the features
-
Structural Damage Detection Based on the Correlation of Variational Autoencoder Neural Networks Using Limited Sensors Sensors (IF 3.9) Pub Date : 2024-04-19 Jun Lin, Hongwei Ma
Identifying the structural state without baseline data is an important engineering problem in the field of structural health monitoring, which is crucial for assessing the safety condition of structures. In the context of limited accelerometers available, this paper proposes a correlation-based damage identification method using Variational Autoencoder neural networks. The approach involves initially
-
An Aerial Image Detection Algorithm Based on Improved YOLOv5 Sensors (IF 3.9) Pub Date : 2024-04-19 Dan Shan, Zhi Yang, Xiaofeng Wang, Xiangdong Meng, Guangwei Zhang
To enhance aerial image detection in complex environments characterized by multiple small targets and mutual occlusion, we propose an aerial target detection algorithm based on an improved version of YOLOv5 in this paper. Firstly, we employ an improved Mosaic algorithm to address redundant boundaries arising from varying image scales and to augment the training sample size, thereby enhancing detection
-
Emotion Classification Based on Pulsatile Images Extracted from Short Facial Videos via Deep Learning Sensors (IF 3.9) Pub Date : 2024-04-19 Shlomi Talala, Shaul Shvimmer, Rotem Simhon, Michael Gilead, Yitzhak Yitzhaky
Most human emotion recognition methods largely depend on classifying stereotypical facial expressions that represent emotions. However, such facial expressions do not necessarily correspond to actual emotional states and may correspond to communicative intentions. In other cases, emotions are hidden, cannot be expressed, or may have lower arousal manifested by less pronounced facial expressions, as
-
An Adaptive Multi-D-Norm-Driven Sparse Unfolding Deconvolutional Network for Bearing Fault Diagnosis Sensors (IF 3.9) Pub Date : 2024-04-19 Jianbo Lin, Han Zhang, Yunfei Li, Zhaohui Du
Impulsive blind deconvolution (IBD) is a popular method to recover impulsive sources for bearing fault diagnosis. Its underpinnings are in the design of objective functions based on prior knowledge of impulsive sources and a transfer function to describe transmission path influences. However, popular objective functions cannot retain waveform impulsiveness and periodicity cyclostationarity simultaneously
-
Investigation of the Efficacy of a Listeria monocytogenes Biosensor Using Chicken Broth Samples Sensors (IF 3.9) Pub Date : 2024-04-19 Or Zolti, Baviththira Suganthan, Sanket Naresh Nagdeve, Ryan Maynard, Jason Locklin, Ramaraja P. Ramasamy
Foodborne pathogens are microbes present in food that cause serious illness when the contaminated food is consumed. Among these pathogens, Listeria monocytogenes is one of the most serious bacterial pathogens, and causes severe illness. The techniques currently used for L. monocytogenes detection are based on common molecular biology tools that are not easy to implement for field use in food production
-
Fusion of Land-Based and Satellite-Based Localization Using Constrained Weighted Least Squares Sensors (IF 3.9) Pub Date : 2024-04-20 Paihang Zhao, Linqiang Jiang, Tao Tang, Zhidong Wu, Ding Wang
Combining multiple devices for localization has important applications in the military field. This paper exploits the land-based short-wave platforms and satellites for fusion localization. The ionospheric reflection height error and satellite position errors have a great impact on the short-wave localization and satellite localization accuracy, respectively. In this paper, an iterative constrained
-
Identification of the Biomechanical Response of the Muscles That Contract the Most during Disfluencies in Stuttered Speech Sensors (IF 3.9) Pub Date : 2024-04-20 Edu Marin, Nicole Unsihuay, Victoria E. Abarca, Dante A. Elias
Stuttering, affecting approximately 1% of the global population, is a complex speech disorder significantly impacting individuals’ quality of life. Prior studies using electromyography (EMG) to examine orofacial muscle activity in stuttering have presented mixed results, highlighting the variability in neuromuscular responses during stuttering episodes. Fifty-five participants with stuttering and 30
-
SSFLNet: A Novel Fault Diagnosis Method for Double Shield TBM Tool System Sensors (IF 3.9) Pub Date : 2024-04-20 Peng Zhou, Chang Liu, Jiacan Xu, Dazhong Ma, Zinan Wang, Enguang He
In tunnel boring projects, wear and tear in the tooling system can have significant consequences, such as decreased boring efficiency, heightened maintenance costs, and potential safety hazards. In this paper, a fault diagnosis method for TBM tooling systems based on SAV−SVDD failure location (SSFL) is proposed. The aim of this method is to detect faults caused by disk cutter wear during the boring
-
Assessing the Effect of Data Quality on Distance Estimation in Smartphone-Based Outdoor 6MWT Sensors (IF 3.9) Pub Date : 2024-04-20 Sara Caramaschi, Carl Magnus Olsson, Elizabeth Orchard, Jackson Molloy, Dario Salvi
As a result of technological advancements, functional capacity assessments, such as the 6-minute walk test, can be performed remotely, at home and in the community. Current studies, however, tend to overlook the crucial aspect of data quality, often limiting their focus to idealised scenarios. Challenging conditions may arise when performing a test given the risk of collecting poor-quality GNSS signal
-
Anomaly Detection in Railway Sensor Data Environments: State-of-the-Art Methods and Empirical Performance Evaluation Sensors (IF 3.9) Pub Date : 2024-04-20 Michał Bałdyga, Kacper Barański, Jakub Belter, Mateusz Kalinowski, Paweł Weichbroth
To date, significant progress has been made in the field of railway anomaly detection using technologies such as real-time data analytics, the Internet of Things, and machine learning. As technology continues to evolve, the ability to detect and respond to anomalies in railway systems is once again in the spotlight. However, railway anomaly detection faces challenges related to the vast infrastructure
-
Analysis and Prediction of Urban Surface Transformation Based on Small Baseline Subset Interferometric Synthetic Aperture Radar and Sparrow Search Algorithm–Convolutional Neural Network–Long Short-Term Memory Model Sensors (IF 3.9) Pub Date : 2024-04-20 Yuejuan Chen, Siai Du, Pingping Huang, Huifang Ren, Bo Yin, Yaolong Qi, Cong Ding, Wei Xu
With the acceleration of urbanisation, urban areas are subject to the combined effects of the accumulation of various natural factors, such as changes in temperature leading to the thermal expansion or contraction of surface materials (rock, soil, etc.) and changes in precipitation and humidity leading to an increase in the self-weight of soil due to the infiltration of water along the cracks or pores
-
Assessing the Impact of COVID-19 on Amateur Runners’ Performance: An Analysis through Monitoring Devices Sensors (IF 3.9) Pub Date : 2024-04-20 María García-Arrabé, María-José Giménez, Juliette Moriceau, Amandine Fevre, Jean-Sebastien Roy, Ángel González-de-la-Flor, Marta de la Plaza San Frutos
This retrospective study aimed to analyze the return to running of non-professional runners after experiencing asymptomatic or mild COVID-19. Participants aged 18–55 years who maintained a training load of ≥10 km/week for at least three months prior to diagnosis and utilized Garmin/Polar apps were included. From these devices, parameters such as pace, distance, total running time, cadence, and heart
-
Comparative Analysis of Anomaly Detection Approaches in Firewall Logs: Integrating Light-Weight Synthesis of Security Logs and Artificially Generated Attack Detection Sensors (IF 3.9) Pub Date : 2024-04-20 Adrian Komadina, Ivan Kovačević, Bruno Štengl, Stjepan Groš
Detecting anomalies in large networks is a major challenge. Nowadays, many studies rely on machine learning techniques to solve this problem. However, much of this research depends on synthetic or limited datasets and tends to use specialized machine learning methods to achieve good detection results. This study focuses on analyzing firewall logs from a large industrial control network and presents
-
Learning Ground Displacement Signals Directly from InSAR-Wrapped Interferograms Sensors (IF 3.9) Pub Date : 2024-04-20 Lama Moualla, Alessio Rucci, Giampiero Naletto, Nantheera Anantrasirichai
Monitoring ground displacements identifies potential geohazard risks early before they cause critical damage. Interferometric synthetic aperture radar (InSAR) is one of the techniques that can monitor these displacements with sub-millimeter accuracy. However, using the InSAR technique is challenging due to the need for high expertise, large data volumes, and other complexities. Accordingly, the development
-
Modelling Inductive Sensors for Arc Fault Detection in Aviation Sensors (IF 3.9) Pub Date : 2024-04-20 Gabriel Barroso-de-María, Guillermo Robles, Juan Manuel Martínez-Tarifa, Alexander Cuadrado
Modern aircraft are being equipped with high-voltage and direct current (HVDC) architectures to address the increase in electrical power. Unfortunately, the rise of voltage in low pressure environments brings about a problem with unexpected ionisation phenomena such as arcing. Series arcs in HVDC cannot be detected with conventional means, and finding methods to avoid the potentially catastrophic hazards
-
COVID-19 Hierarchical Classification Using a Deep Learning Multi-Modal Sensors (IF 3.9) Pub Date : 2024-04-20 Albatoul S. Althenayan, Shada A. AlSalamah, Sherin Aly, Thamer Nouh, Bassam Mahboub, Laila Salameh, Metab Alkubeyyer, Abdulrahman Mirza
Coronavirus disease 2019 (COVID-19), originating in China, has rapidly spread worldwide. Physicians must examine infected patients and make timely decisions to isolate them. However, completing these processes is difficult due to limited time and availability of expert radiologists, as well as limitations of the reverse-transcription polymerase chain reaction (RT-PCR) method. Deep learning, a sophisticated
-
Personalized Machine Learning-Based Prediction of Wellbeing and Empathy in Healthcare Professionals Sensors (IF 3.9) Pub Date : 2024-04-20 Jason Nan, Matthew S. Herbert, Suzanna Purpura, Andrea N. Henneken, Dhakshin Ramanathan, Jyoti Mishra
Healthcare professionals are known to suffer from workplace stress and burnout, which can negatively affect their empathy for patients and quality of care. While existing research has identified factors associated with wellbeing and empathy in healthcare professionals, these efforts are typically focused on the group level, ignoring potentially important individual differences and implications for
-
A High-Quality Sample Generation Method for Improving Steel Surface Defect Inspection Sensors (IF 3.9) Pub Date : 2024-04-20 Yu He, Shuai Li, Xin Wen, Jing Xu
Defect inspection is a critical task in ensuring the surface quality of steel plates. Deep neural networks have the potential to achieve excellent inspection accuracy if defect samples are sufficient. Nevertheless, it is very different to collect enough samples using cameras alone. To a certain extent, generative models can alleviate this problem but poor sample quality can greatly affect the final
-
Electrochemical Impedance Spectroscopy for the Sensing of the Kinetic Parameters of Engineered Enzymes Sensors (IF 3.9) Pub Date : 2024-04-20 Adriána Dusíková, Timea Baranová, Ján Krahulec, Olívia Dakošová, Ján Híveš, Monika Naumowicz, Miroslav Gál
The study presents a promising approach to enzymatic kinetics using Electrochemical Impedance Spectroscopy (EIS) to assess fundamental parameters of modified enteropeptidases. Traditional methods for determining these parameters, while effective, often lack versatility and convenience, especially under varying environmental conditions. The use of EIS provides a novel approach that overcomes these limitations
-
Development of a Two-Finger Haptic Robotic Hand with Novel Stiffness Detection and Impedance Control Sensors (IF 3.9) Pub Date : 2024-04-18 Vahid Mohammadi, Ramin Shahbad, Mojtaba Hosseini, Mohammad Hossein Gholampour, Saeed Shiry Ghidary, Farshid Najafi, Ahad Behboodi
Haptic hands and grippers, designed to enable skillful object manipulation, are pivotal for high-precision interaction with environments. These technologies are particularly vital in fields such as minimally invasive surgery, where they enhance surgical accuracy and tactile feedback: in the development of advanced prosthetic limbs, offering users improved functionality and a more natural sense of touch
-
Action Recognition of Taekwondo Unit Actions Using Action Images Constructed with Time-Warped Motion Profiles Sensors (IF 3.9) Pub Date : 2024-04-18 Junghwan Lim, Chenglong Luo, Seunghun Lee, Young Eun Song, Hoeryong Jung
Taekwondo has evolved from a traditional martial art into an official Olympic sport. This study introduces a novel action recognition model tailored for Taekwondo unit actions, utilizing joint-motion data acquired via wearable inertial measurement unit (IMU) sensors. The utilization of IMU sensor-measured motion data facilitates the capture of the intricate and rapid movements characteristic of Taekwondo
-
Self-Diagnostic and Self-Compensation Methods for Resistive Displacement Sensors Tailored for In-Field Implementation Sensors (IF 3.9) Pub Date : 2024-04-18 Federico Mazzoli, Davide Alghisi, Vittorio Ferrari
This paper presents a suitably general model for resistive displacement sensors where the model parameters depend on the current sensor conditions, thereby capturing wearout and failure, and proposes a novel fault detection method that can be seamlessly applied during sensor operation, providing self-diagnostic capabilities. On the basis of the estimation of model parameters, an innovative self-compensation
-
Optimizing Driving Parameters of the Jumbo Drill Efficiently with XGBoost-DRWIACO Framework: Applied to Increase the Feed Speed Sensors (IF 3.9) Pub Date : 2024-04-18 Hao Guo, Lin Lin, Jinlei Wu, Yancheng Lv, Changsheng Tong
The jumbo drill is a commonly used driving equipment in tunnel engineering. One of the key decision-making issues for reducing tunnel construction costs is to optimize the main driving parameters to increase the feed speed of the jumbo drill. The optimization of the driving parameters is supposed to meet the requirements of high reliability and efficiency due to the high risk and complex working conditions
-
Personal Air-Quality Monitoring with Sensor-Based Wireless Internet-of-Things Electronics Embedded in Protective Face Masks Sensors (IF 3.9) Pub Date : 2024-04-18 Lajos Kuglics, Attila Géczy, Karel Dusek, David Busek, Balázs Illés
In this paper, the design and research of a sensor-based personal air-quality monitoring device are presented, which is retrofitted into different personal protective face masks. Due to its small size and low power consumption, the device can be integrated into and applied in practical urban usage. We present our research and the development of the sensor node based on a BME680-type environmental sensor
-
Hard- and Software Controlled Complex for Gas-Strain Monitoring of Transition Zones Sensors (IF 3.9) Pub Date : 2024-04-18 Grigory Dolgikh, Mariia Bovsun, Stanislav Dolgikh, Igor Stepochkin, Vladimir Chupin, Andrey Yatsuk
The article describes a hard- and software controlled complex for gas-strain monitoring, consisting of stationary laser strainmeters and a laser nanobarograph, a stationary gas analyzer, and a weather station installed at Shultz Cape in the Sea of Japan; and a mobile shipboard complex, consisting of a gas analyzer and a weather station installed in a scientific research vessel. In the course of trial
-
Long-Term Coherent Integration Algorithm for High-Speed Target Detection Sensors (IF 3.9) Pub Date : 2024-04-18 Yao He, Guanghui Zhao, Kai Xiong
Long-term coherent integration (CI) can effectively improve the radar detection capability for high-speed targets. However, the range walk (RW) effect caused by high-speed motion significantly degrades the detection performance. To improve detection performance, this study proposes an improved algorithm based on the modified Radon inverse Fourier transform (denoted as IMRIFT). The proposed algorithm
-
α-Fe2O3/TiO2/Ti3C2Tx Nanocomposites for Enhanced Acetone Gas Sensors Sensors (IF 3.9) Pub Date : 2024-04-18 Zhihua Zhao, Zhenli Lv, Zhuo Chen, Baocang Zhou, Zhigang Shao
Metal oxide semi-conductors are widely applied in various fields due to their low cost, easy processing, and good compatibility with microelectronic technology. In this study, ternary α-Fe2O3/TiO2/Ti3C2Tx nanocomposites were prepared via simple hydrothermal and annealing treatments. The composition, morphology, and crystal structure of the samples were studied using XPS, SEM, EDS, XRD, and multiple
-
Composite ADRC Speed Control Method Based on LTDRO Feedforward Compensation Sensors (IF 3.9) Pub Date : 2024-04-19 Rencheng Jin, Junwei Wang, Yangyi Ou, Jianzhang Li
The performance of the extended state observer (ESO) in an Active Disturbance Rejection Control (ADRC) is limited by the operational load in stepper motor control, which has high real-time requirements and may cause delays. Additionally, the complexity of parameter tuning, especially in high-order systems, further limits the ESO’s performance. This paper proposes a composite ADRC (LTDRO-ADRC) based
-
On the Initialization of Swarm Intelligence Algorithms for Vector Quantization Codebook Design Sensors (IF 3.9) Pub Date : 2024-04-19 Verusca Severo, Felipe B. S. Ferreira, Rodrigo Spencer, Arthur Nascimento, Francisco Madeiro
Vector Quantization (VQ) is a technique with a wide range of applications. For example, it can be used for image compression. The codebook design for VQ has great significance in the quality of the quantized signals and can benefit from the use of swarm intelligence. Initialization of the Linde–Buzo–Gray (LBG) algorithm, which is the most popular VQ codebook design algorithm, is a step that directly
-
AI- and IoT-Enabled Solutions for Healthcare Sensors (IF 3.9) Pub Date : 2024-04-19 Samaneh Kouchaki, Xiaorong Ding, Saeid Sanei
Patient care and management have entered a new arena, where intelligent technology can assist clinicians in both diagnosis and treatment [...]
-
Wideband Current Transducer Traceable Calibration up to 10 A and 1 MHz Sensors (IF 3.9) Pub Date : 2024-04-19 Mohamed Ouameur, Daniela Istrate, François Ziade
Energy efficiency is an important issue in industry, especially with the ever-increasing consumption of electrical energy. The power quality and the traceability of metering devices are essential when integrating energy metering systems for energy efficiency. This management requires an understanding of electrical current events such as pulse and transient currents. Current transducers are widely used
-
108 m Underwater Wireless Optical Communication Using a 490 nm Blue VECSEL and an AOM Sensors (IF 3.9) Pub Date : 2024-04-19 Ruiyang Tian, Tao Wang, Xiaoyu Shen, Renjiang Zhu, Lidan Jiang, Yongle Lu, Huanyu Lu, Yanrong Song, Peng Zhang
Advanced light sources in the blue-green band are crucial for underwater wireless optical communication (UWOC) systems. Vertical-external-cavity surface-emitting lasers (VECSELs) can produce high output power and good beam quality, making them suitable for UWOC. This paper presents a 108 m distance UWOC based on a 100 mW 490 nm blue VECSEL and an acousto-optic modulator (AOM). The high-quality beam
-
Effect of Three-Dimensional-Printed Thermoplastics Used in Sensor Housings on Common Atmospheric Trace Gasses Sensors (IF 3.9) Pub Date : 2024-04-19 Tristalee Mangin, Evan K. Blanchard, Kerry E. Kelly
Low-cost air quality sensors (LCSs) are becoming more ubiquitous as individuals and communities seek to reduce their exposure to poor air quality. Compact, efficient, and aesthetically designed sensor housings that do not interfere with the target air quality measurements are a necessary component of a low-cost sensing system. The selection of appropriate housing material can be an important factor
-
Call to Action: Investigating Interaction Delay in Smartphone Notifications Sensors (IF 3.9) Pub Date : 2024-04-19 Michael Stach, Lena Mulansky, Manfred Reichert, Rüdiger Pryss, Felix Beierle
Notifications are an essential part of the user experience on smart mobile devices. While some apps have to notify users immediately after an event occurs, others can schedule notifications strategically to notify them only on opportune moments. This tailoring allows apps to shorten the users’ interaction delay. In this paper, we present the results of a comprehensive study that identified the factors
-
A Post-Processing Multipath/NLoS Bias Estimation Method Based on DBSCAN Sensors (IF 3.9) Pub Date : 2024-04-19 Yihan Guo, Simone Zocca, Paolo Dabove, Fabio Dovis
Positioning based on Global Navigation Satellite Systems (GNSSs) in urban environments always suffers from multipath and Non-Line-of-Sight (NLoS) effects. In such conditions, the GNSS pseudorange measurements can be affected by biases disrupting the GNSS-based applications. Many efforts have been devoted to detecting and mitigating the effects of multipath/NLoS, but the identification and classification
-
Measurements of Spatial Angles Using Diamond Nitrogen–Vacancy Center Optical Detection Magnetic Resonance Sensors (IF 3.9) Pub Date : 2024-04-19 Zhenrong Shi, Haodong Jin, Hao Zhang, Zhonghao Li, Huanfei Wen, Hao Guo, Zongmin Ma, Jun Tang, Jun Liu
This article introduces a spatial angle measuring device based on ensemble diamond nitrogen–vacancy (NV) center optical detection magnetic resonance (ODMR). This device realizes solid-state all-optical wide-field vector magnetic field measurements for solving the angles of magnetic components in space. The system uses diamond NV center magnetic microscope imaging to obtain magnetic vector distribution
-
Artificial Intelligence Methods for Smart Cities Sensors (IF 3.9) Pub Date : 2024-04-19 Alessandro Sebastian Podda, Salvatore Carta, Silvio Barra
In recent years, the concept of smart cities has garnered increasing attention as urban areas grapple with the challenges of population growth, resource management, and infrastructure optimization [...]
-
Image-Based Approach Applied to Load Torque Estimation in Three-Phase Induction Motors Sensors (IF 3.9) Pub Date : 2024-04-19 Cleber Gustavo Dias, Jhone Fontenele
This paper presents a novel method for load torque estimation in three-phase induction motors using air gap flux measurement and the conversion of this type of time-domain signal into grayscale images for further processing as inputs for an inception-type convolutional neural network. The magnetic flux was measured employing a Hall effect sensor installed inside the machine, near the stator slots,