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Disposable Screen-Printed Microchip Based on Nanoparticles Sensitive Membrane for Potentiometric Determination of Lead J. Sens. (IF 1.9) Pub Date : 2024-3-13 Majed Alrobaian, Munerah Alfadhel, Sayed Zayed, Mohammad AlDosari, Hassan Arida
Realization of screen-printed disposable microchip based on organic membrane sensitive layer highly responsive to lead has been demonstrated for the first time. Fabrication, potentiometric characterization and analytical application of the novel microchip have been reported. A sensitive layer comprises TiO2 nanoparticles and multiwalled carbon nanotubes “MWCNTs” composite incorporated in PVC membrane
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Research on the Factors Influencing the Seismic Performance of Grouting Sleeve Assembled Double-Column Piers J. Sens. (IF 1.9) Pub Date : 2024-3-11 Sheng Li, Zewen Yao, Li Wang, Li Ma, Yongze He, Xuefeng Ban
The present study investigated the influence of key design parameters on the seismic performance of prefabricated precast assembled piers’ connection parts to better adapt to the industrialized construction of prefabricated precast assembled pier connected using grouting sleeves. Relying on a prefabricated assembled bridge in the actual project, the ABAQUS software was used to establish a refined solid
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Monitoring Analysis of Urban Subsidence in Northern Henan Province Based on TS-InSAR Technology J. Sens. (IF 1.9) Pub Date : 2024-3-9 Huabin Chai, Yahui Ding, Jibiao Hu, Sijia Geng, Pengju Guan, Hui Xu, Yuqiao Zhao, Mingtao Xu
The protracted and pervasive incidence of land subsidence emerges as a pivotal factor exerting a substantial impact on the sustainable development of urban landscapes. A nuanced comprehension of the spatiotemporal evolution characteristics of land subsidence within the Northern Henan Plain assumes paramount significance in the context of mitigating potential urban geological disasters. This study endeavors
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Efficacy of Multiseason Sentinel-2 Imagery for Classifying and Mapping Grassland Condition J. Sens. (IF 1.9) Pub Date : 2024-3-8 Diego R. Guevara-Torres, José M. Facelli, Bertram Ostendorf
Assessing the condition of ecosystems is imperative for understanding their degree of degradation and managing their conservation. The increasing availability of remote sensing products offers unprecedented opportunities for mapping vegetation with high detail and accuracy. However, mapping complex ecosystems, like grasslands, remains challenging due to their heterogeneity in vegetation composition
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A Feasibility Study on the Efficacy of Functional Near-Infrared Spectrometry (fNIRS) to Measure Prefrontal Activation in Paediatric HIV J. Sens. (IF 1.9) Pub Date : 2024-2-24 Sizwe Zondo, Aline Ferreira-Correia, Kate Cockcroft
Human immunodeficiency virus (HIV) infection is associated with disturbed neurotransmission and aberrant cortical networks. Although advances in the imaging of brain microarchitecture following neuroHIV has added to our knowledge of structural and functional changes associated with HIV, no data exists on paediatric HIV using optical neuroimaging techniques. This study investigated the feasibility of
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Wrist EMG Monitoring Using Neural Networks Techniques J. Sens. (IF 1.9) Pub Date : 2024-2-16 Miriam Cristina Reyes-Fernandez, Rubén Posada-Gomez, Albino Martinez-Sibaja, Alberto A. Aguilar-Lasserre, J. J. Agustín Flores Cuautle
In rehabilitation, the correct performance of the exercises the specialist prescribes wrist movement is crucial. However, this may have the disadvantage of the patient’s subjectivity. Moreover, recent studies show that feedback through electrostimulation devices is beneficial during the process that leads to neuromotor rehabilitation. Besides, the electromyographic (EMG) signals give information about
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Fault Feature Extraction Method of Rolling Bearing Based on IAFD and TKEO J. Sens. (IF 1.9) Pub Date : 2024-2-15 Kai Guo, Jun Ma, Xin Xiong, Yuming Hu, Xiang Li
The study of bearing fault feature extraction using adaptive Fourier decomposition (AFD) holds significant practical importance. However, AFD is constrained by its reliance on prior knowledge for determining decomposition levels, which can result in either underdecomposition or overdecomposition based on a single indicator. Consequently, an improved adaptive Fourier decomposition (IAFD) is proposed
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A Novel Method for 3D Object Detection in Open-Pit Mine Based on Hybrid Solid-State LiDAR Point Cloud J. Sens. (IF 1.9) Pub Date : 2024-2-14 Cheng Li, Gang Yao, Teng Long, Xiwen Yuan, Peijie Li
In recent years, the mining industry has encountered challenges, such as a shortage of human resources, an ongoing emphasis on safety enhancements, and increased ecological preservation requirements. Autonomous mining trucks have emerged as a novel solution to effectively address these issues within open-pit mining operations. To meet the demanding conditions of open-pit mines, characterized by intense
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Arc-Scanning Synthetic Aperture Radar for Accurate Location of Targets J. Sens. (IF 1.9) Pub Date : 2024-2-12 Félix Pérez-Martínez, Susan Martínez-Cordero, Jaime Calvo-Gallego, Francisco-Javier Romero-Paisano
The purpose of this article is to present the advantages that the use of arc-scanning synthetic aperture radar (Arc-SAR) would provide for accurate location of target to the weapon systems. Arc-SAR systems have an extraordinary capacity of angular discrimination of the targets, this fact make possible they can be used for the precise location of targets by replacing the large antennas required by the
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A Deep Learning Method for Building Extraction from Remote Sensing Images by Fuzing Local and Global Features J. Sens. (IF 1.9) Pub Date : 2024-2-10 Yitong Wang, Shumin Wang, Jing Yuan, Aixia Dou, Ziying Gu
As important disaster-bearing bodies, buildings are the focus of attention in seismic disaster risk assessment and emergency rescue. It is of great practical significance to extract buildings quickly and accurately with complex textures and variable scales and shapes from high-resolution remote sensing images. We proposed an improved TransUnet model based on multiscale grouped convolution and attention
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Efficient Multistage License Plate Detection and Recognition Using YOLOv8 and CNN for Smart Parking Systems J. Sens. (IF 1.9) Pub Date : 2024-2-8 Mejdl Safran, Abdulmalik Alajmi, Sultan Alfarhood
Smart parking systems play a vital role in enhancing the efficiency and sustainability of smart cities. However, most existing systems depend on sensors to monitor the occupancy of parking spaces, which entail high installation and maintenance costs and limited functionality in tracking vehicle movement within the car park. To address these challenges, we propose a multistage learning-based approach
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Automatic Seizure Detection Using Multi-Input Deep Feature Learning Networks for EEG Signals J. Sens. (IF 1.9) Pub Date : 2024-2-5 Qi Sun, Yuanjian Liu, Shuangde Li
Epilepsy, a neurological disease associated with seizures, affects the normal behavior of human beings. The unpredictability of epileptic seizures has caused great obstacles to the treatment of the disease. The automatic seizure detection method based on electroencephalogram (EEG) can assist experts in predicting seizures to improve treatment efficiency. Epileptic seizure detection cannot be achieved
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Automatic Acquisition System for Mine Pressure Monitoring in Coal Mine Working-Face Footage J. Sens. (IF 1.9) Pub Date : 2024-2-5 Miaoer Zhou, Yongkui Shi, Jian Hao, Xin Chen
The existing mine pressure monitoring system has realized the online continuous monitoring of the working-face stent resistance, roadway roof offcuts, and anchor rod/rope working resistance. However, the mine pressure monitoring information of the working face currently includes only the stent resistance and the monitoring time, and there is no information on the working-face advance. The mine pressure
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Research on Direction Finding Method under Impulsive Noise Based on Nonuniform Linear Array J. Sens. (IF 1.9) Pub Date : 2024-2-1 Chunlian An, Guyue Yang, Peng Li, Dengmei Zhou, Liangliang Tian
Direction of arrival (DOA) estimation under impulsive noise has always been an important research area in array signal processing. The traditional methods under impulsive noise mostly rely on prior parameters and have high computational complexity. Based on the filtering theory, we present an effective pretreatment filtering technology to cut out the impulse mixed in the array received data and employ
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The Multisensor Data Fusion Method Based on Improved Fuzzy Evidence Theory in the Coal Mine Environment J. Sens. (IF 1.9) Pub Date : 2024-1-31 Lei Wang, Chenyan Fu, Junyan Qi
An enhanced evidence theory-based multisensor data fusion technique is presented to address the problem of poor data fusion caused by an unknown interference in the fully automated mining face multisensor system of a coal mine. Initially, the set of all measurement values is considered as the identification framework, and the principles of fuzzy mathematics are applied to introduce the membership function
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Fault Detection Method of Medical Equipment Based on Multi-Index Electrical Performance Parameters J. Sens. (IF 1.9) Pub Date : 2024-1-29 Xiaoyu Chen, Haitao Guo, Zihong Wang, Feiba Chang, Xiaomei Ren, Chengqun Ma, Weiben Li, Miao Tian, Rui Yang, Xianju Yuan, Shengting Zhou
There is a lack of study on fault detection methods of medical equipment at home and abroad. The main reason is that the research of fault features is diverse and not systematic. This paper aims to propose a fault recognition method for medical equipment combining the electrical performance parameter features with fault events. First, it treats the equipment as a whole system, setting up the analysis
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Blood Oxygen Saturation Estimation with Laser-Induced Graphene Respiration Sensor J. Sens. (IF 1.9) Pub Date : 2024-1-29 Ana Madevska Bogdanova, Bojana Koteska, Teodora Vićentić, Stefan D. Ilić, Miona Tomić, Marko Spasenović
Measuring blood oxygen saturation (SpO2) is crucial in a triage process for identifying patients with respiratory distress or shock, since low SpO2 levels indicate compromised hemostability and the need for priority treatment. This paper explores the use of wearable mechanical deflection sensors based on laser-induced graphene (LIG) for SpO2 estimation. The LIG sensors are attached to a subject’s chest
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Developing a Hybrid Irrigation System for Smart Agriculture Using IoT Sensors and Machine Learning in Sri Ganganagar, Rajasthan J. Sens. (IF 1.9) Pub Date : 2024-1-29 Amritpal Kaur, Devershi Pallavi Bhatt, Linesh Raja
The agriculture sector is one of the largest consumers of fresh water. Different types of irrigation systems are available, including center pivot, drip and sprinkler systems, and linear motion systems. However, the complex structure of existing irrigation systems and their high maintenance costs encourage Indian farmers to continue using these methods. Due to its ease of use and low energy consumption
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A High Degree of Freedom Radiation Near-Field Source Localization Algorithm with Gain–Phase Error J. Sens. (IF 1.9) Pub Date : 2024-1-23 Qi Zhang, Wenxing Li, Si Li, Yunlong Mao
The limitation of the number of estimable sources in the localization of radiation near-field sources with gain–phase error is examined in this paper. When only the reference element has no gain–phase error, a new method based on an accurate model is proposed to enhance the maximum number of estimable sources. Based on the location parameter details of the auxiliary source, the method first derives
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Fire-PPYOLOE: An Efficient Forest Fire Detector for Real-Time Wild Forest Fire Monitoring J. Sens. (IF 1.9) Pub Date : 2024-1-18 Pei Yu, Wei Wei, Jing Li, Qiuyang Du, Fang Wang, Lili Zhang, Huitao Li, Kang Yang, Xudong Yang, Ning Zhang, Yucheng Han, Huapeng Yu
Forest fire has the characteristics of sudden and destructive, which threatens safety of people’s life and property. Automatic detection and early warning of forest fire in the early stage is very important for protecting forest resources and reducing disaster losses. Unmanned forest fire monitoring is one popular way of forest fire automatic detection. However, the actual forest environment is complex
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Comparison of Resonance Modes in Two-Dimensional and Three-Dimensional Microsphere Structures J. Sens. (IF 1.9) Pub Date : 2024-1-10 Sajjad Heshmati, Abolfazl Rahmani
In this article, we have investigated the performance of a resonator in 2D, in an asymmetric form using the physical method and using the Matlab software, we have analyzed it in 3D. According to the simulation results, in asymmetric 2D and 3D structures, whispering gallery modes, or resonances appeared at similar wavelengths for the same radial and polar mode number. Also, the results obtained from
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Object Detection Algorithm of Transmission Lines Based on Improved YOLOv5 Framework J. Sens. (IF 1.9) Pub Date : 2024-1-9 Hao Zhang, Xianjun Zhou, Yike Shi, Xuan Guo, Hang Liu
Foreign objects easily attach to the transmission lines because of the various laying methods and the complex, changing environment. They have a significant impact on the safe operation capability of transmission lines if these foreign objects are not detected and removed in time. An improved YOLOv5 technique is provided to detect foreign objects in transmission lines due to the low-foreign object
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Direction Consistency-Guided Lightweight Power Line Detection Network for Aerial Images J. Sens. (IF 1.9) Pub Date : 2023-12-19 Guanying Zhang, Yunhao Shu, Wenming Zhu, Jianxun Ma, Yun Liu, Chang Xu
Accurate detection of power lines in aerial images is of great significance in ensuring grid security. However, complex power line scenarios and the thin and light structure of power lines both make it difficult to detect power lines accurately. Most of the existing approaches use traditional deep learning methods, using networks with a large number of parameters, computation, and memory occupation
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Real-Time Medical Image Classification with ML Framework and Dedicated CNN–LSTM Architecture J. Sens. (IF 1.9) Pub Date : 2023-12-19 Imrus Salehin, Md. Shamiul Islam, Nazrul Amin, Md. Abu Baten, S. M. Noman, Mohd Saifuzzaman, Serdar Yazmyradov
In the domain of modern deep learning and classification techniques, the convolutional neural network (CNN) stands out as a highly successful and preferred method for image classification in artificial intelligence. Especially in the medical field, CNN has proven to be an ideal approach for analyzing medical data and accurately identifying diseases. Over the recent years, CNN has demonstrated significant
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Weak Sinusoidal Signal Detection with CSI Model in Chaotic Interference J. Sens. (IF 1.9) Pub Date : 2023-12-14 Liyun Su, Wanlin Zhu, Fenglan Li, Chunquan Pan
In small target detection under strong sea clutter or impact signal detection under machinery fault diagnosis, a weak sinusoidal signal with random amplitude is often contaminated by heavier chaotic noise, and the target information is difficult to detect. Traditional solutions, such as neural networks or stochastic resonance, can not effectively extract heteroscedasticity of data, which leads to weak
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A Fast Global Optimal Strategy for Iteration Closest Point Using 2D-BnB and Its Application to Rail Profile Registration J. Sens. (IF 1.9) Pub Date : 2023-12-14 Dingfei Jin, Hua Ma
Profile registration is critical to rail wear measurement with line structured light, and the most popular registration method is iteration closest point (ICP). Unfortunately, ICP is often invalid in actual applications because it is easy to trap into local minima. To solve this problem, we propose a hybrid 2D-point-set registration method which combined ICP to branch and bound. In this way, we can
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A GAN–SVR Prediction Method of the Metal Tube-Bending Rebound with Small Samples J. Sens. (IF 1.9) Pub Date : 2023-12-7 Pengfei Zhang, Ziluo Fang, Liangyou Li, Tingting Yang
This paper investigates a predictive algorithm for the angle of the metal tube-bending rebound with small samples. First, the generative adversarial network (GAN) approach is introduced to address the issues of insufficient sample data. The proposed method can realize data augmentation through a generator, enhancing training effectiveness compared to conventional model-based and experimental prediction
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Feature Line Extraction from Building Façade Point Clouds by Exploring Spatial Topological Relationship J. Sens. (IF 1.9) Pub Date : 2023-12-7 Feng Xiong, Zongchun Li, Yongjian Fu, Wenqi Wang, Hua He, Zhekun Huang, Jie Min, Jiahuan Ran
After using a terrestrial laser scanner to acquire building facade point clouds, the extraction of feature lines can simplify the expression of building objects, thereby contributing to the accurate construction of building facade geometric models. To address the problems of missing extraction and low accuracy in existing methods, this study proposes a feature line extraction method for building facade
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Segmentation of High-Resolution Remote Sensing Images Using the Gabor Texture Feature-Based Mean Shift Method J. Sens. (IF 1.9) Pub Date : 2023-11-30 Ligang Wang, Dan Liu, Weijiang Kong, Liang Mao, Qiaoyang Liu
High-resolution remote sensing images (HRRSIs) play an important role in the construction and development of society with their rich and detailed information. In the process of remote sensing image segmentation, the conventional method of mean shift usually involves spatial features and spectral features to preserve edge information and reduce the effect of noise. However, this traditional method often
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Mobile Robot Path Planning with a Novel Multiobjective Slime Mold Algorithm J. Sens. (IF 1.9) Pub Date : 2023-11-29 Zhibo Zhai, Feifei Liu, Guoping Jia, Yusen Dai, Tao Wang
In the path planning problem of mobile robot in static complex obstacle environment, the original slime mold algorithm (SMA) has some shortcomings, such as initial solution error and easy to fall into local optimum, which leads to low accuracy of robot path planning. A novel Baldwin learning effect mobile robot Path planning with Multi-Objective Slime Mold Algorithm (BPMOSMA) is proposed to solve the
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SSF-Align: Point Cloud Registration Based on Statistical Shape Features with Manifold Metric J. Sens. (IF 1.9) Pub Date : 2023-11-28 Pu Ren, Chongbin Xu, Xiaomin Sun, Yuan Li, Haiying Tao
As an important topic in 3D vision, the point cloud registration has been widely used in various applications, including location, reconstruction, and shape recognition. In this paper, we propose a new registration method for this topic, which utilizes statistical shape features (SSFs) and manifold metrics to estimate the transformation matrix. The SSFs are extracted to establish a compact representation
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Study on Short-Term Load Combination Forecasting Model Considering Historical Data Interval Construction J. Sens. (IF 1.9) Pub Date : 2023-11-27 Laiqing Yan, Zhenwen Li, Chuhan Zhang, Zutai Yan, Xiaojia Liu, Ning Ma
In response to the insufficient accuracy of load forecasting in power system and the wide range of intervals, a combined short-term power load forecasting model considering the interval construction of historical data is proposed. First, the data are decomposed into relatively stable subsequences using extreme-point symmetric mode decomposition (ESMD), and the adaptive dispersion entropy (DE) of C–C
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Optimal Design of a Six-Dimensional Force Sensor with Three-Beam Structure J. Sens. (IF 1.9) Pub Date : 2023-11-27 Xiliang Chen, Zhiqiang Wang, Zhengyu Xie, Ning Li, Yiwen Sun
A six-dimensional force sensor with a three-beam structure was designed, and its strain and stress distribution under various load conditions were analyzed based on finite element simulation technology. According to the strain distribution, the Wheatstone bridge scheme of the sensor is designed, and the output voltage and sensitivity formulas in each direction are deduced. The optimal patch location
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Hybrid Delay-Minimization Scheduling Algorithm of FT and MPTS in WSN Data Aggregation J. Sens. (IF 1.9) Pub Date : 2023-11-17 Cheng Li, Guoyin Zhang, Yan Mao
The data acquisition of Internet of Things (IoT) is mostly brought out by wireless sensor networks (WSNs), and the efficiency of IoT is directly affected by the time delay in the process of data acquisition, which is researched mainly by the data aggregation of WSNs. Minimizing the delay in the data aggregation process is one of the most important operations. In order to reduce the data aggregation
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Research and Application of Vibration Isolation Platform Based on Nonlinear Vibration Isolation System J. Sens. (IF 1.9) Pub Date : 2023-11-13 Fei Wang, Shengen Zheng, Chengbao Huang, Weijie Wang, Jin Yan, Zhaoqi He, Hongliang Yu, Jianbin Liao
In order to weaken the ground vibration caused by mechanical equipment, a vibration isolation platform of nonlinear vibration isolation system is proposed. The influence of flexible foundation and nonlinear isolator parameters on the performance of nonlinear vibration isolation platform is studied. By optimizing the nonlinear parameters of the vibration isolation platform, the vibration response of
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IETIF: Intelligent Energy-Aware Task Scheduling Technique in IoT/Fog Networks J. Sens. (IF 1.9) Pub Date : 2023-11-10 Amin Nazari, Sakine Sohrabi, Reza Mohammadi, Mohammad Nassiri, Muharram Mansoorizadeh
Nowadays, with the advent of various communication technologies such as the internet of things (IoT), a large volume of data is produced that needs to be processed in real-time. Fog computing is an appropriate solution to address the requirements of different types of IoT applications. In most cases, IoT applications consist of a set of dependent tasks that can be separately processed in a heterogeneous
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Rapid and Accurate Identification of Grass Seedlings in Agricultural Fields Based on Optimized YOLOX Model J. Sens. (IF 1.9) Pub Date : 2023-11-6 Jie Kang, Yi Gu, Zhi Yuan Wang, Xing Yu Lu
Traditional agricultural cultivation is labor-intensive and vulnerable to natural climate conditions, such as heavy rainfall and drought. Concerns over food safety have also brought attention to the growth of weeds and the misuse of agricultural chemicals, which can have a serious negative impact on crop growth and safety. We investigated the feasibility of the YoloX model in the field of agricultural
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Based on Sliding Mode and Adaptive Linear Active Disturbance Rejection Control for a Magnetic Levitation System J. Sens. (IF 1.9) Pub Date : 2023-10-31 Ziwei Wu, Kuangang Fan, Xuetao Zhang, Weichao Li
The magnetic levitation system has evident advantages in reducing energy consumption, but its nonlinear characteristics increase the difficulty of control. This study proposes a control method that combines the improved particle swarm optimisation algorithm with sliding mode control and adaptive linear active disturbance rejection control (IPSO–SMC–ALADRC) to address the problems of weak anti-interference
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Normalizing Flow-Based Industrial Complex Background Anomaly Detection J. Sens. (IF 1.9) Pub Date : 2023-10-31 Pengxv Wen, Xiaorong Gao, Yong Wang, Jinlong Li, Lin Luo
This paper proposes a novel approach called cross-scale with attention normalizing flow (CSA-Flow) enhanced with channel-attention (CA) and self-attention (SA) modules for high-speed railway anomaly detection in complex industrial backgrounds to reduce the manual workload of the primary maintenance of high-speed electric multiple units. Detecting defects in industrial environments, characterized by
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A Partial-to-Partial Point Cloud Registration Method Based on Geometric Attention Network J. Sens. (IF 1.9) Pub Date : 2023-10-27 Yi Chen, Yong Wang, Jinlong Li, Yu Zhang, Xiaorong Gao
Partial point cloud registration is an important step in generating a full 3D model. Many deep learning-based methods show good performance for the registration of complete point clouds but cannot deal with the registration of partial point clouds effectively. Recent methods that seek correspondences over downsampled superpoints show great potential in partial point cloud registration. Therefore, this
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Google Earth Engine for Advanced Land Cover Analysis from Landsat-8 Data with Spectral and Topographic Insights J. Sens. (IF 1.9) Pub Date : 2023-10-26 Abolfazl Abdollahi, Biswajeet Pradhan, Abdullah Alamri, Chang-Wook Lee
The primary goal of this research is to see how effective cloud-based computing services such as Google Earth Engine (GEE) platform are at classifying multitemporal satellite images and producing high-quality land cover maps for the target year of 2020, with the possibility of using it on a larger-scale area such as metropolitan Melbourne as a test site. To create high-quality land cover maps, the
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An Efficient Anycast Mechanism for 802.15.4-TSCH to Improve QoS in IIoT J. Sens. (IF 1.9) Pub Date : 2023-10-21 Sahand Amiri, Mohammad Nassiri, Reza Mohammadi, Fabrice Theoleyre
The Industrial Internet of Things (IIoT) has emerged as a technology that automates industrial processes. In IIoT networks, data are collected from various nodes and sent to a base station for managerial purposes. However, in the industrial environment, network reliability and delay are significant challenges due to the high likelihood of packet loss in radio networks. Anycast is a link layer mechanism
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Paper Recommendation via Correlation Pattern Mining and Attention Mechanism J. Sens. (IF 1.9) Pub Date : 2023-10-18 Weiming Huang, Baisong Liu, Zhaoliang Wang
In this paper, we improve the efficiency and effectiveness of the matrix factorization method in the paper recommendation system. We mainly address two problems. First, the vectors based on citation networks are undertrained because newly added papers are rarely cited. Second, current algorithms are mainly based on keyword search or global popularity and lack the organic combination of considering
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Facial Video-Based Robust Measurement of Respiratory Rates in Various Environmental Conditions J. Sens. (IF 1.9) Pub Date : 2023-10-13 Jinsoo Park, Kwangseok Hong
This paper addresses the process of respiration, which involves providing oxygen to the body’s cells, blood, and tissues. The significance of respiratory signals as predictors of diverse physical conditions, including respiratory health and disorders, is emphasized. Technologies such as pressure-sensor belts for chest or abdominal movement detection, carbon-dioxide sensors placed under the nose, and
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Prediction of the Frost Resistance of Iron Ore Tailings Concrete Based-BP Neural Network J. Sens. (IF 1.9) Pub Date : 2023-10-12 Chun Fu, Xiaohong Li, Jie Han, Haijun Li, Haotian Wu, Songyang Liu
In order to predict the relative dynamic elastic modulus (RDEM), which is used to reflect the frost resistance of iron ore tailings concrete, the backpropagation neural network (BPNN) was used in this study. Here, one hidden layer was chosen in the structure of BPNN. It is well known that the number of neurons in the hidden layer is the key of BPNN; hence, checking the features of overfitting was chosen
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Walk-to-Charge Technology: Exploring Efficient Energy Harvesting Solutions for Smart Electronics J. Sens. (IF 1.9) Pub Date : 2023-10-10 Ruby Beniwal, Shruti Kalra, Narendra Singh Beniwal, Hirak Mazumdar, Ashish Kumar Singhal, Sunil Kumar Singh
Wearable sensors offer great potential in sports, fitness, and medicine. However, their limited battery life poses a major obstacle to their widespread use. This paper explores various energy solutions to extend the battery life of wearable sensors. The first part of the paper focuses on hardware improvements for wearable sensors, such as employing low-power sensors, energy- efficient microcontrollers
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Multisensor Fusion SLAM Research Based on Improved RBPF-SLAM Algorithm J. Sens. (IF 1.9) Pub Date : 2023-10-9 Nan Li, Feng Zhou, Kaiwen Yao, Xinli Hu, Rugang Wang
Simultaneous localization and map construction (SLAM) technology provides the foundation for indoor robots to realize autonomous path planning. The Rao-Blackwellized particle filtering (RBPF) algorithm is widely used to obtain information and perform map construction in unknown environments. This paper proposes a multisensor fusion algorithm that improves the RBPF-SLAM algorithm by addressing the issues
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GDF: A Novel Image Fusion Approach for Compelling Depiction of Earthly Features J. Sens. (IF 1.9) Pub Date : 2023-10-7 Vikash Kumar Mishra, Utsav Nareti, Raghvendra Kumar, Triloki Pant, Abdul Aleem, Ajeet Singh, Seblewongel Esseynew Biable
One of the most challenging aspects of satellite remote sensing is image fusion. Image fusion increases the visual interpretation of the image and has many applications such as monitoring water bodies, land cover, urbanisation, agriculture, national defence, and so forth. Remote sensing applications require images with a high spatial and spectral resolution for accurately processing and distinguishing
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Improving Knowledge Base Updates with CAIA: A Method Utilizing Capsule Network and Attentive Intratriplet Association Features J. Sens. (IF 1.9) Pub Date : 2023-10-5 Jingxiong Qiu, Linfu Sun, Min Han
The ongoing and effective application of the knowledge base hinges on the dynamic updating of the knowledge base, which, in turn, depends on the accurate verification of triplets. Current methods struggle to manage unknown new entities and relations or have issues with relation characterization. This paper presents a novel approach to enhance the precision and efficiency of triplet verification during
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Parametric Analysis of Electrostatic Comb Drive for Resonant Sensors Operating under Atmospheric Pressure J. Sens. (IF 1.9) Pub Date : 2023-9-30 Shiping Chen, Zhanqing Yu, Ya Mou, Jiaxu Shi
The microelectrostatic comb resonator’s issues with high driving voltage and strong feed-through coupling noise limit its practical use. In earlier studies, the design and structural optimization of microcomb resonators generally focused on lowering beam stiffness and raising electrostatic force density to enhance resonance displacement and lower driving voltage. However, for a microresonator that
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An End-to-End Robotic Visual Localization Algorithm Based on Deep Learning J. Sens. (IF 1.9) Pub Date : 2023-9-29 Niansheng Chen, Hongcheng Wang, Guangyu Fan, Dingyu Yang, Lei Rao
Efficient localization plays a significant role in mobile autonomous robots’ navigation systems. Traditional visual simultaneous localization systems based on point feature matching suffer from two shortcomings. First one is that the method of tracking features is not robust for environments with frequent changes in brightness. Another one is the large of consecutive visual keyframes can consume expensive
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Modification of Metal Oxide Semiconductor Gas Sensors Using Conducting Polymer Materials J. Sens. (IF 1.9) Pub Date : 2023-9-28 Jonah Wuloh, Eric Selorm Agorku, Nathaniel Owusu Boadi
Conducting polymers have shown great potential in detecting gases at room temperature. This has caused the rapid development of these materials for gas sensing because their conductivity can be altered when exposed to oxidant or reductant molecules at room temperature. Nevertheless, due to their relatively high attraction to volatile organic and water molecules and low conductivity, they show low sensitivity
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Joint Virtual Machine Selection and Computation Resource Allocation in Mobile Edge Computing J. Sens. (IF 1.9) Pub Date : 2023-9-27 Huifeng Yang, Xianglong Meng, Yichao Li, Yong Wei, Li Shang, Jiucheng Wang, Peng Lin
Mobile edge computing (MEC) is considered as an effective technology to enhance the storage and computation capability of smart power sensors (SPSs) in smart grid networks. The MEC server is composed of multiple virtual machines (VMs) with powerful computation capability, and each VM can process multiple tasks independently, which cannot be ignored during the task computation period. In this work,
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Research on Chaotic Chimp Optimization Algorithm Based on Adaptive Tuning and Its Optimization for Engineering Application J. Sens. (IF 1.9) Pub Date : 2023-9-25 Wenli Lei, Kun Jia, Xin Zhang, Yang Lei
The original Chimp Optimization Algorithm has disadvantages such as slow convergence, the tendency to fall into local optima, and low accuracy in finding the best. To alleviate the existing problems, a chaotic chimp optimization algorithm based on adaptive tuning is proposed. First, sine chaos mapping was used to initialize the chimpanzee population and enhance the quality and diversity of the initialized
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A Digital Lock-in Amplifier Based on Adaptive Kalman Filter for Rail Defect Detection J. Sens. (IF 1.9) Pub Date : 2023-9-20 Hongzhen Chen, Yong Li, Liyun Ou
In this paper, an eddy current testing system equipped with low-performance processor is designed for rail defect detection. A digital lock-in amplifier (DLIA) based on adaptive Kalman filter (AKF) is presented to detect the weak voltage induced in two differential coils. The proposed method can fast demodulate the amplitude of a signal with a randomized phase using one cycle of the sinusoidal signal
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Effect of Annealing on PL Intensity of NaYF4:Er3+, Yb3+/Ca2+ Phosphor and the Application to Temperature Sensor J. Sens. (IF 1.9) Pub Date : 2023-9-19 Fuli Zhang, Chunlai Guo, Zhen Zhang, Xu Chen, Chunlei Song, Chengren Li
We have prepared NaYF4:Er3+/Yb3+/Ca2+ phosphors using the solvothermal method and discussed the influence of annealing on the crystal structure, morphology, and photoluminescence characteristics in this work. The results show that, with the increase of annealing temperature, the crystallization phase changes from a single cubic structure to a mixed phase of cubic and hexagonal phases and the morphology
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Satellite-Synoptic Monitoring of Dominant Dust Entering Western Iran J. Sens. (IF 1.9) Pub Date : 2023-9-19 Himan Shahabi, Taher Safarrad, Mazlan Hashim, Nadhir Al-Ansari
Dust storm in Iran’s western regions has been one of its major environmental problems in recent years, which has not only turned into a yearly phenomenon but is also expanding. This study investigated two events of dominant dust in southwestern Iran using moderate resolution imaging spectroradiometer imagery, Reanalysis Datasets (meteorological fields and atmospheric compositions), in both hot (July
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Driving Profile Optimization Using a Deep Q-Network to Enhance Electric Vehicle Battery Life J. Sens. (IF 1.9) Pub Date : 2023-9-6 Jihoon Kwon, Manho Kim, Hyeongjun Kim, Minwoo Lee, Suk Lee
In the COVID-19 era, automobiles with internal combustion engines are being replaced by eco-friendly vehicles. The demand for battery electric vehicles (BEVs) has increased explosively. Treatment of spent batteries has received much attention. Battery life can be extended via both efficient charging and driving. Consideration of the vehicles ahead when driving a BEV effectively prolongs battery life
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Pi-Score: An Estimation Strategy of the Class Prior in Positive-Unlabeled Learning for Electrical Insulator Defect Detection with Incomplete Annotations J. Sens. (IF 1.9) Pub Date : 2023-9-5 Fengqian Pang, Chenghan Jia, Yue Li, Chunyue Lei, Xi Chen
Insulators in high-voltage power systems serve as brackets for overhead lines and prevent these wires from becoming grounded. Due to long-term exposure to a harsh environment, it is indispensable to apply periodic inspection for defective insulators, facilitating the timely overhaul of insulators. In the field of object detection, convolutional neural networks (CNNs) have been introduced and have achieved
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Dual-Mode Pressure Sensor Integrated with Deep Learning Algorithm for Joint State Monitoring in Tennis Motion J. Sens. (IF 1.9) Pub Date : 2023-9-1 Jianhui Gao, Zhi Li, Zhong Chen
The precise capture and identification of movement features are important for numerous scientific endeavors. In this work, we present a novel multimodal sensor, called the resistance/capacitance dual-mode (RCDM) sensor, which effectively differentiates between compression and stretchable strains during tennis motion; meanwhile, it can also accurately identify various joint movements. The proposed wearable