Pub Date : 2025-08-20DOI: 10.1109/JSEN.2025.3598820
Victoria Arenas-Ramos;Victor Pallares-Lopez;Rafael Real-Calvo;Miguel Gonzalez-Redondo;Isabel Santiago-Chiquero
The increasing integration of photovoltaic (PV) plants into the power grid presents an ongoing challenge to prevent the instability caused by atmospheric conditions from affecting the power distribution network. To adequately control the network and implement such techniques, monitoring has been considerably improved in recent years. Due to the vast number of measurements generated, efficient management of this data has become a significant challenge. This article outlines a procedure used for simultaneously storing synchronized measured data from phasor measurement units (PMUs) and PMU-like data from acquisition data acquisition (DAQ) devices through self-made extended-PMUs (ePMUs). Throughout this article, a communication and storage environment that complies with the IEEE C37.118.2 standard for Synchrophasor Data Transfer for Power Systems will be characterized based on criteria established in the IEC 61850-90-5 standard for power utility automation. In addition, it justifies the chosen storage method through a comprehensive study of data volume and reading accessibility. The procedure and the ePMUs have been experimentally validated in field tests in two grid-connected PV plants. The procedure was proven to be valid for quasi-real-time applications for data registered and sent both in a local area network (LAN) and a wide area network (WAN). Thanks to the dataframes synchronize up to the millisecond, data can also be studied offline seamlessly.
{"title":"Implementation and Characterization of a High-Precision Monitoring System for Photovoltaic Power Plants Using Self-Made Phasor Measurement Units","authors":"Victoria Arenas-Ramos;Victor Pallares-Lopez;Rafael Real-Calvo;Miguel Gonzalez-Redondo;Isabel Santiago-Chiquero","doi":"10.1109/JSEN.2025.3598820","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3598820","url":null,"abstract":"The increasing integration of photovoltaic (PV) plants into the power grid presents an ongoing challenge to prevent the instability caused by atmospheric conditions from affecting the power distribution network. To adequately control the network and implement such techniques, monitoring has been considerably improved in recent years. Due to the vast number of measurements generated, efficient management of this data has become a significant challenge. This article outlines a procedure used for simultaneously storing synchronized measured data from phasor measurement units (PMUs) and PMU-like data from acquisition data acquisition (DAQ) devices through self-made extended-PMUs (ePMUs). Throughout this article, a communication and storage environment that complies with the IEEE C37.118.2 standard for Synchrophasor Data Transfer for Power Systems will be characterized based on criteria established in the IEC 61850-90-5 standard for power utility automation. In addition, it justifies the chosen storage method through a comprehensive study of data volume and reading accessibility. The procedure and the ePMUs have been experimentally validated in field tests in two grid-connected PV plants. The procedure was proven to be valid for quasi-real-time applications for data registered and sent both in a local area network (LAN) and a wide area network (WAN). Thanks to the dataframes synchronize up to the millisecond, data can also be studied offline seamlessly.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 19","pages":"37383-37393"},"PeriodicalIF":4.3,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11131518","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145204537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-19DOI: 10.1109/JSEN.2025.3598361
T. V. Padmavathy;S. Indra Priyadharshini;S. Brindha
The complicated topographical limitations and NP-hard nature of the sensor placement problem make it difficult to achieve optimal coverage and connectivity in wireless sensor networks (WSNs) deployed over irregular terrains. WSNs encounter critical challenges in uneven regions due to irregular obstacles, elevation variations, and dynamic terrain profiles, all of which influence coverage and connectivity. Classical methods, such as greedy or evolutionary strategies, often struggle to scale efficiently as terrain complexity increases and may converge to suboptimal solutions due to their heuristic nature. This article applies Grover’s quantum search algorithm to enhance sensor placement, the primary focus of this study. Unlike classical techniques that traverse solution spaces step by step, the Grover algorithm enhances the search process with a quadratic speedup. This enhancement enables the efficient identification of near-optimal sensor configurations in complicated contexts. This quantum-assisted method not only reduces the number of necessary sensors but also enhances performance in coverage and connectivity metrics, along with a substantial decrease in computation burden. The integration of terrain awareness and quantum search diverges significantly from traditional approaches, providing an intelligent, scalable, and computationally promising methodology for WSN deployment in the real-world terrains.
{"title":"Maximizing Wireless Sensor Networks Coverage and Connectivity in Irregular Terrains Using Grover’s Quantum Algorithm","authors":"T. V. Padmavathy;S. Indra Priyadharshini;S. Brindha","doi":"10.1109/JSEN.2025.3598361","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3598361","url":null,"abstract":"The complicated topographical limitations and NP-hard nature of the sensor placement problem make it difficult to achieve optimal coverage and connectivity in wireless sensor networks (WSNs) deployed over irregular terrains. WSNs encounter critical challenges in uneven regions due to irregular obstacles, elevation variations, and dynamic terrain profiles, all of which influence coverage and connectivity. Classical methods, such as greedy or evolutionary strategies, often struggle to scale efficiently as terrain complexity increases and may converge to suboptimal solutions due to their heuristic nature. This article applies Grover’s quantum search algorithm to enhance sensor placement, the primary focus of this study. Unlike classical techniques that traverse solution spaces step by step, the Grover algorithm enhances the search process with a quadratic speedup. This enhancement enables the efficient identification of near-optimal sensor configurations in complicated contexts. This quantum-assisted method not only reduces the number of necessary sensors but also enhances performance in coverage and connectivity metrics, along with a substantial decrease in computation burden. The integration of terrain awareness and quantum search diverges significantly from traditional approaches, providing an intelligent, scalable, and computationally promising methodology for WSN deployment in the real-world terrains.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 19","pages":"37352-37369"},"PeriodicalIF":4.3,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11130622","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145204538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In satellite-denied environments, the inertial navigation system (INS) suffers from error accumulation, whereas integrated solutions often depend on costly external sensors and fail to exploit motion modes effectively. This article reveals the mechanism of navigation enhancement under sparse motion modes, where effective states are intermittent but critical. We propose a task-driven dynamic decision optimization framework for self-constrained vehicle inertial navigation, combining deep learning with geometric state estimation. First, we establish multidimensional self-constraints from vehicle kinematics to suppress error propagation. Next, a Transformer-based network calculates motion mode confidences from inertial data, followed by a Bayesian optimization strategy to dynamically adjust thresholds, prioritizing false alarm suppression under a bounded missed detection tolerance. Furthermore, a state persistence mechanism (SPM) leverages motion continuity to eliminate short-term misjudgments. Finally, motion constraints are fused with inertial data via a manifold-based invariant extended Kalman filter (IEKF), which embeds states on Lie groups to mitigate inconsistency in linearization. The experiments on the Urban Kaist dataset demonstrate that compared with the existing motion mode decision, the average absolute trajectory error of positioning is reduced by more than 8.19%. This verifies the effectiveness of the proposed method, which provides a new solution for high-precision autonomous navigation of vehicles.
{"title":"Self-Constrained Vehicle Inertial Navigation Method With Task-Driven Dynamic Decision Optimization","authors":"Rui Mao;Zhihong Deng;Ping Zhang;Jiesi Dong;Wenhao Qi","doi":"10.1109/JSEN.2025.3597374","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3597374","url":null,"abstract":"In satellite-denied environments, the inertial navigation system (INS) suffers from error accumulation, whereas integrated solutions often depend on costly external sensors and fail to exploit motion modes effectively. This article reveals the mechanism of navigation enhancement under sparse motion modes, where effective states are intermittent but critical. We propose a task-driven dynamic decision optimization framework for self-constrained vehicle inertial navigation, combining deep learning with geometric state estimation. First, we establish multidimensional self-constraints from vehicle kinematics to suppress error propagation. Next, a Transformer-based network calculates motion mode confidences from inertial data, followed by a Bayesian optimization strategy to dynamically adjust thresholds, prioritizing false alarm suppression under a bounded missed detection tolerance. Furthermore, a state persistence mechanism (SPM) leverages motion continuity to eliminate short-term misjudgments. Finally, motion constraints are fused with inertial data via a manifold-based invariant extended Kalman filter (IEKF), which embeds states on Lie groups to mitigate inconsistency in linearization. The experiments on the Urban Kaist dataset demonstrate that compared with the existing motion mode decision, the average absolute trajectory error of positioning is reduced by more than 8.19%. This verifies the effectiveness of the proposed method, which provides a new solution for high-precision autonomous navigation of vehicles.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 18","pages":"35191-35200"},"PeriodicalIF":4.3,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145078626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wireless sensor networks (WSNs) are a crucial component of modern information technology and are widely used in applications such as environmental monitoring, smart homes, and healthcare. Node localization technology is fundamental to the operation of these applications. Because traditional received signal strength indicator (RSSI) fingerprint matching localization algorithms face significant challenges in practical applications, such as low positioning accuracy and high computational complexity, this article proposes an optimized method for RSSI-based fingerprint matching localization in WSNs, which enhances effectiveness and expands application scope. First, Kalman filtering is applied to preprocess RSSI values, reducing noise interference. Second, the RSSI distance model is used to construct fingerprint node circles, forming a fingerprint database and lessening the data required for matching. Finally, dynamic time warping (DTW) distance measures the similarity between positioning points and fingerprint data nodes, significantly enhancing the accuracy and precision of the matching process. In addition, the optimized algorithm also supports collaborative localization between multiple agents, so as to achieve real-time tracking and positioning of objects in space. The simulation and experimental results indicate that the algorithm delivers remarkable performance in 2-D and 3-D localization, with a 93% improvement in positioning accuracy and a nearly tenfold boost in computational efficiency.
{"title":"Optimization of Fingerprint Matching Localization Algorithm Based on RSSI in Wireless Sensor Network","authors":"Jiahao Xia;Xiu You;Haowei Cui;Yuhang Xin;Xueting Yin","doi":"10.1109/JSEN.2025.3598063","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3598063","url":null,"abstract":"Wireless sensor networks (WSNs) are a crucial component of modern information technology and are widely used in applications such as environmental monitoring, smart homes, and healthcare. Node localization technology is fundamental to the operation of these applications. Because traditional received signal strength indicator (RSSI) fingerprint matching localization algorithms face significant challenges in practical applications, such as low positioning accuracy and high computational complexity, this article proposes an optimized method for RSSI-based fingerprint matching localization in WSNs, which enhances effectiveness and expands application scope. First, Kalman filtering is applied to preprocess RSSI values, reducing noise interference. Second, the RSSI distance model is used to construct fingerprint node circles, forming a fingerprint database and lessening the data required for matching. Finally, dynamic time warping (DTW) distance measures the similarity between positioning points and fingerprint data nodes, significantly enhancing the accuracy and precision of the matching process. In addition, the optimized algorithm also supports collaborative localization between multiple agents, so as to achieve real-time tracking and positioning of objects in space. The simulation and experimental results indicate that the algorithm delivers remarkable performance in 2-D and 3-D localization, with a 93% improvement in positioning accuracy and a nearly tenfold boost in computational efficiency.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 18","pages":"35524-35533"},"PeriodicalIF":4.3,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145073281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-18DOI: 10.1109/JSEN.2025.3597913
Binkai Zou;Qitong Chen;Liang Chen;Changqing Shen
In recent years, deep learning has achieved significant progress in cross-domain fault diagnosis. Currently, most existing models still rely on large-scale labeled data and complex architectures, limiting their practical deployment in industrial applications. To overcome these limitations, this study aims to develop a lightweight method capable of effectively mitigating the impact of cross-domain feature distribution discrepancies, thereby enhancing model generalization and applicability under complex working conditions. First, a multiscale fusion adversarial domain adaptation (MFADA) approach is proposed. It improves interdomain feature alignment by integrating local structural features and high-level semantic information from both the source and target domains. Second, a lightweight feature extraction module is designed by combining pointwise (PW) convolution, depthwise (DW) convolution, and max pooling (MaxPool). This structure achieves strong feature extraction capability with minimal parameters, significantly improving computational efficiency. The proposed method is validated on current signals from industrial robots and vibration signals from bearings under various complex operating conditions. Experimental results demonstrate that MFADA achieves excellent performance on multiple transfer tasks. It reaches a maximum diagnostic accuracy of 99.17%, while maintaining a compact model size and low computational cost, demonstrating excellent performance.
{"title":"Multiscale Fusion Adversarial Domain Adaptation: A Cross-Domain Fault Diagnosis Method Based on Lightweight Model","authors":"Binkai Zou;Qitong Chen;Liang Chen;Changqing Shen","doi":"10.1109/JSEN.2025.3597913","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3597913","url":null,"abstract":"In recent years, deep learning has achieved significant progress in cross-domain fault diagnosis. Currently, most existing models still rely on large-scale labeled data and complex architectures, limiting their practical deployment in industrial applications. To overcome these limitations, this study aims to develop a lightweight method capable of effectively mitigating the impact of cross-domain feature distribution discrepancies, thereby enhancing model generalization and applicability under complex working conditions. First, a multiscale fusion adversarial domain adaptation (MFADA) approach is proposed. It improves interdomain feature alignment by integrating local structural features and high-level semantic information from both the source and target domains. Second, a lightweight feature extraction module is designed by combining pointwise (PW) convolution, depthwise (DW) convolution, and max pooling (MaxPool). This structure achieves strong feature extraction capability with minimal parameters, significantly improving computational efficiency. The proposed method is validated on current signals from industrial robots and vibration signals from bearings under various complex operating conditions. Experimental results demonstrate that MFADA achieves excellent performance on multiple transfer tasks. It reaches a maximum diagnostic accuracy of 99.17%, while maintaining a compact model size and low computational cost, demonstrating excellent performance.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 19","pages":"37512-37521"},"PeriodicalIF":4.3,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145204544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-18DOI: 10.1109/JSEN.2025.3594530
Mariam El Gharbi;Jamal Abounasr;Raúl Fernández-García;Ignacio Gil
Wearable technology has increased interest in monitoring vital signs in various medical fields, especially breathing monitoring. However, existing methods to track breathing patterns face challenges, including a lack of user comfort, robustness, system reliability, and data processing issues. Therefore, it is crucial to develop user-friendly and reliable wireless methods to overcome these challenges. In this article, we present a new wireless communication platform for real-time breathing monitoring. The platform consists of a wearable stretching belt with an embroidered loop antenna-based sensor connected to a compact Bluetooth transmitter, with dimensions of 17.145 × 11.303 mm2, placed on the abdomen. The proposed antenna-based sensor was designed to transmit data over wireless networks operating at a 2.4-GHz frequency. Its sensing mechanism involves detecting a received signal strength indicator (RSSI) wirelessly transmitted by the antenna sensor, which is responsive to strain. The experimental results were conducted to track different breathing patterns from a male volunteer. The system was compared with a Biopac MP36, and the obtained results showed a strong correlation with the measured values. The proposed system demonstrates the ability to detect various breathing patterns, including eupnea, hypopnea, hyperpnea, and Biot’s respiration. Furthermore, its accuracy in detecting these breathing patterns highlights its potential for clinical applications, such as monitoring respiratory health in patients with sleep disorders or chronic respiratory conditions.
{"title":"A Smart Belt With Embroidered Antenna-Based Sensor for Real-Time Respiratory Monitoring","authors":"Mariam El Gharbi;Jamal Abounasr;Raúl Fernández-García;Ignacio Gil","doi":"10.1109/JSEN.2025.3594530","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3594530","url":null,"abstract":"Wearable technology has increased interest in monitoring vital signs in various medical fields, especially breathing monitoring. However, existing methods to track breathing patterns face challenges, including a lack of user comfort, robustness, system reliability, and data processing issues. Therefore, it is crucial to develop user-friendly and reliable wireless methods to overcome these challenges. In this article, we present a new wireless communication platform for real-time breathing monitoring. The platform consists of a wearable stretching belt with an embroidered loop antenna-based sensor connected to a compact Bluetooth transmitter, with dimensions of 17.145 × 11.303 mm2, placed on the abdomen. The proposed antenna-based sensor was designed to transmit data over wireless networks operating at a 2.4-GHz frequency. Its sensing mechanism involves detecting a received signal strength indicator (RSSI) wirelessly transmitted by the antenna sensor, which is responsive to strain. The experimental results were conducted to track different breathing patterns from a male volunteer. The system was compared with a Biopac MP36, and the obtained results showed a strong correlation with the measured values. The proposed system demonstrates the ability to detect various breathing patterns, including eupnea, hypopnea, hyperpnea, and Biot’s respiration. Furthermore, its accuracy in detecting these breathing patterns highlights its potential for clinical applications, such as monitoring respiratory health in patients with sleep disorders or chronic respiratory conditions.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 19","pages":"37327-37338"},"PeriodicalIF":4.3,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11126937","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145204555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In active magnetic bearing (AMB) systems, harmonic currents induced by mass unbalance and sensor runout cause the system to generate harmonic vibrations. Conventional harmonic current suppression methods depend highly on accurate rotational speed information, which limits their application in scenarios without rotational speed sensors. To address this problem, this article first analyses the main sources of harmonic vibration and then proposes an improved multiple second-order generalized integral frequency-locked loop (IMSOGI-FLL) algorithm. The algorithm achieves adaptive estimation of the rotational frequency by using the frequency of the fundamental component in the disturbance signal and effectively suppresses the harmonic currents generated by the mass unbalance and sensor beating. In addition, in order to ensure the stability of the system in the operating range, this article designs a simple phase compensator without parameter switching, which can improve the stability range of the system in the low-frequency band. The experimental results show that the proposed algorithm can not only accurately estimate the rotational frequency but also significantly eliminate the harmonic currents caused by mass unbalance and sensor runout, which provides an effective solution for harmonic current suppression in the case of no speed sensor.
{"title":"Harmonic Current Suppression of AMB Rotor System Based on Improved Multiple SOGI-FLL","authors":"Fayuan Xie;Jiming Zou;Shaobin Li;Lijun Xiao;Yongxiang Xu;Pengcheng Zhu","doi":"10.1109/JSEN.2025.3597315","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3597315","url":null,"abstract":"In active magnetic bearing (AMB) systems, harmonic currents induced by mass unbalance and sensor runout cause the system to generate harmonic vibrations. Conventional harmonic current suppression methods depend highly on accurate rotational speed information, which limits their application in scenarios without rotational speed sensors. To address this problem, this article first analyses the main sources of harmonic vibration and then proposes an improved multiple second-order generalized integral frequency-locked loop (IMSOGI-FLL) algorithm. The algorithm achieves adaptive estimation of the rotational frequency by using the frequency of the fundamental component in the disturbance signal and effectively suppresses the harmonic currents generated by the mass unbalance and sensor beating. In addition, in order to ensure the stability of the system in the operating range, this article designs a simple phase compensator without parameter switching, which can improve the stability range of the system in the low-frequency band. The experimental results show that the proposed algorithm can not only accurately estimate the rotational frequency but also significantly eliminate the harmonic currents caused by mass unbalance and sensor runout, which provides an effective solution for harmonic current suppression in the case of no speed sensor.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 18","pages":"34529-34536"},"PeriodicalIF":4.3,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145090128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-18DOI: 10.1109/JSEN.2025.3597294
Dezhi Zheng;Zonglin Li;Jie Yuan;Chun Hu;Zhen Wang;Peng Peng
Eddy current testing (ECT) is a vital technique for pipeline defect detection, where the sensitivity of detection is heavily influenced by probe design parameters. However, traditional optimization methods for probe parameters often suffer from limitations such as neglecting interactions among parameters, ignoring potential optimal combinations within the step size, and being quite time-consuming. To address these challenges, an advanced optimization framework is proposed, which combines a neural network with Bayesian optimization (BO). A probe configuration consisting of two coaxially arranged coils connected via a bridge circuit is investigated. A multipath residual neural network is developed as a surrogate model to evaluate the design parameters, including coil inner diameter, number of turns, height, and spacing. Bayesian optimization then uses this model as the objective function to identify optimal parameter combinations. Simulation and experimental results validate that the surrogate model demonstrates enhanced prediction accuracy, and the optimization process achieves superior performance with fewer iterations. Compared with the comparison groups, the optimized probes exhibit higher sensitivity for defects in the 1–4-mm depth range. These prove the effectiveness of the proposed method for efficient and high-performance ECT probe design, indicating its significant application potential.
{"title":"High-Sensitivity Eddy Current Probe Design via Multipath ResNet and Bayesian Optimization","authors":"Dezhi Zheng;Zonglin Li;Jie Yuan;Chun Hu;Zhen Wang;Peng Peng","doi":"10.1109/JSEN.2025.3597294","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3597294","url":null,"abstract":"Eddy current testing (ECT) is a vital technique for pipeline defect detection, where the sensitivity of detection is heavily influenced by probe design parameters. However, traditional optimization methods for probe parameters often suffer from limitations such as neglecting interactions among parameters, ignoring potential optimal combinations within the step size, and being quite time-consuming. To address these challenges, an advanced optimization framework is proposed, which combines a neural network with Bayesian optimization (BO). A probe configuration consisting of two coaxially arranged coils connected via a bridge circuit is investigated. A multipath residual neural network is developed as a surrogate model to evaluate the design parameters, including coil inner diameter, number of turns, height, and spacing. Bayesian optimization then uses this model as the objective function to identify optimal parameter combinations. Simulation and experimental results validate that the surrogate model demonstrates enhanced prediction accuracy, and the optimization process achieves superior performance with fewer iterations. Compared with the comparison groups, the optimized probes exhibit higher sensitivity for defects in the 1–4-mm depth range. These prove the effectiveness of the proposed method for efficient and high-performance ECT probe design, indicating its significant application potential.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 18","pages":"34803-34812"},"PeriodicalIF":4.3,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145089998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-18DOI: 10.1109/JSEN.2025.3597683
Xinjiu Jin;Lijian Yang
The accurate assessment of the ultimate tensile strength (UTS) of pipeline materials is crucial for determining the maximum allowable operating pressure of pipelines and predicting potential locations of structural failure. To evaluate the UTS of in-service pipelines, this study investigated the relationship between the UTS of steel and its magnetic permeability based on dislocation dynamics and density functional theory. An eddy current-based detection method for assessing the UTS of pipelines was proposed. The effectiveness of the proposed method was verified through experiments, and the impact of temperature variations and surface corrosion on the detection outcomes was also investigated. The experimental results demonstrate that when the detection frequency is set within the range of 5–50 kHz, the eddy current testing results of Q235 and Q345 steels exhibit an approximately linear distribution on the impedance plane, corresponding to the ascending order of their UTS. The optimal detection frequency for both steel types is identified to be between 10 and 50 kHz. Within this frequency range, both the amplitude and the phase angle of the eddy current impedance display an approximately linear correlation with the UTS of the materials. Under linear regression analysis, the Pearson correlation coefficient between impedance amplitude and UTS exceeds 0.75, while that between phase angle and UTS remains above 0.7. This method exhibits less susceptibility to temperature variations and surface corrosion on steel, making it suitable for complex working conditions, including internal inspection of pipelines.
{"title":"Research on Eddy Current-Based Detection Method for Ultimate Tensile Strength of Pipelines","authors":"Xinjiu Jin;Lijian Yang","doi":"10.1109/JSEN.2025.3597683","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3597683","url":null,"abstract":"The accurate assessment of the ultimate tensile strength (UTS) of pipeline materials is crucial for determining the maximum allowable operating pressure of pipelines and predicting potential locations of structural failure. To evaluate the UTS of in-service pipelines, this study investigated the relationship between the UTS of steel and its magnetic permeability based on dislocation dynamics and density functional theory. An eddy current-based detection method for assessing the UTS of pipelines was proposed. The effectiveness of the proposed method was verified through experiments, and the impact of temperature variations and surface corrosion on the detection outcomes was also investigated. The experimental results demonstrate that when the detection frequency is set within the range of 5–50 kHz, the eddy current testing results of Q235 and Q345 steels exhibit an approximately linear distribution on the impedance plane, corresponding to the ascending order of their UTS. The optimal detection frequency for both steel types is identified to be between 10 and 50 kHz. Within this frequency range, both the amplitude and the phase angle of the eddy current impedance display an approximately linear correlation with the UTS of the materials. Under linear regression analysis, the Pearson correlation coefficient between impedance amplitude and UTS exceeds 0.75, while that between phase angle and UTS remains above 0.7. This method exhibits less susceptibility to temperature variations and surface corrosion on steel, making it suitable for complex working conditions, including internal inspection of pipelines.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 18","pages":"35201-35211"},"PeriodicalIF":4.3,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145078632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-18DOI: 10.1109/JSEN.2025.3597861
Sumaiya Afroz Mila;Sandip Ray
The use of Internet of Things (IoT) technology in the healthcare system has significantly improved the efficiency and effectiveness of patient care, marking a paradigm shift in modern healthcare practices. Continuous monitoring of physiological parameters through wearable devices has the potential to contribute to the early detection of various chronic and infectious diseases. In this survey, we dig into a variety of wearable devices, exploring the sensors they employ and the specific physiological parameters they monitor. Additionally, we demonstrate the wireless communication facilitated by these devices, connecting sensors and external servers or cloud platforms. Ultimately, we showcase the diverse array of applications for these wearable devices in the realms of disease diagnosis and prevention, achieved through the continuous monitoring of physiological data.
{"title":"IoT for Continuous Physiological Parameters Monitoring in Healthcare: A Review","authors":"Sumaiya Afroz Mila;Sandip Ray","doi":"10.1109/JSEN.2025.3597861","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3597861","url":null,"abstract":"The use of Internet of Things (IoT) technology in the healthcare system has significantly improved the efficiency and effectiveness of patient care, marking a paradigm shift in modern healthcare practices. Continuous monitoring of physiological parameters through wearable devices has the potential to contribute to the early detection of various chronic and infectious diseases. In this survey, we dig into a variety of wearable devices, exploring the sensors they employ and the specific physiological parameters they monitor. Additionally, we demonstrate the wireless communication facilitated by these devices, connecting sensors and external servers or cloud platforms. Ultimately, we showcase the diverse array of applications for these wearable devices in the realms of disease diagnosis and prevention, achieved through the continuous monitoring of physiological data.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 18","pages":"34311-34325"},"PeriodicalIF":4.3,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145073158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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