Pub Date : 2025-02-13DOI: 10.1109/TIM.2025.3541812
Zhiqiang Cao;Ran Liu;Billy Pik Lik Lau;Chau Yuen;U-Xuan Tan
Relative localization is crucial for a multirobot system to collaboratively perform tasks, such as exploration and formation. However, this is highly challenging for homogeneous robots with similar appearance in GPS-denied and communication-limited environments. In this article, we propose a fully distributed relative position estimation approach for a team of robots based on onboard ultra-wideband (UWB) and light detection and ranging (LiDAR) sensors, in which LiDAR is utilized to obtain the position of anonymous objects in line-of-sight (LOS), and UWB is used for ranging between robots. We construct two graphs, namely UWB connection graph and LiDAR connection graph, to represent the spatial relationship among objects (robots and obstacles) based on UWB and LiDAR measurements. Identification and relative position estimation are formulated as a common subgraph matching problem. A falsely matched robot identification approach is designed to recognize the falsely matched results caused by obstacle blockage in LiDAR field of view. These robots are then localized by leveraging the well-matched robots and the UWB ranging measurements in the UWB connection graph. We conducted experiments to evaluate the performance of our approach. The results show that the proposed approach is capable of achieving satisfactory positioning accuracy for a team of robots in a distributed manner with only exchanging limited information.
{"title":"Distributed Relative Localization Based on Ultra-Wideband and LiDAR for Multirobot With Limited Communication","authors":"Zhiqiang Cao;Ran Liu;Billy Pik Lik Lau;Chau Yuen;U-Xuan Tan","doi":"10.1109/TIM.2025.3541812","DOIUrl":"https://doi.org/10.1109/TIM.2025.3541812","url":null,"abstract":"Relative localization is crucial for a multirobot system to collaboratively perform tasks, such as exploration and formation. However, this is highly challenging for homogeneous robots with similar appearance in GPS-denied and communication-limited environments. In this article, we propose a fully distributed relative position estimation approach for a team of robots based on onboard ultra-wideband (UWB) and light detection and ranging (LiDAR) sensors, in which LiDAR is utilized to obtain the position of anonymous objects in line-of-sight (LOS), and UWB is used for ranging between robots. We construct two graphs, namely UWB connection graph and LiDAR connection graph, to represent the spatial relationship among objects (robots and obstacles) based on UWB and LiDAR measurements. Identification and relative position estimation are formulated as a common subgraph matching problem. A falsely matched robot identification approach is designed to recognize the falsely matched results caused by obstacle blockage in LiDAR field of view. These robots are then localized by leveraging the well-matched robots and the UWB ranging measurements in the UWB connection graph. We conducted experiments to evaluate the performance of our approach. The results show that the proposed approach is capable of achieving satisfactory positioning accuracy for a team of robots in a distributed manner with only exchanging limited information.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-12"},"PeriodicalIF":5.6,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143489136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Inspection images of transmission lines from autonomous aerial vehicle (AAV) often contain complex backgrounds and multicategory of power defects. Similarities in categories and scale differences in objects make the traditional single-category detection methods of power defects have unacceptable errors. Therefore, in this article, the you only look once (YOLO)–dynamic task alignment detection (DTAD) model for images of multicategory power defects is constructed to ensure real-time detection of AAV. First, the detection head of DTAD embeds a feature extractor built by grouped convolution in a decoupled head structure of classification and localization, which further learns the task interaction features to improve the model performance. Second, based on the idea of exponential moving average (EMA), the EMA SlideLoss (ESLoss) function is proposed to self-study the intersection over union (IoU) threshold of the bounding box to control the balance between positive and negative samples and dynamically regulate the loss weights of the samples. Finally, normalized Wasserstein distance (NWD) is introduced to alleviate the regression bias of the multiscale object bounding box. Compared with other detectors, the proposed model reaches the mAP50 of 48.2% and 150 frames/s (FPS), respectively, in a private power dataset containing six categories of power defects, which achieves the best tradeoff between speed and accuracy. In addition, generalization experiments are also conducted on other four public datasets to prove the versatility and effectiveness of the proposed model, and the precision values are improved by about 3.1% on average.
{"title":"YOLO–DTAD: Dynamic Task Alignment Detection Model for Multicategory Power Defects Image","authors":"Runhai Jiao;Jiaji Liu;Kaihang Li;Ruojiao Qiao;Yanzhi Liu;Wenbiao Zhang","doi":"10.1109/TIM.2025.3541692","DOIUrl":"https://doi.org/10.1109/TIM.2025.3541692","url":null,"abstract":"Inspection images of transmission lines from autonomous aerial vehicle (AAV) often contain complex backgrounds and multicategory of power defects. Similarities in categories and scale differences in objects make the traditional single-category detection methods of power defects have unacceptable errors. Therefore, in this article, the you only look once (YOLO)–dynamic task alignment detection (DTAD) model for images of multicategory power defects is constructed to ensure real-time detection of AAV. First, the detection head of DTAD embeds a feature extractor built by grouped convolution in a decoupled head structure of classification and localization, which further learns the task interaction features to improve the model performance. Second, based on the idea of exponential moving average (EMA), the EMA SlideLoss (ESLoss) function is proposed to self-study the intersection over union (IoU) threshold of the bounding box to control the balance between positive and negative samples and dynamically regulate the loss weights of the samples. Finally, normalized Wasserstein distance (NWD) is introduced to alleviate the regression bias of the multiscale object bounding box. Compared with other detectors, the proposed model reaches the mAP50 of 48.2% and 150 frames/s (FPS), respectively, in a private power dataset containing six categories of power defects, which achieves the best tradeoff between speed and accuracy. In addition, generalization experiments are also conducted on other four public datasets to prove the versatility and effectiveness of the proposed model, and the precision values are improved by about 3.1% on average.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-14"},"PeriodicalIF":5.6,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Electrical impedance myography (EIM) is a noninvasive, painless, rapid, and low-cost method for assessing muscle health proposed in the past two decades. It operates by the nonintrusive injection of a weak, high-frequency current to acquire the electrical characteristics of muscle tissue, thereby determining its physiological properties. In this article, a portable human tissue complex impedance measurement system based on EIM is proposed. Through in-phase and quadrature (I/Q) demodulation, this system is capable of real-time display of the amplitude and phase of human tissue complex impedance on a PC. The reliability of the system was validated by measuring the complex impedance of the Fricke-Morse impedance models under excitations at different frequencies and of the relaxed human neck muscles under a single-frequency excitation across different environmental parameters. Furthermore, to reveal the correlation between muscle tissue complex impedance and muscle states, the swallowing action recognition model based on convolutional k-nearest neighbors (CKNNs) is constructed and deployed in the proposed system. With high accuracy and low floating point operations (FLOPs), CKNN performs effectively in swallowing action recognition. Compared to other swallowing action recognition systems, the proposed system exhibits reliable classification capabilities, achieving a recognition accuracy of 95.09%.
{"title":"A Human Tissue Complex Impedance Measurement System for Swallowing Action Recognition","authors":"Bojun Liu;Zhaosheng Teng;Qiu Tang;Tianyi Deng;Hongqin Lan;Haowen Zhong","doi":"10.1109/TIM.2025.3541660","DOIUrl":"https://doi.org/10.1109/TIM.2025.3541660","url":null,"abstract":"Electrical impedance myography (EIM) is a noninvasive, painless, rapid, and low-cost method for assessing muscle health proposed in the past two decades. It operates by the nonintrusive injection of a weak, high-frequency current to acquire the electrical characteristics of muscle tissue, thereby determining its physiological properties. In this article, a portable human tissue complex impedance measurement system based on EIM is proposed. Through in-phase and quadrature (I/Q) demodulation, this system is capable of real-time display of the amplitude and phase of human tissue complex impedance on a PC. The reliability of the system was validated by measuring the complex impedance of the Fricke-Morse impedance models under excitations at different frequencies and of the relaxed human neck muscles under a single-frequency excitation across different environmental parameters. Furthermore, to reveal the correlation between muscle tissue complex impedance and muscle states, the swallowing action recognition model based on convolutional k-nearest neighbors (CKNNs) is constructed and deployed in the proposed system. With high accuracy and low floating point operations (FLOPs), CKNN performs effectively in swallowing action recognition. Compared to other swallowing action recognition systems, the proposed system exhibits reliable classification capabilities, achieving a recognition accuracy of 95.09%.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-11"},"PeriodicalIF":5.6,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143489162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
There are two problems in the low-noise magnetic field compensation of desktop small magnetic shielding boxes (SMSBs). First, the uniformity of the magnetic field generated by the coil is reduced due to the strong coupling effect of the ferromagnetic boundary. Second, the larger coil constant requires a highly accurate current source. To improve uniformity and reduce the coil constant, an optimization design method with an ultrasmall coil constant biplane coil (BC) based on the same trend coupling effect of the ferromagnetic boundary is proposed. In this article, for the first time, the relationship between coil constant and shielding distance is analyzed by constructing the magnetic field and distance (M-D) function. The parameters of the primary coils and the secondary coils are optimized by particle swarm optimization (PSO). The combined BCs (CBCs) with an ultrasmall coil constant are designed based on the double image method and M-D function. Compared with traditional BCs, the ${B}_{x}$ and ${B}_{y}$ coil constants of CBCs are reduced by two orders of magnitude to 41.8 and 35.4 nT/A, respectively, and the $ {B}_{z} $ coil constant is reduced by three orders of magnitude to 49.9 nT/A. The uniformity of the magnetic field in the target area is improved by 4.4 times in the X-direction and 7.4 times in the Z-direction. The peak-to-peak value of the magnetic field is reduced by 24.7%, and magnetic field noise is reduced by 49% at 1 Hz in the closed loop.
{"title":"Optimization Design of Biplane Coil With Ultrasmall Coil Constant Based on Co-Directional Ferromagnetic Boundary Coupling Effect","authors":"Haoting Wu;Xiuqi Zhao;Peiling Cui;Haifeng Zhang;Jiawen Liu;Tong Wen","doi":"10.1109/TIM.2025.3533616","DOIUrl":"https://doi.org/10.1109/TIM.2025.3533616","url":null,"abstract":"There are two problems in the low-noise magnetic field compensation of desktop small magnetic shielding boxes (SMSBs). First, the uniformity of the magnetic field generated by the coil is reduced due to the strong coupling effect of the ferromagnetic boundary. Second, the larger coil constant requires a highly accurate current source. To improve uniformity and reduce the coil constant, an optimization design method with an ultrasmall coil constant biplane coil (BC) based on the same trend coupling effect of the ferromagnetic boundary is proposed. In this article, for the first time, the relationship between coil constant and shielding distance is analyzed by constructing the magnetic field and distance (M-D) function. The parameters of the primary coils and the secondary coils are optimized by particle swarm optimization (PSO). The combined BCs (CBCs) with an ultrasmall coil constant are designed based on the double image method and M-D function. Compared with traditional BCs, the <inline-formula> <tex-math>${B}_{x}$ </tex-math></inline-formula> and <inline-formula> <tex-math>${B}_{y}$ </tex-math></inline-formula> coil constants of CBCs are reduced by two orders of magnitude to 41.8 and 35.4 nT/A, respectively, and the <inline-formula> <tex-math>$ {B}_{z} $ </tex-math></inline-formula> coil constant is reduced by three orders of magnitude to 49.9 nT/A. The uniformity of the magnetic field in the target area is improved by 4.4 times in the X-direction and 7.4 times in the Z-direction. The peak-to-peak value of the magnetic field is reduced by 24.7%, and magnetic field noise is reduced by 49% at 1 Hz in the closed loop.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-11"},"PeriodicalIF":5.6,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143422781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-13DOI: 10.1109/TIM.2025.3538085
Lingli Cui;Wenhao Sun;Xinyuan Zhao;Dongdong Liu
Maximum cyclostationarity blind deconvolution effectively enhances the periodic components by maximizing the cyclostationary behavior associated with the fault-inducing source. However, the validity of maximum cyclostationarity blind deconvolution depends on prior knowledge of bearing characteristic frequency, which is influenced by the shaft rotation frequency and bearing elements. In addition, it tends to generate false cyclostationary components when the incorrect cyclic frequency is provided as input. To address the above problems, a diagnostic feature-based adaptive maximum cyclostationarity blind deconvolution (DFACYCBD) is proposed for identifying the incipient faults of bearings. A novel estimator known as the diagnostic feature spectrum (DFS) is introduced in this method, which is constructed based on a feature at each frequency in the enhanced envelope spectrum (EES). Specifically, the cyclostationary information of noisy signals is first extracted using the fast spectral correlation (Fast-SC) and then converted into the equal-frequency interval harmonic structure (EIHS) within EES. Subsequently, DFS is used to calculate the cyclic frequency, with the estimated result considered as the desired cyclic frequency. Even in conditions with heavy background noise, DFS is proven to yield precise estimated results as the cyclic frequency to input. Finally, the simulated signal and bearing vibration datasets are applied to validate the efficacy of DFACYCBD.
{"title":"Adaptive Maximum Second-Order Cyclostationarity Blind Deconvolution Based on Diagnostic Feature Spectrum for Rolling Bearing Fault Diagnosis","authors":"Lingli Cui;Wenhao Sun;Xinyuan Zhao;Dongdong Liu","doi":"10.1109/TIM.2025.3538085","DOIUrl":"https://doi.org/10.1109/TIM.2025.3538085","url":null,"abstract":"Maximum cyclostationarity blind deconvolution effectively enhances the periodic components by maximizing the cyclostationary behavior associated with the fault-inducing source. However, the validity of maximum cyclostationarity blind deconvolution depends on prior knowledge of bearing characteristic frequency, which is influenced by the shaft rotation frequency and bearing elements. In addition, it tends to generate false cyclostationary components when the incorrect cyclic frequency is provided as input. To address the above problems, a diagnostic feature-based adaptive maximum cyclostationarity blind deconvolution (DFACYCBD) is proposed for identifying the incipient faults of bearings. A novel estimator known as the diagnostic feature spectrum (DFS) is introduced in this method, which is constructed based on a feature at each frequency in the enhanced envelope spectrum (EES). Specifically, the cyclostationary information of noisy signals is first extracted using the fast spectral correlation (Fast-SC) and then converted into the equal-frequency interval harmonic structure (EIHS) within EES. Subsequently, DFS is used to calculate the cyclic frequency, with the estimated result considered as the desired cyclic frequency. Even in conditions with heavy background noise, DFS is proven to yield precise estimated results as the cyclic frequency to input. Finally, the simulated signal and bearing vibration datasets are applied to validate the efficacy of DFACYCBD.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-9"},"PeriodicalIF":5.6,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-13DOI: 10.1109/TIM.2025.3541659
Fei Teng;Wenyi Zhang;Zhenhai Zhang
This article proposes a sensitivity calibration method for triaxial accelerometers, aiming to eliminate calibration errors caused by the transverse effects of the calibration device on the accelerometers. First, we establish a matrix model that relates the triaxial acceleration excitation loads to the sensor voltage sensitivity. Next, we introduce an orthogonal calibration method based on the Hopkinson bar. Using three laser Doppler velocimeters (LDVs), we simultaneously measure the 3-D orthogonal excitation acceleration at the end of the calibration device. We then calculate the impact of the transverse coupling effect between the elastic rod and the anvil on the accelerometer calibration. Finally, we perform calibration experiments on triaxial high-g accelerometers using the proposed and conventional methods. The sensitivity matrices for each method were computed using the least squares method. We evaluate the calibration accuracy using relative error and root mean square error (RMSE) metrics. The results demonstrate that the proposed orthogonal calibration method reduces the average relative error by 60.3% and the RMSE by 64.3% compared with the conventional calibration method. The proposed orthogonal calibration method achieves higher precision and better reflects the sensitivity characteristics of triaxial accelerometers.
本文提出了一种三轴加速度计灵敏度校准方法，旨在消除校准装置对加速度计的横向影响所造成的校准误差。首先，我们建立了一个矩阵模型，将三轴加速度激励载荷与传感器电压灵敏度联系起来。接下来，我们介绍一种基于霍普金森杆的正交校准方法。我们使用三个激光多普勒测速仪（LDV），同时测量校准装置末端的三维正交激振加速度。然后，我们计算了弹性杆和砧之间的横向耦合效应对加速度计校准的影响。最后，我们使用提出的方法和传统方法对三轴高 g 加速计进行了校准实验。使用最小二乘法计算了每种方法的灵敏度矩阵。我们使用相对误差和均方根误差 (RMSE) 指标来评估校准精度。结果表明，与传统校准方法相比，拟议的正交校准方法将平均相对误差降低了 60.3%，均方根误差降低了 64.3%。拟议的正交校准方法实现了更高的精度，更好地反映了三轴加速度计的灵敏度特性。
{"title":"Sensitivity Calibration of Triaxial High-g Accelerometer Based on the Transverse Effect of Hopkinson Bar","authors":"Fei Teng;Wenyi Zhang;Zhenhai Zhang","doi":"10.1109/TIM.2025.3541659","DOIUrl":"https://doi.org/10.1109/TIM.2025.3541659","url":null,"abstract":"This article proposes a sensitivity calibration method for triaxial accelerometers, aiming to eliminate calibration errors caused by the transverse effects of the calibration device on the accelerometers. First, we establish a matrix model that relates the triaxial acceleration excitation loads to the sensor voltage sensitivity. Next, we introduce an orthogonal calibration method based on the Hopkinson bar. Using three laser Doppler velocimeters (LDVs), we simultaneously measure the 3-D orthogonal excitation acceleration at the end of the calibration device. We then calculate the impact of the transverse coupling effect between the elastic rod and the anvil on the accelerometer calibration. Finally, we perform calibration experiments on triaxial high-g accelerometers using the proposed and conventional methods. The sensitivity matrices for each method were computed using the least squares method. We evaluate the calibration accuracy using relative error and root mean square error (RMSE) metrics. The results demonstrate that the proposed orthogonal calibration method reduces the average relative error by 60.3% and the RMSE by 64.3% compared with the conventional calibration method. The proposed orthogonal calibration method achieves higher precision and better reflects the sensitivity characteristics of triaxial accelerometers.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-9"},"PeriodicalIF":5.6,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-13DOI: 10.1109/TIM.2025.3541698
Qingtian Wu;Xiaoming Wang;Nannan Li;Simon Fong;Liming Zhang;Jinfeng Yang
Facial landmark detection (FLD) is an important task in computer vision, involving the extraction of keypoints from facial images. Traditional methods typically employ a two-stage approach: first detecting faces, then predicting facial landmarks. However, computing deep features for accurate face and landmark detection is time intensive, and the features from each stage are not shared. This makes these methods suboptimal for real-time applications, especially on edge devices. In this article, we present a novel end-to-end deep network for joint face and FLD. Our approach builds upon the YOLO framework with minimal modifications, primarily involving the adjustment of multitarget labels for face detection and the addition of a separate head for landmark localization. Furthermore, we enhance the model using structural reparameterization, channel shuffling, and implicit modules. Experimental evaluations on the 300 W dataset demonstrate that our proposed method achieves high accuracy while maintaining real-time processing speeds, surpassing several state-of-the-art (SOTA) methods. Additional testing on challenging datasets such as Caltech Occluded Faces in the Wild (COFW) and AFLW2000-3D further highlights the robustness of our model in diverse conditions. Our model and source code will be made publicly available.
{"title":"Real-Time Face and Facial Landmark Joint Detection Based on End-to-End Deep Network","authors":"Qingtian Wu;Xiaoming Wang;Nannan Li;Simon Fong;Liming Zhang;Jinfeng Yang","doi":"10.1109/TIM.2025.3541698","DOIUrl":"https://doi.org/10.1109/TIM.2025.3541698","url":null,"abstract":"Facial landmark detection (FLD) is an important task in computer vision, involving the extraction of keypoints from facial images. Traditional methods typically employ a two-stage approach: first detecting faces, then predicting facial landmarks. However, computing deep features for accurate face and landmark detection is time intensive, and the features from each stage are not shared. This makes these methods suboptimal for real-time applications, especially on edge devices. In this article, we present a novel end-to-end deep network for joint face and FLD. Our approach builds upon the YOLO framework with minimal modifications, primarily involving the adjustment of multitarget labels for face detection and the addition of a separate head for landmark localization. Furthermore, we enhance the model using structural reparameterization, channel shuffling, and implicit modules. Experimental evaluations on the 300 W dataset demonstrate that our proposed method achieves high accuracy while maintaining real-time processing speeds, surpassing several state-of-the-art (SOTA) methods. Additional testing on challenging datasets such as Caltech Occluded Faces in the Wild (COFW) and AFLW2000-3D further highlights the robustness of our model in diverse conditions. Our model and source code will be made publicly available.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-13"},"PeriodicalIF":5.6,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143471787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-13DOI: 10.1109/TIM.2025.3541658
Xingchen Liu;Carman K. M. Lee;Jingyuan Huang;Qiuzhuang Sun
Online degradation analysis requires an adaptive model parameter estimation. In addition, measurement errors and outliers are inevitable in real applications of degradation analysis. However, existing online models ignore the measurement error or assume the measurement error to be distributed as Gaussian for mathematical simplicity, which is vulnerable to measurement outliers. To deal with such problems, an online degradation analysis technique with robustness to measurement outliers is developed. More specifically, the underlying degradation is modeled with the Wiener process and the measurement error is modeled by constructing a modified Huber density to enhance the robustness against the outlier. For the adaptive estimation of model parameters, an online expectation-maximization (EM) algorithm is developed. Furthermore, procedures are provided for recursive degradation state identification by maximizing a posteriori based on the Laplace approximation. Numerical and two real case studies are carried out to validate the efficacy of the proposed model.
{"title":"Online Robustness Degradation Analysis With Measurement Outlier","authors":"Xingchen Liu;Carman K. M. Lee;Jingyuan Huang;Qiuzhuang Sun","doi":"10.1109/TIM.2025.3541658","DOIUrl":"https://doi.org/10.1109/TIM.2025.3541658","url":null,"abstract":"Online degradation analysis requires an adaptive model parameter estimation. In addition, measurement errors and outliers are inevitable in real applications of degradation analysis. However, existing online models ignore the measurement error or assume the measurement error to be distributed as Gaussian for mathematical simplicity, which is vulnerable to measurement outliers. To deal with such problems, an online degradation analysis technique with robustness to measurement outliers is developed. More specifically, the underlying degradation is modeled with the Wiener process and the measurement error is modeled by constructing a modified Huber density to enhance the robustness against the outlier. For the adaptive estimation of model parameters, an online expectation-maximization (EM) algorithm is developed. Furthermore, procedures are provided for recursive degradation state identification by maximizing a posteriori based on the Laplace approximation. Numerical and two real case studies are carried out to validate the efficacy of the proposed model.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-12"},"PeriodicalIF":5.6,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143489172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-13DOI: 10.1109/TIM.2025.3541695
Eulalia Balestrieri;Pasquale Daponte;Luca De Vito;Francesco Picariello;Sergio Rapuano;Ioan Tudosa
This article introduces a passive measurement method for diagnosing anomalies in two-wire communication channels using machine learning (ML). The proposed method involves the acquisition of the signal received by a transceiver and the decoded sequence provided by the receiver. In particular, it does not require the acquisition of a particular injected signal and any synchronization of the acquisition with the data transmission, making it suitable for the diagnosis of existing two-wire communication channels without interrupting their operability. An experimental setup has been implemented to generate a dataset of acquired signals through a channel having the following anomalies: air-exposed conductors, water-exposed conductors, and tapping of various lengths. The performance of an ML-based decision tree classifier has been assessed according to features extracted in the time and frequency domains from the acquired signal and an estimated impulse response of the cable obtained from the decoded sequence. The most sensitive features to the anomalies have been analyzed, and the decision tree classifier has been trained according to them by considering several sampling frequencies of the signal acquisition, ranging from 62.5 MHz to 6.25 GHz. The classification accuracy obtained in a set of laboratory experiments carried out on actual anomalies is 99.04% at the sampling frequency of 312.5 MHz. Moreover, an analysis is carried out to assess the sensitivity of the diagnostic tool to the anomaly lengths, thus demonstrating its capability to estimate them.
{"title":"A Passive-Measurement Method for Physical Security and Cable Diagnosis","authors":"Eulalia Balestrieri;Pasquale Daponte;Luca De Vito;Francesco Picariello;Sergio Rapuano;Ioan Tudosa","doi":"10.1109/TIM.2025.3541695","DOIUrl":"https://doi.org/10.1109/TIM.2025.3541695","url":null,"abstract":"This article introduces a passive measurement method for diagnosing anomalies in two-wire communication channels using machine learning (ML). The proposed method involves the acquisition of the signal received by a transceiver and the decoded sequence provided by the receiver. In particular, it does not require the acquisition of a particular injected signal and any synchronization of the acquisition with the data transmission, making it suitable for the diagnosis of existing two-wire communication channels without interrupting their operability. An experimental setup has been implemented to generate a dataset of acquired signals through a channel having the following anomalies: air-exposed conductors, water-exposed conductors, and tapping of various lengths. The performance of an ML-based decision tree classifier has been assessed according to features extracted in the time and frequency domains from the acquired signal and an estimated impulse response of the cable obtained from the decoded sequence. The most sensitive features to the anomalies have been analyzed, and the decision tree classifier has been trained according to them by considering several sampling frequencies of the signal acquisition, ranging from 62.5 MHz to 6.25 GHz. The classification accuracy obtained in a set of laboratory experiments carried out on actual anomalies is 99.04% at the sampling frequency of 312.5 MHz. Moreover, an analysis is carried out to assess the sensitivity of the diagnostic tool to the anomaly lengths, thus demonstrating its capability to estimate them.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-12"},"PeriodicalIF":5.6,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143489068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-13DOI: 10.1109/TIM.2025.3541806
Yanfeng Yao;Zhuoyan Wang;Peizheng Yan;Mohan Ding;Yonghong Wang
Digital shearography (DS) is a nondestructive testing method that provides full-field measurement, with its sensitive direction closely related to shearing direction and its sensitivity related to the shearing amount. Conventional single-direction DS carries the risk of missed detection of defects, while conventional two- or multidirectional DS struggles to balance both rapid measurement and high-quality measurement results. To avoid missed detection of defects and achieve high-performance measurement, we have designed a novel rotational shearography system that can perform defect detection with just a single temporal phase shift (TPS). The system introduces rotational shearing amount using a dove prism (D), effectively preventing missed detections caused by the insensitivity of defect shapes to the shearing direction. A 4f system is embedded in the measurement system to overcome the limitations imposed by optical components on the field of view (FOV), allowing the system to work in a large range of FOV. We constructed an experimental setup to evaluate the performance of the proposed system. The measurement results not only demonstrate the system’s applicability for various loading conditions and defect shapes but also verify its feasibility in both large and tiny FOV measurements.
{"title":"High-Performance Rotational Shearography System Based on Dove Prism for Nondestructive Testing","authors":"Yanfeng Yao;Zhuoyan Wang;Peizheng Yan;Mohan Ding;Yonghong Wang","doi":"10.1109/TIM.2025.3541806","DOIUrl":"https://doi.org/10.1109/TIM.2025.3541806","url":null,"abstract":"Digital shearography (DS) is a nondestructive testing method that provides full-field measurement, with its sensitive direction closely related to shearing direction and its sensitivity related to the shearing amount. Conventional single-direction DS carries the risk of missed detection of defects, while conventional two- or multidirectional DS struggles to balance both rapid measurement and high-quality measurement results. To avoid missed detection of defects and achieve high-performance measurement, we have designed a novel rotational shearography system that can perform defect detection with just a single temporal phase shift (TPS). The system introduces rotational shearing amount using a dove prism (D), effectively preventing missed detections caused by the insensitivity of defect shapes to the shearing direction. A 4f system is embedded in the measurement system to overcome the limitations imposed by optical components on the field of view (FOV), allowing the system to work in a large range of FOV. We constructed an experimental setup to evaluate the performance of the proposed system. The measurement results not only demonstrate the system’s applicability for various loading conditions and defect shapes but also verify its feasibility in both large and tiny FOV measurements.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-7"},"PeriodicalIF":5.6,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143471788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：