IEEE Transactions on Instrumentation and Measurement最新文献

{"title":"An Improved Lempel–Ziv Complexity for Bearing Fault Diagnosis Based on the Time–Frequency Encoding Method","authors":"Jiancheng Yin;Wentao Sui;Xuye Zhuang;Yunlong Sheng;Yongbo Li","doi":"10.1109/TIM.2025.3558777","DOIUrl":"https://doi.org/10.1109/TIM.2025.3558777","url":null,"abstract":"Lempel-Ziv complexity (LZC) is extensively utilized in the identification of bearing faults. The present enhancement in LZC encoding relies on time-domain information. When the magnitude of fluctuation is minimal, LZC utilizing time-domain information encoding is unable to properly differentiate signals with varying frequency components. Thus, an improved LZC based on the time-frequency encoding method is proposed. Initially, the time-domain encoding is obtained according to the quartile of the amplitude. Then, the frequency-domain encoding is calculated based on the statistic value at each frequency along the frequency direction of the Wigner Trispectrum. Finally, the ultimate encoding is derived from both time-domain encoding and frequency-domain encoding. The proposed method is validated through the actual data of bearing. The time-frequency encoding technique can significantly augment the capacity of encoding sequences to depict signal variations and boost the LZC representation of signal complexity.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-12"},"PeriodicalIF":5.6,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143871039","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}

{"title":"Roadside LiDAR Deployment Optimization for Vehicle-to-Infrastructure Cooperative Perception in Urban Occlusion Environments","authors":"Ciyun Lin;Yuying Wang;Bowen Gong;Hui Liu;Hongchao Liu","doi":"10.1109/TIM.2025.3558231","DOIUrl":"https://doi.org/10.1109/TIM.2025.3558231","url":null,"abstract":"Roadside light detection and range sensor (LiDAR) is a way to enhance the perception capabilities of connected autonomous vehicles (CAVs) using the vehicle-to-infrastructure (V2I) cooperation system by providing complementary and beyond visual range information. However, occlusion remains challenging in roadside LiDAR required to be considered in deployment. Therefore, this article proposes a roadside LiDAR deployment optimization framework that considers the spatial-temporal occlusion arising from vehicles on urban streets. First, a pose deployment optimization model was constructed to maximize the utilization of the laser beams and minimize the spatial-temporal occlusion. Also, a network deployment problem was formulated considering the coverage intensity and cost performance with the pose deployment constraints. Then, a stagewise greedy algorithm combined with a particle swarm optimization (GA-PSO) algorithm was proposed and an elitist preservation genetic algorithm (EGA) was improved to optimize the roadside LiDAR layout and pose in the traffic network. Experiment results showed that the detection accuracy of the proposed method can reach 90.6%, outperforming current methods in different traffic conditions. Furthermore, the proposed method enables more complete target point cloud outlines capturing under various traffic and occlusion conditions.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-14"},"PeriodicalIF":5.6,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848879","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}

{"title":"Extended Target-Tracking Algorithm Based on Variational Bayes and Axis Estimation Theory","authors":"Shenghua Wang;Chenkai Men;Renxian Li;Tat-Soon Yeo;Pengchao He","doi":"10.1109/TIM.2025.3553218","DOIUrl":"https://doi.org/10.1109/TIM.2025.3553218","url":null,"abstract":"Aiming at resolving the problem of low tracking accuracy for maneuvering extended targets in lidar systems, an interactive multimodel variational Bayes independent axis estimation (IMM-VB-IAE) algorithm is proposed in this article. First, the algorithm utilizes IMM to adaptively select the appropriate model to track the target according to the changes in the target’s motion state, thereby improving the overall tracking performance. Second, the algorithm uses VB theory to approximate the posterior closed expression, which simplifies the solution process by transforming the original complex inference problem into a parameter optimization problem. Finally, the principle of eigendecomposition is utilized to quadratically estimate the ellipse axes’ lengths, which improves the estimation accuracy of the ellipse parameters. The final simulation and experimental results demonstrate that the proposed algorithm outperforms several other algorithms in the accuracy of tracking maneuvering extended targets, with average OSPA and Gaussian-Wasserstein distances reduced by at least 55.5% and 56.1%, respectively.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-16"},"PeriodicalIF":5.6,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848792","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}

{"title":"Enhancement of Magnetic Field Disturbance Suppression for Wearable Optically Pumped Magnetometers","authors":"Jing Zhang;Xinda Song;Le Jia;Haoyuan Sun;Shengjie Qi;Zhendong Wu;Gang Liu","doi":"10.1109/TIM.2025.3556821","DOIUrl":"https://doi.org/10.1109/TIM.2025.3556821","url":null,"abstract":"Wearable optically pumped magnetometers (OPMs) operating in the spin-exchange relaxation-free (SERF) regime are an emerging alternative for biomagnetic field measurements. Nevertheless, the wearable implementation of OPM inevitably entails positional variations, leading to magnetic field disturbance and the introduction of amplitude errors, consequently impacting the precision of magnetic source localization. To solve this problem, an enhanced method is presented for wearable OPMs, which can achieve real-time suppression of magnetic field disturbance without overshoot. First, we analyze the influence mechanism of magnetic field disturbance on OPMs, thereby clarifying the nonlinear impact that disturbance exerts on the system. Then, a magnetic field disturbance suppression system based on active disturbance rejection control (ADRC) is developed to estimate and compensate for magnetic field disturbance in real time. The experimental results demonstrate that the ADRC method enables overshoot-free fast response and tracking suppression for the sensitive x-axis magnetic field disturbances. Moreover, the settling time is shortened to 3.3 ms compared to 15.4 ms required by the PI method, which extends the effective duration of the signal under measurement. Furthermore, the amplitude errors in the y-axis and z-axis are reduced by 38.29% and 47.10%, respectively. Finally, the enhanced magnetic field disturbance suppression method proposed in this article provides valuable technical support for wearable medical applications of OPM.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-11"},"PeriodicalIF":5.6,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143830502","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}

{"title":"Self-Calibrated Stitching Test of X-Ray Flat Mirrors for Achieving Single-Nanometer and Nanoradian Long-Term Reproductivity","authors":"Shuai Xue;Yunfeng Mao;Zubo Hu;Antong Huang;Yong Liu;Zhiqi Chen","doi":"10.1109/TIM.2025.3556163","DOIUrl":"https://doi.org/10.1109/TIM.2025.3556163","url":null,"abstract":"X-ray flat surfaces utilized in most advanced light sources have posed challenges to achieving form accuracy of peak-to-valley (PV) 2 nm, root mean square (rms) 0.2 nm, and rms 50 nrad. Measurement of the form error with high long-term reproductivity, i.e., repeatability over time of at least one day and with test surface remounted, is the key to a direct deterministic figuring process. To this end, the models of two main error sources—long-term environment disturbance and retrace error—have been established. The different error period components of the long-term environment disturbance and their influence on the full-aperture test accuracy have been investigated. The method of determining the adequate subaperture size, overlapping ratio, and tilt misalignment threshold for decreasing the influence of the long-term environment disturbance and the retrace error is reported. The PV value standard uncertainty is evaluated to be 0.092 nm. Experiments on an X-ray flat with a clear aperture (CA) of <inline-formula> <tex-math>$250times 30$ </tex-math></inline-formula> mm were tested with the optimized test parameters and other sets of parameters for comparisons. Self-consistency experiments have been conducted to verify the long-term reproductivity can reach PV 0.908 nm, rms 0.076 nm, and rms 32 nrad, which meet the typical test accuracy posed by the fourth-generation synchrotron radiation and free electron lasers.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-13"},"PeriodicalIF":5.6,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143839949","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}

{"title":"A Two-Stage Prediction + Detection Framework for Real-Time Epileptic Seizure Monitoring","authors":"Siyuan Qiu;Wenjin Wang;Changchun Zhou;Xiaoyan Song;Jie Yang;Hailong Jiao","doi":"10.1109/TIM.2025.3558236","DOIUrl":"https://doi.org/10.1109/TIM.2025.3558236","url":null,"abstract":"Wearable intelligent devices are essential for the health monitoring of epilepsy patients in nonhospital environments. The existing seizure detection algorithms for wearable health monitoring devices cannot achieve high sensitivity, short detection latency, low false alarm rate (FAR), as well as lightweight computing simultaneously. In this article, we propose a two-stage prediction + detection framework, PDNet, for real-time epileptic seizure monitoring. The proposed two-stage PDNet framework consists of a lightweight seizure predictor and a highly accurate seizure detector. Only when the first-stage seizure predictor forecasts an impending seizure, the second-stage seizure detector is activated to precisely and rapidly classify the seizure states, thereby significantly reducing the amount of computations. A semisupervised learning strategy is employed to enhance the decision boundary of the seizure predictor, which is used for electroencephalogram (EEG) preprocessing instead of prediction only. Soft labels are adopted to enable the seizure detector to precisely distinguish the seizure states. The proposed PDNet is evaluated using the CHB-MIT scalp EEG database. The proposed PDNet achieves 99.0% sensitivity, 0.43/h FAR, and 3.52-s detection latency with 3.02 M multiply-accumulate (MAC) operations, which are competitive compared to the state-of-the-art in terms of sensitivity, detection latency, FAR, and computation complexity. Furthermore, fine-grained information such as the occurrence process of seizures demonstrated by soft labels can help the caregivers or clinicians to come up with targeted healthcare and clinical treatments.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-14"},"PeriodicalIF":5.6,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143865265","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}

{"title":"Enhancing Point Cloud Tracking With Spatio-Temporal Triangle Optimization","authors":"Kaijie Zhao;Haitao Zhao;Zhongze Wang;Jingchao Peng;Lujian Yao","doi":"10.1109/TIM.2025.3553981","DOIUrl":"https://doi.org/10.1109/TIM.2025.3553981","url":null,"abstract":"Single object tracking (SOT) is a critical task in computer vision, playing a substantial role in fields such as autonomous driving. It represents a spatio-temporal learning challenge, aimed at tracking a designated target, indicated by a bounding box (BBox), in video sequences of 3-D point cloud data. Recent developments in the target-motion-estimation (TME) paradigm for SOT have achieved significant performance improvements. This paradigm involves predicting the relative target motion (RTM), which captures the shifts in the target’s center and orientation between consecutive frames. However, existing TME-based methods utilize separate and complex optimizations to regress the RTM, with the RTM’s coordinates shift and rotation angle representing motion properties. Furthermore, RTM fails to offer a comprehensive representation of target motion, such as the motion patterns of objects with varying sizes and movement ranges. Additionally, the current paradigm for multiframe tracking is sensitive to noise and distractors, due to the unrobust proposal selection method based on point counting. To address these limitations, a spatio-temporal triangle (STT) optimization method is proposed. All optimization steps from existing TME-based methods are integrated into a STT optimization, simplifying the process and improving integration. Diagonals (BBox corners) are used to denote the BBox parameters, allowing the RTM to be represented as a spatio-temporal area swept by diagonals, thus providing a consistent motion measurement and comprehensive motion patterns. A novel proposal selection method is introduced, selecting proposals based on the highest intersection over union (IoU) with the ground truth (GT), ensuring more robust tracking in scenarios with multiple distractors. Extensive experiments demonstrate that the proposed STT optimization method significantly enhances tracking performance, resulting in improvements of <inline-formula> <tex-math>$uparrow 1.5$ </tex-math></inline-formula>/<inline-formula> <tex-math>$uparrow 2.2$ </tex-math></inline-formula> on the KITTI dataset and <inline-formula> <tex-math>$uparrow 2.22$ </tex-math></inline-formula>/<inline-formula> <tex-math>$uparrow 2.45$ </tex-math></inline-formula> on nuScenes for the two-frame model, and <inline-formula> <tex-math>$uparrow 2.8$ </tex-math></inline-formula>/<inline-formula> <tex-math>$uparrow 2.4$ </tex-math></inline-formula> on KITTI and <inline-formula> <tex-math>$uparrow 3.46$ </tex-math></inline-formula>/<inline-formula> <tex-math>$uparrow 4.47$ </tex-math></inline-formula> on nuScenes for the multiframe model, achieving state-of-the-art results on both datasets.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-11"},"PeriodicalIF":5.6,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143821816","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}

{"title":"A Sensitivity-Guided Unsupervised Learning Method for Image Reconstruction of Electrical Impedance Tomography","authors":"Yuehui Wu;Jianda Han;Xinhao Bai;Jianeng Lin;Ningbo Yu","doi":"10.1109/TIM.2025.3555752","DOIUrl":"https://doi.org/10.1109/TIM.2025.3555752","url":null,"abstract":"Electrical impedance tomography (EIT) detects time-varying conductivity distribution and has grown to be a promising imaging modality in industrial and biomedical fields. However, current deep learning-based image reconstruction methods require a large number of voltage-conductivity samples for training. This article proposes a sensitivity-guided unsupervised learning method for EIT (SULEIT) image reconstruction. First, the voltage measurements are projected into voltage feature maps and a fully convolutional network (FCN) is designed to nonlinearly reconstruct the conductivity distribution images. Subsequently, the reconstructed images are converted to the measurement domain through the EIT forward modeling. Moreover, the loss function consisting of the mean absolute error and an <inline-formula> <tex-math>$L_{1}$ </tex-math></inline-formula> regularization term (RT) is devised to evaluate the disparity between the measured and converted voltage measurements. By combining data-driven techniques with physical constraints, the neural network is enforced to learn the inherently nonlinear mapping from the voltage measurements to conductivity images. The proposed method enables the training of the neural network without the prior knowledge of the true conductivity distributions. Experiments show that the proposed SULEIT method obtains higher correlation coefficient (CC) values and lower root-mean-square error (RMSE) values, which demonstrate its superior imaging quality to the alternative numerical and unsupervised learning methods.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-12"},"PeriodicalIF":5.6,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848806","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}

{"title":"Design of a Laser Gas Sensor With Phase Compensation in Free Space Using Half-Mirror and Mixer","authors":"Qixing Tang;Lu Liu;Yujun Zhang;Yuan Rao;Juan Liao;Yuwei Wang;Yanwei Gao","doi":"10.1109/TIM.2025.3558168","DOIUrl":"https://doi.org/10.1109/TIM.2025.3558168","url":null,"abstract":"A laser gas sensor with phase compensation in free space is designed based on a half-mirror and mixer in this study. The half-mirror is employed to split the returned beam into transmitted and reflected beams in free space, thereby generating two distinct path signals that carry highly correlated phase fluctuation information. A phase correction method in free space is then proposed to extract the phase information from the two signals, which is subsequently combined with a mixer to effectively mitigate phase fluctuations caused by atmospheric turbulence. To verity the performance of the designed laser gas sensor, NH3 is selected as the target gas for the experiment. Test results demonstrate that the low limit of detection for 5 ppm NH3 is 8 ppb (<inline-formula> <tex-math>$1sigma $ </tex-math></inline-formula>) in static experiments. Additionally, in free space, the fluctuation power spectral density (PSD) is suppressed below the 20-Hz offset frequency. These results indicate that the sensor effectively mitigates phase fluctuations in free space and holds significant potential for applications in airborne observation, ground-based remote sensing, and satellite remote sensing, offering a pathway for high-resolution measurements.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-6"},"PeriodicalIF":5.6,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860869","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}

{"title":"Enhancing Bearing Fault Diagnosis in Real Damages: A Hybrid Multidomain Generalization Network for Feature Comparison","authors":"Zhengyan Yang;Liting Luo;Jitong Ma;Hongpeng Zhang;Lei Yang;Zhanjun Wu","doi":"10.1109/TIM.2025.3556828","DOIUrl":"https://doi.org/10.1109/TIM.2025.3556828","url":null,"abstract":"Recently, domain adaptation methods in the field of fault diagnosis for rotating machinery have gained increasing popularity. However, these methods often require a substantial amount of labeled prior data distribution. In practical diagnostic scenarios, acquiring vast amounts of authentic data proves to be challenging. Additionally, a notable disparity exists between datasets generated through artificially induced damages and those produced from actual real-world damages. Consequently, developing models and applying domain adaptation methods to industrial data with real damages poses a challenging task. Acknowledging these challenges and aiming to maximize the transfer model’s generalization capability, this article proposes a novel mixed domain fusion network (MDFNet) model for fault diagnosis in rotating machinery under unknown real operating conditions. The core innovation of this model lies in capturing invariant representations of different environmental information. Simultaneously, through feature discrimination, the inner product of similar feature representations is averaged higher than other features, enabling the diagnostic model to learn robust information specific to certain working environments and generalize to unseen working environments. Experimental results conducted on the German Paderborn rolling bearing dataset validate the effectiveness of this approach. Furthermore, additional experiments conducted on a planetary parallel-axis gearbox bearing comprehensive fault simulation test bench, involving a substantial amount of diagnostic tasks on collected real-world data, further confirm the improved generalization performance of the proposed method under unknown real operating conditions.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-11"},"PeriodicalIF":5.6,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143821868","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}