IEEE Transactions on Instrumentation and Measurement最新文献

Robust Vital Signs Monitoring of Speaking Subjects Through mmWave Radar 毫米波雷达对说话对象的鲁棒生命体征监测

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-07-15 DOI: 10.1109/TIM.2025.3588997

Zhenyu Liu;Yingjie Ye;Danke Jiang;Silong Tu

{"title":"Robust Vital Signs Monitoring of Speaking Subjects Through mmWave Radar","authors":"Zhenyu Liu;Yingjie Ye;Danke Jiang;Silong Tu","doi":"10.1109/TIM.2025.3588997","DOIUrl":"https://doi.org/10.1109/TIM.2025.3588997","url":null,"abstract":"Vital signs measurement using radio frequency (RF) signals, particularly mmWave-based methods, has gained widespread attention. Speaking, which modifies the pattern of chest wall motion during phonation, leads to intricate couplings with vital signals in both the temporal and spectral domains. Respiratory spectrum broadening and phase noise interference are challenges in vital signs monitoring of speaking subjects. To tackle these issues, a novel method is proposed. First, a dynamic composite model of chest wall motion is developed, and the inherent challenges in vital signs monitoring of speaking subjects are analyzed in detail. Second, a recursive autocorrelation periodic enhancement (RAPE) algorithm is proposed, leveraging the periodicity of respiration and the intermittency of speech to enhance the respiratory signal. A recursive strategy is employed, which incorporates an adaptive termination mechanism based on Shannon entropy and spectral concentration. Third, an adaptive low-rank decomposition (ALRD) algorithm is proposed, exploiting the low-rank property of the Hankel matrix from the heartbeat signal to transform the denoising challenge into a matrix decomposition task. It also models phase noise as energy-bounded interference and adaptively selects the optimal parameter, achieving high-quality separation of weak heartbeat signals from phase noise. Extensive experimental results demonstrate that the proposed method facilitates accurate and reliable vital signs monitoring for speaking subjects. This study bridges a critical gap in the current body of noncontact vital signs measurement methods.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-15"},"PeriodicalIF":5.6,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144671252","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}

引用次数: 0

Design and Optimization of a Three-Coil Nested Inductive Sensor With Inner Sensing and Outer Excitation 内传感外激励三线圈嵌套式电感传感器的设计与优化

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-07-15 DOI: 10.1109/TIM.2025.3584137

Jing Cai;Chong Cheng;Zhenglin Wang;Hangyuan Zhang;Xin Li;Haotian Shi;Ming Li

引用次数: 0

A Novel High-Precision Lightweight Mathematical Model of Underwater Active Electrosense and Its Application on Object Pose and Size Estimation 一种新型高精度轻量级水下有源电感数学模型及其在目标姿态和尺寸估计中的应用

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-07-11 DOI: 10.1109/TIM.2025.3588219

Guangyu Jiang;Qiao Hu;Renhui Fan;Tongqiang Fu;Yi Rong;Qian Yang;Pengtao Wang

{"title":"A Novel High-Precision Lightweight Mathematical Model of Underwater Active Electrosense and Its Application on Object Pose and Size Estimation","authors":"Guangyu Jiang;Qiao Hu;Renhui Fan;Tongqiang Fu;Yi Rong;Qian Yang;Pengtao Wang","doi":"10.1109/TIM.2025.3588219","DOIUrl":"https://doi.org/10.1109/TIM.2025.3588219","url":null,"abstract":"Weakly electric fish employ the self-established electric field to sense the cluttered and turbid underwater environment. Previous works on modeling underwater active electrosense have predominantly involved analytical models derived from the Rasnow model, as well as numerical models such as the finite element method (FEM) and the boundary element method (BEM). Both are hindered by poor precision and heavy computational burden, making them difficult to apply in real-world engineering. In this article, a high-precision and lightweight model excited by an electric dipole source was established for the first time. The simulation and experimental results validate the effectiveness of the proposed model and its superiority compared to the Rasnow model. A simplified slender robot equipped with active electrosense was made to estimate object pose and size with a purely model-based method. Despite some model simplifications and experimental errors, both the pose and size estimation achieved satisfactory results. This article offers promising perspectives on high-precision real-time processing for marine robots equipped with active electrosense.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-15"},"PeriodicalIF":5.6,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144671253","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}

引用次数: 0

Iterative Reconstruction Method With Auto-Updated Seed Point of Monoscopic Deflectometry for Off-Axis Aspheric 离轴非球面单镜偏转法种子点自动更新迭代重建方法

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-07-10 DOI: 10.1109/TIM.2025.3587357

Menghui Lan;Bing Li;Xiang Wei;Emanuele Zappa

{"title":"Iterative Reconstruction Method With Auto-Updated Seed Point of Monoscopic Deflectometry for Off-Axis Aspheric","authors":"Menghui Lan;Bing Li;Xiang Wei;Emanuele Zappa","doi":"10.1109/TIM.2025.3587357","DOIUrl":"https://doi.org/10.1109/TIM.2025.3587357","url":null,"abstract":"The complex surface characteristics and misalignment of the geometric and optical axes of off-axis aspherical surfaces pose major challenges in the work to develop a measurement technique that is accurate, effective, and simple. Phase measurement deflectometry (PMD) is a more convenient and cost-effective method than interferometry for measuring specular objects. Nevertheless, monocular PMD necessitates complicated processes or costly additional equipment to resolve height-slope problems, whereas binocular PMD requires more time-consuming matching calculations. In this article, a novel iterative strategy based on monocular PMD technology with an automatically updated seed point during the iterative process is proposed, which only requires an approximate estimation about the height of one point (either the lowest, middle, or highest) on the measured off-axis aspheric component. The lowest, middle, or highest point of the reconstructed surface is used as the target of the auto-updated seed point in the iterative process, and the surface’s overall height information is then updated until the difference between two neighboring surface profiles is less than a certain threshold. Finally, the off-axis aspherical surface result is sent out. The proposed monocular PMD method is more straightforward, cost-effective, and simple, requiring only basic and low-cost tools to estimate the initial height of the measured component without the need to obtain the seed point’s 3-D coordinate. An experiment was performed to evaluate the feasibility and accuracy of the proposed method with the results of high-precision contact measurements as benchmarks. The results also indicated that even the uncertainty in the estimated initial height using common Vernier calipers has little effect on the measured surface shape, since it only leads to a small offset in the whole surface location.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-12"},"PeriodicalIF":5.6,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144653886","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}

引用次数: 0

Corrections to “LPDD-SSAN: Limited Physics Data Diffusion Guided Selection Sparsity-Aware Attention Network for Engine Valve Wear Prediction” 修正“LPDD-SSAN：有限物理数据扩散引导选择稀疏感知关注网络发动机气门磨损预测”

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-07-10 DOI: 10.1109/TIM.2025.3581816

Jilong Guo;Rui Li;Guodong Li;Yao Gong;Enzhe Song;Yanmin Zhou;Xianghui Meng

引用次数: 0

Evaluating and Optimizing Conventional Training Circuits for Analog Fault Diagnosis via Transfer Learning 基于迁移学习的模拟故障诊断常规训练电路评估与优化

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-07-10 DOI: 10.1109/TIM.2025.3586376

Yurong Chen;Kun Peng;Dan Huang;Qinglin Tang

{"title":"Evaluating and Optimizing Conventional Training Circuits for Analog Fault Diagnosis via Transfer Learning","authors":"Yurong Chen;Kun Peng;Dan Huang;Qinglin Tang","doi":"10.1109/TIM.2025.3586376","DOIUrl":"https://doi.org/10.1109/TIM.2025.3586376","url":null,"abstract":"The soft-fault diagnosis in analog circuits increasingly relies on neural networks; yet, diagnostic accuracy is fundamentally constrained by the quality of the training data supplied to those networks. Most studies still generate that data with a few long-standing “benchmark” training circuits (e.g., Sallen–Key bandpass filter and four-op-amp biquad high-pass filter) and tacitly assume that the circuit choice is inconsequential. Our experiments reveal the opposite: component symmetries in these classic topologies create fault-response overlap, producing ambiguous feature sets that hamper classification and generalization. To overcome this limitation, we structurally modify the original four-op-amp biquad filter to break the component symmetries responsible for fault confusion, thereby yielding more diverse and separable fault responses. Three training circuits are evaluated—Sallen–Key, original four-op-amp, and the optimized design—under consistent fault category count, excitation signal, and sample length. A fixed 1-D residual network (ResNet) is trained on each dataset; transfer learning then tests generalization on a more complex leapfrog circuit. Besides overall accuracy, we visualize embeddings with t-distributed stochastic neighbor embedding (t-SNE) and quantify separability through interclass centroid distance and intraclass variance. Across 100 independent trials, the optimized circuit achieves higher classification accuracy, the largest average interclass distance, and the lowest intraclass variance. Moreover, it consistently shows smaller or comparable standard deviations (SDs) across all metrics, indicating more stable and reliable diagnostic performance compared with the conventional circuits.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-15"},"PeriodicalIF":5.6,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144657535","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}

引用次数: 0

A Dual-Driven SOC Estimation Framework: Fusion of Multiscale Temporal Encoding Network and EKF Based on Feature Dimensionality Reduction 一种双驱动SOC估计框架：多尺度时间编码网络与基于特征降维的EKF融合

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-07-10 DOI: 10.1109/TIM.2025.3587361

Xiongbo Wan;Ziwen Chen;Chuan-Ke Zhang;Wenkai Hu;Tao Wu;Weilong Zhang

{"title":"A Dual-Driven SOC Estimation Framework: Fusion of Multiscale Temporal Encoding Network and EKF Based on Feature Dimensionality Reduction","authors":"Xiongbo Wan;Ziwen Chen;Chuan-Ke Zhang;Wenkai Hu;Tao Wu;Weilong Zhang","doi":"10.1109/TIM.2025.3587361","DOIUrl":"https://doi.org/10.1109/TIM.2025.3587361","url":null,"abstract":"Accurate state of charge (SOC) estimation is crucial for battery safety. Although the mechanism and data fusion estimation methods are relatively accurate and interpretable, the existing fusion strategies mostly rely on a single feature, ignoring the influence of multiple features. Directly designing fusion strategies based on multiple features will undoubtedly increase the complexity. To address these issues, a novel multifeature dimensionality reduction fusion framework is proposed. The battery is characterized by the Thevenin model, and its parameters are identified by the forgetting factor recursive least squares FFRLS) method. With these parameters, the SOC and open-circuit voltage are then estimated by the extended Kalman filter (EKF) algorithm. A multiscale temporal encoding network (MSTEN) is proposed to mine temporal information at different scales to estimate the SOC. The input features of the MSTEN are subjected to feature dimensionality reduction by kernel principal component analysis (KPCA), and the fusion strategies are designed according to the results of dimensionality reduction. The final SOC estimation results are integrated based on these fusion strategies. The effectiveness of the proposed method is validated by multiple driving cycle experiments on the LG 18650-HG2 dataset. These experiments demonstrate that the root mean square error (RMSE) of the proposed method is less than 0.44%, and the mean absolute error (MAE) is less than 0.32%, under different operating conditions.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-10"},"PeriodicalIF":5.6,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144671243","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}

引用次数: 0

SiamMTS: Self-Supervised Representation Learning for High-Speed Train Traction System State Prediction 高速列车牵引系统状态预测的自监督表示学习

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-07-10 DOI: 10.1109/TIM.2025.3587364

Zhiqiang Yang;Honghui Dong;Huipeng Zhang;Ruojin Wang

{"title":"SiamMTS: Self-Supervised Representation Learning for High-Speed Train Traction System State Prediction","authors":"Zhiqiang Yang;Honghui Dong;Huipeng Zhang;Ruojin Wang","doi":"10.1109/TIM.2025.3587364","DOIUrl":"https://doi.org/10.1109/TIM.2025.3587364","url":null,"abstract":"Accurate prediction of the high-speed train traction system state ensures safe train operation. Currently, common prediction methods involve dividing the traction system into multiple equipment and establishing separate models based on multisensor signals for each. While effective, this method requires repeated training for different prediction tasks, consuming significant computational resources and limiting the flexibility of model application. Therefore, developing a universal learning framework for predicting the state of various equipment within the traction system is crucial. To this end, this article proposes SiamMTS, a self-supervised representation learning (SSRL) framework based on a Siamese network architecture for multisensor time-series signals. SiamMTS performs self-supervised learning by minimizing the distance between different augmented views of the same sensor sequence in the feature space, thereby extracting a universal time-series representation for improved state prediction performance from the multisensor signals monitoring the traction system. Experimental results demonstrate that SiamMTS performs well when processing datasets from multiple high-speed train traction systems. The encoder obtained during its pretraining phase provides reasonable initialization parameters for downstream tasks, enabling effective prediction of the state of various equipment within the system. Compared with the Supervised model with the same encoder architecture, SiamMTS reduces the average root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) by 22.27%, 23.13%, and 33.21%, respectively, at a prediction step of 10 and by 15.31%, 16.67%, and 20.53%, respectively, at a prediction step of 20. In addition, the total computation time of SiamMTS in predicting the state of four traction system equipment is 41.31% of that required by the Supervised model.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-16"},"PeriodicalIF":5.6,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144657353","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}

引用次数: 0

Dual Spatio-Temporal Contrastive Learning Network With Adaptive Threshold Generation for Anomaly Detection of Electric Submersible Pump 电潜泵异常检测的双时空对比学习网络自适应阈值生成

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-07-10 DOI: 10.1109/TIM.2025.3587359

Kang Li;Shuang Li;Qiang Li;Zhikuan Jiao;Jun Fu;Xiaoyong Gao;Laibin Zhang

引用次数: 0

Two 3D-Printed Sensitive Cylindrical Sensors for Characterizing Organic Liquids 两个用于表征有机液体的3d打印灵敏圆柱形传感器

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-07-10 DOI: 10.1109/TIM.2025.3587363

Xiue Bao;Chenhao Yin;Jinkai Li;Li Wang;Dominique Schreurs;Liming Si;Giovanni Crupi;Zhuangzhuang Liu;Houjun Sun

{"title":"Two 3D-Printed Sensitive Cylindrical Sensors for Characterizing Organic Liquids","authors":"Xiue Bao;Chenhao Yin;Jinkai Li;Li Wang;Dominique Schreurs;Liming Si;Giovanni Crupi;Zhuangzhuang Liu;Houjun Sun","doi":"10.1109/TIM.2025.3587363","DOIUrl":"https://doi.org/10.1109/TIM.2025.3587363","url":null,"abstract":"In this article, two highly sensitive sensors based on cylindrical cavities for measuring the complex permittivity of organic liquids are presented. To analyze the sensing performance, the two sensors are designed at the working frequency of around 20 GHz, where the relaxation frequencies of some common lossy liquids are located. For liquid sensing, a Teflon tube is designed at the sensing area. Based on full-wave simulations, the characterization principles are provided, and additionally, the sensing range for the complex permittivity of lossy liquids is analyzed. Next, by using selective laser melting (SLM) additive manufacturing technology, the two sensors are fabricated. However, due to manufacturing tolerance, there is a slight difference between the fabricated sensors and the simulated ones. Therefore, further simulations are performed for calibration of the complex permittivity characterization formulas. The two sensors are used to measure seven pure organic liquids and ten liquid mixtures, which are commonly used for industrial applications. The measurement procedure is simple and nondestructive. By comparing with literature data, the two sensors are validated to provide reliable results. The experimental validation also demonstrates that the proposed devices have good sensitivity to the complex permittivity of liquids. Their good performance is also validated by comparing with other sensors reported in the literature.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-14"},"PeriodicalIF":5.6,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144671254","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}

引用次数: 0