For high-resolution measurements of solid insulation space charge in complex stress environments, an optical measurement method of space charge based on the electro-optical effect is proposed. In this article, the whole process signal conversion mechanism and transmission model of the space charge electro-optical detection system are constructed. Furthermore, the key factors affecting measurement sensitivity are analyzed for the core electro-optical detection system. The results show that the measurement sensitivity shows a positive and negative correlation with the sensor electro-optical coefficient and sensor thickness, respectively. Simultaneously, it shows a decreasing trend in measurement sensitivity as the angle of incidence deviates from the Brewster angle of the electro-optical sensor. The optimal configuration of the measurement sensitivity can be realized by adjusting the incidence angle of the detection light, the electro-optical coefficient, and the thickness of the sensor. Expanding the measuring bandwidth further decreases the signal distortion. The detector bandwidth is the primary parameter that limits the behavior of the detecting system. Ultimately, a refined design for the electro-optical detecting system is suggested for sensitivity improvement. The significant sensitivity is obtained by incorporating the optical sampling technique. The experimental results demonstrate that the established detection system is capable of accurately measuring the picosecond pulsed electric field. The enhanced electro-optical detection system achieves measurement sensitivity at the $mu $ V level and bandwidth in the THz range. The space charge measurements with nm resolution can be realized through the proposed detection system.
针对复杂应力环境下固体绝缘空间电荷的高分辨率测量，提出了一种基于电光效应的空间电荷光学测量方法。本文构建了空间电荷光电检测系统的全过程信号转换机制和传输模型。此外，还分析了影响核心光电检测系统测量灵敏度的关键因素。结果表明，测量灵敏度分别与传感器电光系数和传感器厚度呈正相关和负相关。同时，当入射角偏离电光传感器的布鲁斯特角时，测量灵敏度呈下降趋势。通过调整检测光的入射角、电光系数和传感器厚度，可以实现测量灵敏度的最佳配置。扩大测量带宽可进一步减少信号失真。探测器带宽是限制探测系统性能的主要参数。最终，为提高灵敏度，建议对电子光学探测系统进行改进设计。通过采用光学采样技术，灵敏度大大提高。实验结果表明，所建立的检测系统能够精确测量皮秒脉冲电场。增强型光电检测系统实现了 $mu $ V 级的测量灵敏度和太赫兹范围的带宽。通过所提出的检测系统，可以实现纳米分辨率的空间电荷测量。
{"title":"Optimization of Space Charge Electro-Optical Detection System in Power Electronic Insulation: Toward High Sensitivity","authors":"Tianrun Qi;Hanwen Ren;Haoyu Gao;Qingmin Li;Xinjun He;Yidan Ma;Yiqun Ma;Tao Xiao","doi":"10.1109/TIM.2024.3485397","DOIUrl":"https://doi.org/10.1109/TIM.2024.3485397","url":null,"abstract":"For high-resolution measurements of solid insulation space charge in complex stress environments, an optical measurement method of space charge based on the electro-optical effect is proposed. In this article, the whole process signal conversion mechanism and transmission model of the space charge electro-optical detection system are constructed. Furthermore, the key factors affecting measurement sensitivity are analyzed for the core electro-optical detection system. The results show that the measurement sensitivity shows a positive and negative correlation with the sensor electro-optical coefficient and sensor thickness, respectively. Simultaneously, it shows a decreasing trend in measurement sensitivity as the angle of incidence deviates from the Brewster angle of the electro-optical sensor. The optimal configuration of the measurement sensitivity can be realized by adjusting the incidence angle of the detection light, the electro-optical coefficient, and the thickness of the sensor. Expanding the measuring bandwidth further decreases the signal distortion. The detector bandwidth is the primary parameter that limits the behavior of the detecting system. Ultimately, a refined design for the electro-optical detecting system is suggested for sensitivity improvement. The significant sensitivity is obtained by incorporating the optical sampling technique. The experimental results demonstrate that the established detection system is capable of accurately measuring the picosecond pulsed electric field. The enhanced electro-optical detection system achieves measurement sensitivity at the \u0000<inline-formula> <tex-math>$mu $ </tex-math></inline-formula>\u0000V level and bandwidth in the THz range. The space charge measurements with nm resolution can be realized through the proposed detection system.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"73 ","pages":"1-16"},"PeriodicalIF":5.6,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587603","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}
The infrared small target detection (IRSTD) has significant value in many civilian and military applications. However, most current methods still struggle to maintain tiny targets with low contrast and are prone to false alarms caused by confusing noise similar to the targets. Furthermore, challenges remain in accurately segmenting target shapes due to the difficulty in acquiring sufficient fine-grained details. To address these issues, we propose the adaptive feature enhancement and hierarchical refinement network (AER-Net) to extract discriminative features of small targets while leveraging the distinct semantics of different layers for reducing false alarms and preserving edge contours of targets. Specifically, we present a U-shaped architecture tailored for IRSTD by a feature representation enhancement module (FREM), which combines the local correlation of convolution neural networks (CNNs) and the global correlation of Transformers to enhance features while preventing the loss of tiny targets. Based on this, we further devise a daisy-shaped correlation-aware module (DCM) to capture sufficient global contexts for mitigating clutter interference in deeper layers. Subsequently, a gated refinement module (GRM) is designed to refine low-level features from the encoder utilizing high-level features as guidance for preserving local details and further removing noise in shallower layers. Moreover, we integrate our method with the monitoring device to extend the application of IRSTD to automatic monitoring of birds in plateau lakes and wetlands, while contributing an infrared bird dataset named BSIRST_v1 with targets exhibiting high morphological uncertainty to advance research in this field. Extensive experiments on three public and the proposed datasets have demonstrated that our method achieves a balance between detection capability and segmentation accuracy. Notably, our approach has significant improvements in the mean intersection over union (IoU) metric on the challenging benchmark NUDT-SIRST and IRSTD-1k datasets with 4.46% and 3.86%, respectively. The source code and dataset are available at: https://github.com/fuqingzhang/AER-Net .
{"title":"AER-Net: Adaptive Feature Enhancement and Hierarchical Refinement Network for Infrared Small Target Detection","authors":"Fuqing Zhang;Jing Yang;Shen Deng;Anning Pan;Yang Yang;Chengjiang Zhou","doi":"10.1109/TIM.2024.3485456","DOIUrl":"https://doi.org/10.1109/TIM.2024.3485456","url":null,"abstract":"The infrared small target detection (IRSTD) has significant value in many civilian and military applications. However, most current methods still struggle to maintain tiny targets with low contrast and are prone to false alarms caused by confusing noise similar to the targets. Furthermore, challenges remain in accurately segmenting target shapes due to the difficulty in acquiring sufficient fine-grained details. To address these issues, we propose the adaptive feature enhancement and hierarchical refinement network (AER-Net) to extract discriminative features of small targets while leveraging the distinct semantics of different layers for reducing false alarms and preserving edge contours of targets. Specifically, we present a U-shaped architecture tailored for IRSTD by a feature representation enhancement module (FREM), which combines the local correlation of convolution neural networks (CNNs) and the global correlation of Transformers to enhance features while preventing the loss of tiny targets. Based on this, we further devise a daisy-shaped correlation-aware module (DCM) to capture sufficient global contexts for mitigating clutter interference in deeper layers. Subsequently, a gated refinement module (GRM) is designed to refine low-level features from the encoder utilizing high-level features as guidance for preserving local details and further removing noise in shallower layers. Moreover, we integrate our method with the monitoring device to extend the application of IRSTD to automatic monitoring of birds in plateau lakes and wetlands, while contributing an infrared bird dataset named BSIRST_v1 with targets exhibiting high morphological uncertainty to advance research in this field. Extensive experiments on three public and the proposed datasets have demonstrated that our method achieves a balance between detection capability and segmentation accuracy. Notably, our approach has significant improvements in the mean intersection over union (IoU) metric on the challenging benchmark NUDT-SIRST and IRSTD-1k datasets with 4.46% and 3.86%, respectively. The source code and dataset are available at: \u0000<uri>https://github.com/fuqingzhang/AER-Net</uri>\u0000.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"73 ","pages":"1-18"},"PeriodicalIF":5.6,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600113","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 : 2024-10-23DOI: 10.1109/TIM.2024.3485434
Tian Jin;Pei Zhang;James Chakwizira;Yuchen Wang
Cellular signals of opportunity (CSOPs) can be utilized as a backup positioning system for global navigation satellite systems (GNSSs) in urban. Current studies on the fusion of GNSS-CSOPs require sufficient signals. However, in low-observability environments where the total number of visible signals does not exceed four, the existing GNSS-CSOPs pseudorange fusion methods lack robust strategies and have poor performance. In addition, GNSS-CSOPs are not spatiotemporally synchronized and have not yet been considered by current studies. Moreover, GNSS-CSOPs pseudorange measurements noise exhibit differences under the kinematic receiver, and this challenge has not been well dealt with by the existing weighting methods. To address the above-mentioned issues, a novel fusion positioning system based on an iterated extended Kalman filter (IEKF) for GNSS-CSOP is formulated under low observability, and the degree of observability is analyzed. In this system, the influence of spatiotemporal asynchronicity is addressed. Then, a Helmert unit variance estimation (HUVE) algorithm is proposed to obtain the measurement weights in GNSS-CSOPs fusion. Moreover, a double Dog-leg incremental estimation (DDIE) algorithm is proposed to enhance convergence when solving under low observability. In field tests, results show that the positioning accuracy of the proposed system can reach approximately 6.5 m under low observability. Compared with other state-of-the-art studies, such as weighting with power-of-signal, quasi-Newton (QN), Levenberg-Marquardt (LM), and Dog-leg methods, the performance of the proposed system has been improved by 62.4%, 82.4%, 72.9%, and 64.7%, respectively. This study presents a novel fusion positioning strategy for the GNSS-CSOP in low-observability environments.
{"title":"Fusion Positioning of GNSS-Cellular Signals of Opportunity Under Low Observability","authors":"Tian Jin;Pei Zhang;James Chakwizira;Yuchen Wang","doi":"10.1109/TIM.2024.3485434","DOIUrl":"https://doi.org/10.1109/TIM.2024.3485434","url":null,"abstract":"Cellular signals of opportunity (CSOPs) can be utilized as a backup positioning system for global navigation satellite systems (GNSSs) in urban. Current studies on the fusion of GNSS-CSOPs require sufficient signals. However, in low-observability environments where the total number of visible signals does not exceed four, the existing GNSS-CSOPs pseudorange fusion methods lack robust strategies and have poor performance. In addition, GNSS-CSOPs are not spatiotemporally synchronized and have not yet been considered by current studies. Moreover, GNSS-CSOPs pseudorange measurements noise exhibit differences under the kinematic receiver, and this challenge has not been well dealt with by the existing weighting methods. To address the above-mentioned issues, a novel fusion positioning system based on an iterated extended Kalman filter (IEKF) for GNSS-CSOP is formulated under low observability, and the degree of observability is analyzed. In this system, the influence of spatiotemporal asynchronicity is addressed. Then, a Helmert unit variance estimation (HUVE) algorithm is proposed to obtain the measurement weights in GNSS-CSOPs fusion. Moreover, a double Dog-leg incremental estimation (DDIE) algorithm is proposed to enhance convergence when solving under low observability. In field tests, results show that the positioning accuracy of the proposed system can reach approximately 6.5 m under low observability. Compared with other state-of-the-art studies, such as weighting with power-of-signal, quasi-Newton (QN), Levenberg-Marquardt (LM), and Dog-leg methods, the performance of the proposed system has been improved by 62.4%, 82.4%, 72.9%, and 64.7%, respectively. This study presents a novel fusion positioning strategy for the GNSS-CSOP in low-observability environments.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"73 ","pages":"1-18"},"PeriodicalIF":5.6,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595875","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}
In miniaturized streak tube (MST), the scanning electric field, while deflecting dynamic imaging, inevitably causes dynamic spatial distortion (DSD), declining dynamic spatial resolution (DSR) uniformity and hindering large-area applications. To mitigate these cases, a compensation system has been devised, aiming to reduce DSD and enhance DSR uniformity by the application of the falling edge of V-shaped high-voltage pulse (VHVP). The research results demonstrate the VHVP, featuring a rising edge slope of 12.68 kV/ns and a falling edge slope of 15.22 kV/ns, which successfully generated by the Marx circuit combined with the LC filter. When the VHVP, in conjunction with the deflection compensation technique (DCT), was implemented in the MST, a notable reduction in DSD was achieved. Specifically, within a $Phi 30$ -mm detection area, the DSD ratio has been decreased from 35.61% to 12.09%, accompanied by 5.45% improvement in DSR uniformity at 15-mm OFF-axis. Furthermore, comparative analysis revealed that compared to an MST with $Phi 16$ -mm detection area, which utilized a curved cathode and spherical phosphor screen (PS) to achieve 2.31% improvement ratio, the proposed DCT demonstrated significant 17.36% improvement ratio at an 8-mm OFF-axis point. These findings present a universal and effective approach for the technical enhancement of MST performance.
{"title":"Reduction of Dynamic Spatial Distortion of Miniaturized Streak Tube Using Deflection Compensation Technique","authors":"Yanli Bai;Yi Jiang;Wenlong Lv;Songchun Li;Guochun Huang;Luye Liang;Si Zhong","doi":"10.1109/TIM.2024.3485443","DOIUrl":"https://doi.org/10.1109/TIM.2024.3485443","url":null,"abstract":"In miniaturized streak tube (MST), the scanning electric field, while deflecting dynamic imaging, inevitably causes dynamic spatial distortion (DSD), declining dynamic spatial resolution (DSR) uniformity and hindering large-area applications. To mitigate these cases, a compensation system has been devised, aiming to reduce DSD and enhance DSR uniformity by the application of the falling edge of V-shaped high-voltage pulse (VHVP). The research results demonstrate the VHVP, featuring a rising edge slope of 12.68 kV/ns and a falling edge slope of 15.22 kV/ns, which successfully generated by the Marx circuit combined with the LC filter. When the VHVP, in conjunction with the deflection compensation technique (DCT), was implemented in the MST, a notable reduction in DSD was achieved. Specifically, within a \u0000<inline-formula> <tex-math>$Phi 30$ </tex-math></inline-formula>\u0000-mm detection area, the DSD ratio has been decreased from 35.61% to 12.09%, accompanied by 5.45% improvement in DSR uniformity at 15-mm OFF-axis. Furthermore, comparative analysis revealed that compared to an MST with \u0000<inline-formula> <tex-math>$Phi 16$ </tex-math></inline-formula>\u0000-mm detection area, which utilized a curved cathode and spherical phosphor screen (PS) to achieve 2.31% improvement ratio, the proposed DCT demonstrated significant 17.36% improvement ratio at an 8-mm OFF-axis point. These findings present a universal and effective approach for the technical enhancement of MST performance.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"73 ","pages":"1-7"},"PeriodicalIF":5.6,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672010","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 : 2024-10-23DOI: 10.1109/TIM.2024.3485430
Lusheng Zhai;Junxi Liu;Bo Xu;Yukun Huang;Ningde Jin
Horizontal gas-liquid slug flows are widely encountered in important industrial processes. Bubble velocity measurement is of great significance for understanding the heat/mass transfer and flow pattern transitions, and thus optimizing the industrial processes. Multiscale bubbles in slug flow are characterized by complicated interactions and thus exhibit intricate transient behaviors, making the velocity measurement exceedingly challenging. In this study, a multichannel ultrasonic Doppler sensor (MCUDS) is proposed to measure the bubble velocities in gas-liquid slug flows. The MCUDS features region-focused characteristics and thus yields high sensitivity at different measurement depths. A field programmable gate array (FPGA)-based measurement system is designed to generate pulse ultrasonic signals and implement rapid and stable data acquisition, processing, and transmission. A Taylor bubble tracking method based on the effective information ratio is proposed to measure the velocities of the nose and tail of Taylor bubbles. Doppler pulse repetition method (DPRM) with a sliding window is combined with the expanded autocorrelation (EAC) algorithm to derive the Doppler shift. The research results indicate that integrating a sliding window into the DPRM effectively improves the temporal resolution of bubble velocity measurements, thereby accurately constructing the spatiotemporal distribution of the velocity vectors of dispersed bubbles. This allows for the revelation of the complex motion processes of bubbles and the slug aeration characteristics under different gas-liquid flow conditions.
{"title":"Velocity Measurement of Gas–Liquid Slug Flows Using Multichannel Ultrasonic Doppler Sensor System","authors":"Lusheng Zhai;Junxi Liu;Bo Xu;Yukun Huang;Ningde Jin","doi":"10.1109/TIM.2024.3485430","DOIUrl":"https://doi.org/10.1109/TIM.2024.3485430","url":null,"abstract":"Horizontal gas-liquid slug flows are widely encountered in important industrial processes. Bubble velocity measurement is of great significance for understanding the heat/mass transfer and flow pattern transitions, and thus optimizing the industrial processes. Multiscale bubbles in slug flow are characterized by complicated interactions and thus exhibit intricate transient behaviors, making the velocity measurement exceedingly challenging. In this study, a multichannel ultrasonic Doppler sensor (MCUDS) is proposed to measure the bubble velocities in gas-liquid slug flows. The MCUDS features region-focused characteristics and thus yields high sensitivity at different measurement depths. A field programmable gate array (FPGA)-based measurement system is designed to generate pulse ultrasonic signals and implement rapid and stable data acquisition, processing, and transmission. A Taylor bubble tracking method based on the effective information ratio is proposed to measure the velocities of the nose and tail of Taylor bubbles. Doppler pulse repetition method (DPRM) with a sliding window is combined with the expanded autocorrelation (EAC) algorithm to derive the Doppler shift. The research results indicate that integrating a sliding window into the DPRM effectively improves the temporal resolution of bubble velocity measurements, thereby accurately constructing the spatiotemporal distribution of the velocity vectors of dispersed bubbles. This allows for the revelation of the complex motion processes of bubbles and the slug aeration characteristics under different gas-liquid flow conditions.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"73 ","pages":"1-13"},"PeriodicalIF":5.6,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600399","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 : 2024-10-23DOI: 10.1109/TIM.2024.3485448
Tie Liu;Dianchun Bai;Le Ma;Qiang Du;Hiroshi Yokoi
Surface electromyography-based gesture recognition and prosthetic hand control using deep learning (DL) have become increasingly significant in the field of human-computer interaction. This study aims to enhance the control of prosthetic hands driven by complex gestures, addressing the challenge of low-resolution gesture differentiation caused by the coupling and superposition of surface electromyography signals in DL models. We propose a DL-based framework for the recognition of complex surface electromyography signals, utilizing a multipathway approach to acquire raw surface electromyography signals, process them in the time-frequency domain, and extract features using multiscale convolutional networks. The processed surface electromyography features are then analyzed in parallel to enhance accuracy. This method effectively processes multiple signals concurrently and extracts diverse feature sets. By collecting data from six channels, it achieves an 88.56% recognition rate for 16 complex hand gestures, enabling control of ten distinct prosthetic hand movements. By leveraging multidimensional continuous surface electromyography images, we have developed a feature model that resolves the issues of signal coupling and superposition in multichannel surface electromyography data, allowing for precise control of prosthetic hand movements.
{"title":"Complex Surface Electromyography Signal Gesture Recognition Based on Multipathway Featured Scale Convolutional Neural Network","authors":"Tie Liu;Dianchun Bai;Le Ma;Qiang Du;Hiroshi Yokoi","doi":"10.1109/TIM.2024.3485448","DOIUrl":"https://doi.org/10.1109/TIM.2024.3485448","url":null,"abstract":"Surface electromyography-based gesture recognition and prosthetic hand control using deep learning (DL) have become increasingly significant in the field of human-computer interaction. This study aims to enhance the control of prosthetic hands driven by complex gestures, addressing the challenge of low-resolution gesture differentiation caused by the coupling and superposition of surface electromyography signals in DL models. We propose a DL-based framework for the recognition of complex surface electromyography signals, utilizing a multipathway approach to acquire raw surface electromyography signals, process them in the time-frequency domain, and extract features using multiscale convolutional networks. The processed surface electromyography features are then analyzed in parallel to enhance accuracy. This method effectively processes multiple signals concurrently and extracts diverse feature sets. By collecting data from six channels, it achieves an 88.56% recognition rate for 16 complex hand gestures, enabling control of ten distinct prosthetic hand movements. By leveraging multidimensional continuous surface electromyography images, we have developed a feature model that resolves the issues of signal coupling and superposition in multichannel surface electromyography data, allowing for precise control of prosthetic hand movements.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"73 ","pages":"1-11"},"PeriodicalIF":5.6,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142598607","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 : 2024-10-23DOI: 10.1109/TIM.2024.3485401
Kai Zhou;Yang Jiao;Qing Chen;Hongbin Li;Tong Wu;Zemin Qu
Due to the great diversity of loads in low-voltage systems, the detection based on characteristic parameters of the current often confuses series arc faults (SAFs) with complex loads. To address this issue, an SAF detection method is proposed based on the inevitable dc component. First, comprehensive analyses, as well as observations, are made on the electrode-arcing-current asymmetry (EACA) to demonstrate that an inevitable dc component is inevitably induced during an SAF. Then, a dc-related dominated index and several asymmetry-related supplemental indices are gathered to form a feature set with strong generality. Afterward, a specific scheme is developed based on the uni-period state evaluation and the multiperiod fault judgment to reduce the false detection, where the eXtreme gradient boosting (XGBoost) algorithm is employed as a classifier. After that, experiments are made to verify the proposed method’s validity. Finally, with monitored samples used to construct an ultrageneral testing set, simulations are conducted to prove its superiority in generality.
由于低压系统中的负载种类繁多，基于电流特征参数的检测往往会混淆复杂负载的串联电弧故障（SAF）。针对这一问题，我们提出了一种基于不可避免的直流分量的 SAF 检测方法。首先，对电弧电流不对称性（EACA）进行了全面分析和观测，以证明在 SAF 期间不可避免地会诱发直流分量。然后，收集了一个与直流相关的主导指数和几个与不对称相关的补充指数，形成了一个通用性很强的特征集。之后，在单周期状态评估和多周期故障判断的基础上开发了一种特定方案来减少误检测，其中采用了极端梯度提升（XGBoost）算法作为分类器。随后，实验验证了所提方法的有效性。最后，利用监测到的样本构建超通用测试集，并进行模拟以证明其通用性的优越性。
{"title":"A Detection Method for a Series Arc Fault Based on the Inevitable DC Component Due to the Arcing Process’s Asymmetry","authors":"Kai Zhou;Yang Jiao;Qing Chen;Hongbin Li;Tong Wu;Zemin Qu","doi":"10.1109/TIM.2024.3485401","DOIUrl":"https://doi.org/10.1109/TIM.2024.3485401","url":null,"abstract":"Due to the great diversity of loads in low-voltage systems, the detection based on characteristic parameters of the current often confuses series arc faults (SAFs) with complex loads. To address this issue, an SAF detection method is proposed based on the inevitable dc component. First, comprehensive analyses, as well as observations, are made on the electrode-arcing-current asymmetry (EACA) to demonstrate that an inevitable dc component is inevitably induced during an SAF. Then, a dc-related dominated index and several asymmetry-related supplemental indices are gathered to form a feature set with strong generality. Afterward, a specific scheme is developed based on the uni-period state evaluation and the multiperiod fault judgment to reduce the false detection, where the eXtreme gradient boosting (XGBoost) algorithm is employed as a classifier. After that, experiments are made to verify the proposed method’s validity. Finally, with monitored samples used to construct an ultrageneral testing set, simulations are conducted to prove its superiority in generality.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"73 ","pages":"1-11"},"PeriodicalIF":5.6,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142565601","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 : 2024-10-23DOI: 10.1109/TIM.2024.3485399
Alfred Albert;Silvano Donati;San-Liang Lee
We consider the operation of a self-mixing interferometer on distances larger than the laboratory scale, that is, tens to hundreds of meters, and develop for the first time the theoretical analysis of SMI performances in the near and far field (FF), presenting results about signal amplitude (AM), SNR, C factor, spot size, and linewidth. Theory is also valid for the realistic case of an elliptical laser spot. Thereafter, we compare the theoretical findings with experimental data measured on white paper target using a diode laser SMI operating at 1550 nm, with a SNR =3.6 at 12-m distance and find good agreement. These results open the way to long stand-off vibration, displacement, and distance/velocity measurements.
{"title":"Self-Mixing Interferometry on Long Distance: Theory and Experimental Validation","authors":"Alfred Albert;Silvano Donati;San-Liang Lee","doi":"10.1109/TIM.2024.3485399","DOIUrl":"https://doi.org/10.1109/TIM.2024.3485399","url":null,"abstract":"We consider the operation of a self-mixing interferometer on distances larger than the laboratory scale, that is, tens to hundreds of meters, and develop for the first time the theoretical analysis of SMI performances in the near and far field (FF), presenting results about signal amplitude (AM), SNR, C factor, spot size, and linewidth. Theory is also valid for the realistic case of an elliptical laser spot. Thereafter, we compare the theoretical findings with experimental data measured on white paper target using a diode laser SMI operating at 1550 nm, with a SNR =3.6 at 12-m distance and find good agreement. These results open the way to long stand-off vibration, displacement, and distance/velocity measurements.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"73 ","pages":"1-8"},"PeriodicalIF":5.6,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595874","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}
With the continuous improvement of the accuracy of inertial devices and systems, the effects of gravity disturbance on autonomous inertial navigation system (INS) calculations cannot be overlooked. The traditional gravity disturbance compensation method directly introduces it into the INS calculation link, but the errors of gravity disturbance will lead to irreversible INS calculation errors, ultimately rendering the traditional compensation method ineffective. In this article, a new gravity disturbance compensation method for INS is proposed based on Newtonian mechanics. The INS calculation errors caused by horizontal gravity disturbance are directly corrected in the navigation system in a direct manner, which avoids coupling attitude calculation errors. The physical quantity of direct compensation is derived, and the influences of different compensation periods on the algorithm are tested. The effectiveness of the proposed method is validated using various sources of gravity disturbance. Both simulation and experimental results demonstrate that our method can effectively mitigate the influence of gravity disturbances on high-precision INSs.
随着惯性设备和系统精度的不断提高，重力干扰对自主惯性导航系统（INS）计算的影响不容忽视。传统的重力扰动补偿方法直接将其引入 INS 计算环节，但重力扰动的误差会导致不可逆的 INS 计算误差，最终导致传统补偿方法失效。本文基于牛顿力学提出了一种新的 INS 重力扰动补偿方法。以直接方式在导航系统中直接修正由水平重力扰动引起的 INS 计算误差，避免了耦合姿态计算误差。推导了直接补偿的物理量，并测试了不同补偿周期对算法的影响。利用各种重力干扰源验证了所提方法的有效性。模拟和实验结果表明，我们的方法可以有效减轻重力干扰对高精度 INS 的影响。
{"title":"A Novel Gravity Disturbance Compensation Inertial Navigation Method Based on Newtonian Mechanics","authors":"Kaixin Luo;Ruihang Yu;Meiping Wu;Juliang Cao;Yulong Huang","doi":"10.1109/TIM.2024.3485437","DOIUrl":"https://doi.org/10.1109/TIM.2024.3485437","url":null,"abstract":"With the continuous improvement of the accuracy of inertial devices and systems, the effects of gravity disturbance on autonomous inertial navigation system (INS) calculations cannot be overlooked. The traditional gravity disturbance compensation method directly introduces it into the INS calculation link, but the errors of gravity disturbance will lead to irreversible INS calculation errors, ultimately rendering the traditional compensation method ineffective. In this article, a new gravity disturbance compensation method for INS is proposed based on Newtonian mechanics. The INS calculation errors caused by horizontal gravity disturbance are directly corrected in the navigation system in a direct manner, which avoids coupling attitude calculation errors. The physical quantity of direct compensation is derived, and the influences of different compensation periods on the algorithm are tested. The effectiveness of the proposed method is validated using various sources of gravity disturbance. Both simulation and experimental results demonstrate that our method can effectively mitigate the influence of gravity disturbances on high-precision INSs.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"73 ","pages":"1-12"},"PeriodicalIF":5.6,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595907","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 : 2024-10-23DOI: 10.1109/TIM.2024.3485442
Chao Sun;Xing Wu;Changyin Sun
Localization in an unexplored environment is a fundamental capability for robotic vision navigation. However, due to the static world assumption, it still suffers the impoverishment of robustness and accuracy in complex dynamic workspaces. Moving objects, with indeterminate motion status in dynamic scenarios, usually increase the difficulty and complexity to the localization of the robotic vehicles. To address this problem, a robust and real-time RGB-D vision navigation system based on motion saliency measurement (MSM) and twin reprojection (TR) optimization is proposed to allow accurate localization for the robotic vehicles under complex dynamic scenes. Firstly, a novel saliency-induced dense motion removal (SDMR) method is developed to detect and eliminate the dynamic regions in RGB-D inputs, which can effectively filter out the outlier data that are associated with the moving objects. Then, a robust matching strategy for edge drawing lines (EDLines) feature is devised to acquire fine line inliers by constructing keypoint correspondence. Furthermore, the TR error is built by depth measurement for the line features. It is incorporated into a new error optimization function to achieve optimal pose estimation. The experimental results demonstrate that the SDMR can accurately detect dynamic objects and eliminate movement regions in complex dynamic scenarios. The proposed navigation system proves to attain at least 26% improvement of localization accuracy over other advanced dynamic navigation solutions. Test code is available on https://github.com/SunIMLab/TL-REE .
{"title":"RGB-D Vision Navigation via Motion Saliency Measurement and Twin Reprojection Optimization in Complex Dynamic Scenes","authors":"Chao Sun;Xing Wu;Changyin Sun","doi":"10.1109/TIM.2024.3485442","DOIUrl":"https://doi.org/10.1109/TIM.2024.3485442","url":null,"abstract":"Localization in an unexplored environment is a fundamental capability for robotic vision navigation. However, due to the static world assumption, it still suffers the impoverishment of robustness and accuracy in complex dynamic workspaces. Moving objects, with indeterminate motion status in dynamic scenarios, usually increase the difficulty and complexity to the localization of the robotic vehicles. To address this problem, a robust and real-time RGB-D vision navigation system based on motion saliency measurement (MSM) and twin reprojection (TR) optimization is proposed to allow accurate localization for the robotic vehicles under complex dynamic scenes. Firstly, a novel saliency-induced dense motion removal (SDMR) method is developed to detect and eliminate the dynamic regions in RGB-D inputs, which can effectively filter out the outlier data that are associated with the moving objects. Then, a robust matching strategy for edge drawing lines (EDLines) feature is devised to acquire fine line inliers by constructing keypoint correspondence. Furthermore, the TR error is built by depth measurement for the line features. It is incorporated into a new error optimization function to achieve optimal pose estimation. The experimental results demonstrate that the SDMR can accurately detect dynamic objects and eliminate movement regions in complex dynamic scenarios. The proposed navigation system proves to attain at least 26% improvement of localization accuracy over other advanced dynamic navigation solutions. Test code is available on \u0000<uri>https://github.com/SunIMLab/TL-REE</uri>\u0000.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"73 ","pages":"1-17"},"PeriodicalIF":5.6,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645545","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}
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