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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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}
As an indicator of grain safety, grain temperature data assumes great importance in the analysis of grain storage conditions and the decision-making of preventive measures such as ventilation and cooling. However, obtaining a thorough picture of grain temperature distribution via grain IoT with sensors deployed in the granary remains a challenge, given numerous data gaps across various areas due to insufficient coverage of the sensor network that fails to encompass the entire granary. Interpolation of grain temperature data, in this regard, is able to fill in the “unsensored” areas that are vacant in the records of data. Yet little literature is found in the frontier scholarship of grain temperature interpolation. To fill this noticeable niche, this study develops a novel data fusion interpolation model named convolutional neural network-attention-multilayer perceptron neural network (CAMNN) featuring an integration of convolutional neural network (CNN), attention mechanism, and multilayer perceptron (MLP). CNN is used to capture local spatial features of the temperature data, the attention mechanism enables the location of key and sensitive temperature areas, and MLP is incorporated for deep feature fusion. Performances of the proposed model are evaluated in a bin granary located in Shaanxi, China, and further validated in a larger bin granary of different storage types situated in Ningxia, China. Comparative assessments are conducted with five machine learning and deep learning (DL) models. Results indicate that CAMNN outperforms the other models, with a mean absolute error (MAE) of 0.5251 and a mean square error (mse) of 1.0881, demonstrating robust cross-context applicability across bin granaries varying in terms of sizes, storage types, and climatic zones.
{"title":"On Grain Security by Temperature Interpolation: A Deep Learning Method for Comprehensive Data Fusion in Smart Granaries","authors":"Zhongke Qu;Ke Yang;Yue Li;Xuemei Jiang;Yang Zhang;Yanyan Zhao;Wenfei Wu;Yuan Gao;Zhaolin Gu;Zhibin Zhao","doi":"10.1109/TIM.2024.3485435","DOIUrl":"https://doi.org/10.1109/TIM.2024.3485435","url":null,"abstract":"As an indicator of grain safety, grain temperature data assumes great importance in the analysis of grain storage conditions and the decision-making of preventive measures such as ventilation and cooling. However, obtaining a thorough picture of grain temperature distribution via grain IoT with sensors deployed in the granary remains a challenge, given numerous data gaps across various areas due to insufficient coverage of the sensor network that fails to encompass the entire granary. Interpolation of grain temperature data, in this regard, is able to fill in the “unsensored” areas that are vacant in the records of data. Yet little literature is found in the frontier scholarship of grain temperature interpolation. To fill this noticeable niche, this study develops a novel data fusion interpolation model named convolutional neural network-attention-multilayer perceptron neural network (CAMNN) featuring an integration of convolutional neural network (CNN), attention mechanism, and multilayer perceptron (MLP). CNN is used to capture local spatial features of the temperature data, the attention mechanism enables the location of key and sensitive temperature areas, and MLP is incorporated for deep feature fusion. Performances of the proposed model are evaluated in a bin granary located in Shaanxi, China, and further validated in a larger bin granary of different storage types situated in Ningxia, China. Comparative assessments are conducted with five machine learning and deep learning (DL) models. Results indicate that CAMNN outperforms the other models, with a mean absolute error (MAE) of 0.5251 and a mean square error (mse) of 1.0881, demonstrating robust cross-context applicability across bin granaries varying in terms of sizes, storage types, and climatic zones.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595101","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}
This article presents the method to evaluate the fast neutron energy spectrum using the single-crystal chemical vapor deposition (CVD) diamond detector to be applicable on the radiation monitoring in advanced scientific/engineering systems usually characterized with mixed and high-dose radiation field. The pulse shape discrimination (PSD) based on the shape and the width of a pulse was applied to extract events, in which fast neutron hits at the specific depth of the single-crystal diamond. Unfolding of the measured spectrum for extracted pulses could deduce the neutron energy spectrum. Experiments using monoenergetic neutron sources demonstrated the reliable capability of this method to evaluate the neutron energy spectrum quantitatively.
{"title":"Application of a Single-Crystal CVD Diamond Detector for Fast Neutron Measurement in High Dose and Mixed Radiation Fields","authors":"Makoto I. Kobayashi;Sachiko Yoshihashi;Kunihiro Ogawa;Mitsutaka Isobe;Tsukasa Aso;Masanori Hara;Siriyaporn Sangaroon;Sachie Kusaka;Shingo Tamaki;Isao Murata;Sho Toyama;Misako Miwa;Shigeo Matsuyama;Masaki Osakabe","doi":"10.1109/TIM.2024.3481590","DOIUrl":"https://doi.org/10.1109/TIM.2024.3481590","url":null,"abstract":"This article presents the method to evaluate the fast neutron energy spectrum using the single-crystal chemical vapor deposition (CVD) diamond detector to be applicable on the radiation monitoring in advanced scientific/engineering systems usually characterized with mixed and high-dose radiation field. The pulse shape discrimination (PSD) based on the shape and the width of a pulse was applied to extract events, in which fast neutron hits at the specific depth of the single-crystal diamond. Unfolding of the measured spectrum for extracted pulses could deduce the neutron energy spectrum. Experiments using monoenergetic neutron sources demonstrated the reliable capability of this method to evaluate the neutron energy spectrum quantitatively.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595871","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.2024.3485439
Huabin Wang;Dongxu Shang;Zhe Jin;Fei Liu
Alzheimer’s disease (AD) is a degenerative disorder that encompasses multiple stages during its onset. There are certain shared characteristics among patients at various stages of AD, which results in the presence of incorrect edges in the graph structure constructed using graph convolutional network (GCN) for AD diagnosis. Due to the presence of incorrect edges, a singular graph structure faces challenges in accurately capturing the relationships between nodes. To tackle such a problem, this article proposes a screening strategy that constructs a large number of graphs, and selects an optimal graph combination. For each graph, the model adaptively aggregates lesion area features of similar nodes. Such a graph-selecting strategy alleviates the impact of incorrect edges and yields better performance. First, a multiscale composition module is designed to find the potential relationship between nodes, and the graph structure at different scales is constructed by extracting the significant pathogenic features from the node features. Second, a multihop node aggregation (MHNA) algorithm is proposed to find the correlation between multihop nodes in the same category, and highly correlated multihop nodes are found by traversing the features of different hop nodes. Third, an optimal multigraph combination screening strategy is proposed to select the optimal multihop graph combinations under the optimal multiscale combinations, and further adaptive fusion by using the multigraph attention mechanism. This enables the whole model to capture the distinctive features of AD while enhancing aggregation among similar nodes. The proposed model achieves an average accuracy of 90.21% and 94.10% on the NACC and Tadpole datasets, respectively, surpassing state-of-the-art results.
阿尔茨海默病（AD）是一种退行性疾病，发病过程包含多个阶段。处于不同阶段的阿尔茨海默病患者具有某些共同特征，这导致在使用图卷积网络（GCN）构建的用于诊断阿尔茨海默病的图结构中存在不正确的边。由于存在错误的边，奇异图结构在准确捕捉节点之间的关系方面面临挑战。为解决这一问题，本文提出了一种筛选策略，即构建大量图，并选择最佳图组合。对于每个图，该模型会自适应地汇总相似节点的病变区域特征。这种图选择策略可减轻错误边缘的影响，并产生更好的性能。首先，设计了一个多尺度组成模块，以发现节点之间的潜在关系，并通过从节点特征中提取重要的致病特征来构建不同尺度的图结构。其次，提出了多跳节点聚合（MHNA）算法，通过遍历不同跳节点的特征，找到同类多跳节点之间的相关性，从而找到高度相关的多跳节点。第三，提出最优多图组合筛选策略，在最优多尺度组合下选择最优多跳图组合，并利用多图关注机制进一步自适应融合。这使得整个模型能够捕捉到 AD 的显著特征，同时加强相似节点之间的聚合。所提出的模型在 NACC 和 Tadpole 数据集上的平均准确率分别达到了 90.21% 和 94.10%，超过了最先进的结果。
{"title":"A Multigraph Combination Screening Strategy Enabled Graph Convolutional Network for Alzheimer’s Disease Diagnosis","authors":"Huabin Wang;Dongxu Shang;Zhe Jin;Fei Liu","doi":"10.1109/TIM.2024.3485439","DOIUrl":"https://doi.org/10.1109/TIM.2024.3485439","url":null,"abstract":"Alzheimer’s disease (AD) is a degenerative disorder that encompasses multiple stages during its onset. There are certain shared characteristics among patients at various stages of AD, which results in the presence of incorrect edges in the graph structure constructed using graph convolutional network (GCN) for AD diagnosis. Due to the presence of incorrect edges, a singular graph structure faces challenges in accurately capturing the relationships between nodes. To tackle such a problem, this article proposes a screening strategy that constructs a large number of graphs, and selects an optimal graph combination. For each graph, the model adaptively aggregates lesion area features of similar nodes. Such a graph-selecting strategy alleviates the impact of incorrect edges and yields better performance. First, a multiscale composition module is designed to find the potential relationship between nodes, and the graph structure at different scales is constructed by extracting the significant pathogenic features from the node features. Second, a multihop node aggregation (MHNA) algorithm is proposed to find the correlation between multihop nodes in the same category, and highly correlated multihop nodes are found by traversing the features of different hop nodes. Third, an optimal multigraph combination screening strategy is proposed to select the optimal multihop graph combinations under the optimal multiscale combinations, and further adaptive fusion by using the multigraph attention mechanism. This enables the whole model to capture the distinctive features of AD while enhancing aggregation among similar nodes. The proposed model achieves an average accuracy of 90.21% and 94.10% on the NACC and Tadpole datasets, respectively, surpassing state-of-the-art results.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587637","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.2024.3485460
ZhenZhou Wang
The visual sensing system is one of the most important parts of the welding robots to realize intelligent and autonomous welding. The active visual sensing methods have been widely adopted in robotic welding because of their higher accuracies compared to the passive visual sensing methods. In this article, a comprehensive review of the active visual sensing methods for robotic welding is given. According to their uses, the state-of-the-art active visual sensing methods are divided into four categories: seam tracking, weld bead defect detection, 3-D weld pool geometry measurement, and welding path planning. First, the principles of these active visual sensing methods are reviewed. Then, a tutorial on the 3-D calibration methods for the active visual sensing systems used in intelligent welding robots is given to fill the gaps in the related fields. At last, the reviewed active visual sensing methods are compared and the prospects are given based on their advantages and disadvantages.
{"title":"The Active Visual Sensing Methods for Robotic Welding: Review, Tutorial, and Prospect","authors":"ZhenZhou Wang","doi":"10.1109/TIM.2024.3485460","DOIUrl":"https://doi.org/10.1109/TIM.2024.3485460","url":null,"abstract":"The visual sensing system is one of the most important parts of the welding robots to realize intelligent and autonomous welding. The active visual sensing methods have been widely adopted in robotic welding because of their higher accuracies compared to the passive visual sensing methods. In this article, a comprehensive review of the active visual sensing methods for robotic welding is given. According to their uses, the state-of-the-art active visual sensing methods are divided into four categories: seam tracking, weld bead defect detection, 3-D weld pool geometry measurement, and welding path planning. First, the principles of these active visual sensing methods are reviewed. Then, a tutorial on the 3-D calibration methods for the active visual sensing systems used in intelligent welding robots is given to fill the gaps in the related fields. At last, the reviewed active visual sensing methods are compared and the prospects are given based on their advantages and disadvantages.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595774","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}
Transceivers are critical components of phased array radar (PAR) systems, and accurate fault diagnosis is essential for ensuring their reliability. However, many transceiver faults exhibit similar characteristics, making them difficult to identify. To address this challenge, a two-stage fault diagnosis method employing both rough and fine classifiers is proposed for PAR transceivers. In the first stage, a weighted support vector machine serves as the rough classifier to effectively separate easily distinguishable faults. For more complex faults that remain ambiguous, Fisher’s discriminating ratio is used to identify the most significant monitoring variables, refining the analysis further. In the second stage, a sparse momentum deep belief network (DBN) is developed as the fine classifier to accurately identify these challenging faults. The configuration parameters for both classifiers are optimized using a modified equilibrium optimizer to maximize performance. The proposed method is validated using a real-world dataset of PAR transceivers, with test results demonstrating superior accuracy compared to several existing intelligent diagnostic methods.
收发器是相控阵雷达（PAR）系统的关键部件，准确的故障诊断对确保其可靠性至关重要。然而，许多收发器故障具有相似的特征，因此难以识别。为了应对这一挑战，我们提出了一种针对 PAR 收发器的两阶段故障诊断方法，同时采用粗分类器和精分类器。在第一阶段，加权支持向量机作为粗略分类器，可有效区分易于区分的故障。对于仍不明确的较复杂故障，则使用费雪判别率来识别最重要的监测变量，进一步细化分析。在第二阶段，开发了稀疏动量深度信念网络（DBN）作为精细分类器，以准确识别这些具有挑战性的故障。两个分类器的配置参数都使用改进的平衡优化器进行优化，以最大限度地提高性能。使用 PAR 收发器的真实数据集对所提出的方法进行了验证，测试结果表明，与现有的几种智能诊断方法相比，该方法具有更高的准确性。
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Pub Date : 2024-10-23DOI: 10.1109/TIM.2024.3485398
Enrique M. Spinelli;Marcelo A. Haberman
A general-purpose instrumentation amplifier must be dc-coupled and has a differential input to handle both differential and single-ended input signals. It must also exhibit low input noise in both voltage and current to accommodate a wide range of signal source impedances. Additionally, having a differential output is desirable to allow direct connection to current high-resolution analog-to-digital converters (ADCs), which have differential inputs. There are commercially available devices with $e_n$ voltage noise spectral densities as low as $1~mathrm{nV} / sqrt{mathrm{Hz}}$ but present high current noise spectral densities $i_n$ of a few $mathrm{pA} / sqrt{mathrm{Hz}}$ . On the other hand, there are also devices with $i_n$ as low as a few $mathrm{fA} / sqrt{mathrm{Hz}}$ but presenting $e_n$ around $10~mathrm{nV} / sqrt{mathrm{Hz}}$ . To obtain low values of both $e_n$ and $i_n$ , a fully differential circuit topology combining discrete junction field transistors (JFETs) and operational amplifiers (OAs) is proposed. Design equations, stability analysis, and experimental results are presented. As an example, a fully differential instrumentation amplifier has been designed, built, and tested showing $e_n < 1~mathrm{nV} / sqrt{mathrm{Hz}}$ at 1 kHz and $i_n < 10~mathrm{fA} / sqrt{mathrm{Hz}}$ at 1 kHz. The proposed topology finds applications, such as front ends for measuring and testing instruments, industrial instrumentation, and audio circuits.
{"title":"An Ultralow-Noise Fully Differential Amplifier","authors":"Enrique M. Spinelli;Marcelo A. Haberman","doi":"10.1109/TIM.2024.3485398","DOIUrl":"https://doi.org/10.1109/TIM.2024.3485398","url":null,"abstract":"A general-purpose instrumentation amplifier must be dc-coupled and has a differential input to handle both differential and single-ended input signals. It must also exhibit low input noise in both voltage and current to accommodate a wide range of signal source impedances. Additionally, having a differential output is desirable to allow direct connection to current high-resolution analog-to-digital converters (ADCs), which have differential inputs. There are commercially available devices with \u0000<inline-formula> <tex-math>$e_n$ </tex-math></inline-formula>\u0000 voltage noise spectral densities as low as \u0000<inline-formula> <tex-math>$1~mathrm{nV} / sqrt{mathrm{Hz}}$ </tex-math></inline-formula>\u0000 but present high current noise spectral densities \u0000<inline-formula> <tex-math>$i_n$ </tex-math></inline-formula>\u0000 of a few \u0000<inline-formula> <tex-math>$mathrm{pA} / sqrt{mathrm{Hz}}$ </tex-math></inline-formula>\u0000. On the other hand, there are also devices with \u0000<inline-formula> <tex-math>$i_n$ </tex-math></inline-formula>\u0000 as low as a few \u0000<inline-formula> <tex-math>$mathrm{fA} / sqrt{mathrm{Hz}}$ </tex-math></inline-formula>\u0000 but presenting \u0000<inline-formula> <tex-math>$e_n$ </tex-math></inline-formula>\u0000 around \u0000<inline-formula> <tex-math>$10~mathrm{nV} / sqrt{mathrm{Hz}}$ </tex-math></inline-formula>\u0000. To obtain low values of both \u0000<inline-formula> <tex-math>$e_n$ </tex-math></inline-formula>\u0000 and \u0000<inline-formula> <tex-math>$i_n$ </tex-math></inline-formula>\u0000, a fully differential circuit topology combining discrete junction field transistors (JFETs) and operational amplifiers (OAs) is proposed. Design equations, stability analysis, and experimental results are presented. As an example, a fully differential instrumentation amplifier has been designed, built, and tested showing \u0000<inline-formula> <tex-math>$e_n < 1~mathrm{nV} / sqrt{mathrm{Hz}}$ </tex-math></inline-formula>\u0000 at 1 kHz and \u0000<inline-formula> <tex-math>$i_n < 10~mathrm{fA} / sqrt{mathrm{Hz}}$ </tex-math></inline-formula>\u0000 at 1 kHz. The proposed topology finds applications, such as front ends for measuring and testing instruments, industrial instrumentation, and audio circuits.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587629","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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