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}
Deriving a unique cryptographic key from biometric measurements is a challenging task due to the existing noise gap between the biometric measurements and error correction coding. Additionally, privacy and security concerns arise as biometric measurements are inherently linked to the user. Bio-cryptosystems represent a key branch of solutions aimed at addressing these issues. However, many existing bio-cryptosystems rely on handcrafted feature extractors and error correction codes (ECC), often leading to performance degradation. To address these challenges and improve the reliability of biometric measurements, we propose a novel biometric cryptosystem (BC) named WiFaKey, for generating cryptographic keys from face in unconstrained settings. Specifically, WiFaKey first introduces an adaptive random masking-driven feature transformation pipeline, AdaMTrans. AdaMTrans effectively quantizes and binarizes real-valued features and incorporates an adaptive random masking scheme to align the bit error rate (BER) with error correction requirements, thereby mitigating the noise gap. Besides, WiFaKey incorporates a supervised learning-based neural decoding scheme called neural-MS decoder, which delivers a more robust error correction performance with less iteration than nonlearning decoders, thereby alleviating the performance degradation. We evaluated WiFaKey using widely adopted face feature extractors on six large unconstrained and two constrained datasets. On the labeled faces in the wild database (LFW) dataset, WiFaKey achieved an average genuine match rate (GMR) of 85.45% and 85.20% at a 0% false match rate (FMR) for MagFace and AdaFace features, respectively. Our comprehensive comparative analysis shows a significant performance improvement of WiFaKey. The source code of our work is available at github.com/xingbod/WiFaKey.
{"title":"WiFaKey: Generating Cryptographic Keys From Face in the Wild","authors":"Xingbo Dong;Hui Zhang;Yen Lung Lai;Zhe Jin;Junduan Huang;Wenxiong Kang;Andrew Beng Jin Teoh","doi":"10.1109/TIM.2024.3485436","DOIUrl":"https://doi.org/10.1109/TIM.2024.3485436","url":null,"abstract":"Deriving a unique cryptographic key from biometric measurements is a challenging task due to the existing noise gap between the biometric measurements and error correction coding. Additionally, privacy and security concerns arise as biometric measurements are inherently linked to the user. Bio-cryptosystems represent a key branch of solutions aimed at addressing these issues. However, many existing bio-cryptosystems rely on handcrafted feature extractors and error correction codes (ECC), often leading to performance degradation. To address these challenges and improve the reliability of biometric measurements, we propose a novel biometric cryptosystem (BC) named WiFaKey, for generating cryptographic keys from face in unconstrained settings. Specifically, WiFaKey first introduces an adaptive random masking-driven feature transformation pipeline, AdaMTrans. AdaMTrans effectively quantizes and binarizes real-valued features and incorporates an adaptive random masking scheme to align the bit error rate (BER) with error correction requirements, thereby mitigating the noise gap. Besides, WiFaKey incorporates a supervised learning-based neural decoding scheme called neural-MS decoder, which delivers a more robust error correction performance with less iteration than nonlearning decoders, thereby alleviating the performance degradation. We evaluated WiFaKey using widely adopted face feature extractors on six large unconstrained and two constrained datasets. On the labeled faces in the wild database (LFW) dataset, WiFaKey achieved an average genuine match rate (GMR) of 85.45% and 85.20% at a 0% false match rate (FMR) for MagFace and AdaFace features, respectively. Our comprehensive comparative analysis shows a significant performance improvement of WiFaKey. The source code of our work is available at github.com/xingbod/WiFaKey.","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":"142636306","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":"73 ","pages":"1-20"},"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":"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":"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.2024.3485460
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.
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