Pub Date : 2025-09-12DOI: 10.1109/TIM.2025.3609383
M. R. Soleimani;Z. Nasiri-Gheidari;F. Tootoonchian;H. Oraee
This article presents an optimized design for a multiturn outer rotor variable reluctance (VR) resolver, focusing on enhancing its accuracy, manufacturability, and overall performance. An analytical model is developed to evaluate the influence of key design parameters, including rotor contour, winding configuration, and the number of turns per layer. Through a comprehensive optimization process, the best combinations of these parameters are identified, improving both the precision and efficiency of the resolver. The study also explores the impact of rotor yoke thickness on sensor accuracy, offering insights into the tradeoffs between compactness and precision. Experimental validation is conducted by fabricating a prototype based on the optimized design and comparing its performance with simulation results. The prototype demonstrates excellent agreement with the simulations, exhibiting low position errors and confirming the effectiveness of the proposed design and optimization strategy. The findings provide a practical framework for designing high-precision VR resolvers, balancing accuracy, cost-effectiveness, and ease of construction.
{"title":"Optimization and Performance Evaluation of a Multiturn, Outer Rotor VR Resolver for Enhanced Accuracy and Manufacturability","authors":"M. R. Soleimani;Z. Nasiri-Gheidari;F. Tootoonchian;H. Oraee","doi":"10.1109/TIM.2025.3609383","DOIUrl":"https://doi.org/10.1109/TIM.2025.3609383","url":null,"abstract":"This article presents an optimized design for a multiturn outer rotor variable reluctance (VR) resolver, focusing on enhancing its accuracy, manufacturability, and overall performance. An analytical model is developed to evaluate the influence of key design parameters, including rotor contour, winding configuration, and the number of turns per layer. Through a comprehensive optimization process, the best combinations of these parameters are identified, improving both the precision and efficiency of the resolver. The study also explores the impact of rotor yoke thickness on sensor accuracy, offering insights into the tradeoffs between compactness and precision. Experimental validation is conducted by fabricating a prototype based on the optimized design and comparing its performance with simulation results. The prototype demonstrates excellent agreement with the simulations, exhibiting low position errors and confirming the effectiveness of the proposed design and optimization strategy. The findings provide a practical framework for designing high-precision VR resolvers, balancing accuracy, cost-effectiveness, and ease of construction.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-8"},"PeriodicalIF":5.9,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145090046","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}
Accurate characterization of pipeline defects is crucial for maintaining structural integrity and ensuring operational safety. This study introduces an innovative pipeline defect evaluation method integrating the gravitational search algorithm (GSA) with the compressed sampling matching pursuit (CoSaMP), aimed at improving the accuracy and robustness of ultrasonic guided wave (UGW) signal decomposition and reconstruction. GSA is applied to dynamically optimize signal sparsity, overcoming the limitations of traditional methods that rely on predefined sparsity levels. Moreover, an optimized waveform dictionary, which incorporates prior knowledge of guided wave reflection characteristics, is constructed to improve the accuracy of defect signal decomposition and reconstruction. The proposed method effectively separates overlapping reflection signals from the front and rear edges of pipeline defects, enabling precise characterization of defect axial dimensions. Finite element (FE) simulations and experimental validations using a piezoelectric (PZT) sensor array installed on the surface of a stainless steel pipeline illustrate the enhanced effectiveness of the proposed methodology, achieving average defect size evaluation errors of 0.68 and 2.20 mm, respectively, significantly outperforming conventional matching pursuit (MP), standard CoSaMP, orthogonal matching pursuit (OMP), and basis pursuit (BP) algorithms. This method addresses the limitations of existing approaches by adaptively optimizing signal sparsity, enhancing robustness against noise, and providing a reliable tool for pipeline integrity assessment. The findings contribute to the development of predictive maintenance strategies and advance real-time defect monitoring applications for complex pipeline networks.
{"title":"Pipeline Defect Assessment Method Based on Ultrasonic Guided Wave Sensor Array and GSA-CoSaMP Algorithm","authors":"Zhirong Lin;Yishou Wang;Linlin Fang;Xiaodie Hu;Xinlin Qing","doi":"10.1109/TIM.2025.3609325","DOIUrl":"https://doi.org/10.1109/TIM.2025.3609325","url":null,"abstract":"Accurate characterization of pipeline defects is crucial for maintaining structural integrity and ensuring operational safety. This study introduces an innovative pipeline defect evaluation method integrating the gravitational search algorithm (GSA) with the compressed sampling matching pursuit (CoSaMP), aimed at improving the accuracy and robustness of ultrasonic guided wave (UGW) signal decomposition and reconstruction. GSA is applied to dynamically optimize signal sparsity, overcoming the limitations of traditional methods that rely on predefined sparsity levels. Moreover, an optimized waveform dictionary, which incorporates prior knowledge of guided wave reflection characteristics, is constructed to improve the accuracy of defect signal decomposition and reconstruction. The proposed method effectively separates overlapping reflection signals from the front and rear edges of pipeline defects, enabling precise characterization of defect axial dimensions. Finite element (FE) simulations and experimental validations using a piezoelectric (PZT) sensor array installed on the surface of a stainless steel pipeline illustrate the enhanced effectiveness of the proposed methodology, achieving average defect size evaluation errors of 0.68 and 2.20 mm, respectively, significantly outperforming conventional matching pursuit (MP), standard CoSaMP, orthogonal matching pursuit (OMP), and basis pursuit (BP) algorithms. This method addresses the limitations of existing approaches by adaptively optimizing signal sparsity, enhancing robustness against noise, and providing a reliable tool for pipeline integrity assessment. The findings contribute to the development of predictive maintenance strategies and advance real-time defect monitoring applications for complex pipeline networks.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-13"},"PeriodicalIF":5.9,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145078639","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 : 2025-09-11DOI: 10.1109/TIM.2025.3608336
Shuang Zhao;Yuanxi Yang;Shuqiang Xue;Zhenjie Wang;Zhen Xiao;Baojin Li
The seafloor hybrid constellation, composed of fixed and moored stations equipped with acoustic beacons, serves as a crucial infrastructure and holds promising prospects for possible applications in ocean submesoscale current monitoring and acoustic navigation when compared with traditionally unalloyed seafloor constellations. However, most of the acoustic positioning models are designed to handle fixed seafloor stations and do not match the actual motion characteristics of moored stations in a hybrid constellation, which may degrade the accuracy of beacon position estimation. To address this gap, a novel GNSS-acoustic (GNSS-A) positioning model is proposed in this contribution. First, the critical factor of acoustic measurements, namely, observation error of sound speed, is processed by error modeling based on the geometric angle of acoustic rays. Second, the smooth variation characteristic of physical marine signal processing is taken into consideration to estimate parameters related to time-delay error. Furthermore, the motion depiction of moored beacons is established and introduced into the observation equation system to obtain more reasonable positioning results of seafloor beacons. Finally, the proposed model is validated through tests on a sea-trial experimental dataset, along with an analysis of seafloor baseline measurements. Results and analysis show that, compared with those of traditional methods, the motion of moored beacons can be tracked in detail, and the trajectories of the four beacons maintain an overall consistency, which is expected to aid in deriving the possible ocean submesoscale currents.
{"title":"A Novel GNSS-Acoustic Positioning Model for a Seafloor Hybrid Constellation With Fixed and Moored Beacons","authors":"Shuang Zhao;Yuanxi Yang;Shuqiang Xue;Zhenjie Wang;Zhen Xiao;Baojin Li","doi":"10.1109/TIM.2025.3608336","DOIUrl":"https://doi.org/10.1109/TIM.2025.3608336","url":null,"abstract":"The seafloor hybrid constellation, composed of fixed and moored stations equipped with acoustic beacons, serves as a crucial infrastructure and holds promising prospects for possible applications in ocean submesoscale current monitoring and acoustic navigation when compared with traditionally unalloyed seafloor constellations. However, most of the acoustic positioning models are designed to handle fixed seafloor stations and do not match the actual motion characteristics of moored stations in a hybrid constellation, which may degrade the accuracy of beacon position estimation. To address this gap, a novel GNSS-acoustic (GNSS-A) positioning model is proposed in this contribution. First, the critical factor of acoustic measurements, namely, observation error of sound speed, is processed by error modeling based on the geometric angle of acoustic rays. Second, the smooth variation characteristic of physical marine signal processing is taken into consideration to estimate parameters related to time-delay error. Furthermore, the motion depiction of moored beacons is established and introduced into the observation equation system to obtain more reasonable positioning results of seafloor beacons. Finally, the proposed model is validated through tests on a sea-trial experimental dataset, along with an analysis of seafloor baseline measurements. Results and analysis show that, compared with those of traditional methods, the motion of moored beacons can be tracked in detail, and the trajectories of the four beacons maintain an overall consistency, which is expected to aid in deriving the possible ocean submesoscale currents.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-14"},"PeriodicalIF":5.9,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145210058","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}
Unsupervised anomaly segmentation plays a critical role in real-world industrial product quality inspection. While feature reconstruction-based methods have shown promising performance by detecting anomalies through differences between pretrained features and their reconstructions, existing approaches often suffer from shortcut learning, and leading to reconstruction failures and inaccurate anomaly representation across multistage features. To address these limitations, we propose feature cross-channel projection (FC2P), a novel approach for anomaly segmentation. FC2P divides features into two subsets based on neighboring channels and employs two autoencoders for closed-loop prediction, effectively mitigating shortcut effects while capturing semantic relationships for efficient reconstruction. In addition, we introduce an anomaly exposure network (AExNet), which progressively amplifies anomalies across multistage feature residuals, generating precise anomaly score maps for accurate segmentation. Extensive experiments on MVTec AD and Visa benchmark datasets demonstrate that the proposed FC2P achieves state-of-the-art (SOTA) performance, with average precision (AP) scores of 79.8% and 44.8%, respectively. Moreover, visualization results on real industrial data further show the practicality of our proposed method. The code will be made publicly available at https://github.com/Karma1628/work-2 to ensure reproducibility and facilitate further research.
{"title":"FC2P: Feature Cross-Channel Projection for Unsupervised Anomaly Segmentation","authors":"Yichi Chen;Weizhi Xian;Junjie Wang;Xian Tao;Bin Chen","doi":"10.1109/TIM.2025.3608319","DOIUrl":"https://doi.org/10.1109/TIM.2025.3608319","url":null,"abstract":"Unsupervised anomaly segmentation plays a critical role in real-world industrial product quality inspection. While feature reconstruction-based methods have shown promising performance by detecting anomalies through differences between pretrained features and their reconstructions, existing approaches often suffer from shortcut learning, and leading to reconstruction failures and inaccurate anomaly representation across multistage features. To address these limitations, we propose feature cross-channel projection (FC2P), a novel approach for anomaly segmentation. FC2P divides features into two subsets based on neighboring channels and employs two autoencoders for closed-loop prediction, effectively mitigating shortcut effects while capturing semantic relationships for efficient reconstruction. In addition, we introduce an anomaly exposure network (AExNet), which progressively amplifies anomalies across multistage feature residuals, generating precise anomaly score maps for accurate segmentation. Extensive experiments on MVTec AD and Visa benchmark datasets demonstrate that the proposed FC2P achieves state-of-the-art (SOTA) performance, with average precision (AP) scores of 79.8% and 44.8%, respectively. Moreover, visualization results on real industrial data further show the practicality of our proposed method. The code will be made publicly available at <uri>https://github.com/Karma1628/work-2</uri> to ensure reproducibility and facilitate further research.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-13"},"PeriodicalIF":5.9,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145090245","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 : 2025-09-10DOI: 10.1109/TIM.2025.3608340
Han Yao;Ferruccio Renzoni
High-sensitivity operation of radio frequency atomic magnetometers (AMs) in unshielded environment requires compensation of low-frequency fluctuations of the ambient magnetic field. Here, we demonstrate the use of phase-lock (PL) techniques to stabilize the magnetic environment and achieve high sensitivity at high frequencies. This is achieved by using the output of the AM both for stabilization and for measurement purposes. The approach is validated by a proof-of-concept in unshielded environment. The PL approach is also compared to the standard approach, where the magnetic environment is stabilized with the help of a set of fluxgate magnetometers, and it is shown that the PL approach features superior performances in signal detection.
{"title":"High-Sensitivity Operation of Unshielded Radio Frequency Atomic Magnetometers Using Phase-Lock Techniques","authors":"Han Yao;Ferruccio Renzoni","doi":"10.1109/TIM.2025.3608340","DOIUrl":"https://doi.org/10.1109/TIM.2025.3608340","url":null,"abstract":"High-sensitivity operation of radio frequency atomic magnetometers (AMs) in unshielded environment requires compensation of low-frequency fluctuations of the ambient magnetic field. Here, we demonstrate the use of phase-lock (PL) techniques to stabilize the magnetic environment and achieve high sensitivity at high frequencies. This is achieved by using the output of the AM both for stabilization and for measurement purposes. The approach is validated by a proof-of-concept in unshielded environment. The PL approach is also compared to the standard approach, where the magnetic environment is stabilized with the help of a set of fluxgate magnetometers, and it is shown that the PL approach features superior performances in signal detection.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-9"},"PeriodicalIF":5.9,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145090047","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 : 2025-09-10DOI: 10.1109/TIM.2025.3608359
He Zhu;Kun Zhao;Chao Yu;Xichao Yang
Received signal strength (RSS)-based localization methods are widely used in indoor positioning scenarios within 5G systems due to their cost-effectiveness and broad device compatibility. However, the path loss exponent (PLE) in the path loss model is highly sensitive to the localization environment, and precisely measuring the reference signal received power (RSRP) at the reference point remains challenging in practice. Consequently, in different localization application scenarios, continuous measurement and adjustment of the RSRP at the reference point and the PLE are required. Otherwise, the localization accuracy will be degraded. In this article, we first employ a dynamic difference of RSS (DRSS) model to eliminate the impact of RSRP measurement errors at the reference point. The model also addresses variations in PLE at different locations within the same localization scenario, as well as dynamic changes in PLE within the environment. Subsequently, a localization coordinate adjudicator is proposed to iteratively update the UE position and determine the optimal PLE for the current UE. Finally, under the optimal PLE, the UE’s localization coordinates are obtained using a genetic algorithm with a dynamic elite retention mechanism. Experimental validation was performed using both publicly available 5G simulation datasets and real-world data. The results show that the proposed dynamic DRSS model achieves a root mean square error (RMSE) of 2.44 m, outperforming existing techniques by 29%.
{"title":"Indoor Localization Using Dynamic DRSS Model in 5G System","authors":"He Zhu;Kun Zhao;Chao Yu;Xichao Yang","doi":"10.1109/TIM.2025.3608359","DOIUrl":"https://doi.org/10.1109/TIM.2025.3608359","url":null,"abstract":"Received signal strength (RSS)-based localization methods are widely used in indoor positioning scenarios within 5G systems due to their cost-effectiveness and broad device compatibility. However, the path loss exponent (PLE) in the path loss model is highly sensitive to the localization environment, and precisely measuring the reference signal received power (RSRP) at the reference point remains challenging in practice. Consequently, in different localization application scenarios, continuous measurement and adjustment of the RSRP at the reference point and the PLE are required. Otherwise, the localization accuracy will be degraded. In this article, we first employ a dynamic difference of RSS (DRSS) model to eliminate the impact of RSRP measurement errors at the reference point. The model also addresses variations in PLE at different locations within the same localization scenario, as well as dynamic changes in PLE within the environment. Subsequently, a localization coordinate adjudicator is proposed to iteratively update the UE position and determine the optimal PLE for the current UE. Finally, under the optimal PLE, the UE’s localization coordinates are obtained using a genetic algorithm with a dynamic elite retention mechanism. Experimental validation was performed using both publicly available 5G simulation datasets and real-world data. The results show that the proposed dynamic DRSS model achieves a root mean square error (RMSE) of 2.44 m, outperforming existing techniques by 29%.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-10"},"PeriodicalIF":5.9,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145090246","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 laser powder bed fusion (LPBF) additive manufacturing, unstable melt pool and keyhole can result in defects such as pores, lack of fusion, and cracks. In three-dimension (3D) monitoring of melt pool and keyhole is essential for preventing process deviations and optimizing part quality. This study proposed a novel binocular imaging system for in situ 3D monitoring of melt pool and keyhole. A coaxial binocular imaging optical path is designed to capture dual-view melt pools and an unsupervised adaptive weighted-loss residual U-net (Res-Unet) is adopted to achieve accurate disparity extraction. The performance of the network is validated, demonstrating subpixel accuracy using the HCI light field dataset. The binocular imaging system’s spatial resolution is validated at $6.2~mu $ m using a standard resolution board, while its surface 3D reconstruction accuracy is confirmed to be $10.6~mu $ m through a standard gauge block. The effectiveness of the binocular imaging system for in situ monitoring of melt pool keyhole depth is validated through both experiments and simulations, which reveals dynamic variation in keyhole depth. This work represents the first integration of optical imaging and artificial intelligence (AI) for coaxial in situ monitoring of 3D morphology of both LPBF melt pool and keyhole. It provides valuable tool for monitoring the evolution of keyhole depth, serving as a critical reference for enhancing the reliability and consistency of additive manufacturing processes.
{"title":"In Situ Three-Dimension Monitoring of Laser Powder Bed Fusion Melt Pool and Keyhole by Binocular Imaging","authors":"Xiuhua Li;Hui Li;Shengnan Shen;Mingliang Li;Ruiqin Ma;Rong Chen;Yuanhong Qian;Zheyu Yang;Kai Zhang","doi":"10.1109/TIM.2025.3608360","DOIUrl":"https://doi.org/10.1109/TIM.2025.3608360","url":null,"abstract":"In laser powder bed fusion (LPBF) additive manufacturing, unstable melt pool and keyhole can result in defects such as pores, lack of fusion, and cracks. In three-dimension (3D) monitoring of melt pool and keyhole is essential for preventing process deviations and optimizing part quality. This study proposed a novel binocular imaging system for in situ 3D monitoring of melt pool and keyhole. A coaxial binocular imaging optical path is designed to capture dual-view melt pools and an unsupervised adaptive weighted-loss residual U-net (Res-Unet) is adopted to achieve accurate disparity extraction. The performance of the network is validated, demonstrating subpixel accuracy using the HCI light field dataset. The binocular imaging system’s spatial resolution is validated at <inline-formula> <tex-math>$6.2~mu $ </tex-math></inline-formula>m using a standard resolution board, while its surface 3D reconstruction accuracy is confirmed to be <inline-formula> <tex-math>$10.6~mu $ </tex-math></inline-formula>m through a standard gauge block. The effectiveness of the binocular imaging system for in situ monitoring of melt pool keyhole depth is validated through both experiments and simulations, which reveals dynamic variation in keyhole depth. This work represents the first integration of optical imaging and artificial intelligence (AI) for coaxial in situ monitoring of 3D morphology of both LPBF melt pool and keyhole. It provides valuable tool for monitoring the evolution of keyhole depth, serving as a critical reference for enhancing the reliability and consistency of additive manufacturing processes.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-11"},"PeriodicalIF":5.9,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145110326","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 : 2025-09-10DOI: 10.1109/TIM.2025.3608322
Jie Yang;Zhengjie Ying;Keya Yuan;Renhui Ding;Qingquan Liu
This study presents a novel radiation measurement system capable of simultaneously measuring solar and upward longwave radiation, with the goal of achieving measurement accuracy within ±5% under the tested experimental conditions. A multiphysics heat transfer analysis based on computational fluid dynamics (CFDs) was first conducted to quantify the influence of key environmental factors on the thermal response of the sensing elements. Subsequently, an environmental correction model was developed using a multilayer perceptron (MLP) neural network to compensate for the nonlinear effects of meteorological variables. Finally, a field comparison platform was constructed to assess the system’s performance. During the experiments, solar radiation data from a Kipp and Zonen CMP10 pyranometer and longwave radiation values derived from the Stefan–Boltzmann law were used as reference standards. The results showed that the relative errors for solar and longwave radiation measurements ranged from –3.66% to 3.69% and –3.86% to 3.81%, respectively. The root mean square errors (RMSEs) between the estimated and measured values were 15.4 W/m2 for solar radiation and 16.7 W/m2 for longwave radiation, with corresponding mean absolute errors (MAEs) of 9.8 and 11.4 W/m2. The correlation coefficients were 0.98 and 0.96, respectively, indicating a strong agreement with the reference data. These results demonstrate the high accuracy and robustness of the proposed system, highlighting its potential for applications in energy balance analysis, climate monitoring, and agroecological research.
{"title":"Design and Experimental Study of a Measurement System for Total Solar Radiation and Upward Longwave Radiation","authors":"Jie Yang;Zhengjie Ying;Keya Yuan;Renhui Ding;Qingquan Liu","doi":"10.1109/TIM.2025.3608322","DOIUrl":"https://doi.org/10.1109/TIM.2025.3608322","url":null,"abstract":"This study presents a novel radiation measurement system capable of simultaneously measuring solar and upward longwave radiation, with the goal of achieving measurement accuracy within ±5% under the tested experimental conditions. A multiphysics heat transfer analysis based on computational fluid dynamics (CFDs) was first conducted to quantify the influence of key environmental factors on the thermal response of the sensing elements. Subsequently, an environmental correction model was developed using a multilayer perceptron (MLP) neural network to compensate for the nonlinear effects of meteorological variables. Finally, a field comparison platform was constructed to assess the system’s performance. During the experiments, solar radiation data from a Kipp and Zonen CMP10 pyranometer and longwave radiation values derived from the Stefan–Boltzmann law were used as reference standards. The results showed that the relative errors for solar and longwave radiation measurements ranged from –3.66% to 3.69% and –3.86% to 3.81%, respectively. The root mean square errors (RMSEs) between the estimated and measured values were 15.4 W/m2 for solar radiation and 16.7 W/m2 for longwave radiation, with corresponding mean absolute errors (MAEs) of 9.8 and 11.4 W/m2. The correlation coefficients were 0.98 and 0.96, respectively, indicating a strong agreement with the reference data. These results demonstrate the high accuracy and robustness of the proposed system, highlighting its potential for applications in energy balance analysis, climate monitoring, and agroecological research.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-13"},"PeriodicalIF":5.9,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145073276","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 : 2025-09-10DOI: 10.1109/TIM.2025.3608335
Qingying He;Xiao Li;Chengming Tian;Fangyu Shen;Yuanyuan Liu;Hao Sun
High-precision pose estimation using fiducial markers has many applications in medical device tracking, virtual reality alignment, navigation, and more. However, the accuracy of pose estimation and detection capabilities are often constrained by the shape and scale of the fiducial marker plane. In this article, we propose a triangular planar fiducial marker affixed to a positive icosahedron for pose estimation. This design expands the angular observation range, increases the marker scale, and consequently enhances estimation accuracy and recognition distance. The 2-D coordinates of the feature points from the markers are detected and extracted from the environment. Subsequently, the 3-D coordinates of these feature points are obtained using the triangulation method. This process results in the formation of 2-D–3-D point pairs. High-quality interior points are then filtered using the random sample consensus (RANSAC) method. The initial position is determined through the efficient perspective-n-point (EPnP) method, followed by the application of Levenberg–Marquardt (LM) optimization. We evaluated the performance of IcoTag3D through both simulations and physical experiments. The results from the simulation experiments indicate that IcoTag3D exhibits significantly lower maximum rotation angle error, reprojection error, and translation error at the submillimeter level. In addition, it demonstrates an improved recognition distance compared with the method of attaching ArUco markers to icosahedra. Physical experiments have further confirmed the feasibility of IcoTag3D.
{"title":"IcoTag3D: Enhanced 6-DoF Pose Estimation for Robotic Arms Using TriangleTag Markers on an Icosahedron","authors":"Qingying He;Xiao Li;Chengming Tian;Fangyu Shen;Yuanyuan Liu;Hao Sun","doi":"10.1109/TIM.2025.3608335","DOIUrl":"https://doi.org/10.1109/TIM.2025.3608335","url":null,"abstract":"High-precision pose estimation using fiducial markers has many applications in medical device tracking, virtual reality alignment, navigation, and more. However, the accuracy of pose estimation and detection capabilities are often constrained by the shape and scale of the fiducial marker plane. In this article, we propose a triangular planar fiducial marker affixed to a positive icosahedron for pose estimation. This design expands the angular observation range, increases the marker scale, and consequently enhances estimation accuracy and recognition distance. The 2-D coordinates of the feature points from the markers are detected and extracted from the environment. Subsequently, the 3-D coordinates of these feature points are obtained using the triangulation method. This process results in the formation of 2-D–3-D point pairs. High-quality interior points are then filtered using the random sample consensus (RANSAC) method. The initial position is determined through the efficient perspective-n-point (EPnP) method, followed by the application of Levenberg–Marquardt (LM) optimization. We evaluated the performance of IcoTag3D through both simulations and physical experiments. The results from the simulation experiments indicate that IcoTag3D exhibits significantly lower maximum rotation angle error, reprojection error, and translation error at the submillimeter level. In addition, it demonstrates an improved recognition distance compared with the method of attaching ArUco markers to icosahedra. Physical experiments have further confirmed the feasibility of IcoTag3D.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-15"},"PeriodicalIF":5.9,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145090079","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}
Deep learning techniques have made impressive progress in the field of remote sensing change detection (RSCD) in recent years. However, existing RSCD methods still exhibit limitations in bi-temporal feature fusion, making it difficult to adequately mine critical change information. Moreover, they often overlook the semantic inconsistency between features at different levels during feature aggregation, which limits the accurate reconstruction of the internal structure of change objects. To address the above issues, this article proposes a multiscale spatial frequency fusion and prior change guidance network, called MPNet, aiming to enhance the complete reconstruction of change objects. The proposed MPNet has two advantages. First, a multiscale spatial frequency fusion (MSFF) module is proposed to capture the bi-temporal features in the frequency domain and different scale spatial domains, and perform dynamic adaptive fusion through the attention mechanism, thereby realizing the adequate mining of global and local change information. Second, a prior change guidance (PCG) module is designed to generate a prior change mapping by fusing high-level semantic information with low-level texture details. This prior mapping guides multilevel feature learning, effectively correcting semantic discrepancies across different feature layers and enabling the extraction of more discriminative change feature representations. Experimental results on the LEVIR-CD, WHU-CD, and SYSU-CD datasets demonstrate that the proposed MPNet significantly outperforms other state-of-the-art (SOTA) methods in the complete detection of the internal structure of change objects. The code is available at https://github.com/NjustHGWei/MPNet.
{"title":"Multiscale Spatial Frequency Fusion and Prior Change Guidance Network for Remote Sensing Change Detection","authors":"Hongguang Wei;Yuan Liu;Yueran Ma;Dongdong Pang;Yuanxin Ye;Xiubao Sui;Qian Chen","doi":"10.1109/TIM.2025.3608333","DOIUrl":"https://doi.org/10.1109/TIM.2025.3608333","url":null,"abstract":"Deep learning techniques have made impressive progress in the field of remote sensing change detection (RSCD) in recent years. However, existing RSCD methods still exhibit limitations in bi-temporal feature fusion, making it difficult to adequately mine critical change information. Moreover, they often overlook the semantic inconsistency between features at different levels during feature aggregation, which limits the accurate reconstruction of the internal structure of change objects. To address the above issues, this article proposes a multiscale spatial frequency fusion and prior change guidance network, called MPNet, aiming to enhance the complete reconstruction of change objects. The proposed MPNet has two advantages. First, a multiscale spatial frequency fusion (MSFF) module is proposed to capture the bi-temporal features in the frequency domain and different scale spatial domains, and perform dynamic adaptive fusion through the attention mechanism, thereby realizing the adequate mining of global and local change information. Second, a prior change guidance (PCG) module is designed to generate a prior change mapping by fusing high-level semantic information with low-level texture details. This prior mapping guides multilevel feature learning, effectively correcting semantic discrepancies across different feature layers and enabling the extraction of more discriminative change feature representations. Experimental results on the LEVIR-CD, WHU-CD, and SYSU-CD datasets demonstrate that the proposed MPNet significantly outperforms other state-of-the-art (SOTA) methods in the complete detection of the internal structure of change objects. The code is available at <uri>https://github.com/NjustHGWei/MPNet</uri>.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-13"},"PeriodicalIF":5.9,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145090085","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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