The effective representation and feature extraction of 3-D scenes from sparse and unstructured point clouds pose a significant challenge in 3-D object detection. In this article, we propose TransMRE, a network that enables fully sparse multiple observation plane feature fusion using LiDAR point clouds as single-modal input. TransMRE achieves this by sparsely factorizing a 3-D voxel scene into three separate observation planes: XY, XZ, and YZ planes. In addition, we propose Observation Plane Sparse Fusion and Interaction to explore the internal relationship between different observation planes. The Transformer mechanism is employed to realize feature attention within a single observation plane and feature attention across multiple observation planes. This recursive application of attention is done during multiple observation plane projection feature aggregation to effectively model the entire 3-D scene. This approach addresses the limitation of insufficient feature representation ability under a single bird’s-eye view (BEV) constructed by extremely sparse point clouds. Furthermore, TransMRE maintains the full sparsity property of the entire network, eliminating the need to convert sparse feature maps into dense feature maps. As a result, it can be effectively applied to LiDAR point cloud data with large scanning ranges, such as Argoverse 2, while ensuring low computational complexity. Extensive experiments were conducted to evaluate the effectiveness of TransMRE, achieving 64.9 mAP and 70.4 NDS on the nuScenes detection benchmark, and 32.3 mAP on the Argoverse 2 detection benchmark. These results demonstrate that our method outperforms state-of-the-art methods.
{"title":"TransMRE: Multiple Observation Planes Representation Encoding With Fully Sparse Voxel Transformers for 3-D Object Detection","authors":"Ziming Zhu;Yu Zhu;Kezhi Zhang;Hangyu Li;Xiaofeng Ling","doi":"10.1109/TIM.2024.3480206","DOIUrl":"https://doi.org/10.1109/TIM.2024.3480206","url":null,"abstract":"The effective representation and feature extraction of 3-D scenes from sparse and unstructured point clouds pose a significant challenge in 3-D object detection. In this article, we propose TransMRE, a network that enables fully sparse multiple observation plane feature fusion using LiDAR point clouds as single-modal input. TransMRE achieves this by sparsely factorizing a 3-D voxel scene into three separate observation planes: XY, XZ, and YZ planes. In addition, we propose Observation Plane Sparse Fusion and Interaction to explore the internal relationship between different observation planes. The Transformer mechanism is employed to realize feature attention within a single observation plane and feature attention across multiple observation planes. This recursive application of attention is done during multiple observation plane projection feature aggregation to effectively model the entire 3-D scene. This approach addresses the limitation of insufficient feature representation ability under a single bird’s-eye view (BEV) constructed by extremely sparse point clouds. Furthermore, TransMRE maintains the full sparsity property of the entire network, eliminating the need to convert sparse feature maps into dense feature maps. As a result, it can be effectively applied to LiDAR point cloud data with large scanning ranges, such as Argoverse 2, while ensuring low computational complexity. Extensive experiments were conducted to evaluate the effectiveness of TransMRE, achieving 64.9 mAP and 70.4 NDS on the nuScenes detection benchmark, and 32.3 mAP on the Argoverse 2 detection benchmark. These results demonstrate that our method outperforms state-of-the-art methods.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"73 ","pages":"1-13"},"PeriodicalIF":5.6,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636362","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 : 2024-11-04DOI: 10.1109/TIM.2024.3488141
Tangwen Yin;Hongtian Chen;Dan Huang;Hesheng Wang
Causality is an active relationship that transforms possibility into actuality, underscoring the limitation of relying on averages to address rare events. This study proposes a counterfactual covariate causal discovery mechanism on nonlinear extremal quantiles (CCCD-NEQs) to impute potential outcomes, measure unobservable causalities, and unveil hidden causal relationships in safety-critical systems. We created a multilevel statistical model called mixed-effect and causal-covariate statistical model with dynamic quantiles (MCSM-DQs), which incorporates mixed effects, causal covariates, and dynamic quantiles. Leveraging the exponential family distribution over MCSM-DQ ensures simplified parameter estimation and enhanced computation efficiency, enabling the bootstrapping prediction of counterfactual outcomes at dynamic quantiles to reveal causal relationships and mitigate confounding effects. We applied the CCCD-NEQ approach to identify the potential causal effects among aircraft configuration, decision-making capabilities, and flight safety. Results revealed previously unknown causal relationships concerning rare safety incidents that cannot be detected using conventional instrumental analytics. Our new counterfactual causal discovery mechanism provides opportunities to uncover hidden causality on nonlinear extremal quantiles, highlighting the forward design and optimization of systems for adaptability, robustness, intelligence, and safety.
{"title":"Counterfactual Covariate Causal Discovery on Nonlinear Extremal Quantiles","authors":"Tangwen Yin;Hongtian Chen;Dan Huang;Hesheng Wang","doi":"10.1109/TIM.2024.3488141","DOIUrl":"https://doi.org/10.1109/TIM.2024.3488141","url":null,"abstract":"Causality is an active relationship that transforms possibility into actuality, underscoring the limitation of relying on averages to address rare events. This study proposes a counterfactual covariate causal discovery mechanism on nonlinear extremal quantiles (CCCD-NEQs) to impute potential outcomes, measure unobservable causalities, and unveil hidden causal relationships in safety-critical systems. We created a multilevel statistical model called mixed-effect and causal-covariate statistical model with dynamic quantiles (MCSM-DQs), which incorporates mixed effects, causal covariates, and dynamic quantiles. Leveraging the exponential family distribution over MCSM-DQ ensures simplified parameter estimation and enhanced computation efficiency, enabling the bootstrapping prediction of counterfactual outcomes at dynamic quantiles to reveal causal relationships and mitigate confounding effects. We applied the CCCD-NEQ approach to identify the potential causal effects among aircraft configuration, decision-making capabilities, and flight safety. Results revealed previously unknown causal relationships concerning rare safety incidents that cannot be detected using conventional instrumental analytics. Our new counterfactual causal discovery mechanism provides opportunities to uncover hidden causality on nonlinear extremal quantiles, highlighting the forward design and optimization of systems for adaptability, robustness, intelligence, and safety.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"73 ","pages":"1-12"},"PeriodicalIF":5.6,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636329","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 : 2024-11-04DOI: 10.1109/TIM.2024.3485462
Ze Chen;Yuan Zhang;Zifei Cao;Yongmeng Liu
As the “heart” of the aviation industry, high-performance aero-engines have always been a stumbling block restricting rapid development. Curvic couplings are widely used in the assembly of multistage aero-engine rotors. The coaxiality of the assembly significantly influences the performance and life of the aero-engine, so it is necessary to predict and optimize the assembly coaxiality. Aiming at three key problems, we propose an assembly coaxiality optimization and prediction approach. In this approach, we measure 3-D point clouds by a line-structured light array scanning measurement system and come up with a weighted iterative closest point (ICP) algorithm to perform a virtual assembly of the point cloud model to regulate the assembly precision. Ultimately, rotors with curvic couplings are used to experimentally validate the coaxiality prediction and optimization approach. According to the experimental findings, the two-/ three-stage rotors assemblies’ maximum coaxiality prediction errors under eight distinct assembly phases are 4.8 and $7.7~mu $ m, respectively. The two-/three-stage rotors optimization assemblies’ coaxiality errors are decreased by 11.9 and $31.8~mu $ m, respectively, compared with the direct assembly without optimization. The three-stage rotors’ assembly accuracy is improved by 12.09%. The results show the effectiveness of the proposed method.
{"title":"Precision Regulation in Multistage Aero-Engine Rotors With Curvic Couplings Using Line-Structured Light Array Scanning and Virtual Assembly","authors":"Ze Chen;Yuan Zhang;Zifei Cao;Yongmeng Liu","doi":"10.1109/TIM.2024.3485462","DOIUrl":"https://doi.org/10.1109/TIM.2024.3485462","url":null,"abstract":"As the “heart” of the aviation industry, high-performance aero-engines have always been a stumbling block restricting rapid development. Curvic couplings are widely used in the assembly of multistage aero-engine rotors. The coaxiality of the assembly significantly influences the performance and life of the aero-engine, so it is necessary to predict and optimize the assembly coaxiality. Aiming at three key problems, we propose an assembly coaxiality optimization and prediction approach. In this approach, we measure 3-D point clouds by a line-structured light array scanning measurement system and come up with a weighted iterative closest point (ICP) algorithm to perform a virtual assembly of the point cloud model to regulate the assembly precision. Ultimately, rotors with curvic couplings are used to experimentally validate the coaxiality prediction and optimization approach. According to the experimental findings, the two-/ three-stage rotors assemblies’ maximum coaxiality prediction errors under eight distinct assembly phases are 4.8 and \u0000<inline-formula> <tex-math>$7.7~mu $ </tex-math></inline-formula>\u0000m, respectively. The two-/three-stage rotors optimization assemblies’ coaxiality errors are decreased by 11.9 and \u0000<inline-formula> <tex-math>$31.8~mu $ </tex-math></inline-formula>\u0000m, respectively, compared with the direct assembly without optimization. The three-stage rotors’ assembly accuracy is improved by 12.09%. The results show the effectiveness of the proposed method.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"73 ","pages":"1-11"},"PeriodicalIF":5.6,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636361","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}
An easy-to-fabricate, full circle range (0°–360°), planar coil-based variable reluctance (VR) angle transducer with enhanced linearity is presented in this article. The proposed sensor system aims to mitigate the limitations of the existing VR angle sensors, particularly their limited accuracy and nonlinearity, resulting from the inherent sensor output characteristics. By carefully designing the coil geometry to achieve uniform flux distribution and implementing a simple semicircular-shaped rotor, the sensor system offers enhanced performance and linearity. The proposed sensor employs a semicircular-shaped rotor plate (RP) placed between two printed circuit board (PCBs) with four coils each. These coils are strategically designed to ensure a linear variation of inductance with respect to the RP position, resulting in improved linearity in the sensor output. After validating the sensor design through analytical methods and finite-element analysis (FEA), a suitable algorithm was developed for accurately estimating the rotor angle. A sensor prototype was manufactured to evaluate the performance of the sensor system. The prototype showed an excellent linearity with a worst case error of 0.31% and a resolution of 0.11°. The sensor shows negligible sensitivity to axial misalignment of the shaft and the presence of external magnetic objects, highlighting the practical usefulness of the system.
本文介绍了一种易于制造、全圆范围（0°-360°）、基于平面线圈的可变磁阻（VR）角度传感器，具有更高的线性度。拟议的传感器系统旨在缓解现有 VR 角度传感器的局限性，特别是其固有的传感器输出特性所导致的有限精度和非线性。通过精心设计线圈的几何形状以实现均匀的磁通量分布，并采用简单的半圆形转子，该传感器系统的性能和线性度都得到了提高。拟议的传感器采用了一个半圆形转子板（RP），置于两块印刷电路板（PCB）之间，每块印刷电路板有四个线圈。这些线圈经过精心设计，可确保电感随 RP 位置的线性变化，从而提高传感器输出的线性度。通过分析方法和有限元分析（FEA）对传感器设计进行验证后，开发出一种合适的算法，用于准确估算转子角度。为评估传感器系统的性能，制造了一个传感器原型。原型显示出极佳的线性度，最坏情况下误差为 0.31%，分辨率为 0.11°。传感器对轴的轴向偏差和外部磁性物体的灵敏度几乎可以忽略不计，突出了该系统的实用性。
{"title":"A Variable Reluctance-Based Planar Dual-Coil Angle Sensor With Enhanced Linearity","authors":"Anil Kumar Appukuttan Nair Syamala Amma;P.P. Narayanan;Jeshma Thalapil Vaheeda;Sreenath Vijayakumar","doi":"10.1109/TIM.2024.3451596","DOIUrl":"https://doi.org/10.1109/TIM.2024.3451596","url":null,"abstract":"An easy-to-fabricate, full circle range (0°–360°), planar coil-based variable reluctance (VR) angle transducer with enhanced linearity is presented in this article. The proposed sensor system aims to mitigate the limitations of the existing VR angle sensors, particularly their limited accuracy and nonlinearity, resulting from the inherent sensor output characteristics. By carefully designing the coil geometry to achieve uniform flux distribution and implementing a simple semicircular-shaped rotor, the sensor system offers enhanced performance and linearity. The proposed sensor employs a semicircular-shaped rotor plate (RP) placed between two printed circuit board (PCBs) with four coils each. These coils are strategically designed to ensure a linear variation of inductance with respect to the RP position, resulting in improved linearity in the sensor output. After validating the sensor design through analytical methods and finite-element analysis (FEA), a suitable algorithm was developed for accurately estimating the rotor angle. A sensor prototype was manufactured to evaluate the performance of the sensor system. The prototype showed an excellent linearity with a worst case error of 0.31% and a resolution of 0.11°. The sensor shows negligible sensitivity to axial misalignment of the shaft and the presence of external magnetic objects, highlighting the practical usefulness of the system.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"73 ","pages":"1-8"},"PeriodicalIF":5.6,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595032","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 : 2024-11-01DOI: 10.1109/TIM.2024.3481591
Xiangyang Yang;Haitao Hu;Donghua Xiao;Haidong Tao;Yitong Song;Zhengyou He
Accurate impedance measurements of the train and traction network are crucial for small-signal stability analysis of railway train-network system (RTNS). Although impedance measurement methods for four-quadrant converters (4QCs) in electric trains based on harmonic voltage disturbance injection have been proposed, few studies have investigated the impact of integrating a harmonic generator on RTNS stability. To address this issue, this article proposes a wideband harmonic disturbance generator (WHDG) and evaluates its impact on RTNS stability. The WHDG primarily comprises the back-to-back converter-based cascaded H-bridge (CHB) structure and a wideband coupling transformer. This generator can produce multifrequency perturbations with uniformly distributed spectrum energy. Subsequently, an accurate output impedance model is established based on the detailed topology and parameters of the WHDG. The model accounts for the impact of the dc impedance of the front-stage rectifier on the post-stage inverter. The close alignment between the modeling and simulation results demonstrates the accuracy of the deduced impedance model. Furthermore, an impedance-matching analysis of the RTNS with integrated WHDG is performed, indicating that the internal impedance of the WHDG weakens the stability of the tested RTNS. Finally, the effectiveness of the proposed WHDG is validated via a hardware-in-the-loop (HIL) experimental platform, and the impedance-matching analysis results are verified.
{"title":"Impedance-Matching Analysis of Wideband Harmonic Disturbance Generator for Railway Train-Network System","authors":"Xiangyang Yang;Haitao Hu;Donghua Xiao;Haidong Tao;Yitong Song;Zhengyou He","doi":"10.1109/TIM.2024.3481591","DOIUrl":"https://doi.org/10.1109/TIM.2024.3481591","url":null,"abstract":"Accurate impedance measurements of the train and traction network are crucial for small-signal stability analysis of railway train-network system (RTNS). Although impedance measurement methods for four-quadrant converters (4QCs) in electric trains based on harmonic voltage disturbance injection have been proposed, few studies have investigated the impact of integrating a harmonic generator on RTNS stability. To address this issue, this article proposes a wideband harmonic disturbance generator (WHDG) and evaluates its impact on RTNS stability. The WHDG primarily comprises the back-to-back converter-based cascaded H-bridge (CHB) structure and a wideband coupling transformer. This generator can produce multifrequency perturbations with uniformly distributed spectrum energy. Subsequently, an accurate output impedance model is established based on the detailed topology and parameters of the WHDG. The model accounts for the impact of the dc impedance of the front-stage rectifier on the post-stage inverter. The close alignment between the modeling and simulation results demonstrates the accuracy of the deduced impedance model. Furthermore, an impedance-matching analysis of the RTNS with integrated WHDG is performed, indicating that the internal impedance of the WHDG weakens the stability of the tested RTNS. Finally, the effectiveness of the proposed WHDG is validated via a hardware-in-the-loop (HIL) experimental platform, and the impedance-matching analysis results are verified.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"73 ","pages":"1-14"},"PeriodicalIF":5.6,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595836","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}
Blade vibration testing is crucial for understanding the dynamic behavior of rotating machinery. This article presents a theoretical analysis and experimental validation of electromagnetic excitation for blade vibration testing in static conditions. The study focuses on investigating the effect of electromagnets on static blades to establish a theoretical foundation. The Timoshenko beam theory is utilized to analyze the vibration parameters, including amplitude and frequency while considering associated uncertainties. The theoretical analysis is complemented by numerical modeling using the finite-element method and experimental measurements employing laser Doppler vibrometer (LDV). The results demonstrate the effectiveness of electromagnetic excitation in generating controlled vibrations in static blades. These findings provide valuable insights and serve as a basis for subsequent investigations into the behavior of blades during rotation. The mathematical model’s frequency estimation error was approximately 4% compared to numerical results, and the numerical amplitude results differed by 6.4% from the experimental measurements. These contributions enhance the understanding and design of blade vibration monitoring systems in rotating machinery and provide valuable information on the blade’s dynamic parameters for the calibration of blade tip timing (BTT) systems.
{"title":"Electromagnetic Excitation for Blade Vibration Analysis in Static Conditions: Theoretical Insights and Experimental Evaluation","authors":"Mohammed Lamine Mekhalfia;Pavel Procházka;Radislav Smid;Philip Bonello;Peter Russhard;Dušan Maturkanič;Mohamed Elsayed Mohamed;Eder Batista Tchawou Tchuisseu","doi":"10.1109/TIM.2024.3488153","DOIUrl":"https://doi.org/10.1109/TIM.2024.3488153","url":null,"abstract":"Blade vibration testing is crucial for understanding the dynamic behavior of rotating machinery. This article presents a theoretical analysis and experimental validation of electromagnetic excitation for blade vibration testing in static conditions. The study focuses on investigating the effect of electromagnets on static blades to establish a theoretical foundation. The Timoshenko beam theory is utilized to analyze the vibration parameters, including amplitude and frequency while considering associated uncertainties. The theoretical analysis is complemented by numerical modeling using the finite-element method and experimental measurements employing laser Doppler vibrometer (LDV). The results demonstrate the effectiveness of electromagnetic excitation in generating controlled vibrations in static blades. These findings provide valuable insights and serve as a basis for subsequent investigations into the behavior of blades during rotation. The mathematical model’s frequency estimation error was approximately 4% compared to numerical results, and the numerical amplitude results differed by 6.4% from the experimental measurements. These contributions enhance the understanding and design of blade vibration monitoring systems in rotating machinery and provide valuable information on the blade’s dynamic parameters for the calibration of blade tip timing (BTT) systems.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"73 ","pages":"1-8"},"PeriodicalIF":5.6,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672061","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 : 2024-11-01DOI: 10.1109/TIM.2024.3488145
Linfeng Li;Yucheng Song;Tian Tian;Jinwen Tian
Infrared small target detection (IRSTD) is important in military and civilian applications. In recent years, numerous methods based on convolutional neural networks (CNNs) have already been explored in the field of IRSTD. However, due to the mismatch between the network’s receptive field and the size of the target, conventional CNN-based methods struggle to fully differentiate between the background and the small target and are prone to losing the small target in deeper layers. A dense condition-driven diffusion network (DCDNet) based on the conditional diffusion model is proposed to address the IRSTD task. The diffusion model can easily fit the distribution of infrared background images, thereby isolating the small targets from the distribution. Extracted features from original images are used as conditions to guide the diffusion model in gradually transforming Gaussian noise into the target image. A dense conditioning module is introduced to provide richer guidance to the diffusion model. This module incorporates multiscale information from the conditional image into the diffusion model. Multiple samplings can reduce the amplitude of background noise to enhance the target. Comprehensive experiments performed on two public datasets demonstrate the proposed method’s effectiveness and superiority over other comparative methods in terms of probability of detection ( $P_{d}$ ), intersection over union (IoU), and signal-to-clutter ratio gain (SCRG).
{"title":"Dense Condition-Driven Diffusion Network for Infrared Small Target Detection","authors":"Linfeng Li;Yucheng Song;Tian Tian;Jinwen Tian","doi":"10.1109/TIM.2024.3488145","DOIUrl":"https://doi.org/10.1109/TIM.2024.3488145","url":null,"abstract":"Infrared small target detection (IRSTD) is important in military and civilian applications. In recent years, numerous methods based on convolutional neural networks (CNNs) have already been explored in the field of IRSTD. However, due to the mismatch between the network’s receptive field and the size of the target, conventional CNN-based methods struggle to fully differentiate between the background and the small target and are prone to losing the small target in deeper layers. A dense condition-driven diffusion network (DCDNet) based on the conditional diffusion model is proposed to address the IRSTD task. The diffusion model can easily fit the distribution of infrared background images, thereby isolating the small targets from the distribution. Extracted features from original images are used as conditions to guide the diffusion model in gradually transforming Gaussian noise into the target image. A dense conditioning module is introduced to provide richer guidance to the diffusion model. This module incorporates multiscale information from the conditional image into the diffusion model. Multiple samplings can reduce the amplitude of background noise to enhance the target. Comprehensive experiments performed on two public datasets demonstrate the proposed method’s effectiveness and superiority over other comparative methods in terms of probability of detection (\u0000<inline-formula> <tex-math>$P_{d}$ </tex-math></inline-formula>\u0000), intersection over union (IoU), and signal-to-clutter ratio gain (SCRG).","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"73 ","pages":"1-13"},"PeriodicalIF":5.6,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600213","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 : 2024-11-01DOI: 10.1109/TIM.2024.3484531
Weihao Zhang;Cai Yi;Lei Yan;Qi Liu;Qiuyang Zhou;Pengfei He;Le Ran;Yunzhi Lin
It has been demonstrated that fast convolutional sparse dictionary learning (FCSDL) is a useful instrument for diagnosing rolling bearing faults and can recover rolling bearing fault shocks unaffected by random slippage. However, although FCSDL is not impacted by random fluctuations and can rapidly reconstruct fault shock without truncating the signal, its performance for repetitive fault shock reconstruction is not optimal when dealing with strong noise vibration signals. Therefore, this article proposes cyclostationary convolutional sparse dictionary learning (CCSDL), which is guided by fault features (cyclostationarity) to achieve the greatest signal reconstruction performance. First, the proposed method is based on the rotation frequency, and various frequency-band-covering components in the vibration signal are reconstructed successively. In the meanwhile, the harmonic significance index (HSI), which can indicate the cyclostationarity of the fault shock, evaluates the fault characteristics of each reconstruction result and finally obtains the most significant reconstruction result. Compared with FCSDL and variational mode decomposition (VMD), the proposed method performs far superior in signal reconstruction when processing low SNR vibration data.
{"title":"Dictionary Learning Method for Cyclostationarity Maximization and Its Application to Bearing Fault Feature Extraction","authors":"Weihao Zhang;Cai Yi;Lei Yan;Qi Liu;Qiuyang Zhou;Pengfei He;Le Ran;Yunzhi Lin","doi":"10.1109/TIM.2024.3484531","DOIUrl":"https://doi.org/10.1109/TIM.2024.3484531","url":null,"abstract":"It has been demonstrated that fast convolutional sparse dictionary learning (FCSDL) is a useful instrument for diagnosing rolling bearing faults and can recover rolling bearing fault shocks unaffected by random slippage. However, although FCSDL is not impacted by random fluctuations and can rapidly reconstruct fault shock without truncating the signal, its performance for repetitive fault shock reconstruction is not optimal when dealing with strong noise vibration signals. Therefore, this article proposes cyclostationary convolutional sparse dictionary learning (CCSDL), which is guided by fault features (cyclostationarity) to achieve the greatest signal reconstruction performance. First, the proposed method is based on the rotation frequency, and various frequency-band-covering components in the vibration signal are reconstructed successively. In the meanwhile, the harmonic significance index (HSI), which can indicate the cyclostationarity of the fault shock, evaluates the fault characteristics of each reconstruction result and finally obtains the most significant reconstruction result. Compared with FCSDL and variational mode decomposition (VMD), the proposed method performs far superior in signal reconstruction when processing low SNR vibration data.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"73 ","pages":"1-13"},"PeriodicalIF":5.6,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595849","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 : 2024-11-01DOI: 10.1109/TIM.2024.3485452
Chaozheng Xue;Tao Li;Yongzhao Li;Yuhan Ruan;Rui Zhang;Octavia A. Dobre
Radio frequency fingerprint (RFF) identification is a promising technique that exploits hardware impairment-induced features to achieve specific device identification. Among RFF features, carrier frequency offset (CFO) as a hotspot feature has received widespread attention. Since CFO is time-variant, existing research suggests compensating for its drift; however, this article emphasizes using the drift of CFO. Correspondingly, a novel RFF feature, named cyclic similarity (cyc-similarity), is proposed to depict the oscillator drift. Simply combining the cyc-similarity feature with a K-nearest neighbor (KNN) classifier, the system can achieve superior temporal and receiver generalization performance. On a public dataset of WiFi devices, the proposed method outperforms the existing methods.
射频指纹（RFF）识别是一种很有前途的技术，它利用硬件损伤引起的特征来实现特定设备的识别。在射频指纹特征中，载波频率偏移（CFO）作为一种热点特征受到广泛关注。由于载波频率偏移是时变的，现有研究建议对其漂移进行补偿；但本文强调利用载波频率偏移的漂移。因此，本文提出了一种名为 "循环相似性（cyc-similarity）"的新型 RFF 特征来描述振荡器漂移。只需将循环相似性特征与 K 近邻（KNN）分类器相结合，系统就能实现卓越的时间和接收器泛化性能。在一个公开的 WiFi 设备数据集上，所提出的方法优于现有的方法。
{"title":"Radio Frequency Fingerprinting for WiFi Devices Using Oscillator Drifts","authors":"Chaozheng Xue;Tao Li;Yongzhao Li;Yuhan Ruan;Rui Zhang;Octavia A. Dobre","doi":"10.1109/TIM.2024.3485452","DOIUrl":"https://doi.org/10.1109/TIM.2024.3485452","url":null,"abstract":"Radio frequency fingerprint (RFF) identification is a promising technique that exploits hardware impairment-induced features to achieve specific device identification. Among RFF features, carrier frequency offset (CFO) as a hotspot feature has received widespread attention. Since CFO is time-variant, existing research suggests compensating for its drift; however, this article emphasizes using the drift of CFO. Correspondingly, a novel RFF feature, named cyclic similarity (cyc-similarity), is proposed to depict the oscillator drift. Simply combining the cyc-similarity feature with a K-nearest neighbor (KNN) classifier, the system can achieve superior temporal and receiver generalization performance. On a public dataset of WiFi devices, the proposed method outperforms the existing methods.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"73 ","pages":"1-4"},"PeriodicalIF":5.6,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595872","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}
A novel optical fiber-coupled waveguide grating chip device based on Fano resonance effect is proposed and demonstrated for the first time, to the best of our knowledge. It is fabricated by ultraviolet nanoimprint lithography (UV-NIL), with the advantages of low-cost and easy mass production. Such a device can perform multifunctional sensing such as refractive index (RI) and pressure because the variations of the effective RI of the guide mode and the grating period will cause the resonant wavelength shifts under the change of external parameters. Through experimental verification, a RI sensitivity of 59.29 nm/RIU and a pressure sensitivity of 0.89 nm/MPa, a copper ion concentration detection sensitivity of 3.40 pm/ $mu $ M are achieved, respectively. Furthermore, the chip sensing function is realized, and each arrayed sensing unit is interrogated through optical fiber scanning. This kind of optical fiber-coupled waveguide grating chip sensor can not only realize array sensing but also measure various physical and chemical parameters. It could find important applications in biochemical and industrial fields.
{"title":"Optical Fiber-Coupled Waveguide Grating Chip Sensor Fabricated by Ultraviolet Nanoimprint Lithography","authors":"Qiaoling Chen;Jianxin Cui;Zengling Ran;Xiu He;Xiaoxue Ruan;Shengyi Qiu;Yanbo Xiao;Qingqiang Zhu;Fei Zhang;Gaoli Xiao;Ziqiang Chen;Jiahui Yu;Yuan Gong","doi":"10.1109/TIM.2024.3485400","DOIUrl":"https://doi.org/10.1109/TIM.2024.3485400","url":null,"abstract":"A novel optical fiber-coupled waveguide grating chip device based on Fano resonance effect is proposed and demonstrated for the first time, to the best of our knowledge. It is fabricated by ultraviolet nanoimprint lithography (UV-NIL), with the advantages of low-cost and easy mass production. Such a device can perform multifunctional sensing such as refractive index (RI) and pressure because the variations of the effective RI of the guide mode and the grating period will cause the resonant wavelength shifts under the change of external parameters. Through experimental verification, a RI sensitivity of 59.29 nm/RIU and a pressure sensitivity of 0.89 nm/MPa, a copper ion concentration detection sensitivity of 3.40 pm/\u0000<inline-formula> <tex-math>$mu $ </tex-math></inline-formula>\u0000M are achieved, respectively. Furthermore, the chip sensing function is realized, and each arrayed sensing unit is interrogated through optical fiber scanning. This kind of optical fiber-coupled waveguide grating chip sensor can not only realize array sensing but also measure various physical and chemical parameters. It could find important applications in biochemical and industrial fields.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"73 ","pages":"1-9"},"PeriodicalIF":5.6,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636358","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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