IEEE Transactions on Instrumentation and Measurement最新文献_第5页

Simultaneous Estimation of Conductivity and Radial Eccentricity of Metallic Cylinders Using Eddy Current Testing 用涡流试验同时估计金属圆柱体的电导率和径向偏心

IF 5.9 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-09-04 DOI: 10.1109/TIM.2025.3606056

Xun Zou;Saibo She;Xinnan Zheng;Kuohai Yu;Jialong Shen;Anthony Peyton;Wuliang Yin

{"title":"Simultaneous Estimation of Conductivity and Radial Eccentricity of Metallic Cylinders Using Eddy Current Testing","authors":"Xun Zou;Saibo She;Xinnan Zheng;Kuohai Yu;Jialong Shen;Anthony Peyton;Wuliang Yin","doi":"10.1109/TIM.2025.3606056","DOIUrl":"https://doi.org/10.1109/TIM.2025.3606056","url":null,"abstract":"Metallic cylinders are extensively used across a range of industries. The inspection of their properties through eddy current testing (ECT) is crucial to ensure the desired performance of the piece in practical applications. This article proposes for the first time an analytical model for the mutual inductance variation of a coil pair encircling an eccentric metallic cylinder, applicable to 3-D asymmetric cases where vibration and wobble exist. The analytical solution is further simplified for faster calculation while maintaining high consistency with the complete model. Moreover, an inverse approach is proposed to simultaneously measure rod conductivity and its eccentricity from the center based on the simplified analytical model, exploiting the crossing frequency between the real and imaginary parts of the inductance spectra. A modified Newton–Raphson method is employed to reduce the estimation error further. Experiments are carried out using a multifrequency eddy current sensor to test different metallic specimens, the results of which validated the effectiveness of the analytical solution. Finally, the proposed inverse approach achieves high-accuracy estimations for both conductivity and eccentricity.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-10"},"PeriodicalIF":5.9,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145078624","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}

引用次数: 0

A 2-D Indoor Localization System Using 3-D Structural Features for Mobile Robots 基于三维结构特征的移动机器人室内二维定位系统

IF 5.9 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-09-04 DOI: 10.1109/TIM.2025.3606048

Wei Li;Guohui Tian;Xuyang Shao

{"title":"A 2-D Indoor Localization System Using 3-D Structural Features for Mobile Robots","authors":"Wei Li;Guohui Tian;Xuyang Shao","doi":"10.1109/TIM.2025.3606048","DOIUrl":"https://doi.org/10.1109/TIM.2025.3606048","url":null,"abstract":"Indoor environments, as typical unstructured settings, present significant challenges for the localization of mobile robots. In such environments, robots are prone to getting lost or mismatched to incorrect locations. Existing solutions heavily rely on 2-D light detection and ranging (LiDAR), which can only scan the horizontal plane of the environment, thus failing to fully observe spatial objects, resulting in insufficient available features for the localization of robots. In response to this challenge, this article introduces a system that measures 3-D structural features for 2-D localization. First, a vision sensor is employed to capture the 3-D structural features of the scene. A hierarchical strategy is then introduced to extract key structural features, mapping the 3-D features into 2-D hierarchical submaps. A map selection algorithm is further proposed to filter the localization map. Next, we propose a method to convert point cloud data into 2-D pseudo-laser representations, allowing for parallel matching between the hierarchical submaps and the pseudo-laser data to obtain multiple localization results. Building on this, we investigate an observation residual evaluation method to assess the performance of multiple localization results, enabling fused localization. Both simulation and real-world experiments demonstrate that the introduced approach significantly improves the accuracy and robustness of localization for mobile robots.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-16"},"PeriodicalIF":5.9,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145027908","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}

引用次数: 0

Nonconvex Sparse Regularization Method for Eyeblink Artifact Suppression From Single-Channel EEG Signals 单通道脑电信号眨眼伪影抑制的非凸稀疏正则化方法

IF 5.9 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-09-04 DOI: 10.1109/TIM.2025.3606042

Lin Zou;Mingming Dong;Yun Kong;Wei Li;Weiwei Lv

{"title":"Nonconvex Sparse Regularization Method for Eyeblink Artifact Suppression From Single-Channel EEG Signals","authors":"Lin Zou;Mingming Dong;Yun Kong;Wei Li;Weiwei Lv","doi":"10.1109/TIM.2025.3606042","DOIUrl":"https://doi.org/10.1109/TIM.2025.3606042","url":null,"abstract":"Recent advancements in affordable single-channel electroencephalogram (EEG) devices have garnered considerable attention due to their ability to reduce hardware complexity. However, effectively suppressing eyeblink artifacts in single-channel EEG signals remains a substantial challenge for biomedical applications. This article proposes a nonconvex sparse regularization methodology (NSRM), which explores the generalized minimax-concave (GMC) penalty for eyeblink artifact suppression from single-channel EEG signals. The contaminated EEG signals can be initially modeled within the sparse representation framework as a combination of target and noise components. The proposed methodology preserves the convexity of the sparsity-regularized least square objective function, allowing the global minimum to be reached through convex optimization. Specifically, a forwardbackward splitting (FBS) algorithm is developed to resolve the nonconvex sparse regularization problem of eyeblink artifact suppression. In addition, we introduce an adaptive selection strategy for the regularization parameter. The advantage over conventional methods is that NSRM can better preserve useful information from EEG signals while suppressing eyeblink artifacts. To validate the efficacy of NSRM, a semisimulated EEG dataset and two real experiment datasets have been analyzed. Results demonstrate that our NSRM methodology eliminates eyeblink artifacts effectively and accurately from single-channel EEG signals, outperforming the <inline-formula> <tex-math>$L1$ </tex-math></inline-formula> norm-based sparse regularization method, as evidenced by quantitative metrics. Finally, comparison results with the advanced K-means singular value decomposition (K-SVD) have also confirmed the superiority of our proposed NSRM for eyeblink artifact suppression in the context of the sparse representation paradigm.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-14"},"PeriodicalIF":5.9,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145057478","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}

引用次数: 0

Enhanced SAR Image Generation Using Ego-Motion Estimation Based on Ground Scatterers for Automotive Radar Systems 基于地面散射体的自运动估计增强汽车雷达SAR图像生成

IF 5.9 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-09-04 DOI: 10.1109/TIM.2025.3606067

Gunhwi Moon;Seongwook Lee;Jeong-Hoon Park;Young-Jun Yoon;Seong-Cheol Kim

引用次数: 0

Heart Rate and HRV Estimation Using PPG Based on Superlet Transform and LSTM Network 基于超let变换和LSTM网络的PPG心率和HRV估计

IF 5.9 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-09-04 DOI: 10.1109/TIM.2025.3606047

Alisha Gupta;Suresh R. Devasahayam;Badri Narayan Subudhi

{"title":"Heart Rate and HRV Estimation Using PPG Based on Superlet Transform and LSTM Network","authors":"Alisha Gupta;Suresh R. Devasahayam;Badri Narayan Subudhi","doi":"10.1109/TIM.2025.3606047","DOIUrl":"https://doi.org/10.1109/TIM.2025.3606047","url":null,"abstract":"Photoplethysmography (PPG) is a widely used, noninvasive method for monitoring cardiovascular parameters in wearable devices. However, wrist-based PPG signals are often affected by motion artifacts and poor sensor contact, which can compromise heart rate (HR) estimation accuracy. This article presents a novel algorithm that combines deep learning and spectro-temporal analysis to enhance HR estimation and HR variability (HRV) assessment from PPG signals. A long short-term memory (LSTM) network is employed to model temporal patterns in preprocessed signals, followed by spectral analysis to extract HR-relevant features. The method is evaluated on a custom dataset collected from 15 subjects under six motion conditions, including walking, climbing stairs, and hand movements. Experimental results show that the proposed approach achieves a mean absolute error (MAE) of 0.93 beats per minute (bpm), outperforming existing state-of-the-art methods with improvements ranging from 7.92% to 66.06% in the MAE across all subjects. The method demonstrates consistently low absolute errors (AEs) in diverse motion scenarios, with a minimum AE of 0.15 bpm, indicating high precision in HR estimation. Additionally, the proposed method aligns closely with ground truth in all HRV metrics, with an IBI mean difference of 0.051 s, SDNN difference of 0.063 s, and RMSSD difference of 0.127 s. In the frequency domain, low-frequency (LF) and high-frequency (HF) power differ by 0.01 normalized units (n.u.) each, while the LF/HF ratio differs by 0.13. Nonlinear measures also show close alignment, with approximate entropy (ApEn) and detrended fluctuation analysis (DFA) differing by just 0.031 and 0.07, respectively. These findings highlight the method’s robustness in capturing both linear and nonlinear HRV characteristics and its effectiveness in improving the reliability of wearable PPG monitoring in real-world scenarios.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-11"},"PeriodicalIF":5.9,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145078619","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}

引用次数: 0

A Graph Neural Network With Dual-Stage Feature Aggregation for Industrial Soft Sensors 基于双阶段特征聚合的工业软传感器图神经网络

IF 5.9 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-09-04 DOI: 10.1109/TIM.2025.3606039

Jince Li;You Fan;Ziyan Wang;Yongjian Wang

{"title":"A Graph Neural Network With Dual-Stage Feature Aggregation for Industrial Soft Sensors","authors":"Jince Li;You Fan;Ziyan Wang;Yongjian Wang","doi":"10.1109/TIM.2025.3606039","DOIUrl":"https://doi.org/10.1109/TIM.2025.3606039","url":null,"abstract":"Soft sensing, as a key engineering methodology, leverages readily accessible information from auxiliary variables to estimate hard-to-measure targets. Deep learning frameworks have significantly advanced intelligent data-driven modeling in this field. However, most multivariate data reside in structured spaces, where the interactions among different variables are accompanied by scale disparities, posing significant challenges to conventional neural networks. In response, we propose a novel graph neural network (GNN) with dual-stage feature aggregation (DA-GNN) for soft sensor modeling. Initially, multivariate time spans associated with graph nodes are chronologically segmented to build small-scale node subareas, which serve as the basic units for graph state updates. Subsequently, in the first stage, an attention mechanism is adopted to select subregion states of adjacent nodes guided by their importance scores. In the second stage, a gated recurrent module is embedded in the graph architecture to aggregate temporal features of the subregions based on the evolution orders of the industrial process. As a result, this dual-stage mechanism reconciles the scale differences while capturing local dependencies within the structured multivariate space, leading to enhanced performance. The proposed framework is applied to soft sensing of chemical oxygen demand (COD) in a real-world wastewater treatment process. Its effectiveness is validated through comparative studies with some classical and advanced algorithms.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-10"},"PeriodicalIF":5.9,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145027903","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}

引用次数: 0

MASSLOC: A Massive Sound Source Localization System Based on Direction-of-Arrival Estimation and Complementary Zadoff–Chu Sequences 基于到达方向估计和互补Zadoff-Chu序列的海量声源定位系统

IF 5.9 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-09-04 DOI: 10.1109/TIM.2025.3606066

Georg K. J. Fischer;Thomas Schaechtle;Moritz Schabinger;Alexander Richter;Ivo Häring;Fabian Höflinger;Stefan J. Rupitsch

{"title":"MASSLOC: A Massive Sound Source Localization System Based on Direction-of-Arrival Estimation and Complementary Zadoff–Chu Sequences","authors":"Georg K. J. Fischer;Thomas Schaechtle;Moritz Schabinger;Alexander Richter;Ivo Häring;Fabian Höflinger;Stefan J. Rupitsch","doi":"10.1109/TIM.2025.3606066","DOIUrl":"https://doi.org/10.1109/TIM.2025.3606066","url":null,"abstract":"Acoustic indoor localization offers the potential for highly accurate position estimation while generally exhibiting low hardware requirements compared to radio frequency (RF)-based solutions. Furthermore, angular-based localization significantly reduces installation effort by minimizing the number of required fixed anchor nodes. In this article, we propose the so-called MASSLOC system, which leverages sparse 2-D array geometries to localize and identify a large number of concurrently active sources. Additionally, the use of complementary Zadoff–Chu sequences is introduced to enable efficient, beamforming-based source identification. These sequences provide a tradeoff between favorable correlation properties and accurate, unsynchronized direction-of-arrival (DoA) estimation by exhibiting a spectrally balanced waveform. The system is evaluated in both a controlled anechoic chamber and a highly reverberant lobby environment with a reverberation time of 1.6 s. In a laboratory setting, successful DoA estimation and identification of up to 14 simultaneously emitting sources are demonstrated. Adopting a Perspective-n-Point (PnP) calibration approach, the system achieves a median 3-D localization error of 55.7 mm and a median angular error of 0.84° with dynamic source movement of up to 1.9 ms<sup>−1</sup> in the challenging reverberant environment. The multisource capability is also demonstrated and evaluated in that environment with a total of three tags. These results indicate the scalability and robustness of the MASSLOC system, even under challenging acoustic conditions.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-13"},"PeriodicalIF":5.9,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11151304","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145061827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Regular RGB-Video-Based Eye Movement Assessment for Parkinson’s Disease 基于rgb视频的帕金森病常规眼动评估

IF 5.9 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-09-04 DOI: 10.1109/TIM.2025.3606068

Jiaxun Gao;Luke Bidulka;Martin J. McKeown;Z. Jane Wang

{"title":"Regular RGB-Video-Based Eye Movement Assessment for Parkinson’s Disease","authors":"Jiaxun Gao;Luke Bidulka;Martin J. McKeown;Z. Jane Wang","doi":"10.1109/TIM.2025.3606068","DOIUrl":"https://doi.org/10.1109/TIM.2025.3606068","url":null,"abstract":"Eye-tracking, as an accessible, noninvasive technology, offers valuable insights into the human motor and cognitive functions, and it is an essential tool in studying neurodegenerative diseases such as Parkinson’s disease (PD). While current eye movement assessment for PD diagnosis mainly relies on high-end, specialized eye-tracker equipment, this work demonstrates that advanced deep learning (DL) methods using RGB-video from regular cameras (with 60 f/s sampling rate, <inline-formula> <tex-math>$1920times 1080$ </tex-math></inline-formula> image resolution) can provide promising performance on PD eye movement assessment. Our contributions are twofold: first, we show the potential and feasibility of using readily accessible, regular RGB camera data for PD eye movement assessment, making it more attractive for wide applicability in practice. Second, we propose a novel PD classification model by exploring temporal eye movement patterns from regular RGB-video data, and it can achieve performance comparable to or even better than current standard methods reliant on commercial, specialized eye-tracking equipment. The results highlight the promise of regular RGB-video-based PD assessment and the potential for more accessible diagnostic tools in PD studies.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-12"},"PeriodicalIF":5.9,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036835","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}

引用次数: 0

Hybrid Deep Learning and Fractional Brownian Motion Approach for Probabilistic RUL Prediction in Industrial Equipment 混合深度学习和分数布朗运动方法用于工业设备的概率RUL预测

IF 5.9 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-09-04 DOI: 10.1109/TIM.2025.3606063

Jialong He;Jichao Guo;Liming Zhou;Yan Liu

{"title":"Hybrid Deep Learning and Fractional Brownian Motion Approach for Probabilistic RUL Prediction in Industrial Equipment","authors":"Jialong He;Jichao Guo;Liming Zhou;Yan Liu","doi":"10.1109/TIM.2025.3606063","DOIUrl":"https://doi.org/10.1109/TIM.2025.3606063","url":null,"abstract":"Accurate prediction of the remaining useful life (RUL) constitutes the core task of predictive maintenance, and the selection of an appropriate degradation model is pivotal to enhancing the accuracy of RUL prediction. However, deep learning models cannot characterize RUL uncertainty, and traditional stochastic process degradation models are challenging in depicting the long-range dependence (LRD) of the degradation process, adversely impacting the accuracy and credibility of RUL prediction. To address these challenges, this article unveils a synergistic temporal convolutional network Kolmogorov–Arnold network fractional Brownian motion (TCN-KAN-FBM) prediction framework using TCNs and KAN for robust device RUL prognostication. The TCN-KAN module is designed to realize RUL prediction. The TCN-KAN module captures temporal features of degraded data and adaptively learns degradation knowledge for point estimation prediction. Complementing this, the FBM module, then, masterfully constructs the probability distribution of the prediction results based on its LRD and self-similarity, thus realizing RUL prediction’s uncertainty quantification (UQ). The effectiveness of the proposed method is confirmed by practical examples of rolling bearings and two sets of servo tool holder power head systems under different operating conditions.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-13"},"PeriodicalIF":5.9,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036896","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}

引用次数: 0

Differential Efficiency Testing Method for Power Drive Systems With Measurement Uncertainty Analysis 带测量不确定度分析的动力传动系统差动效率试验方法

IF 5.9 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-09-04 DOI: 10.1109/TIM.2025.3606023

João Paulo Zomer Machado;Rodolfo César Costa Flesch

{"title":"Differential Efficiency Testing Method for Power Drive Systems With Measurement Uncertainty Analysis","authors":"João Paulo Zomer Machado;Rodolfo César Costa Flesch","doi":"10.1109/TIM.2025.3606023","DOIUrl":"https://doi.org/10.1109/TIM.2025.3606023","url":null,"abstract":"Absolute efficiency measurements in power drive systems (PDSs) require low-uncertainty instrumentation, making them costly and impractical for properly characterizing small incremental performance gains. Differential measurements offer a practical alternative by focusing on variations between operating points and attenuating systematic uncertainties. This article proposes a structured method for differential efficiency measurement in PDSs and derives equations for the quantification of the measurement uncertainty in the proposed method. The results are presented in a general way, so best practices are highlighted to improve the applicability of differential measurements in different PDS configurations, motor types, and industrial applications. The proposed method is evaluated in an experimental case study considering several switching frequencies in a frequency converter, and the results show that the method enables the reliable characterization of efficiency variations of the order of 0.05%, even when using instrumentation with insufficient accuracy for absolute efficiency assessments.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-9"},"PeriodicalIF":5.9,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11151634","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145073298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0