IEEE Transactions on Instrumentation and Measurement最新文献_第6页

Optimized Fuzzy Slope Entropy: A Complexity Measure for Nonlinear Time Series 优化的模糊斜率熵：非线性时间序列的复杂性度量

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2024-11-08 DOI: 10.1109/TIM.2024.3493878

Yuxing Li;Ge Tian;Yuan Cao;Yingmin Yi;Dingsong Zhou

{"title":"Optimized Fuzzy Slope Entropy: A Complexity Measure for Nonlinear Time Series","authors":"Yuxing Li;Ge Tian;Yuan Cao;Yingmin Yi;Dingsong Zhou","doi":"10.1109/TIM.2024.3493878","DOIUrl":"https://doi.org/10.1109/TIM.2024.3493878","url":null,"abstract":"Entropy has long been a subject that has attracted researchers from a diverse range of fields, including healthcare, finance, and fault detection. Slope entropy (SE) has recently been proposed as a new approach to address the shortcomings of permutation entropy (PE), which ignores magnitude information; however, SE is sensitive to parameters \u0000<inline-formula> <tex-math>$boldsymbol {gamma }$ </tex-math></inline-formula>\u0000 and \u0000<inline-formula> <tex-math>$boldsymbol {delta }$ </tex-math></inline-formula>\u0000, and some information may be lost when segmenting symbols. The \u0000<inline-formula> <tex-math>$boldsymbol {delta }$ </tex-math></inline-formula>\u0000, moreover, has only a limited gain on the time series classification performance of SE and increases the algorithm complexity. Considering the aforementioned limitations, this study introduces the concept of fuzzification to the SE and eliminates the \u0000<inline-formula> <tex-math>$boldsymbol {delta }$ </tex-math></inline-formula>\u0000 to simplify the parameters, resulting in the proposal of fuzzy SE (FuSE); furthermore, we incorporate the artificial rabbit optimization (ARO) algorithm to optimize the parameter \u0000<inline-formula> <tex-math>$boldsymbol {gamma }$ </tex-math></inline-formula>\u0000 to enhance the effectiveness of FuSE for time series classification and finally proposed an optimized FuSE (OFuSE). OFuSE can greatly reduce the information loss in the mapping process and adaptively search for the optimal parameter. The study evaluated FuSE and OFuSE on several synthetic datasets and concluded that FuSE is more sensitive to changes in signal amplitude and frequency while confirming the advantage of OFuSE in classification. The application of OFuSE on three different real datasets verifies that its classification performance and generalization ability are better than other entropy methods.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"73 ","pages":"1-14"},"PeriodicalIF":5.6,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636492","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 Multisensor Feature Fusion Attention Convolutional Neural Network for Complex Magnetic Leakage 针对复杂磁泄漏的多传感器特征融合注意卷积神经网络

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2024-11-08 DOI: 10.1109/TIM.2024.3493872

Xianming Lang;Ze Wang

{"title":"A Multisensor Feature Fusion Attention Convolutional Neural Network for Complex Magnetic Leakage","authors":"Xianming Lang;Ze Wang","doi":"10.1109/TIM.2024.3493872","DOIUrl":"https://doi.org/10.1109/TIM.2024.3493872","url":null,"abstract":"Among complex defects, small defects in oil and gas pipelines are easily submerged and difficult to detect. To improve the detection accuracy for small defects in complex magnetic flux leakage (MFL) signals, we propose a weak supervision method called multisensor feature fusion attention convolutional neural network (FACNN). First, an improved conditional adversarial generation network is presented, which introduces a supervised loss function at the analog signal level to reduce the number of parameter iterations for sample generation. Second, the feature extraction module uses the decoupled fully connected (DFC) attention mechanism and a convolutional neural network parallel structure to aggregate the features gathered at the center of the image and the features of the convolutional neural network, from which the small defect features can be fully extracted. Third, the feature fusion module uses the proposed loss function to guide the fusion of axial, radial, and circumferential signal feature maps, which enhances the effective propagation among small defect features. Finally, the experimental results show that the average detection accuracy of the proposed method for detecting small defects reaches 96.7%, which is 5.5% higher than the best detection accuracy of the existing methods.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"73 ","pages":"1-9"},"PeriodicalIF":5.6,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142691774","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

Robust Surface Area Measurement of Unorganized Point Clouds Based on Multiscale Supervoxel Segmentation 基于多尺度超像素分割的无组织点云鲁棒性表面积测量技术

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2024-11-08 DOI: 10.1109/TIM.2024.3485393

Pengju Tian;Xianghong Hua

{"title":"Robust Surface Area Measurement of Unorganized Point Clouds Based on Multiscale Supervoxel Segmentation","authors":"Pengju Tian;Xianghong Hua","doi":"10.1109/TIM.2024.3485393","DOIUrl":"https://doi.org/10.1109/TIM.2024.3485393","url":null,"abstract":"Most of the existing surface area measurement methods suffer from poor efficiency, low precision, and high computational cost, especially for inaccessible, large-scale, rough, and curved surfaces. In this article, we propose a method to directly measure the surface areas of unorganized point clouds applicable to various scenes. First, an adaptive supervoxel segmentation algorithm is adopted to divide the input point cloud into a collection of facets with multiple scales. For each facet, all points belonging to it are projected onto its corresponding accurately fit plane. Second, for each projected facet, rigid transform is performed so that its normal vector is parallel to the Z-axis. For each 2-D facet point cloud, the x-coordinates and-coordinates are utilized to abstract its boundary points. Third, the boundary points are sorted in clockwise order so that every two adjacent points and the center point determine a triangle. Next, an improved interpolation method is adopted to interpolate the sparse edge points. The surface area calculation results of different scales can be obtained by counting the sum of the triangular area inside each facet. Finally, the optimum value is determined from these results. The proposed method is tested on various types of point clouds acquired in different ways. Comprehensive experiments demonstrate that the proposed method is efficient and effective and is capable of obtaining good performances in both simple regular planes and complex surfaces. In particular, compared with traditional reconstruction-based methods, the proposed method significantly outperforms when dealing with large-scale and complex scenes.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"73 ","pages":"1-17"},"PeriodicalIF":5.6,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636360","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

Anti-Drift Gas Detection Algorithm Based on Neural Network 基于神经网络的防漂移气体检测算法

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2024-11-07 DOI: 10.1109/TIM.2024.3488159

Jiayi Guo;Xu Li;Xiulei Li;Zheng Liang;Juexian Cao;Xiaolin Wei

{"title":"Anti-Drift Gas Detection Algorithm Based on Neural Network","authors":"Jiayi Guo;Xu Li;Xiulei Li;Zheng Liang;Juexian Cao;Xiaolin Wei","doi":"10.1109/TIM.2024.3488159","DOIUrl":"https://doi.org/10.1109/TIM.2024.3488159","url":null,"abstract":"Recently, long-term gas detection has attracted much attention due to its being a key factor for electronic nose (E-Nose) applications. However, the sensor drift effect can significantly reduce the performance of the sensor. Therefore, in this work, we proposed a new drift compensation method by optimizing feature selection, model construction, and training methods to study drift-resistant gas detection based on convolutional neural network (CNN) methods. First, the attention mechanism is used to screen the specific features of the gas data and remove the low-weight features. Moreover, a multiscale feature extraction network is designed so that the features fused by the three-layer convolution are used as the final classification feature input to extract the depth features keeping the drift unchanged. Simultaneously, the segmented training method and the targeted cyclic training model are adopted to reduce the required experimental data. Importantly, based on the largest gas drift dataset currently, the proposed method maintains the average gas detection accuracy beyond 80% in three years, and the long-term stability of gas detection is effectively improved. Therefore, our findings provide an effective way to solve the sensor drift effect.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"73 ","pages":"1-8"},"PeriodicalIF":5.6,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636491","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

An Adaptive Defect-Aware Attention Network for Accurate PCB-Defect Detection 用于准确检测 PCB 缺陷的自适应缺陷感知注意力网络

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2024-11-07 DOI: 10.1109/TIM.2024.3488158

Xiang Liu

引用次数: 0

Equivalent Bandwidth Matrix of Relative Locations: Image Modeling Method for Defect Degree Identification of In-Vehicle Cable Termination 相对位置的等效带宽矩阵：车载电缆终端缺陷度识别的图像建模方法

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2024-11-05 DOI: 10.1109/TIM.2024.3481567

Kai Liu;Shibo Jiao;Guangbo Nie;Hui Ma;Bo Gao;Chuanming Sun;Dongli Xin;Tapan Kumar Saha;Guangning Wu

{"title":"Equivalent Bandwidth Matrix of Relative Locations: Image Modeling Method for Defect Degree Identification of In-Vehicle Cable Termination","authors":"Kai Liu;Shibo Jiao;Guangbo Nie;Hui Ma;Bo Gao;Chuanming Sun;Dongli Xin;Tapan Kumar Saha;Guangning Wu","doi":"10.1109/TIM.2024.3481567","DOIUrl":"https://doi.org/10.1109/TIM.2024.3481567","url":null,"abstract":"The detection of defect severity in cable terminations plays a critical role in ensuring the safe and stable operation of high-speed trains (HSTs). However, the partial discharge (PD) characteristics of the same type of defect can appear similar across different severities, posing challenges for accurate insulation defect degree identification. Consequently, this article proposes an image transformation method, named the equivalent bandwidth matrix of relative locations (EBMRLs), coupled with the self-guided transformer (SG-Former) algorithm, which is more effective for fine-grained image recognition, to accurately identify different degrees of defects with similar PD characteristics. In the proposed approach, the original PD signals are first converted into images using EBMRL. This transformation embeds the characteristic and bandwidth information from the original PD data into the images, thereby reducing the similarity of information between classes in the transformed images and enhancing their distinguishability. Subsequently, the local and global features of the transformed EBMRL images are extracted to train the SG-Former model. The model is finally utilized to identify the severity of defects in cable terminations. The results demonstrate that the method proposed in this article achieves better performance compared with some of the state-of-the-art methods.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"73 ","pages":"1-10"},"PeriodicalIF":5.6,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142598615","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

High-Speed Train Brake Pads Condition Monitoring Based on Trade-Off Contrastive Learning Network 基于权衡对比学习网络的高速列车制动片状态监测

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2024-11-05 DOI: 10.1109/TIM.2024.3485406

Min Zhang;Jiamin Li;Jiliang Mo;Mingxue Shen;Zaiyu Xiang;Zhongrong Zhou

{"title":"High-Speed Train Brake Pads Condition Monitoring Based on Trade-Off Contrastive Learning Network","authors":"Min Zhang;Jiamin Li;Jiliang Mo;Mingxue Shen;Zaiyu Xiang;Zhongrong Zhou","doi":"10.1109/TIM.2024.3485406","DOIUrl":"https://doi.org/10.1109/TIM.2024.3485406","url":null,"abstract":"The braking system of high-speed trains is directly related to the operation safety of the train. The brake pads, which play a crucial role, will inevitably undergo uneven wear in long-term use, posing safety hazards to train braking. As the trains are in normal operating condition for long periods, it is difficult to collect usable uneven wear data, and there is a situation of data imbalance. This article proposes a trade-off contrastive learning network (TCLN), utilizing the differences between data and balancing the weights of different classes, which can realize the condition monitoring under the data imbalance of brake pads. First, data augmentation is employed to provide sufficient and diverse data for contrastive learning, and nonlinear features are extracted by a quadratic convolutional neural network (QCNN). Then, the designed class-weighted method is utilized to improve the characterization ability of the minority class data and realize the equidistant representation of features for each class, which in turn achieves the purpose of paying equal attention to all classes. Finally, the effectiveness of the proposed method is verified using the dataset collected from the scaling experiments, and the results show that the proposed method has higher accuracy and efficiency compared to other methods, which can still accurately identify the brake pad condition when the data are highly imbalanced.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"73 ","pages":"1-10"},"PeriodicalIF":5.6,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636293","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

TransMRE: Multiple Observation Planes Representation Encoding With Fully Sparse Voxel Transformers for 3-D Object Detection TransMRE：利用完全稀疏体素变换器进行多观测平面表示编码，用于三维物体检测

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2024-11-04 DOI: 10.1109/TIM.2024.3480206

Ziming Zhu;Yu Zhu;Kezhi Zhang;Hangyu Li;Xiaofeng Ling

{"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}

引用次数: 0

Counterfactual Covariate Causal Discovery on Nonlinear Extremal Quantiles 非线性极值定量上的反事实共变因果发现

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2024-11-04 DOI: 10.1109/TIM.2024.3488141

Tangwen Yin;Hongtian Chen;Dan Huang;Hesheng Wang

{"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}

引用次数: 0

Precision Regulation in Multistage Aero-Engine Rotors With Curvic Couplings Using Line-Structured Light Array Scanning and Virtual Assembly 利用线型结构光阵扫描和虚拟装配实现带曲柄联轴器的多级航空发动机转子的精确调节

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2024-11-04 DOI: 10.1109/TIM.2024.3485462

Ze Chen;Yuan Zhang;Zifei Cao;Yongmeng Liu

{"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}

引用次数: 0