IEEE Transactions on Emerging Topics in Computational Intelligence最新文献_第3页

MA-MFCNet: Mixed Attention-Based Multi-Scale Feature Calibration Network for Image Dehazing MA-MFCNet：基于混合注意力的多尺度图像去重特征校准网络

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-04-11 DOI: 10.1109/TETCI.2024.3382233

Luqiao Li;Zhihua Chen;Lei Dai;Ran Li;Bin Sheng

{"title":"MA-MFCNet: Mixed Attention-Based Multi-Scale Feature Calibration Network for Image Dehazing","authors":"Luqiao Li;Zhihua Chen;Lei Dai;Ran Li;Bin Sheng","doi":"10.1109/TETCI.2024.3382233","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3382233","url":null,"abstract":"High-quality clear images are the basis for advanced vision tasks such as target detection and semantic segmentation. This paper proposes an image dehazing algorithm named mixed attention-based multi-scale feature calibration network, aiming at solving the problem of uneven haze distribution in low-quality fuzzy images acquired in foggy environments, which is difficult to remove effectively. Our algorithm adopts a U-shaped structure to extract multi-scale features and deep semantic information. In the encoding module, a mixed attention module is designed to assign different weights to each position in the feature map, focusing on the important information and regions where haze is difficult to be removed in the image. In the decoding module, a self-calibration recovery module is designed to fully integrate different levels of features, calibrate feature information, and restore spatial texture details. Finally, the multi-scale feature information is aggregated by the reconstruction module and accurately mapped into the solution space to obtain a clear image after haze removal. Extensive experiments show that our algorithm outperforms state-of-the-art image dehazing algorithms in various synthetic datasets and real hazy scenes in terms of qualitative and quantitative comparisons, and can effectively remove haze in different scenes and recover images with high quality.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 5","pages":"3408-3421"},"PeriodicalIF":5.3,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predicting Citywide Crowd Flows in Critical Areas Based on Dynamic Spatio-Temporal Network 基于动态时空网络预测重要区域的全市人流量

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-04-10 DOI: 10.1109/TETCI.2024.3372420

Heli Sun;Ruirui Xue;Tingting Hu;Tengfei Pan;Liang He;Yuan Rao;Zhi Wang;Yingxue Wang;Yuan Chen;Hui He

{"title":"Predicting Citywide Crowd Flows in Critical Areas Based on Dynamic Spatio-Temporal Network","authors":"Heli Sun;Ruirui Xue;Tingting Hu;Tengfei Pan;Liang He;Yuan Rao;Zhi Wang;Yingxue Wang;Yuan Chen;Hui He","doi":"10.1109/TETCI.2024.3372420","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3372420","url":null,"abstract":"Citywide crowd flow prediction is an important problem for traffic control, risk assessment, and public safety, especially in critical areas. However, the large scale of the city and the interactions between multiple regions make this problem more challenging. Furthermore, it is impacted by temporal closeness, period, and trend features. Besides, geographic information and meta-features, such as periods of a day and days of a week also affect spatio-temporal correlation. Simultaneously, the influence between different regions will change over time, which is called dynamic correlation. We concentrate on how to concurrently model the important features and dynamic spatial correlation to increase prediction accuracy and simplify the problem. To forecast the crowd flow in critical areas, we propose a two-step framework. First, the grid density peak clustering algorithm is used to set the temporal attenuation factor, which selects the critical areas. Then, the effects of geographic information on spatio-temporal correlation are modeled by graph embedding and the effects of different temporal features are represented by graph convolutional neural networks. In addition, we use the multi-attention mechanism to capture the dynamic spatio-temporal correlation. On two real datasets, experimental results show that our model can balance time complexity and prediction accuracy well. It is 20% better in accuracy than other baselines, and the prediction speed is better than most models.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 5","pages":"3703-3715"},"PeriodicalIF":5.3,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142377137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Machine Unlearning: Solutions and Challenges 机器学习：解决方案与挑战

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-04-04 DOI: 10.1109/TETCI.2024.3379240

Jie Xu;Zihan Wu;Cong Wang;Xiaohua Jia

引用次数: 0

Unifying Global-Local Representations in Salient Object Detection With Transformers 利用变换器统一突出物体检测中的全局-局部表征

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-04-02 DOI: 10.1109/TETCI.2024.3380442

Sucheng Ren;Nanxuan Zhao;Qiang Wen;Guoqiang Han;Shengfeng He

{"title":"Unifying Global-Local Representations in Salient Object Detection With Transformers","authors":"Sucheng Ren;Nanxuan Zhao;Qiang Wen;Guoqiang Han;Shengfeng He","doi":"10.1109/TETCI.2024.3380442","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3380442","url":null,"abstract":"The fully convolutional network (FCN) has dominated salient object detection for a long period. However, the locality of CNN requires the model deep enough to have a global receptive field and such a deep model always leads to the loss of local details. In this paper, we introduce a new attention-based encoder, vision transformer, into salient object detection to ensure the globalization of the representations from shallow to deep layers. With the global view in very shallow layers, the transformer encoder preserves more local representations to recover the spatial details in final saliency maps. Besides, as each layer can capture a global view of its previous layer, adjacent layers can implicitly maximize the representation differences and minimize the redundant features, making every output feature of transformer layers contribute uniquely to the final prediction. To decode features from the transformer, we propose a simple yet effective deeply-transformed decoder. The decoder densely decodes and upsamples the transformer features, generating the final saliency map with less noise injection. Experimental results demonstrate that our method significantly outperforms other FCN-based and transformer-based methods in five benchmarks by a large margin, with an average of 12.17% improvement in terms of Mean Absolute Error (MAE).","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 4","pages":"2870-2879"},"PeriodicalIF":5.3,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hierarchical Relational Inference for Few-Shot Learning in 3D Left Atrial Segmentation 三维左心房分段中的分层关系推理（Hierarchical Relational Inference for Few-Shot Learning in 3D Left Atrial Segmentation

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-04-02 DOI: 10.1109/TETCI.2024.3377267

Xuejiao Li;Jun Chen;Heye Zhang;Yongwon Cho;Sung Ho Hwang;Zhifan Gao;Guang Yang

{"title":"Hierarchical Relational Inference for Few-Shot Learning in 3D Left Atrial Segmentation","authors":"Xuejiao Li;Jun Chen;Heye Zhang;Yongwon Cho;Sung Ho Hwang;Zhifan Gao;Guang Yang","doi":"10.1109/TETCI.2024.3377267","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3377267","url":null,"abstract":"Three-dimensional left atrial (LA) segmentation from late gadolinium-enhanced cardiac magnetic resonance (LGE CMR) images is of great significance in the prevention and treatment of atrial fibrillation. Despite deep learning-based approaches have made significant progress in 3D LA segmentation, they usually require a large number of labeled images for training. Few-shot learning can quickly adapt to novel tasks with only a few data samples. However, the resolution discrepancy of LGE CMR images presents challenges for few-shot learning in 3D LA segmentation. To address this issue, we propose the Hierarchical Relational Inference Network (HRIN), which extracts the interactive features of support and query volumes through a bidirectional hierarchical relationship learning module. HRIN learns the commonality and discrepancy between support and query volumes by modeling the higher-order relations. Notably, we embed the bidirectional interaction information between support and query volumes into the prototypes to adaptively predict the query. Additionally, we leverage prior knowledge of foreground and background information in the support volume to model queries. We validated the performance of our method on a total of 369 scans from two centers. Our proposed HRIN achieves higher segmentation performance compared to other state-of-the-art segmentation methods. With only 5% data samples, the average Dice Similarity Coefficient of the two centers respectively reaches 0.8454 and 0.8110. Compared with other methods under the same conditions, the highest values only reach 0.7012 and 0.6898. Our approach improves the adaptability and generalization of few-shot segmentation from LGE CMR images, enabling precise evaluation of LA remodeling.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 5","pages":"3352-3367"},"PeriodicalIF":5.3,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advancements in Deep Learning for B-Mode Ultrasound Segmentation: A Comprehensive Review 深度学习在 B 型超声波分割中的应用进展：全面回顾

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-04-02 DOI: 10.1109/TETCI.2024.3377676

Mohammed Yusuf Ansari;Iffa Afsa Changaai Mangalote;Pramod Kumar Meher;Omar Aboumarzouk;Abdulla Al-Ansari;Osama Halabi;Sarada Prasad Dakua

{"title":"Advancements in Deep Learning for B-Mode Ultrasound Segmentation: A Comprehensive Review","authors":"Mohammed Yusuf Ansari;Iffa Afsa Changaai Mangalote;Pramod Kumar Meher;Omar Aboumarzouk;Abdulla Al-Ansari;Osama Halabi;Sarada Prasad Dakua","doi":"10.1109/TETCI.2024.3377676","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3377676","url":null,"abstract":"Ultrasound (US) is generally preferred because it is of low-cost, safe, and non-invasive. US image segmentation is crucial in image analysis. Recently, deep learning-based methods are increasingly being used to segment US images. This survey systematically summarizes and highlights crucial aspects of the deep learning techniques developed in the last five years for US segmentation of various body regions. We investigate and analyze the most popular loss functions and metrics for training and evaluating the neural network for US segmentation. Furthermore, we study the patterns in neural network architectures proposed for the segmentation of various regions of interest. We present neural network modules and priors that address the anatomical challenges associated with different body organs in US images. We have found that variants of U-Net that have dedicated modules to overcome the low-contrast and blurry nature of images are suitable for US image segmentation. Finally, we also discuss the advantages and challenges associated with deep learning methods in the context of US image segmentation.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 3","pages":"2126-2149"},"PeriodicalIF":5.3,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141095529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Impact of Mental Activities and Age on Brain Network: An Analysis From Complex Network Perspective 智力活动和年龄对大脑网络的影响：复杂网络视角下的分析

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-04-02 DOI: 10.1109/TETCI.2024.3374957

Cemre Candemir;Vahid Khalilpour Akram;Ali Saffet Gonul

{"title":"The Impact of Mental Activities and Age on Brain Network: An Analysis From Complex Network Perspective","authors":"Cemre Candemir;Vahid Khalilpour Akram;Ali Saffet Gonul","doi":"10.1109/TETCI.2024.3374957","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3374957","url":null,"abstract":"The functional connections in the human brain offer many opportunities to explore changing dynamic patterns of the brain under different circumstances. Different factors such as age, mental activity, and health status may affect functional connectivity, connected regions, and the robustness of connections in the brain. In this study, we evaluate the functional connectivity of the whole brain changing with age from a complex network perspective during different processes in healthy adults. We conducted a functional Magnetic Resonance Imaging (fMRI) study that includes both resting and cognitive states with elderly and young participants (n = 38). To analyze the functional connectivity structure in view of graph theory, we used the minimum dominating sets (MDS) and then minimum hitting sets (MHS) of the connectivity networks. Based on our analysis, age, and mental activity show a significant effect on the hitting sets and dominating sets of the brain regions. The results also indicate that the working mechanism of the brain changes from local to diffused under the circumstances of a particular computational load with age. In this manner, the proposed method can be used as a complementary method for clinical procedures to evaluate and measure the effect of aging on the human brain.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 4","pages":"2791-2803"},"PeriodicalIF":5.3,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Physics-Informed Graph Capsule Generative Autoencoder for Probabilistic AC Optimal Power Flow 面向概率交流优化功率流的物理信息图囊生成式自动编码器

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-04-02 DOI: 10.1109/TETCI.2024.3377671

Mohsen Saffari;Mahdi Khodayar;Mohammad E. Khodayar

{"title":"Physics-Informed Graph Capsule Generative Autoencoder for Probabilistic AC Optimal Power Flow","authors":"Mohsen Saffari;Mahdi Khodayar;Mohammad E. Khodayar","doi":"10.1109/TETCI.2024.3377671","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3377671","url":null,"abstract":"Due to the increasing demand for electricity and the inherent uncertainty in power generation, finding efficient solutions to the stochastic alternating current optimal power flow (AC-OPF) problem has become crucial. However, the nonlinear and non-convex nature of AC-OPF, coupled with the growing stochasticity resulting from the integration of renewable energy sources, presents significant challenges in achieving fast and reliable solutions. To address these challenges, this study proposes a novel graph-based generative methodology that effectively captures the uncertainties in power system measurements, enabling the learning of probability distribution functions for generation dispatch and voltage setpoints. Our approach involves modeling the power system as a weighted graph and utilizing a deep spectral graph convolution network to extract powerful spatial patterns from the input graph measurements. A unique variational approach is introduced to identify the most relevant latent features that accurately describe the setpoints of the AC-OPF problem. Additionally, a capsule network with a new greedy dynamic routing algorithm is proposed to precisely decode the latent features and estimate the probabilistic AC-OPF problem. Further, a set of carefully designed physics-informed loss functions is incorporated in the training procedure of the model to ensure adherence to the fundamental physics rules governing power systems. Notably, the proposed physics-informed loss functions not only enhance the accuracy of AC-OPF estimation by effectively regularizing the deep learning model but also significantly reduce the time complexity. Extensive experimental evaluations conducted on various benchmarks demonstrate our proposed model's superiority over both probabilistic and deterministic approaches in terms of relevant criteria.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 5","pages":"3382-3395"},"PeriodicalIF":5.3,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhanced Adjacency-Constrained Hierarchical Clustering Using Fine-Grained Pseudo Labels 使用细粒度伪标签的增强型邻接约束分层聚类法

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-04-02 DOI: 10.1109/TETCI.2024.3367811

Jie Yang;Chin-Teng Lin

{"title":"Enhanced Adjacency-Constrained Hierarchical Clustering Using Fine-Grained Pseudo Labels","authors":"Jie Yang;Chin-Teng Lin","doi":"10.1109/TETCI.2024.3367811","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3367811","url":null,"abstract":"Hierarchical clustering is able to provide partitions of different granularity levels. However, most existing hierarchical clustering techniques perform clustering in the original feature space of the data, which may suffer from overlap, sparseness, or other undesirable characteristics, resulting in noncompetitive performance. In the field of deep clustering, learning representations using pseudo labels has recently become a research hotspot. Yet most existing approaches employ coarse-grained pseudo labels, which may contain noise or incorrect labels. Hence, the learned feature space does not produce a competitive model. In this paper, we introduce the idea of fine-grained labels of supervised learning into unsupervised clustering, giving rise to the enhanced adjacency-constrained hierarchical clustering (ECHC) model. The full framework comprises four steps. One, adjacency-constrained hierarchical clustering (CHC) is used to produce relatively pure fine-grained pseudo labels. Two, those fine-grained pseudo labels are used to train a shallow multilayer perceptron to generate good representations. Three, the corresponding representation of each sample in the learned space is used to construct a similarity matrix. Four, CHC is used to generate the final partition based on the similarity matrix. The experimental results show that the proposed ECHC framework not only outperforms 14 shallow clustering methods on eight real-world datasets but also surpasses current state-of-the-art deep clustering models on six real-world datasets. In addition, on five real-world datasets, ECHC achieves comparable results to supervised algorithms.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 3","pages":"2481-2492"},"PeriodicalIF":5.3,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141096325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Transformer and Graph Convolution-Based Unsupervised Detection of Machine Anomalous Sound Under Domain Shifts 基于变换器和图卷积的域偏移下机器异常声音无监督检测

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-04-02 DOI: 10.1109/TETCI.2024.3377728

Jingke Yan;Yao Cheng;Qin Wang;Lei Liu;Weihua Zhang;Bo Jin

{"title":"Transformer and Graph Convolution-Based Unsupervised Detection of Machine Anomalous Sound Under Domain Shifts","authors":"Jingke Yan;Yao Cheng;Qin Wang;Lei Liu;Weihua Zhang;Bo Jin","doi":"10.1109/TETCI.2024.3377728","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3377728","url":null,"abstract":"Thanks to the development of deep learning, machine abnormal sound detection (MASD) based on unsupervised learning has exhibited excellent performance. However, in the task of unsupervised MASD, there are discrepancies between the acoustic characteristics of the test set and the training set under the physical parameter changes (domain shifts) of the same machine's operating conditions. Existing methods not only struggle to stably learn the sound signal features under various domain shifts but also inevitably increase computational overhead. To address these issues, we propose an unsupervised machine abnormal sound detection model based on Transformer and Dynamic Graph Convolution (Unsuper-TDGCN) in this paper. Firstly, we design a network that models time-frequency domain features to capture both global and local spatial and time-frequency interactions, thus improving the model's stability under domain shifts. Then, we introduce a Dynamic Graph Convolutional Network (DyGCN) to model the dependencies between features under domain shifts, enhancing the model's ability to perceive changes in domain features. Finally, a Domain Self-adaptive Network (DSN) is employed to compensate for the performance decline caused by domain shifts, thereby improving the model's adaptive ability for detecting anomalous sounds in MASD tasks under domain shifts. The effectiveness of our proposed model has been validated on multiple datasets.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 4","pages":"2827-2842"},"PeriodicalIF":5.3,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0