Engineering Applications of Artificial Intelligence最新文献

{"title":"Complete coverage planning using Deep Reinforcement Learning for polyiamonds-based reconfigurable robot","authors":"Anh Vu Le ,&nbsp;Dinh Tung Vo ,&nbsp;Nguyen Tien Dat ,&nbsp;Minh Bui Vu ,&nbsp;Mohan Rajesh Elara","doi":"10.1016/j.engappai.2024.109424","DOIUrl":"10.1016/j.engappai.2024.109424","url":null,"abstract":"<div><div>Achieving complete coverage in complex areas is a critical objective for tilling tasks such as cleaning, painting, maintenance, and inspection. However, existing robots in the market, with their fixed morphologies, face limitations when it comes to accessing confined spaces. Reconfigurable tiling robots provide a feasible solution to this challenge. By shapeshifting among the available morphologies to adapt to the different conditions of complex environments, these robots can enhance the efficiency of complete coverage. However, the ability to change shape is constrained by energy usage considerations. Hence, it is important to have an optimal strategy to generate a trajectory that covers confined areas with minimal reconfiguration actions while taking into account the finite set of possible shapes. This paper proposes a complete coverage planning (CCP) framework for a reconfigurable tiling robot called hTetrakis, which consists of three polyiamonds blocks. The CCP framework leverages Deep Reinforcement Learning (DRL) to derive an optimal action policy within a polyiamonds shape-based workspace. By maximizing cumulative rewards to optimize the overall kinetic energy-based costweight, the proposed DRL model plans the hTetrakis shapes and its trajectories simultaneously. To this end, the DRL model utilizes Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) network and adopts the Actor–Critic deep reinforcement learning agent with Experience Replay (ACER) approach for off-policy decision-making. By producing trajectories with reduced costs and time, the proposed CCP framework surpasses conventional heuristic optimization methods like Particle Swarm Optimization (PSO), Differential Evolution (DE), Genetic Algorithm (GA) and Ant Colony Optimization (ACO) rely on tiling strategies.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142535246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Resilient dynamic microgrid formation by deep reinforcement learning integrating physics-informed neural networks","authors":"Mingze Xu ,&nbsp;Shunbo Lei ,&nbsp;Chong Wang ,&nbsp;Liang Liang ,&nbsp;Junhua Zhao ,&nbsp;Chaoyi Peng","doi":"10.1016/j.engappai.2024.109470","DOIUrl":"10.1016/j.engappai.2024.109470","url":null,"abstract":"<div><div>Dynamic microgrid formation can enhance topological flexibility within the distribution system, particularly during extreme events, thereby facilitating a more efficient restoration process. However, existing research has overlooked the impact of cold load pickup on system restoration efforts. A sudden load spike can lead to the overloading of generators and transformers, which can result in the failure of the system restoration process. This study leverages the topological flexibility through dynamic microgrid formation of the system to mitigate the impact of cold load pickup, thereby enhancing the efficiency of sequential load restoration. To alleviate the computational complexity arising from intricate operational constraints and the uncertainties inherent in cold load pickup conditions, this paper proposes a novel model-free framework. Unlike existing deep reinforcement learning models, we incorporate physical constraint information into the model by means of physics-informed neural networks, where the solution of an optimization problem is regarded as knowledge, enabling the agent to learn operational constraints more efficiently and stably. The proposed approach is compatible with and can be integrated into any deep reinforcement learning algorithm that utilizes the advantage actor–critic framework with neural networks. This research employs the deep deterministic policy gradient algorithm as a representative example for investigation. The effectiveness and generalization performance of the proposed method are validated on a modified IEEE 123-node test feeder, while its scalability is assessed using the IEEE 8500-node test feeder system.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reconstruction error based implicit regularization method and its engineering application to lung cancer diagnosis","authors":"Qinghe Zheng ,&nbsp;Xinyu Tian ,&nbsp;Mingqiang Yang ,&nbsp;Shuang Han ,&nbsp;Abdussalam Elhanashi ,&nbsp;Sergio Saponara ,&nbsp;Kidiyo Kpalma","doi":"10.1016/j.engappai.2024.109439","DOIUrl":"10.1016/j.engappai.2024.109439","url":null,"abstract":"<div><div>The automatic diagnosis of lung cancer via artificial intelligence faces two hotspot issues: (1) insufficient data and (2) excessive redundant information, which make it difficult for convolutional neural networks (CNNs) to learn discriminative information of lung cancer. In this paper, we present the reconstruction error based implicit regularization method (REbIRM) that regularizes CNNs at the loss layer. During each training iteration, the reconstruction errors introduced by the two-stage discriminative auto-encoder are used to sharpen the generalization ability of deep CNNs by improving the decision boundary. In the application process, the trained deep CNN is used for completing computed tomography (CT) diagnostics. The main clinical benefit of our approach is that it is domain independent, requiring no specialized knowledge, and can therefore be applied to different types of datasets. To the best of our knowledge, this is the first attempt to implicitly regularize CNNs based on the reconstruction errors. Finally, experimental results on three CT image classification datasets show that REbIRM can achieve impressive results and that, in conjunction with Dropout, it obtains the state-of-the-art performance. REbIRM is also robust to the selection of hyper-parameters and only has the sublinear influence on the convergence of deep CNNs. Besides, empirical and theoretical evidence are provided to indicate that REbIRM prefers to converges in a constrained parameter space with flatter minima, which explains why it can generalize to new data. Finally, the nature of REbIRM is further explored through visualization techniques to analyze how it works in training deep CNNs.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic model-based intelligent fault diagnosis method for fault detection in a rod fastening rotor","authors":"Wuhui Xu ,&nbsp;Hui Wang ,&nbsp;Jiabin Jin ,&nbsp;Ronggang Yang ,&nbsp;Jiawei Xiang","doi":"10.1016/j.engappai.2024.109499","DOIUrl":"10.1016/j.engappai.2024.109499","url":null,"abstract":"<div><div>A complete fault sample database is of great significance for the intelligent fault diagnosis method of rod fastening rotor. However, the lack of fault samples makes the fault diagnosis results unbelievable. To solve this issue, the dynamic model-based intelligent fault diagnosis method is established for a rod fastening rotor, and the fault sample database is enriched by numerical simulations. First, the lumped parameter model of the rod fastening rotor system is constructed and further updated using Euclidean Distance between measurement and numerical simulation of the intact system. Second, mathematical models of various fault types are incorporate into the updated model to obtain numerical simulation fault samples. Thirdly, the utilization of numerical simulation fault samples is severed as training data to the artificial intelligence (AI) models and the unknown measurement test samples will be finally classified. In this paper, Support Vector Machine, Random Forest, Bayesian Network and Decision Tree are selected as the typical AI models. Subsequently, the feasibility of classification is validated by the test bench of the rod fastening rotor system, and the problem of insufficient fault samples can be solved.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Two-stage encoder multi-decoder network with global–local up-sampling for defect segmentation of strip steel surface defects","authors":"Mingxian Xu ,&nbsp;Jingliang Wei ,&nbsp;Xinglong Feng","doi":"10.1016/j.engappai.2024.109469","DOIUrl":"10.1016/j.engappai.2024.109469","url":null,"abstract":"<div><div>Precisely segmenting surface defects in steel strips is essential for enhancing product quality. Despite the potential improvements in defect segmentation accuracy and robustness offered by deep learning methods, including autoencoder, the following challenges still persist. Firstly, in industrial environments characterized by low-contrast defects against the background and high noise-to-signal ratios, current defect detection methods still face challenges in accurately segmenting defects. Secondly, in industrial production, defects often follow a long-tail distribution, current defect detection methods exhibit lower accuracy in identifying defects in the tail-end categories. To tackle these challenges, a novel two-stage encoder multi-decoder network was introduced, comprising an initial defect detection stage and a category-specific refined stage. In the initial defect detection stage, the network’s decoder employs global–local up-sampling modules to utilize deconvolution of multiple receptive fields for up-sampling feature maps. Subsequently, in the category-specific refined stage, the network initially separates the defect feature maps by employing a category separation module. It integrates prior information through a defect refinement module and a fusion module, fusing prior decoder features with corresponding one. Simulation experiments were conducted using the real-world strip steel defect dataset, and validation experiments were performed on the industrial imbalanced dataset collected from an actual project. The experimental results demonstrate the proposed method reliability in industrial production, with the segmentation mean intersection over union achieving 87.35% and 84.98% on these respective datasets.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence-powered precision: Unveiling the landscape of liver disease diagnosis—A comprehensive review","authors":"Sireesha Vadlamudi ,&nbsp;Vimal Kumar ,&nbsp;Debjani Ghosh ,&nbsp;Ajith Abraham","doi":"10.1016/j.engappai.2024.109452","DOIUrl":"10.1016/j.engappai.2024.109452","url":null,"abstract":"<div><div>The global significance of diagnosing liver diseases is heightened, particularly in under-resourced regions with limited healthcare facilities. Traditional diagnostic methods, characterized by time and labor-intensive processes, have led to a growing demand for telemedicine-based solutions. The incorporation of Artificial Intelligence is deemed essential to enhance the efficiency and accuracy of diagnostic models. This review explores the seamless integration of diverse data modalities, including clinical records, demographics, laboratory values, biopsy specimens, and imaging data, emphasizing the importance of combining both types for comprehensive liver disease diagnosis. The study rigorously examines various approaches, focusing on pre-processing and feature engineering in non-image data diagnostic model development. Additionally, it analyzes studies employing Convolutional Neural Networks for cutting-edge solutions in image data interpretation. The paper provides insights into existing liver disease datasets, encompassing both image and non-image data, offering a comprehensive understanding of crucial research data sources. Emphasis is placed on performance evaluation metrics and their correlation in assessing diagnostic model efficiency. The review also explores open-source software tools dedicated to computer-aided liver analysis, enhancing exploration in liver disease diagnostics. Serving as a concise handbook, it caters to novice and experienced researchers alike, offering essential insights, summarizing the latest research, and providing a glimpse into emerging trends, challenges, and future trajectories in liver disease diagnosis.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-source information fused loose particle localization and material identification method for sealed electronic equipment","authors":"Zhigang Sun ,&nbsp;Qi Liang ,&nbsp;Guofu Zhai ,&nbsp;Guotao Wang ,&nbsp;Min Zhang ,&nbsp;Jingting Sun","doi":"10.1016/j.engappai.2024.109529","DOIUrl":"10.1016/j.engappai.2024.109529","url":null,"abstract":"<div><div>Sealed electronic equipment are1 an important component of aerospace defense systems, and loose particles pose a significant threat to their reliable operation. Loose particle detection is crucial. For sealed electronic equipment with large scale and complex structure, loose particle detection should not only include the judgment of existence, but also obtain location and material information to facilitate the cleaning and control work. In this paper, the authors proposed a multi-source information fused loose particle localization and material identification method. Firstly, the equipment model was designed, the loose particle samples were made, and loose particle signals were collected. Secondly, the two-stage adaptive energy threshold pulse extraction algorithm was newly proposed to extract effective pulses, and the threshold-judgement-search pulse matching algorithm was improved to match the effective pulse groups. Next, spectrograms were transformed from effective pulses to create the localization and material image set. The time-domain, frequency-domain and gray-level co-occurrence matrix features were used to construct the joint feature library. Then, the channel-weighting feature selection method was used to create the localization and material data set. Finally, PReLU-VGG19-Plus was trained on the localization and material image set to obtain the optimal localization and material neural network, while parameter-optimized XGBoost was trained on the localization and material data set to obtain the optimal localization and material classifier. On this basis, combined with the triple majority voting process, the combined localization and material framework were constructed. Extensive test results show that, the location-identification achieved by combined localization framework and the material-identification accuracy achieved by combined material framework are all 100%. The feasibility, stability, and superiority of the method proposed in this paper have been fully verified. It is an important supplement to the existing loose particle detection research, providing important reference for signal detection and classification research in similar fields, and effectively improving the reliability of sealed electronic equipment.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unsupervised domain adaptation for drive-by condition monitoring of multiple railway tracks","authors":"Ramin Ghiasi ,&nbsp;Nicolas Lestoille ,&nbsp;Cassandre Diaine ,&nbsp;Abdollah Malekjafarian","doi":"10.1016/j.engappai.2024.109516","DOIUrl":"10.1016/j.engappai.2024.109516","url":null,"abstract":"<div><div>Monitoring railway tracks through drive-by vibration data collected by in-service trains offers a cost-effective and adaptable solution for inspecting multiple railway lines. However, numerous existing drive-by monitoring methods rely on supervised learning models, necessitating extensive labelled data for each line. In this paper, a novel framework is proposed based on Unsupervised Domain Adaptation (UDA) concept which facilitates the transfer of a geometric defects diagnosis model learned from one line to a new line without the need for any labelled data from the new line. The proposed framework learns the dynamic-based features that are sensitive to damage and also invariant to different railway tracks. It comprises three components: data pre-processing, UDA implementation, and damage diagnosis. The framework uses the data from the source domain, including corresponding labels, as well as the unlabelled data from the target domain as input. The outputs of the framework consist of the predicted labels for the target domain. The performance of the proposed framework is evaluated using a comprehensive dataset of field measurements of a high-speed train passing 4 different lines within the French high-speed rail network. The proposed UDA framework is implemented using four common UDA algorithms including Information-Theoretical Learning (ITL), Geodesic Flow Kernel (GFK), Transfer Component Analysis (TCA), and Subspace Alignment (SA). The results show that the proposed framework has a 14% increase in the anomaly detection accuracy compared to traditional unsupervised learning methods in which UDA is not used. Furthermore, this study investigates the impact of incorporating a percentage of target data labels during training (semi-supervised domain adaptation), along with various sensor layouts and different tuning parameters, on the accuracy of the proposed approach. The results show that the proposed framework can significantly facilitate the monitoring of railway track conditions using the data collected by in-service trains which could be great interest of railway owners.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BABE: Backdoor attack with bokeh effects via latent separation suppression","authors":"Junjian Li ,&nbsp;Honglong Chen ,&nbsp;Yudong Gao ,&nbsp;Shaozhong Guo ,&nbsp;Kai Lin ,&nbsp;Yuping Liu ,&nbsp;Peng Sun","doi":"10.1016/j.engappai.2024.109462","DOIUrl":"10.1016/j.engappai.2024.109462","url":null,"abstract":"<div><div>The escalating menace of backdoor attacks constitutes a formidable obstacle to the ongoing advancement of deep neural networks (DNNs), particularly in the security-sensitive applications such as face recognition and self-driving. Backdoored models render deliberately incorrect predictions on the inputs with the crafted triggers while behaving normally with the benign ones. Despite demonstrating the varying degrees of threat, existing backdoor attack strategies often prioritize stealthiness and defense evasions but neglect the practical feasibility in the real-world deployment scenarios. In this paper, we develop a backdoor attack leveraging bokeh effects (<span><math><mrow><mi>B</mi><mi>A</mi><mi>B</mi><mi>E</mi></mrow></math></span>), which introduces the bokeh effects as the trigger. Once the backdoored model is deployed in the vision application, the model’s malicious behaviors can be activated only by utilizing the captured bokeh images without any other modifications. Specially, we employ the saliency and depth estimation maps to derive the bokeh images, thereby serving as the poisoned samples. Furthermore, to avoid the latent separation of the generated poisoned images, we propose distinct attack strategies on the basis of the adversary’s prior abilities. For the adversary only with the data manipulation, we retain the original semantic labels for a subset of poisoned data during the training process. For the adversary with the manipulation of both the data and models, we construct a reference model trained on the clean samples to impose constraints on the latent representations of the poisoned images. Extensive experiments demonstrate the attack effects of the proposed <span><math><mrow><mi>B</mi><mi>A</mi><mi>B</mi><mi>E</mi></mrow></math></span>, even on the bokeh photos captured from Digital Still Cameras (DSC) and smartphones.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142535033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spiking neural networks for autonomous driving: A review","authors":"Fernando S. Martínez ,&nbsp;Jordi Casas-Roma ,&nbsp;Laia Subirats ,&nbsp;Raúl Parada","doi":"10.1016/j.engappai.2024.109415","DOIUrl":"10.1016/j.engappai.2024.109415","url":null,"abstract":"<div><div>The rapid progress of autonomous driving (AD) has triggered a surge in demand for safer and more efficient autonomous vehicles, owing to the intricacy of modern urban environments. Traditional approaches to autonomous driving have heavily relied on conventional machine learning methodologies, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), for tasks such as perception, decision-making, and control. Presently, major companies such as Tesla, Waymo, Uber, and Volkswagen Group (VW) leverage neural networks for advanced perception and autonomous decision-making. However, concerns have been raised about the escalating computational requirements of training these neural models, primarily in terms of energy consumption and environmental impact. In the situation of optimisation and sustainability, Spiking Neural Networks (SNNs), inspired by the temporal processing of the human brain, have come forth as a third-generation of neural networks, famed for their energy efficiency, potential for handling real-time driving scenarios and processing temporal information efficiently. However, SNNs have not yet achieved the performance levels of their predecessors in critical AD tasks, partly due to the intricate dynamics of neurons, their non-differentiable spike operations, and the lack of specialised benchmark workloads and datasets, among others. This paper examines the principles, models, learning rules, and recent advancements of SNNs in the AD domain. Neuromorphic hardware, hand in hand with SNNs, shows potential but has challenges in accessibility, cost, integration, and scalability. This examination aims to bridge gaps by providing a comprehensive understanding of SNNs in the AD field. It emphasises the role of SNNs in shaping the future of AD while considering optimisation and sustainability.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142535248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}