Expert Systems with Applications最新文献

{"title":"A frequency channel-attention based vision Transformer method for bearing fault identification across different working conditions","authors":"Ling Xiang,&nbsp;Hankun Bing,&nbsp;Xianze Li,&nbsp;Aijun Hu","doi":"10.1016/j.eswa.2024.125686","DOIUrl":"10.1016/j.eswa.2024.125686","url":null,"abstract":"<div><div>Fault identification of rolling bearings plays a crucial role in maintaining the efficient and stable operation of equipment. Although traditional fault identification methods have made certain progress, they still lack in model feature extraction capabilities and generalization ability. In this paper, a frequency channel-attention based vision Transformer method is proposed for rolling bearings intelligent fault identification. Using frequency domain channel-attention mechanism, the proposed method is able to preserve fundamental fault information and integrate the frequency characteristics of the vibration signals. The proposed method also leverages the inherent self-attention mechanism of vision Transformer to recognize long-range dependencies within the signal data. This integration of attention not only enhances the model’s sensitivity to signal frequency characteristics but also enables the visualization of the attention mechanism, thereby increasing the model’s interpretability. Additionally, a shift linear layer is proposed to reduce the model’s computational demands while maintaining its robust feature extraction capabilities. This proposed method directly uses the collected vibration raw signals to achieve precise fault identification of rolling bearings, and experimental validation on two datasets demonstrates the model’s diagnostic accuracy under across working conditions.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"262 ","pages":"Article 125686"},"PeriodicalIF":7.5,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"Informer-FDR: A short-term vehicle speed prediction model in car-following scenario based on traffic environment","authors":"Qifan Xue ,&nbsp;Jian Ma ,&nbsp;Xuan Zhao ,&nbsp;Rui Liu ,&nbsp;Hongji Li ,&nbsp;Xichan Zhu","doi":"10.1016/j.eswa.2024.125655","DOIUrl":"10.1016/j.eswa.2024.125655","url":null,"abstract":"<div><div>Drivers’ car-following behaviors on urban roads are influenced by various factors, including pedestrians, cyclists, adjacent vehicles, and roadside parking. However, few models consider these factors’ influence on drivers’ speed selections during car-following, limiting the human-like driving capability of advanced driver assistance systems (ADAS). This paper proposes a vehicle speed prediction model in car-following scenario that considers the influences of the traffic environment. The vehicle speed is predicted using Informer-FDR (Informer with fusion features, dilated causal convolution, and residual connection), which adopts an improved encoder-decoder structure based on the Informer model. Fusing features of traffic environment characteristics and vehicle dynamics parameters enables the dynamic interaction characteristics between drivers and the traffic environment and potential traffic conflicts to be effectively reflected, which enhances the model’s understanding of the complex driving environment. Moreover, the high computational complexity is reduced by using the ProbSparse self-attention mechanism, which will help to address the difficulty of applying Transformer class models to on-board platforms. Totally 3,980 car-following cases were extracted from naturalistic driving data (NDD), vehicle dynamics parameters and traffic environment characteristics in the car-following scenarios were obtained through target detection and ranging algorithm. The optimal feature set was mined using the combined feature selection method. The dilated causal convolution and average pooling layer are introduced to expand the receptive field of the model, enhance global feature extraction, and ensure the causality of temporal predictions. Furthermore, the residual connection was added to the encoder, realizing the direct deep transfer of cross-layer information. Verifications on the test set show that Informer-FDR has the lowest MAE (0.583), MSE (2.942), RMSE (1.715), and the highest speed prediction accuracy (97.76%), spacing gap accuracy (94.27%), acceleration accuracy (95.35%), which outperforms other baseline models in terms of prediction performance. The ablation study confirms the importance of the improved distilling layer module, residual connection module, and fusion features for predictive performance improvement. Additionally, the road-type experiment reveals performance differences of the model on different road types, emphasizing the importance of incorporating traffic environment on urban road.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"262 ","pages":"Article 125655"},"PeriodicalIF":7.5,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142593549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"A high-effective swarm intelligence-based multi-robot cooperation method for target searching in unknown hazardous environments","authors":"Xiankun Lin,&nbsp;Feng Gao,&nbsp;Wenhui Bian","doi":"10.1016/j.eswa.2024.125609","DOIUrl":"10.1016/j.eswa.2024.125609","url":null,"abstract":"<div><div>To solve target searching problems for multi-robot cooperation with inaccurate target distance perception in unknown hazardous environments, a hybrid adaptive robotic particle swarm optimizer (RPSO) and grey wolf optimizer (GWO) based algorithm with continuous virtual target guidance is proposed for high effective path planning in the searching. In the initial searching stages, both the wolf behavior-generated position and the <em>gbest</em> position and the <em>pbesti</em> positions from RPSO are employed to guide the motions of robots. With the information provided by these initial robot movement paths, a geometric model is established to generate potential targets. The K-means cluster algorithm is introduced to estimate a virtual target position online from potential targets, with new robot-presenting route information to update the history path information. Then the virtual position is employed as one of the direction components to help the robots approach the actual target. In addition, to avoid mobile robots falling into local convergence, a heuristic moving direction determination scheme is utilized to make robots circumvent obstacles in swarm motions, as well as a mutual repulsion algorithm to keep them in a scattering state. Simulation experiments on different types of unknown environments with varied robot numbers and adaptability testing for a dynamic target are carried out to verify the feasibility of the proposed target searching method with comparisons to the other three famous target searching algorithms. It is verified from the results that the presented method can not only contribute a 100% success rate in all runs of searching for a stochastic dynamic target under a limited maximal velocity, but also produce both the shortest path length and minimum iterations in terms of statistical metrics over the comparative methods.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"262 ","pages":"Article 125609"},"PeriodicalIF":7.5,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142571809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"Founder resources and innovation investment: A configuration perspective","authors":"Rong Xiao,&nbsp;Chongkai Wang","doi":"10.1016/j.eswa.2024.125680","DOIUrl":"10.1016/j.eswa.2024.125680","url":null,"abstract":"<div><div>Based on the theory of resource bricolage, this study adopts the method of fuzzy-set qualitative comparative analysis (fsQCA) to explore the multiple interaction mechanisms of the founder’s internal powerful resources (knowledge resources, management rights resources, and control rights resources) and external relationship resources (political relations resources and social relations resources) on firm innovation investment. The main findings are as follows: (1) the five key resources of founders can’t contribute to the result of high firm innovation investment alone, but they can play a role together through arrangement and management, resulting in “resource synergy” effect; (2) the internal-external resource combination of founders can be divided into two paths: “internal-driven” and “internal-external matching”, and each path contains different ways of resource combination, however, the “external-driven” path does not exist; (3) according to the analysis of configuration, it is found that compared with the external relationship resources, the founders pay more attention into cultivating internal powerful resources is more conducive to the improvement of the level of firm innovation investment; (4) the external political relationship resources of the founder play a supplementary role in firm innovation decision-making with all paths. This study shows that the value of founder resources comes from the combination management of internal and external resources. From the perspective of configuration, this study studies the relationship between founders’ individual resources and firm innovation investment, which provides a new idea for the research of firm innovation field.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"262 ","pages":"Article 125680"},"PeriodicalIF":7.5,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"On the local convergence of ADAM-DPGAN with simultaneous and alternating gradient decent training methods","authors":"Maryam Azadmanesh,&nbsp;Behrouz Shahgholi Ghahfarokhi ,&nbsp;Maede Ashouri Talouki","doi":"10.1016/j.eswa.2024.125646","DOIUrl":"10.1016/j.eswa.2024.125646","url":null,"abstract":"<div><div>Generative Adversarial Networks (GANs) do not ensure the privacy of the training datasets and may memorize sensitive details. To maintain privacy of data during inference, various privacy-preserving GAN mechanisms have been proposed. Despite the different approaches and their characteristics, advantages, and disadvantages, there is a lack of a systematic review on them. This paper first presents a comprehensive survey on privacy-preserving mechanisms and offers a taxonomy based on their characteristics. The survey reveals that many of these mechanisms modify the GAN learning algorithm to enhance privacy, highlighting the need for theoretical and empirical analysis of the impact of these modifications on GAN convergence. Among the surveyed methods, ADAM-DPGAN is a promising approach that ensures differential privacy in GANs for both the discriminator and the generator networks when using the ADAM optimizer, by introducing appropriate noise based on the global sensitivity of discriminator parameters. Therefore, this paper conducts a theoretical and empirical analysis of the convergence of ADAM-DPGAN. In the presented theoretical analysis, assuming that simultaneous/alternating gradient descent method with ADAM optimizer converges locally to a fixed point and its operator is L-Lipschitz with L &lt; 1, the effect of ADAM-DPGAN-based noise disturbance on local convergence is investigated and an upper bound for the convergence rate is provided. The analysis highlights the significant impact of differential privacy parameters, the number of training iterations, the discriminator’s learning rate, and the ADAM hyper-parameters on the convergence rate. The theoretical analysis is further validated through empirical analysis. Both theoretical and empirical analyses reveal that a stronger privacy guarantee leads to a slower convergence, highlighting the trade-off between privacy and performance. The findings also indicate that there exists an optimal value for the number of training iterations regarding the privacy needs. The optimal settings for each parameter are calculated and outlined in the paper.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"262 ","pages":"Article 125646"},"PeriodicalIF":7.5,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142593552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"Fighting against forest fire: A lightweight real-time detection approach for forest fire based on synthetic images","authors":"Guanbo Wang,&nbsp;Haiyan Li,&nbsp;Qing Xiao,&nbsp;Pengfei Yu,&nbsp;Zhaisheng Ding,&nbsp;Zongshan Wang,&nbsp;Shidong Xie","doi":"10.1016/j.eswa.2024.125620","DOIUrl":"10.1016/j.eswa.2024.125620","url":null,"abstract":"<div><div>Forest fires are known for their high level of randomness and unpredictability, which often lead to significant ecological damage and human life loss. Existing forest fire detection technologies are not capable of detecting small-scale flames or smoke in real time, thus failing to meet the demands of real-time detection of forest fires using Unmanned Aerial Vehicles (UAVs). To overcome these limitations, we propose an efficient and lightweight forest fire detection method that utilizes synthetic images and UAVs to achieve real-time and high-precision detection of forest fires against complex backgrounds. Firstly, we propose the Dilation Repconv Cross Stage Partial Network (DRCSPNet), which enhances the detection capabilities for multiscale flames and smoke using multi-branch parallel joint dilation convolution and batch normalization, while effectively extracting features from different stages of forest fires. Secondly, to mitigate challenges associated with extreme lighting in forest scenes and large contrast variation in fire images, we propose a Global Mixed-Attention (GMA) model across feature pyramids to enhance information lost in high-dimensional feature maps and increase the robustness of the model through a multiscale fusion strategy. Finally, we present the Lite-Path Aggregation Network (Lite-PAN) with varying scales to improve effective feature flow for multilevel forest fires, addressing challenges that arise from various climatic conditions. Furthermore, we employ Unreal Engine 5 to generate forest fire datasets in four scenarios to address the issue of relatively limited aerial forest fire datasets. According to the results of the experiment, our proposed method achieves 58.39% mAP(mean Average Precision) with 5.703 GFLOPs (Giga Floating Point Operations Per Second) while yielding a frame rate of 33.5 Frames Per Second (FPS) on NVIDIA Jetson NX. Extensive experiment results demonstrate our method has the advantage of being in real time, extremely accurate, and easily implementable compared to state-of-the-art techniques.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"262 ","pages":"Article 125620"},"PeriodicalIF":7.5,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"How to assess measurement capabilities of a security monitoring infrastructure and plan investment through a graph-based approach","authors":"Alessandro Palma ,&nbsp;Andrea Sorrentino ,&nbsp;Silvia Bonomi","doi":"10.1016/j.eswa.2024.125623","DOIUrl":"10.1016/j.eswa.2024.125623","url":null,"abstract":"<div><div>Security monitoring is a crucial activity in managing cybersecurity for any organization, as it plays a foundational role in various security processes and systems, such as risk identification and threat detection. To be effective, security monitoring is currently implemented by orchestrating multiple data sources to provide corrective actions promptly. Poor monitoring management can compromise an organization’s cybersecurity posture and waste resources. This issue is further exacerbated by the fact that monitoring infrastructures are typically managed with a limited resource budget. This paper addresses the problem of supporting security experts in managing security infrastructures efficiently and effectively by considering the trade-off cost-benefit between using specific monitoring tools and the benefit of including them in the organization’s infrastructure. To this aim, we introduce a graph-based model named <em>Metric Graph Model</em> (MGM) to represent dependencies between security metrics and the monitoring infrastructure. It is used to solve a set of security monitoring problems: (i) <em>Metrics Computability</em>, to assess the measurement capabilities of the monitoring infrastructure, (ii) <em>Instrument Redundancy</em>, to assess the utility of the instruments used for the monitoring, and (iii) <em>Cost-Bounded Constraint</em>, to identify the optimal monitoring infrastructure in terms of cost-benefit trade-off. We prove the NP-hardness of some of these problems, propose heuristics for solving them based on the Metric Graph Model and provide an experimental evaluation that shows their better performance than existing solutions. Finally, we present a usage scenario based on an instance of the Metric Graph Model derived from a state-of-the-art security metric taxonomy currently employed by organizations. It demonstrates how the proposed approach supports an administrator in optimizing the security monitoring infrastructure in terms of saving resources and speeding up the decision-making process.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"262 ","pages":"Article 125623"},"PeriodicalIF":7.5,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142586042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep reinforcement learning-based multi-objective optimization for electricity–gas–heat integrated energy systems","authors":"Feng Li,&nbsp;Lei Liu,&nbsp;Yang Yu","doi":"10.1016/j.eswa.2024.125558","DOIUrl":"10.1016/j.eswa.2024.125558","url":null,"abstract":"<div><div>With the increasing global attention on energy efficiency and carbon emissions, the optimization of integrated energy systems (IES) has become the key to improve energy efficiency and reduce pollution emissions. However, most of the existing optimization methods cannot effectively deal with the complexity of high dimensional continuous action space. Therefore, this paper focuses on a novel multi-objective optimization strategy for the electricity–gas–heat integrated energy systems (EGH-IES). Firstly, considering the absorption capacity of wind power and the emission of pollutant gases, a multi-objective optimization model is constructed based on the mechanism model and operation constraints of each device in EGH-IES, in which the integrated operation cost and the environmental factors are taken as optimization objectives. Then, the multi-objective optimization problem is designed as the optimal strategy of interaction learning between agent and environment in reinforcement learning, and the output power of the devices constitutes the action of reinforcement learning. Additionally, the Ornstein–Uhlenbeck process is introduced to enhance the training efficiency and exploration performance of the agent, and the deep deterministic policy gradients (DDPG) algorithm is employed to optimize the action, thus the output power of the appliances could be obtained. Finally, the simulation results show that compared with deep Q network (DQN) method and proximal policy optimization (PPO) method, the reward function value of the proposed method increases by 2.43% and 6.09%, respectively, which represents a reduction in economic cost and pollutant emissions. These verify the effectiveness and superiority of the proposed multi-objective optimization scheme in cost reduction and benefit improvement for the EGH-IES.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"262 ","pages":"Article 125558"},"PeriodicalIF":7.5,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142586289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"Acoustic fault diagnosis of three-phase induction motors using smartphone and deep learning","authors":"Adam Glowacz ,&nbsp;Maciej Sulowicz ,&nbsp;Jakub Zielonka ,&nbsp;Zhixiong Li ,&nbsp;Witold Glowacz ,&nbsp;Anil Kumar","doi":"10.1016/j.eswa.2024.125633","DOIUrl":"10.1016/j.eswa.2024.125633","url":null,"abstract":"<div><div>Faults in induction motors can halt production lines in factories, leading to downtime and resulting in production and economic losses. Therefore, it is crucial to ensure that motors operate reliably. This paper describes an approach for the acoustic fault diagnosis of rotor bars in three-phase induction motors (IM). The authors analyzed the following conditions: a healthy IM, an IM with one broken rotor bar, an IM with two broken rotor bars, and an IM with three broken rotor bars. The FFT method was used to compute the FFT spectrum of the acoustic signals. An original feature extraction method DWV (Differences of Word Vectors) was proposed to compute the acoustic features. DenseNet-201, ResNet-18, ResNet-50, and EfficientNet-b0 were used to classify these acoustic features. The computed recognition efficiency is 100 %. The proposed method was also verified using a low-pass filter of 1–1225 Hz and word coding.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"262 ","pages":"Article 125633"},"PeriodicalIF":7.5,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142593548","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"Design of adaptive recommendation system for autism children using optimal feature selection-based adaptive dilated 1DCNN-LSTM with attention mechanism","authors":"Balaji V. ,&nbsp;Mohana M. ,&nbsp;Hema M. ,&nbsp;Gururama Senthilvel P.","doi":"10.1016/j.eswa.2024.125399","DOIUrl":"10.1016/j.eswa.2024.125399","url":null,"abstract":"<div><div>One kind of neurological disorder is caused in the brain which is defined as Autism Spectrum Disorder (ASD). It has acquired the symptoms that appear in young children. In addition to that, it influences how the individual behaves and learns as well as communicates and interacts with others. More specifically, the term Autism is defined as a developmental disorder that impacts communication and social skills and it may vary from mental handicap cases to relieving superior cognitive abilities, intact, and the characteristic pattern of poor. Moreover, the school activities have acquired various difficulties to the given model that include changes in expected routines, intense sensory stimulation, noisy or disordered environments, and social interactions. Consequently, the conventional approaches face certain limitations like user privacy, scalability, and cold-start. Here, a novel suggestion system for autistic children is developed to detect distractions and anxious situations using deep learning and then treat the children based on their abilities. It has helped to prevent the risk to children. The data is given to the selection of the feature stage. The weight optimization is performed using the Modified Garter Snake Optimization Algorithm (MGSOA) during the selection of features. Then, the selected features are given to the Adaptive Dilated One Dimensional Conventional Neural Network (1DCNN) and Long Short-Term Memory (LSTM) with Attention Mechanism termed AD-1DCNN + LSTM-AMfor detecting the autism disorder for children. Here, the parameter optimization is performed using MGSOA optimization. It effectively forecasts the symptoms in a short time. This optimization helps to provide reliable and flexible outcomes for the developed recommendation system for autistic children. The developed recommendation system for autistic children is compared to baseline techniques with efficacy metrics to visualize elevated results.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"262 ","pages":"Article 125399"},"PeriodicalIF":7.5,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142552635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}