Engineering Applications of Artificial Intelligence最新文献

{"title":"Building image reconstruction and dimensioning of the envelope from two-dimensional perspective drawings","authors":"Andrew Fisher ,&nbsp;Lucas Moreira ,&nbsp;Muntasir Billah ,&nbsp;Pawan Lingras ,&nbsp;Vijay Mago","doi":"10.1016/j.engappai.2024.109657","DOIUrl":"10.1016/j.engappai.2024.109657","url":null,"abstract":"<div><div>In the construction industry, a project typically begins with the creation of two-dimensional (2D) building plans, defining the client’s specifications. Using these plans, a digital three-dimensional (3D) model is developed to visualize the anticipated outcome and to verify the model’s alignment with the client’s expectations. The process of converting from 2D to 3D can become time-intensive if there is a need for modifications or if the project’s overall complexity is high. To enhance efficiency and accuracy, this research introduces an end-to-end framework referred to as <em>BIRD</em> which stands for <u>B</u>uilding <u>I</u>mage <u>R</u>econstruction and <u>D</u>imensioning. <em>BIRD</em> is capable of accepting five 2D perspective drawings of a building as inputs and generating a proportionate 3D model of the building envelope as an output. This is accomplished through the integration of multiple techniques that use convolutional neural networks to extract a refined set of line segments, identify measurements, and align each perspective with the floor plan drawing. The key contributions of this study includes: (1) a novel deep learning model designed for the identification of line segments in building plans; (2) novel algorithms that facilitate the generation of information required for 3D modeling; (3) an end-to-end framework for building reconstruction; and (4) novel performance metrics specifically tailored for the 2D to 3D conversion challenge. The practical application of this research was validated through the use of complete building plans provided by an industry partner. In summary, it was observed that <em>BIRD</em> demonstrated high accuracy in the creation of 3D visualizations, highlighting its real-world efficacy.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109657"},"PeriodicalIF":7.5,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142720673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Dynamic localization based-utility decision approach under type-2 Pythagorean fuzzy set for developing internet of modular self-reconfiguration robot things","authors":"Nahia Mourad ,&nbsp;A.A. Zaidan ,&nbsp;Hassan A. Alsattar ,&nbsp;Sarah Qahtan ,&nbsp;B.B. Zaidan ,&nbsp;Muhammet Deveci ,&nbsp;Dragan Pamucar ,&nbsp;Witold Pedrycz","doi":"10.1016/j.engappai.2024.109671","DOIUrl":"10.1016/j.engappai.2024.109671","url":null,"abstract":"<div><div>The Internet of Modular Robot Things (IoMRT) has emerged through the integration of robotic systems into the Internet of Things (IoT), offering a wide range of solutions to meet continuously growing demands. Six self-reconfiguration functionalities/criteria have been proposed for developing IoMRT. However, no study has fully developed an IoMRT that satisfies all the necessary functionalities. Additionally, there is a lack of scholarly research proposing a decision-based approach for evaluating and ranking IoMRT, which highlights a significant research gap. A complex multiple-criteria decision-making (MCDM) problem has arisen in evaluating and ranking IoMRT due to the diversity of functionalities, the need to prioritize these functionalities based on their importance, and data variability. To address this issue, the study proposes a novel decision-based approach for evaluating and ranking IoMRT, which consists of three phases: (i) Developing a novel weighting method called T2PFS-FWZICbIP (Type-2 Pythagorean Fuzzy Set–Fuzzy Weighted Zero Inconsistency based on Interrelationship Process) to measure the importance of the identified functionalities; (ii) Formulating a decision matrix by cross-referencing potential IoMRT developments with the six self-reconfiguration functionalities resulted in the selection of a random sample of 50 IoMRTs as proof of concept. Following this, the DLbU (Dynamic Localization-based Utility) method was proposed, integrating dynamic localization and utility procedures to manage binary data within the decision matrix; (iii) Developing a novel ranking method, T2PFS-DNMA (Type-2 Pythagorean Fuzzy Set–Double Normalization-based Multiple Aggregation), to address the diversity of functionalities and concerns regarding data variance. The results revealed that the Distributed functionality (C1) received the highest weight value of 0.4060 according to T2PFS-FWZICbIP, indicating its high importance in the ranking of IoMRT. In contrast, the High-Fidelity functionality (C5) received a weight value of 0.0733, indicating its very low importance in the ranking. IoMRT2 and IoMRT35 were identified as the most and least favored, respectively, according to T2PFS-DNMA. The robustness of the proposed approach was assessed through sensitivity analysis and comparative studies.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109671"},"PeriodicalIF":7.5,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142720674","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":"The multi-depot pickup and delivery vehicle routing problem with time windows and dynamic demands","authors":"Yong Wang ,&nbsp;Mengyuan Gou ,&nbsp;Siyu Luo ,&nbsp;Jianxin Fan ,&nbsp;Haizhong Wang","doi":"10.1016/j.engappai.2024.109700","DOIUrl":"10.1016/j.engappai.2024.109700","url":null,"abstract":"<div><div>The rapid development of the urban logistics recycling industry, combined with the complexity of the pickup and delivery networks, has created a surge in dynamic customer demands and exacerbated the difficulty of logistics resource sharing. Accordingly, this work focuses on a multi-depot pickup and delivery vehicle routing problem with time windows and dynamic demands, which incorporates resource sharing. A bi-objective mathematical model is formulated to minimize the total operating cost and number of vehicles. A three-dimensional affinity propagation clustering and an adaptive nondominated sorting genetic algorithm-II are combined to find Pareto optimal solutions. A dynamic demand insertion strategy is proposed to determine the vehicle service sequences for dynamic situations. Combined with an elite iteration mechanism to prevent the proposed algorithm from falling into search stagnation and improve the convergence performance. The superiority of the proposed algorithm is verified by comparing with CPLEX solver (i.e., ILOG CPLEX Optimization Studio 12.10), multi-objective ant colony optimization, multi-objective particle swarm optimization, multi-objective evolutionary algorithm, multi-objective genetic algorithm, and decomposition-based multi-objective evolutionary algorithm with tabu search. Besides, the proposed model and algorithm are tested by a real-world case study in Chongqing city, China, and the further analysis indicates that significant improvement can be achieved. Furthermore, by incorporating the recognition and prediction techniques of artificial intelligence on dynamic demand data, the proposed approach can realize the self-optimization of multi-depot vehicle routes and the precise allocation of logistics resources in dynamic environments. This study is conducive to the construction of a digitally-intelligent urban logistics system.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109700"},"PeriodicalIF":7.5,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142720891","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":"Model-agnostic local explanation: Multi-objective genetic algorithm explainer","authors":"Hossein Nematzadeh ,&nbsp;José García-Nieto ,&nbsp;Sandro Hurtado ,&nbsp;José F. Aldana-Montes ,&nbsp;Ismael Navas-Delgado","doi":"10.1016/j.engappai.2024.109628","DOIUrl":"10.1016/j.engappai.2024.109628","url":null,"abstract":"<div><div>Late detection of plant diseases leads to irreparable losses for farmers, threatening global food security, economic stability, and environmental sustainability. This research introduces the Multi-Objective Genetic Algorithm Explainer (MOGAE), a novel model-agnostic local explainer for image data aimed at the early detection of citrus diseases. MOGAE enhances eXplainable Artificial Intelligence (XAI) by leveraging the Non-dominated Sorting Genetic Algorithm II (NSGA-II) with an adaptive Bit Flip Mutation (BFM) incorporating densify and sparsify operators to adjust superpixel granularity automatically. This innovative approach simplifies the explanation process by eliminating several critical hyperparameters required by traditional methods like Local Interpretable Model-Agnostic Explanations (LIME). To develop the citrus disease classification model, we preprocess the leaf dataset through stratified data splitting, oversampling, and augmentation techniques, then fine-tuning a pre-trained Residual Network 50 layers (ResNet50) model. MOGAE’s effectiveness is demonstrated through comparative analyses with the Ensemble-based Genetic Algorithm Explainer (EGAE) and LIME, showing superior accuracy and interpretability using criteria such as numeric accuracy of explanation and Number of Function Evaluations (NFE). We assess accuracy both intuitively and numerically by measuring the Euclidean distance between expert-provided explanations and those generated by the explainer. The appendix also includes an extensive evaluation of MOGAE on the melanoma dataset, highlighting its versatility and robustness in other domains. The related implementation code for the fine-tuned ResNet50 and MOGAE is available at <span><span>https://github.com/KhaosResearch/Plant-disease-explanation</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109628"},"PeriodicalIF":7.5,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142720889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Quantum-inspired metaheuristic algorithms for Industry 4.0: A scientometric analysis","authors":"Pooja,&nbsp;Sandeep Kumar Sood","doi":"10.1016/j.engappai.2024.109635","DOIUrl":"10.1016/j.engappai.2024.109635","url":null,"abstract":"<div><div>Quantum-inspired Metaheuristic algorithms have redefined non-deterministic polynomial time hard optimization challenges by leveraging quantum mechanics principles. These algorithms herald a broad range of application scenarios in Industry 4.0 and offer feasible time solutions for complex, large-scale industrial landscapes. The potential benefits provided by the quantum-inspired metaheuristic algorithms have accelerated the scientific advancements in this domain. Consequently, the present research contributes to the existing knowledge base by presenting the intellectual landscape through scientometric and systematic literature analysis. The study is conducted on the dataset derived from the Scopus and Web of Science databases, covering 2001 to 2023. The study employs co-citation and co-occurrence analyses to discern prominent research topics, emerging research frontiers, significant authors, and the most collaborating countries. The research findings underscore that electric vehicles, energy efficiency, and combinatorial optimization are prominent research topics, while carbon emission, resource management, and path planning are burgeoning areas of exploration in this knowledge domain. The intricate and entangled network linkage determines that the research community in this domain fosters a dynamic and synergistic relationship. Overall, the pivotal insights and the research challenges articulated in this article offer valuable insights to researchers and the academic community, aiding in discerning the intellectual terrain and emerging research patterns in quantum-inspired metaheuristic algorithms. This, in turn, fosters the advancement of innovation and facilitates well-informed decision-making within this evolving research paradigm.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109635"},"PeriodicalIF":7.5,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142720595","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":"Automatic collaborative learning for drug repositioning","authors":"Yi Wang ,&nbsp;Yajie Meng ,&nbsp;Chang Zhou ,&nbsp;Xianfang Tang ,&nbsp;Pan Zeng ,&nbsp;Chu Pan ,&nbsp;Qiang Zhu ,&nbsp;Bengong Zhang ,&nbsp;Junlin Xu","doi":"10.1016/j.engappai.2024.109653","DOIUrl":"10.1016/j.engappai.2024.109653","url":null,"abstract":"<div><div>Drug repositioning seeks to identify new therapeutic uses for existing drugs, accelerating development and reducing costs. While traditional wet lab experiments are costly, computational methods offer a low-cost, efficient alternative. Despite their potential, most research in this field has uncritically employed the standard message-passing mechanism of Graph Neural Network (GNN), limiting the assessment of collaborative effects on prediction accuracy. In this paper, we introduce a novel model, an automatic collaborative learning framework for drug repositioning. Initially, we propose a metric to measure the interaction levels among neighbors and integrate it with the intrinsic message-passing mechanism of GNN, thereby enhancing the impact of various collaborative effects on prediction accuracy. Furthermore, we introduce an advanced contrastive learning technique to align feature consistency between the disease–drug association space and the customized neighbor space. This approach leverages the inherent regularities across different feature dimensions to minimize feature redundancy. Extensive experiments conducted on three benchmark datasets demonstrate substantial improvements of this novel model over various state-of-the-art methods. Case studies further highlight the practical utility of this model.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109653"},"PeriodicalIF":7.5,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142720771","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":"A knowledge-refined hybrid graph model for quality prediction of industrial processes","authors":"Yang Wang ,&nbsp;Feifan Shen ,&nbsp;Lingjian Ye","doi":"10.1016/j.engappai.2024.109711","DOIUrl":"10.1016/j.engappai.2024.109711","url":null,"abstract":"<div><div>The complexity of industrial processes has spurred the application of soft sensor techniques for predicting key quality variables based on easy-measurable process variables. Currently, data-driven soft sensors based on Artificial Intelligence techniques have become the mainstream. However, these soft sensing models deeply rely on the quality of training data, where the domain knowledge is often ignored. Meanwhile, a significant amount of labeled data is not fully utilized. To address these issues, this paper proposes a supervised framework based on a knowledge-refined hybrid graph network, which contributes to the artificial intelligence application of nonlinear dynamic soft sensors. The problems of applying traditional artificial intelligence models in soft sensor have been addressed by reconstructing the input module of graph neural networks with knowledge-guided approaches. Both spatial and temporal correlations of process data are captured and the hybrid network significantly improves the reliability and interpretability of the soft sensing model. By incorporating labeled data into the model, the representation of quality information is also enhanced. Finally, the proposed framework was applied to an industrial debutanizer column, and the experimental results fully demonstrate the effectiveness and superiority of the method.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109711"},"PeriodicalIF":7.5,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142720890","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":"A novel multi-objective artificial bee colony algorithm for solving the two-echelon load-dependent location-routing problem with pick-up and delivery","authors":"Dekun Tan,&nbsp;Xuhui Liu,&nbsp;Ruchun Zhou,&nbsp;Xuefeng Fu,&nbsp;Zhenzhen Li","doi":"10.1016/j.engappai.2024.109636","DOIUrl":"10.1016/j.engappai.2024.109636","url":null,"abstract":"<div><div>This study considers a two-echelon load-dependent location routing problem with pick-up and delivery (2E-LDLRPPD). As a variant of the two-echelon vehicle routing problem with pick-up and delivery (2E-VRPPD), the 2E-LDLRPPD includes additional variants such as two-echelon location-routing problem (2E-LRP) and load-dependent vehicle routing problem (LDVRP). However, much of the existing research work has traditionally focused on a single objective, predominantly aimed to minimize costs. In our case, we build a multi-objective model that concurrently minimizes costs, carbon emissions, and the number of vehicles used. Heuristic algorithms are commonly used to solve complex location-routing problems. Therefore, we propose a hybrid heuristic algorithm named the improved elite-guided multi-objective artificial bee colony algorithm with variable neighborhood search (IEMOABC-VNS). Base on elite-guided multi-objective artificial bee colony algorithm (EMOABC), a two-archive elite-guide strategy is deployed to strike a balance between diversity and convergence. The efficacy of the IEMOABC-VNS is compared experimentally with four other hybrid heuristic algorithms on test instances and a real-world case. Computational results demonstrate that the IEMOABC-VNS outperforms the competing algorithms in solving 2E-LDLRPPD, and obtains a high-quality Pareto front in a relatively short time. Especially, the algorithm exhibits significant performance enhancements when applied to large-scale instances.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109636"},"PeriodicalIF":7.5,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142720887","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":"A collaborative surface target detection and localization method for an unmanned surface vehicle swarm","authors":"Bo Wang ,&nbsp;Chenyu Mao ,&nbsp;Kaixin Wei ,&nbsp;Xueyi Wu ,&nbsp;Ye Li","doi":"10.1016/j.engappai.2024.109679","DOIUrl":"10.1016/j.engappai.2024.109679","url":null,"abstract":"<div><div>A single unmanned surface vehicle (USV) designed for marine missions suffers from limited payload, low efficiency and weak intelligence, while a swarm of USVs shows significant advantages in mission flexibility, diverse payload and task efficiency. One of the key issues for an USV swarm is how to achieve highly efficient collaborative perception. To address this issue, a method framework of collaborative surface target detection and localization based on multiple sensors for a swarm including 4 USVs is designed. First, perception systems are constructed, a joint calibration method for different sensors is proposed, and a lightweight target detection method improved with attention mechanism and lightweight adaptive spatial feature fusion is designed. Second, a specialized fusion method using sensor principles based on an extended Kalman filter (EKF) is proposed for a single USV to obtain a target state model. Third, the obtained target models from different USVs are registered with fuzzy matching and integrated into the complete model in a geographic coordinate system. The proposed method is applied to the collaborative perception system on our developed 4 USV swarm and verified in real marine environment and simulation. Experimental results show that our proposed method framework significantly improves the accuracy, efficiency, and reliability of the target detection and localization. The proposed LAF-YOLOv8-s reduces the model size by 5.1M, while the mean average precision (mAP) reaches 68.7%, which is significantly superior to other methods. The average collaborative localization error is reduced by 2.9m. The dataset is available at <span><span>https://github.com/maochenyu1/WSLight</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109679"},"PeriodicalIF":7.5,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142720768","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":"Positive discrimination of minority classes through data generation and distribution: A case study in olive disease classification","authors":"Hicham El Akhal,&nbsp;Aissa Ben Yahya,&nbsp;Abdelbaki El Belrhiti El Alaoui","doi":"10.1016/j.engappai.2024.109646","DOIUrl":"10.1016/j.engappai.2024.109646","url":null,"abstract":"<div><div>Deep learning models have achieved remarkable success in various tasks, especially in classification. This success is particularly evident in the precise classification of plant diseases, which is crucial for effective agricultural management. However, accurate classification faces challenges, particularly in data collection, where certain classes are underrepresented, namely the minority classes. This issue can significantly impact model performance. To tackle this challenge, this paper introduces a novel methodology that differs from existing approaches. We focus on addressing the issue of minority classes in image-based classification tasks, particularly for olive diseases. We employ data generation methods, including basic transformations, to produce augmented data and utilize Deep Convolutional Generative Adversarial Networks (DCGAN) to produce generated data. Next, we apply the Frechet Inception Distance (FID) to the generated dataset to select the highest-quality images. We then distribute varying percentages (25%, 50%, 75%, 100%) of this new data into the minority classes of the original dataset. Our data distribution strategies involve incorporating specific amounts of (1) augmented data, (2) generated data, and (3) a combination of both augmented and generated data to achieve target percentages (T.P) in the resulting dataset. Our experiments focus on classifying olive diseases into seven distinct categories using a pre-trained Convolutional Neural Network (CNN) architecture. We observe significant improvements in the model’s performance, particularly in the accurate classification of minority classes. This approach enhances diagnostic accuracy and optimizes data distribution, which is crucial for effectively addressing the challenges posed by minority classes.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109646"},"PeriodicalIF":7.5,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142720766","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}