Engineering Applications of Artificial Intelligence最新文献_第8页

Mixed-integer linear programming and composed heuristics for three-stage remanufacturing system scheduling problem 三阶段再制造系统调度问题的混合整数线性规划和组成启发式方法

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-09-05 DOI: 10.1016/j.engappai.2024.109257

{"title":"Mixed-integer linear programming and composed heuristics for three-stage remanufacturing system scheduling problem","authors":"","doi":"10.1016/j.engappai.2024.109257","DOIUrl":"10.1016/j.engappai.2024.109257","url":null,"abstract":"<div><p>The three-stage remanufacturing system scheduling problem (3T-RSSP) has been a hot research topic recently. The remanufacturing system in this paper is equipped with a novel configuration of unrelated parallel disassembly/reassembly workstations and parallel dedicated flow-shop-type reprocessing lines. To this end, a mixed-integer linear programming (MILP) model based on the adjacent sequence-based modeling idea is first proposed to address the 3T-RSSP for a makespan minimization. Compared with other ideas, the adjacent sequence-based modeling idea is effective in deciding precedence relationship between two adjacent operations, especially for the investigated 3T-RSSP. The 3T-RSSP is NP (non-deterministic polynomial)-hard, we also design 18 composed heuristics for large-sized problems to gain a better performance, compared to traditional isolated heuristics. Simulation experiments are carried out on a publicly available dataset to test the performance the MILP model and composed heuristics. Results imply that the MILP model solved by CPLEX can seek optimum solutions within a short time when the problem size is small. It is found that when problem size becomes 2.0, 4.0, 8.0 times large, performance indicators NCs (number of constraints) and NBVs (number of binary variables) of the model become 3.29, 11.81, 44.60 and 3.43, 12.57, 48.00 times large. Besides, compared with other composed heuristics, LTRT-F (longest total reprocessing time-first available machine) gains the best performance. Instance P5-C3-D2/A2 is selected to quantitatively analyze the MILP model by presenting the detailed 0–1 binary variable values. Finally, by comparing with position-based MILP model, the adjacent sequence-based MILP model has better performance in characterizing the investigated 3T-RSSP.</p></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142151462","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}

引用次数: 0

Identification of product definition patterns in mass customization by multi-information fusion weighted support vector machine 通过多信息融合加权支持向量机识别大规模定制中的产品定义模式

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-09-05 DOI: 10.1016/j.engappai.2024.109253

{"title":"Identification of product definition patterns in mass customization by multi-information fusion weighted support vector machine","authors":"","doi":"10.1016/j.engappai.2024.109253","DOIUrl":"10.1016/j.engappai.2024.109253","url":null,"abstract":"<div><p>In mass customization, companies have built product families to enhance design efficiency and meet customer requirements. However, the complex and diverse customer requirements make the traditional process of mapping customer needs to product families challenging and heavily reliant on prior knowledge. To address this challenge, the mapping task is treated as a classification problem, with customer requirements as classification features and product families as category labels. Based on information theory, this study considers the information gain (IG) and mutual information (MI) between the classification features and the labels. The uncertainty relationship between the two is explored using grey relational analysis (GRA). A hybrid weighting matrix is constructed by combining the effects of these three aspects, which is then used to improve the calculation of the classical support vector machine (CSVM) kernel function, forming a multi-information fusion weighted support vector machine (MIFWSVM) model. This model can take new requirements as input and output product variants that may satisfy the customer. To demonstrate the effectiveness of the proposed method, a case study of a mechanical press company was reported, comparing the MIFWSVM model with classical classifiers and exploring the impact of different weighting methods on the performance of CSVM. The MIFWSVM model achieved an average accuracy of 0.9205 with a standard deviation of 0.0506 and a macro F1 score of 0.9032 with a standard deviation of 0.0589, outperforming other methods. These results indicate that the MIFWSVM model significantly improves the accuracy and stability of customer demand mapping.</p></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142151368","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}

引用次数: 0

A short-term forecasting for multi-factor time series with multiple linear trend fuzzy information granule and cross-association 具有多线性趋势模糊信息颗粒和交叉关联的多因素时间序列短期预测

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-09-05 DOI: 10.1016/j.engappai.2024.109232

{"title":"A short-term forecasting for multi-factor time series with multiple linear trend fuzzy information granule and cross-association","authors":"","doi":"10.1016/j.engappai.2024.109232","DOIUrl":"10.1016/j.engappai.2024.109232","url":null,"abstract":"<div><p>Multi-factor time series forecasting is of great significance in research and application, where capturing data characteristic and association are the main works. For data characteristic, the multiple linear trend fuzzy information granule is developed on multi-factor time series. This kind of granule accurately describes the multi-linear-trend information within the data, and exhibits high semantic and temporal interpretation. To distinguish the diverse trend information hidden in such granule, a fuzzy information granule clustering algorithm is raised, yielding the multi-factor cluster label series. Notably, each cluster label represents a class of trend patterns. Leveraging the characterized trend information, two multi-factor fuzzy association rules are mined, the multi-factor cluster label association rule and the multi-factor cluster label cross-association rule, reflecting the association and cross-association in multi-factor time series respectively. By combing the excavated data characteristic with fuzzy association rules, a short-term forecasting model is designed. This model wins the smallest root mean squared error, mean absolute percentage error, and mean absolute percentage error values in five stock time series forecasting analysis after comparing with other models, and the prediction comparisons of a statistical index (Wilcoxon signed rank test) are smaller than 0.05. The superiority of the novel forecasting model can be demonstrated through the performance across various metrics and indicators.</p></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142151373","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}

引用次数: 0

Steering knuckle surface defect detection and segmentation based on reverse residual distillation 基于反向残余蒸馏的转向节表面缺陷检测与分割

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-09-05 DOI: 10.1016/j.engappai.2024.109161

{"title":"Steering knuckle surface defect detection and segmentation based on reverse residual distillation","authors":"","doi":"10.1016/j.engappai.2024.109161","DOIUrl":"10.1016/j.engappai.2024.109161","url":null,"abstract":"<div><p>Although the supervised deep learning method effectively detects and segments the surface defects of the steering knuckle, in the absence of sufficient defect samples, the model is prone to tend to learn normal sample features and ignore defective features during the training process, leading to a higher defect detection error rate. In this paper, we propose an unsupervised defect detection method called reverse residual distillation, which can be trained using only defect-free steering knuckle surface images and can accurately detect and segment surface defects in steering knuckles. In this method, we adopt the encoder–decoder structure as the basic structure of the teacher–student network and integrate the reverse distillation and progressive distillation methods into the distillation process, which solves the overgeneralization problem in the student network and improves the distillation efficiency. Additionally, we introduce a trainable one-class bottleneck embedding module and a multi-scale channel attention feature fusion module to enhance the model’s performance in detecting and segmenting defects. Experimental results on the mvtec anomaly detection (MVTec AD) dataset and the steering knuckle dataset demonstrate the effectiveness of our method in detecting and segmenting surface defects in industrial products. Especially in the steering knuckle dataset, the area under the receiver operating characteristic curve (AUROC) scores for defect detection and pixel-level segmentation achieved remarkable levels of 98.6% and 99.8%, respectively.</p></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142151371","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}

引用次数: 0

A novel Complementary Dual-aware Network for point cloud classification 用于点云分类的新型互补双感知网络

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-09-05 DOI: 10.1016/j.engappai.2024.109224

{"title":"A novel Complementary Dual-aware Network for point cloud classification","authors":"","doi":"10.1016/j.engappai.2024.109224","DOIUrl":"10.1016/j.engappai.2024.109224","url":null,"abstract":"<div><p>As an elementary research, three-dimensional (3D) point cloud classification study can further serve high-level downstream applications such as 3D reconstruction, generation, and completion. Recently, excellent performance for synthetic point cloud data classification have achieved, but most of them do not work well on point cloud shape collected from real-world scenarios due to its complexity with noises, varies background, occlusion, etc. As we all known, human can handle it easily. Thus, this paper proposed a <em>Complementary Dual-aware Network</em> (ComDa-Net) inspired by the neurobiological basis of human visual system, aiming to enhance the ability of perceiving 3D objects in real-world scenarios. Specifically, the <em>Essential Perceived Unit</em> (EPU) is proposed to realize the primary complementary dual-aware mechanism through elaborated variational resolutions and receptive fields, then multiple EPUs stack to form the cross-complemented hierarchical system. The proposed method achieves advanced and stable accuracy on the wild-used real-world point cloud benchmarks, and its efficiency in terms of computational and storage is also satisfied, which validates the proposed method’s expected performances. In addition, the proposed method also achieves competitive performance on the synthetic point cloud benchmarks.</p></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142151372","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}

引用次数: 0

A comprehensive survey of weapon target assignment problem: Model, algorithm, and application 武器目标分配问题综述：模型、算法和应用

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-09-05 DOI: 10.1016/j.engappai.2024.109212

{"title":"A comprehensive survey of weapon target assignment problem: Model, algorithm, and application","authors":"","doi":"10.1016/j.engappai.2024.109212","DOIUrl":"10.1016/j.engappai.2024.109212","url":null,"abstract":"<div><p>This paper provides an overview of the weapon target assignment problem, which aims to optimize the assignment of weapons to targets in order to maximize weapon damage to targets. The weapon target assignment problem can be viewed as a specialized instance of the optimal resource assignment problem. With the advancement of weapons technology, high-speed and high-lethality missiles have become more advanced, and their tactical applications more diverse. These missiles can strike targets with greater accuracy and improved concealment, posing a significant threat to both attackers and defenders. Consequently, the weapon target assignment problem has become a pressing concern in the field of military offense and defense. Subsequently, researchers worldwide are devoting significant efforts to address the weapon target assignment problem through the utilization of exact algorithms, heuristic algorithms, meta-heuristic algorithms, and artificial intelligence methods. This paper provides a brief review of the weapon target assignment problem development history, formula, solution techniques, and applications. We categorize weapon target assignment problems into four different formulas, considering the complexity of combat scenarios, and summarize various solution methods for each category. Furthermore, we also emphasize the relevance of weapon target assignment problems in national defense applications. Lastly, we conclude by discussing potential avenues for future research in addressing the weapon target assignment problem.</p></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142151459","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}

引用次数: 0

Efficient hardware design of spiking neurons and unsupervised learning module in large scale pattern classification network 大规模模式分类网络中尖峰神经元和无监督学习模块的高效硬件设计

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-09-05 DOI: 10.1016/j.engappai.2024.109255

{"title":"Efficient hardware design of spiking neurons and unsupervised learning module in large scale pattern classification network","authors":"","doi":"10.1016/j.engappai.2024.109255","DOIUrl":"10.1016/j.engappai.2024.109255","url":null,"abstract":"<div><p>The main interest of high-precision, low-energy computing in machines with superior intelligence capabilities is to improve the performance of biologically spiking neural networks (SNNs). In this paper, we address this by presenting a new power-law update of synaptic weights based on burst time-dependent plasticity (Pow-BTDP) as a digital learning block in a SNN model with multiplier-less neuron modules. Propelled by the request for accurate and fast computations that diminishes costly resources in neural network applications, this paper introduces an efficient hardware methodology based on linear approximations. The presented hardware designs based on linear approximation of non-linear terms in learning module (exponential and fractional power) and neuron blocks (second power) are carefully elaborated to guarantee optimal speedup, low resource consumption, and accuracy. The architectures developed for Exp and Power implementations are illustrated and evaluated, leading to the presentation of digital learning module and neuron block that enable efficient and accurate hardware computation. The proposed digital modules of learning mechanism and neuron was used to construct large scale event-based spiking neural network comprising of three layers, enabling unsupervised training with variable learning rate utilizing excitatory and inhibitory neural connections. As a results, the proposed bio-inspired SNN as a spiking pattern classification network with the proposed Pow-BTDP learning approach, by training on MNIST, EMNIST digits, EMNIST letters, and CIFAR10 datasets with respectively 6, 2, 2 and 6 training epochs, achieved superior accuracy 97.9%, 97.8%, 94.2%, and 93.3% which indicate higher accuracy and convergence speed compare to previous works.</p></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142151322","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}

引用次数: 0

An adaptive network fusing light detection and ranging height-sliced bird’s-eye view and vision for place recognition 融合光探测与测距高度切片鸟瞰图和视觉的自适应网络用于地点识别

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-09-05 DOI: 10.1016/j.engappai.2024.109230

{"title":"An adaptive network fusing light detection and ranging height-sliced bird’s-eye view and vision for place recognition","authors":"","doi":"10.1016/j.engappai.2024.109230","DOIUrl":"10.1016/j.engappai.2024.109230","url":null,"abstract":"<div><p>Place recognition, a fundamental component of robotic perception, aims to identify previously visited locations within an environment. In this study, we present a novel global descriptor that uses height-sliced Bird’s Eye View (BEV) from Light Detection and Ranging (LiDAR) and vision images, to facilitate high-recall place recognition in autonomous driving field. Our descriptor generation network, incorporates an adaptive weights generation branch to learn weights of visual and LiDAR features, enhancing its adaptability to different environments. The generated descriptor exhibits excellent yaw-invariance. The entire network is trained using a self-designed quadruplet loss, which discriminates inter-class boundaries and alleviates overfitting to one particular modality. We evaluate our approach on three benchmarks derived from two public datasets and achieve optimal performance on these evaluation sets. Our approach demonstrates excellent generalization ability and efficient runtime, which are indicative of its practical viability in real-world scenarios. For those interested in applying this Artificial Intelligence contribution to engineering, the implementation of our approach can be found at: <span><span>https://github.com/Bryan-ZhengRui/LocFuse</span><svg><path></path></svg></span>.</p></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142151361","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}

引用次数: 0

Autonomous collaborative optimization control of earth pressure balance shield machine based on hierarchical control architecture 基于分层控制架构的土压平衡盾构机自主协同优化控制

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-09-05 DOI: 10.1016/j.engappai.2024.109200

{"title":"Autonomous collaborative optimization control of earth pressure balance shield machine based on hierarchical control architecture","authors":"","doi":"10.1016/j.engappai.2024.109200","DOIUrl":"10.1016/j.engappai.2024.109200","url":null,"abstract":"<div><p>Due to manual operations are unable to adjust parameters promptly to adapt to continuously changing geological conditions, which can easily lead to safety accidents. Therefore, this study aims to solve the real-time dynamic optimization problem of shield machines and achieve autonomous optimal control. Based on cyber-physical system (CPS), this paper proposes a Twin Double Deep Deterministic Policy Gradient-Model Predictive Control (TD3-MPC) hierarchical autonomous control scheme, which is divided into coordination level and execution level. Based on Twin Double Deep Deterministic Policy Gradient (TD3) algorithm, TD3 agent is designed. A virtual tunnel environment has been designed based on sealed cabin pressure mechanism model. TD3 agent and virtual environment are used as coordination level. A dynamic state-space model for sealed cabin pressure is established, incorporating shield machine dynamics model and mechanism of sealed cabin pressure. On this basis, model predictive controller is designed as execution level. TD3 agent interacts with virtual tunneling environment based on geological information parameters of real-time sampling. An optimal sealed cabin pressure curve under current sampling interval is found and transmitted to executive level as the control target. Execution level solves the quadratic programming through real-time feedback of sealed cabin pressure and continuous rolling optimizes screw conveyor speed and propulsion speed to achieve accurate tracking of pressure controlling target. Simulated results demonstrated that this control approach has a good pressure control effect and strong soil adaptive ability. From multiple perspectives, viability and efficiency are further verified for the proposed controlling scheme. This paper further promotes the application of AI technology in underground construction.</p></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142151370","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}

引用次数: 0

Tracking interval control for urban rail trains based on safe reinforcement learning 基于安全强化学习的城市轨道交通列车跟踪间隔控制

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-09-04 DOI: 10.1016/j.engappai.2024.109226

{"title":"Tracking interval control for urban rail trains based on safe reinforcement learning","authors":"","doi":"10.1016/j.engappai.2024.109226","DOIUrl":"10.1016/j.engappai.2024.109226","url":null,"abstract":"<div><p>In order to solve the problem of controlling the interval between trains in the new train control system, which aims to ensure the safe operation of trains and improve traffic density, the process of managing train speed is treated as a decision-making process. The utilization of Safe Reinforcement Learning is implemented to attain immediate control of the train interval within the train section. Firstly, utilizing vehicle-to-vehicle communication, the train obtains state information about its surroundings. A constrained Markov Decision Process model is created that takes into account the dynamic characteristics of both the leading and tracking trains. Secondly, by integrating the minimal safety distance and the maximum operating efficiency distance, safety and optimality are connected. An augmented Lagrange multiplier method is utilized to design and implement the safe reinforcement learning algorithm. To enhance the convergence speed of the algorithm, a dual-priority system is implemented, classifying and extracting samples based on their varying levels of importance in empirical samples. Ultimately, simulations were performed to examine various train tracking scenarios. The findings demonstrate that, in the same scenarios, this algorithm surpasses both the Lagrange-based deep deterministic policy gradient algorithm and the fixed lambda based deep deterministic policy gradient algorithm. The safety performance has been improved by 30% and 60%, and the optimality performance has been improved by 40% and 30%, respectively. This algorithm, when paired with safety experience prioritized replay, achieves faster convergence compared to the enhanced version. In general, this algorithm exhibits robust suitability for train tracking interval control.</p></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142136582","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}

引用次数: 0