Robotics and Computer-integrated Manufacturing最新文献

{"title":"A robotic framework for high-throughput and multi-view 3D digital image correlation (3D-DIC): Increasing measurement volume and versatility for deformation analysis","authors":"Özgüç Bertuğ Çapunaman ,&nbsp;Alale Mohseni ,&nbsp;Dennis Dombrovskij ,&nbsp;Kaiyang Yin ,&nbsp;Benay Gürsoy ,&nbsp;Max David Mylo","doi":"10.1016/j.rcim.2025.103187","DOIUrl":"10.1016/j.rcim.2025.103187","url":null,"abstract":"<div><div>Three-dimensional digital image correlation (3D-DIC) is a widely applicable, non-contact optical imaging technique for accurately quantifying full-field surface displacements and strains in materials and structures. However, conventional 3D-DIC implementations relying on fixed stereo camera positions face trade-offs between the field-of-view and spatial resolution and lack high-throughput for long-duration measurements. Here we present an integrated robotic 3D-DIC framework that employs an industrial robotic arm to autonomously and repeatedly reposition stereo cameras. This enables automated calibration, monitoring of multiple samples over extended periods, and expansion of the effective spatial coverage and data throughput, all while maintaining calibration stability and measurement fidelity. We validate this approach on rigid and deforming reference samples and demonstrate its ability to quantify material deformation of bio-composite samples simultaneously during the drying process. Under robotic repositioning, rigid samples exhibit stable displacement and strain measurements while benefiting from significantly increased volumetric coverage and reduced manual oversight. Thus, the proposed system improves experimental efficiency and allows for the incorporation of advanced techniques, such as multi-view stitching, to characterize complex geometries with higher effective resolution. When applied to slowly deforming bio-composites, the system can capture time-lapse images from multiple viewpoints, providing a more comprehensive assessment of complex, evolving material behaviors. These enhancements in 3D-DIC further improve geometric accuracy, increase data density, and expand applicability to a broader range of materials and experimental conditions. Ultimately, the proposed robot-assisted 3D-DIC system creates a robust, high-throughput monitoring framework for bio-fabrication, additive manufacturing, and advanced composite processing, paving the way for targeted programming of shape changes, among other applications.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"99 ","pages":"Article 103187"},"PeriodicalIF":11.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145595008","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":"Graph-based multi-scale fusion learning for STEP-NC machining feature recognition","authors":"Zichuan Chai ,&nbsp;Wenlei Xiao ,&nbsp;Gang Zhao ,&nbsp;Tianze Qiu ,&nbsp;Yan Liu ,&nbsp;Songyuan Xue ,&nbsp;Oluwasheyi Oyename ,&nbsp;Zheng Shi","doi":"10.1016/j.rcim.2025.103210","DOIUrl":"10.1016/j.rcim.2025.103210","url":null,"abstract":"<div><div>The integration of AI into next-generation CAM systems has attracted significant research interest. Wherein, automatic feature recognition is a critical prerequisite before machining paths could be generated accordingly. Consequently, researchers have increasingly leveraged deep learning methodologies for geometric feature recognition from B-rep models. However, research targeting the recognition of machining features that ensure compatibility with downstream CAM toolpath generation remains limited. This paper proposes a multi-scale fusion graph neural network framework that embeds STEP-NC machining features to enhance their potency on the subsequent toolpath generation. Initially, feature semantics are extracted in accordance with the STEP-NC ISO 14649 standard, and a fusion network is constructed by integrating the adjacent-face aggregation of the GIN with the multi-head self-attention mechanism of the Graph Transformer. In the output layer, fine-grained label decomposition is performed based on standard definitions, enabling concurrent prediction of feature categories and their associated EXPRESS representations. Following pre-training, the model undergoes unsupervised fine-tuning on unlabeled real-world workpiece data to improve its generalization performance in practical manufacturing scenarios. Experimental results achieve over 85% recognition accuracy for real-part machining features in the automated manufacturing tasks.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"99 ","pages":"Article 103210"},"PeriodicalIF":11.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145823067","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":"Time–torque coordinated optimization for trajectory planning of industrial robots","authors":"Zeyun Xiao,&nbsp;Danfeng Sun,&nbsp;Donglai Zhu,&nbsp;Yong Wang,&nbsp;Yi Yan,&nbsp;Huifeng Wu","doi":"10.1016/j.rcim.2025.103199","DOIUrl":"10.1016/j.rcim.2025.103199","url":null,"abstract":"<div><div>Trajectory optimization is vital for improving the operational efficiency and reliability of industrial robotic arms. However, increasing task execution speed frequently induces sharp fluctuations in joint torque, which elevates energy consumption, places excessive stress on actuators, and excites structural vibrations. To tackle these issues, this study introduces a unified time–torque optimization framework that combines predictive modeling with heuristic search. The framework adopts a neural network incorporating frequency-domain correlation and a delay-aware mechanism to model dynamic torque variations. Based on the predicted torque profiles, the robot’s motion trajectory is parameterized by quintic polynomials, and a multi-objective loss function is constructed to jointly minimize execution time and torque variation rate. Particle Swarm Optimization (PSO) is employed to perform a global search for intermediate joint velocities and accelerations, improving convergence toward near-optimal solutions. Experiments on a six-axis industrial robotic platform demonstrate that the proposed method effectively reduces execution time and smooths torque transitions, confirming its practicality for industrial applications.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"99 ","pages":"Article 103199"},"PeriodicalIF":11.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145651051","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":"Target-oriented collision-free robot grasping using task-attendance teachers-student knowledge distillation for various dense-clutter scenarios","authors":"Shaodong Li ,&nbsp;Cheng Xiang ,&nbsp;Wei Du ,&nbsp;Xi Liu ,&nbsp;Huajian Song ,&nbsp;Feng Shuang","doi":"10.1016/j.rcim.2025.103201","DOIUrl":"10.1016/j.rcim.2025.103201","url":null,"abstract":"<div><div>The target-oriented collision-free robot grasping in dense-clutter scenarios has made significant progress. However, the prior studies primarily focus on the complex problem brought about by an increase in object quantity. Actually, the various scenarios will also result in the challenge. That means skill models will inevitably start to obtain bloat, as more subtasks appear. Our previous study presents a multi-teacher distillation strategy, thereby effectively avoiding bloat. Unfortunately, it still has trouble in execution capability when the scenarios have more subtasks and highly similar subtasks. The root cause is attributed to the inadequate capability of distilled student in subtask identification. It is naive to develop the subtask classifier based on the traditional data-driven way when human experience fails to work. Therefore, we propose a dataset-free classifier by migrating the classification capability of teachers to the student. Then, we further propose a task-attendance teachers-student knowledge distillation strategy to afford the various scenarios involving more and highly similar subtasks, thus significantly enhancing the performance of grasping. And we also leverage the language instruction to ensure the mask map of target object through LLM, improving the intuitiveness of human-robot cooperation. Extensive comparative experiment verifies the advantage of our framework. We measure the capability of teachers-student distillation, and value of dataset-free classifier in our framework. Importantly, the performance of segmentation strategy is tested under ambiguous instruction, visual occlusion, and color conflict. Furthermore, the excellent generalization and robustness are exhibited according to the real-world experiments involving unseen object, unseen task modality, and disturbance existing.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"99 ","pages":"Article 103201"},"PeriodicalIF":11.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145731770","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":"From drawings to decisions: A hybrid vision-language framework for parsing 2D engineering drawings into structured manufacturing knowledge","authors":"Muhammad Tayyab Khan ,&nbsp;Lequn Chen ,&nbsp;Zane Yong ,&nbsp;Jun Ming Tan ,&nbsp;Wenhe Feng ,&nbsp;Seung Ki Moon","doi":"10.1016/j.rcim.2025.103186","DOIUrl":"10.1016/j.rcim.2025.103186","url":null,"abstract":"<div><div>Efficient and accurate extraction of key information from 2D engineering drawings is essential for advancing digital manufacturing workflows. This information includes elements such as geometric dimensioning and tolerancing (GD&amp;T), measures, material specifications, and textual annotations. Manual extraction remains slow and labor-intensive, while generic optical character recognition (OCR) models often fail to interpret 2D drawings accurately due to complex layouts, engineering symbols, and rotated annotations. These limitations result in incomplete and unreliable outputs. To address these challenges, this paper proposes a hybrid vision-language framework that integrates a rotation-aware object detection model (YOLOv11-obb) with a transformer-based vision-language parser. We introduce a structured parsing pipeline that first applies YOLOv11-obb to localize annotations and extract oriented bounding box (OBB) image patches, which are subsequently parsed into structured outputs using a fine-tuned, lightweight vision-language model (VLM). To develop and evaluate this pipeline, we curate a dataset of 1367 2D mechanical drawings manually annotated across nine key categories: GD&amp;Ts, General Tolerances, Measures, Materials, Notes, Radii, Surface Roughness, Threads, and Title Blocks. YOLOv11-obb is trained on this dataset to detect OBBs and extract annotation patches. These image patches are then parsed using two fine-tuned open-source VLMs. The first is Donut, a transformer-based model that combines a Swin-B visual encoder with a BART text decoder, enabling end-to-end parsing directly from images without relying on OCR. The second is Florence-2, a prompt-driven encoder–decoder model that integrates a DaViT vision backbone and supports structured output generation through multimodal token alignment. Both models are lightweight and well-suited for specialized industrial tasks under limited computational overhead. Following fine-tuning of both models on the curated dataset of image patches paired with structured annotation labels, a comparative experiment is conducted to evaluate parsing performance across four key metrics. Donut outperforms Florence-2, achieving 89.2 % precision, 99.2 % recall, and a 94 % F1-score, with a hallucination rate of 10.8 %. Finally, a case study demonstrates how the extracted structured information supports downstream manufacturing tasks such as process and tool selection, showcasing the practical utility of the proposed framework in modernizing 2D drawing interpretation.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"99 ","pages":"Article 103186"},"PeriodicalIF":11.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145614041","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":"Dual-service combination optimization of manufacturing and logistics: models for self-managed and third-party logistics in cloud manufacturing","authors":"Chunhua Tang ,&nbsp;Shuangyao Zhao ,&nbsp;Ting Huang ,&nbsp;Mark Goh","doi":"10.1016/j.rcim.2025.103178","DOIUrl":"10.1016/j.rcim.2025.103178","url":null,"abstract":"<div><div>Service combination (SC) is a critical technique in cloud manufacturing, enabling the integration of multiple services to deliver value-added solutions. Logistics plays a pivotal role in SC by ensuring seamless coordination across various manufacturing stages, thereby maximizing the efficiency of production flows. This implies that the SC process must integrate both manufacturing services (MSs) and logistics services (LSs) to determine the optimal combination strategy. Prior research has focused mainly on MS performance, often overlooking the critical impact of logistics on SC outcomes. Although some studies have incorporated logistics considerations, they have largely treated logistics attributes as secondary components of MS evaluations or adopted linear aggregation methods to jointly configure MSs and LSs. These approaches fail to capture the dynamic nature of logistics performance and the interdependencies between MSs and LSs. To address these gaps, this study develops two optimization models for SC that integrate both MSs and LSs, tailored for self-managed and third-party logistics modes. In particular, an innovative bi-level optimization model is introduced to capture the sequential dependencies and dynamic interactions between MSs and LSs in logistics outsourcing, ensuring seamless integration. The upper level focuses on optimizing the MS selection, while the lower level identifies the optimal LSs based on the determined MSs. Improved genetic algorithms incorporating adaptive and parallel mechanisms are developed to address the models, dynamically adjusting parameters to improve solution accuracy and efficiency. Case studies and numerical experiments validate the effectiveness of the proposed models and algorithms, offering actionable managerial insights grounded in the results.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"99 ","pages":"Article 103178"},"PeriodicalIF":11.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145614037","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":"Graph-driven Single-Robot Multi-Cognitive Agent System architecture for human–robot collaborative disassembly","authors":"Jianhao Lv,&nbsp;Jiahui Si,&nbsp;Wenchao Li,&nbsp;Ding Gao,&nbsp;Jinsong Bao","doi":"10.1016/j.rcim.2025.103207","DOIUrl":"10.1016/j.rcim.2025.103207","url":null,"abstract":"<div><div>The inherent limitations of single-agent systems in tackling complex tasks, combined with the inefficiencies of traditional multi-agent paradigms—where task decomposition requires distribution among multiple robots, resulting in resource redundancy and escalated costs. To address this critical constraint, a graph-driven Single-Robot Multi-Cognitive Agent System architecture is proposed. Firstly, scene graphs are constructed to transform unstructured visual data from the environment into graph-based triplets. By aligning these triplets with pre-constructed knowledge graphs, historical memories are activated through graph matching to inform system decision-making with precedented insights. Then, an attention-driven collaboration mechanism dynamically designates leader and supporter roles among the different agents, ensuring adaptive role assignment based on contextual demands. Complementing this, a global optimization framework facilitates the collective evolution of the Single-Robot Multi-Cognitive Agent System, enhancing both individual agent performance and inter-agent collaboration. Finally, the Model Context Protocol orchestrates robotic execution by harmonizing external resource utilization with computational processes, ensuring seamless translation of decision outputs into physical actions. Experimental results demonstrate that the method exhibits strong robustness and generalizability in dynamic disassembly queries.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"99 ","pages":"Article 103207"},"PeriodicalIF":11.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145785002","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 unified framework for large language model-guided reinforcement learning in digital twin industrial environments","authors":"Haolin Fan ,&nbsp;Edward Chow ,&nbsp;Thomas Lu ,&nbsp;Jerry Ying Hsi Fuh ,&nbsp;Wen Feng Lu ,&nbsp;Bingbing Li","doi":"10.1016/j.rcim.2025.103215","DOIUrl":"10.1016/j.rcim.2025.103215","url":null,"abstract":"<div><div>Digital twin (DT) optimization in industrial environments faces persistent challenges, including sample inefficiency, extensive training requirements, and limited cross-domain adaptability. This paper presents a unified three-phase framework that integrates large language models (LLMs) with reinforcement learning (RL) via imitation learning (IL). The proposed approach comprises three key components: (1) offline expert demonstration collection using LLM-generated multi-agent coordination strategies, (2) offline and supervised IL to clone these strategies using a centralized training and decentralized execution (CTDE) architecture, and (3) lightweight RL fine-tuning to optimize the pre-trained policy. The system resolves equipment assignment conflicts and leverages coordination history for adaptive decision-making. Experiments in multi-agent industrial scenarios, including human–machine collaboration and fatigue-aware maintenance, demonstrate that our IL+RL hybrid reduces online training time by up to 96% while maintaining over 66% of optimal task performance, using only 4% of the training episodes required by standard RL. The approach also achieves 30%–40% task completion in zero-shot cross-domain settings (e.g., warehouse, manufacturing), and up to 99.7% with minimal fine-tuning. Conceptually, the framework establishes a new paradigm of ”language-conditioned IL,” where reasoning from general-purpose LLMs serves as an adaptive prior for efficient multi-agent coordination in DT. The results highlight how LLM-guided demonstrations can bridge symbolic reasoning and adaptive learning, offering both conceptual and practical advances for scalable, sample-efficient decision-making in Industry 5.0 systems.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"99 ","pages":"Article 103215"},"PeriodicalIF":11.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145883839","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 hierarchical spatial–aware algorithm with efficient reinforcement learning for human–robot task planning and allocation in production","authors":"Jintao Xue,&nbsp;Xiao Li,&nbsp;Nianmin Zhang","doi":"10.1016/j.rcim.2025.103159","DOIUrl":"10.1016/j.rcim.2025.103159","url":null,"abstract":"<div><div>In advanced manufacturing systems, humans and robots collaborate to conduct the production process. Effective task planning and allocation (TPA) is crucial for achieving high production efficiency, yet it remains challenging in complex and dynamic manufacturing environments. The dynamic nature of humans and robots, particularly the need to consider spatial information (e.g., humans’ real-time position and the distance they need to move to complete a task), substantially complicates TPA. To address the above challenges, we decompose production tasks into manageable subtasks. We then implement a real-time hierarchical human–robot TPA algorithm, including a high-level agent for task planning and a low-level agent for task allocation. For the high-level agent, we propose an efficient buffer-based deep Q-learning method (EBQ), which reduces training time and enhances performance in production problems with long-term and sparse reward challenges. For the low-level agent, a path planning-based spatially aware method (SAP) is designed to allocate tasks to the appropriate human–robot resources, thereby achieving the corresponding sequential subtasks. We conducted experiments on a complex real-time production process in a 3D simulator. The results demonstrate that our proposed EBQ&amp;SAP method effectively addresses human–robot TPA problems in complex and dynamic production processes.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"98 ","pages":"Article 103159"},"PeriodicalIF":11.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145261988","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 lightweight object detection approach for precision gripping in multiple peg-in-hole assembly tasks","authors":"Jianjun Jiao ,&nbsp;Zonggang Li ,&nbsp;Guangqing Xia ,&nbsp;Guoping Wang ,&nbsp;Yinjuan Chen ,&nbsp;Ruibing Gao","doi":"10.1016/j.rcim.2025.103185","DOIUrl":"10.1016/j.rcim.2025.103185","url":null,"abstract":"<div><div>Automating product assembly using manipulators in manufacturing remains challenging. This is mainly because detection and gripping prior to component assembly still depend heavily on manual operations and traditional teaching methods, resulting in a low overall level of automation. The primary difficulty in detection and gripping arises from the precise recognition of rotation angles and the complex demands for accuracy, real-time performance, and stability. This paper presents an improved lightweight model, IDPC-YOLOv8, for multiple peg-in-hole workpiece detection and gripping to address these challenges. The proposed approach integrates adaptive image preprocessing to enhance visual clarity under varying lighting conditions and employs an efficient network architecture that jointly exploits global and local features to improve detection precision and computational efficiency. In addition, a rotation-aware detection strategy is introduced to enable accurate prediction of object orientation. Moreover, a network optimization scheme further reduces model parameters, making the system suitable for real-time deployment. Experimental results reveal that the IDPC-YOLOv8 model achieves an accuracy of 97.8% and a detection speed of 126.59 FPS, representing improvements of 4% and 8.3%, respectively, over the original YOLOv8-OBB model. Compared to several state-of-the-art rotation detection models, IDPC-YOLOv8 demonstrates superior integration and generalization capabilities. The effectiveness of the proposed method is further validated through excellent gripping success rates achieved in real-world experiments using the AUBO-i5 manipulator.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"98 ","pages":"Article 103185"},"PeriodicalIF":11.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145567450","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}