Engineering Applications of Artificial Intelligence最新文献

{"title":"Coupled flows as guidance for model-based policy optimization","authors":"Shengrong Gong ,&nbsp;Yi Wang ,&nbsp;Xin Du ,&nbsp;Yuya Sun ,&nbsp;Lifan Zhou ,&nbsp;Shan Zhong","doi":"10.1016/j.engappai.2025.111528","DOIUrl":"10.1016/j.engappai.2025.111528","url":null,"abstract":"<div><div>Model-based reinforcement learning (MBRL) offers high sample efficiency but suffers from cumulative multi-step prediction errors that degrade long-term performance. To address this, we propose a coupled flows-guided policy optimization framework, where two coupled flows quantify and minimize the discrepancy between the true and learned state–action distributions. By reducing this divergence, the loss functions serve as both a discriminator, selecting more accurate rollouts for policy learning, and a reward signal, refining the dynamics model to mitigate multi-step errors. Theoretical analysis establishes a bound on the expected return discrepancy. Empirical evaluations demonstrate that our method achieves higher cumulative rewards than the representative model-based approaches across diverse control tasks. This highlights its applicability in data-scarce domains such as robotics, recommendation systems, and autonomous driving.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"159 ","pages":"Article 111528"},"PeriodicalIF":7.5,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523210","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 dual-objective contrastive learning approach with dynamic self-adaption for zero-shot fault diagnosis","authors":"Yifan Wu,&nbsp;Min Xia","doi":"10.1016/j.engappai.2025.111660","DOIUrl":"10.1016/j.engappai.2025.111660","url":null,"abstract":"<div><div>Fault type classification and fault severity identification are two critical and complementary tasks in fault diagnosis of industrial machines, providing essential information for the maintenance and safety of the machines. However, variable operating conditions in industrial settings make it hard to collect comprehensive fault data covering all possible types and severities, thereby limiting diagnostic efficiency. To overcome these challenges, a novel multi-task network approach is proposed to detect fault type and severity simultaneously even with zero novel samples. Discriminative features are extracted through a contrastive network with task-specific projection heads, enabling the capture of distinct representations for fault type and severity. Two zero-shot mapping spaces are constructed to diagnose fault types and severity by aligning feature representations with the semantic information of fault types and severity. A dynamic self-adaptation optimization mechanism is introduced considering the dependency of fault severity on fault types. It enhances the identification of fault severity. The proposed method was evaluated on two bearing datasets. It achieved up to 89.4 % accuracy for fault type and 83.42 % for fault severity under zero-shot settings, outperforming baselines and demonstrating strong real-world applicability.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"159 ","pages":"Article 111660"},"PeriodicalIF":7.5,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523221","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":"An efficient deep learning strategy for real-time semantic segmentation of trees for embedded systems","authors":"Pierre Leroy ,&nbsp;Emmanuelle Abisset-Chavanne ,&nbsp;Régis Pommier ,&nbsp;Marco Montemurro","doi":"10.1016/j.engappai.2025.111516","DOIUrl":"10.1016/j.engappai.2025.111516","url":null,"abstract":"<div><div>Real-time segmentation plays a critical role in semantic simultaneous localization and mapping (SLAM) and autonomous navigation, where speed of inference is often prioritized over pixel-level accuracy. Existing segmentation models, such as “You Only Look Once” version 8 (YOLOv8) or tree detection and diameter estimation algorithm based on deep learning (known as “Perceptree”) are designed for generic use cases, leading to unnecessary computational overhead in structured environments such as managed pine forests. In this paper, we propose a lightweight and optimized method for real-time tree segmentation using red, green, blue, and depth channels (RGB-D) data. Our contribution is threefold. The first contribution focuses on depth-guided region proposal: we extract candidate regions from the depth map using mathematical filtering techniques, thus reducing the search space of the supervised model. The second one deals with the development of an embedded-friendly backbone: we simplify the YOLOv8 backbone while integrating depth information, improving inference speed without compromising key features for similarly shaped and sized objects. The last one focuses on the development of a compact segmentation head: instead of pixel-wise classification, we estimate polynomial coefficients to represent object contours, drastically reducing the number of parameters and accelerating inference. Our model achieves 53 frames per second on a ray tracing 2060 super graphics processing unit (GPU), which is 2.7 times faster than YOLOv8 and 10.8 times faster than Perceptree, while achieving a mean average precision score of 78.13% on real forest data.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"159 ","pages":"Article 111516"},"PeriodicalIF":7.5,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523257","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":"Development of intelligent equipment for weed identification and variable spraying in lettuce fields based on instance segmentation framework","authors":"Long-Tao Niu,&nbsp;Wen-Hao Su,&nbsp;He-Yi Zhang,&nbsp;Qi Wang,&nbsp;Bo-Wen Dong,&nbsp;Yankun Peng","doi":"10.1016/j.engappai.2025.111634","DOIUrl":"10.1016/j.engappai.2025.111634","url":null,"abstract":"<div><div>Weeds in the field compete with crops for nutrients, water and sunlight, hindering the early growth of crops. If not controlled in time, weeds may adversely affect crop growth and yield. Although chemical weed control is low cost, efficient and widely applicable, excessive use of chemical agents may lead to herbicide residues and environmental pollution. In this study, an instance segmentation-based intelligent equipment was developed for weed recognition and targeted variable-rate spraying in lettuce fields. The You-Only-Look-Once version 8 segmentation (YOLOv8-seg) model was optimized through three key enhancements. Initially, Depthwise Separable Convolution (DSConv) was adopted to replace standard convolutional layers, effectively reducing model complexity, and improving computational efficiency. After that, a novel Faster Implementation of Cross Stage Partial Bottleneck with 2 Convolutions-Star shaped Convolutional (C2f_Star) module was proposed, which integrated the StarBlock from the Star-shaped Convolutional Neural Network (StarNet) into the existing structure, thereby enhancing the feature extraction capabilities of the model. Finally, the Simple Attention Module (SimAM), a parameter-free attention mechanism, was introduced to improve the model's attention to relevant features without increasing the number of parameters. These improvements led to the development of the YOLOv8n-seg model, which achieved a mean Average Precision (mAP) of 90.15 % at 0.5 Intersection over Union (IoU), with 2,281,702 parameters and an inference speed of 15.7 ms per frame. Compared with the original model, the average precision and inference speed increased by 2.65 % and 4.3 %, respectively, while the number of parameters was reduced by 30 %. By combining this model with post-processing algorithms, a precision variable spraying algorithm and equipment were developed. Laboratory experiments at three different weed density levels demonstrated that the system achieved an average recognition accuracy of 95.2 % and a target spraying success rate of 97.2 % for weeds in lettuce fields. Herbicide dosage was reduced by 88.42 %, 65.25 %, and 37.30 % at the three density levels, respectively. This research provides essential theoretical and technical support for the development of precision spraying and weeding robots.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"159 ","pages":"Article 111634"},"PeriodicalIF":7.5,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144522982","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":"Wind turbine blade surface defect detection model based on improved you only look once version 10 small and integrated compression","authors":"Hang Liu,&nbsp;Sheng Liu,&nbsp;Zhijian Liu,&nbsp;Ben Niu,&nbsp;Jing Xie,&nbsp;Chi Luo,&nbsp;Zhiyu Shi","doi":"10.1016/j.engappai.2025.111645","DOIUrl":"10.1016/j.engappai.2025.111645","url":null,"abstract":"<div><div>This paper introduces a new model built on the You Only Look Once version 10 small (YOLOv10s) baseline to address challenges in wind turbine blade surface defect detection, including low accuracy due to complex backgrounds, small targets, and dense defects, as well as issues of model over-parameterization and high memory consumption. Several improvements are incorporated to enhance detection accuracy: (1) the original Spatial Pyramid Pooling Fast (SPPF) module is replaced with a lightweight Contextual Augmentation Module (CAM-DW) to improve feature fusion, (2) Efficient Multi-Scale Attention (EMA) substitutes Partial Self-Attention (PSA) for better feature extraction, and (3) the Wise-Intersection over Union version 1 (WIoU-V1) loss function optimizes detection performance for high-density defect samples. To tackle the problem of excessive parameters and memory usage, an integrated compression method is proposed, which combines isomorphic pruning to reduce parameters and memory usage with channel-wise knowledge distillation to recover accuracy lost during pruning, thus striking a balance between model complexity and performance. Experimental results show that the proposed model reduces parameters by 69.7 % and memory usage by 68.1 % compared to the baseline. Its mean Average Precision mAP50 (prediction confidence threshold: 0.5) and mAP50-95 (prediction confidence thresholds: 0.5–0.95) improved by 3.3 % and 3.8 %, respectively, while detection speed increased by 46.7 Frames Per Second (FPS). These results demonstrate that the proposed model outperforms mainstream models, significantly enhancing the accuracy and efficiency of wind turbine blade surface defect detection, and providing crucial support for intelligent wind power equipment operation and maintenance.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"159 ","pages":"Article 111645"},"PeriodicalIF":7.5,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523125","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":"Distribution line inspection method using multi-scale information augmentation and ensemble learning","authors":"Yihao Liang ,&nbsp;Liangwu Wei ,&nbsp;Yanzhi Song ,&nbsp;Zhouwang Yang","doi":"10.1016/j.engappai.2025.111461","DOIUrl":"10.1016/j.engappai.2025.111461","url":null,"abstract":"<div><div>This inspection of power distribution lines is evolving from manual to digitalized and intelligent methods with the advancement of Unmanned Aerial Vehicle (UAV) and artificial intelligence algorithms. However, existing universal or those focusing on specific defect detectors struggle to handle the complexity of real-world inspection tasks. To bridge this gap, we proposed a comprehensive distribution line inspection method based on You Only Look Once Exceeding (YOLOX), capable of accurately detecting 14 key defects across 6 critical components. Through large-scale UAV data analysis, we identified two major challenges in practical scenarios: Small Object Detection and Long-tailed Distribution. These insights guide the development of more robust and generalizable inspection methods. And to address these two challenges, we introduced a Multi-Scale Information Augmentation module to enhance the detection of small defects in high-resolution images, and a Dual-Branch Ensemble Classifier structure to mitigate the impact of imbalanced defect distribution. Extensive experiments demonstrated the practical effectiveness of our method, achieving a <span><math><mrow><mi>R</mi><mi>e</mi><mi>c</mi><mi>a</mi><mi>l</mi><mi>l</mi></mrow></math></span> of 76.2%, <span><math><mrow><mi>P</mi><mi>r</mi><mi>e</mi><mi>c</mi><mi>i</mi><mi>s</mi><mi>i</mi><mi>o</mi><mi>n</mi></mrow></math></span> of 59.7%, and <span><math><mrow><mi>A</mi><mi>v</mi><mi>e</mi><mi>r</mi><mi>a</mi><mi>g</mi><mi>e</mi><mspace></mspace><mi>P</mi><mi>r</mi><mi>e</mi><mi>c</mi><mi>i</mi><mi>s</mi><mi>i</mi><mi>o</mi><mi>n</mi></mrow></math></span> at 50% Intersection over Union Threshold (<span><math><mrow><mi>A</mi><msub><mrow><mi>P</mi></mrow><mrow><mn>50</mn></mrow></msub></mrow></math></span>) of 52.6%. Notably, our method delivered a 51.9% improvement in small-defect <span><math><mrow><mi>R</mi><mi>e</mi><mi>c</mi><mi>a</mi><mi>l</mi><mi>l</mi></mrow></math></span> and a 24.3% increase in tail-category performance compared to the baseline YOLOX. A fully implemented inspection system built on our framework has been deployed to assist field engineers, significantly reducing manual workload and improving inspection efficiency. These results demonstrated the robustness and adaptability of our method, offering a valuable contribution to the intelligent operation and maintenance of modern power distribution networks.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"159 ","pages":"Article 111461"},"PeriodicalIF":7.5,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523121","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":"Enhancing road crack detection with Neural Architecture Seeks Large Neural Network: Leveraging deep learning and Augmented Minority Over-Sampling Technique on public and custom developed datasets","authors":"Asad Ullah ,&nbsp;Sanam Shahla Rizvi ,&nbsp;Shengjun Xu ,&nbsp;Amna Khatoon ,&nbsp;Se Jin Kwon","doi":"10.1016/j.engappai.2025.111507","DOIUrl":"10.1016/j.engappai.2025.111507","url":null,"abstract":"<div><div>Deep neural networks for identifying road cracks have emerged as a crucial field of study, marking a significant advancement in infrastructural maintenance. The proposed research presents a novel Neural Architecture Seeks Large Neural Network for detecting road cracks, featuring 27 convolutional layers and ten modules, leveraging Softmax for classification. Initially, the custom developed dataset contained 30,283 images, expanded to 218,073 images using the Augmented Minority Over-Sampling Technique. For better comparison, only 30,350 images are utilized from this expanded data set. Similarly, the Karlsruhe Institute of Technology and Toyota Technological Institute dataset grew from 30,274 to 217,972 images after Augmented Minority Over-Sampling Technique processing. However, 30,274 images from the original dataset and 30,327 images from the Augmented Minority Over-Sampling Technique dataset have been processed. This normalization process aimed to ensure a balanced comparative study between the original and augmented datasets, minimizing differences and enhancing the reliability of results across the datasets. Utilizing a 70/30 train-test split, the network effectively classifies seven types of crack anomalies. The model achieves 83.7% and 89.8% accuracy on the original and augmented Karlsruhe Institute of Technology and Toyota Technological Institute datasets. The custom dataset reaches up to 91.0% accuracy for post-augmentation, while the pre-augmentation accuracy is 90.7%.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"159 ","pages":"Article 111507"},"PeriodicalIF":7.5,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523252","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":"Interpretable adaptive multiwavelet kernel-driven two-dimensional convolutional neural network for mechanical fault diagnosis","authors":"Tianheng Hai,&nbsp;Jing Yuan,&nbsp;Huiming Jiang,&nbsp;Qian Zhao","doi":"10.1016/j.engappai.2025.111614","DOIUrl":"10.1016/j.engappai.2025.111614","url":null,"abstract":"<div><div>Despite extensive research on convolutional neural networks (CNNs) for intelligent fault diagnosis, several challenges remain, including the limited effectiveness of one-dimensional CNNs, susceptibility to noise, and lack of interpretability. To address these issues, an interpretable adaptive multiwavelet kernel-driven two-dimensional convolutional neural network model called MWKN has been designed for mechanical fault diagnosis in this paper. Specifically, a newly designed adaptive natural convolutional layer based on two-dimensional multiwavelet transform is embedded as a feature extraction module in the shallow layer of a two-dimensional CNN model. Crucially, this novel multiwavelet convolutional layer is jointly optimized with the entire network, enabling the adaptive optimization of its intrinsic multiwavelet convolutional kernel. Additionally, this model incorporates a specifically designed embedded two-dimensional neighboring coefficient shrinkage module to address the issue of CNN susceptibility to strong noise. This study investigates the interpretability of the MWKN model through simulated fault experiments, addressing interpretability deficits observed in CNN. The results demonstrate that the embedded multiwavelet kernel and the inclusive WMKN model as a whole possess synchronized learning and matching rules, substantiating that the learning process of the multiwavelet kernel is neither isolated nor random but follows an inherent adaptive learning mechanism based on the principle of error minimization. Finally, the excellent fault identification capability and robust noise resistance of MWKN are validated through experimental cases of variable speed bearing faults and pump circulation bearing faults under strong noise background. In addition, the industrial applicability and interpretability of the method were further validated in an industrial scenario case.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"159 ","pages":"Article 111614"},"PeriodicalIF":7.5,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523253","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":"An initiative in medical diagnosis for detecting the factors of hypoglycemia disease with a new approach of interval-valued Pythagorean fuzzy linear Diophantine Aczel Alsina aggregation operators","authors":"Sarah Asghar ,&nbsp;Syed Tauseef Saeed ,&nbsp;Zeeshan Ali ,&nbsp;Amir Hussain ,&nbsp;Abdulrahman A. Almehizia ,&nbsp;Salman Saleem","doi":"10.1016/j.engappai.2025.111499","DOIUrl":"10.1016/j.engappai.2025.111499","url":null,"abstract":"<div><div>Hypoglycemia is a situation when blood sugar levels drop below normal. It is most commonly associated with Diabetes, particularly in individuals who are taking insulin or other medications that increase insulin secretion. However, it can also occur in people without Diabetes due to various factors like fasting, alcohol consumption, or certain medical conditions. Our main goal is to create a path to calculate the ranking of the most risky factor that can cause Hypoglycemia in the human body. By adopting interval-based fuzzy logic and preserving the Pythagorean constraints, interval-valued Pythagorean fuzzy sets (IVPyFS) provide an effective method for modeling and resolving the issues that involve ambiguity, uncertainty, and incomplete information. The IVPyFS allows the experts to describe their opinions independently using the degree of membership (MD) and non-membership (NMD). We use the Aczel-Alsina operations to enhance the flexibility when the information is obtained to detect hypoglycemia. Consequently, we created a new idea of interval-valued Pythagorean fuzzy (IVPyF) linear Diophantine set (IVPyFLDS). We have formed the weighted average and geometric operators using Aczel-Alsina triangular norms. We investigate some fundamental properties of the developed operators. Furthermore, we observe the sensitivity of the results and compare the results for the justification.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"158 ","pages":"Article 111499"},"PeriodicalIF":7.5,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144517096","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":"Transformer based lifetime interval prediction for dynamic operating proton exchange membrane fuel cells","authors":"Haolong Li ,&nbsp;Liang Xie ,&nbsp;Dongqi Zhao ,&nbsp;Ze Zhou ,&nbsp;Liyan Zhang ,&nbsp;Qihong Chen","doi":"10.1016/j.engappai.2025.111444","DOIUrl":"10.1016/j.engappai.2025.111444","url":null,"abstract":"<div><div>Remaining useful life is crucial for proton exchange membrane fuel cell (PEMFC). However, the complex decay mechanism makes existing methods incapable of quantifying the PEMFC decay uncertainty. To address above issues, a hybrid interval prediction method (HIPM) is proposed. First, multi-feature fusion based on incremental empirical modal decomposition (IEMD) decomposes and reorganizes the nonlinear features of the PEMFC into multiscale degradation components. Second, the temporal Transformer effectively addresses the challenge of modeling long-term dependencies in PEMFC degradation prediction. Third, a novel interval prediction method precisely quantize the uncertainty of PEMFC degradation. Experimental results show HIPM achieves a root mean square error of 0.0047 with limited training data while accurately quantifying PEMFC degradation uncertainty across all conditions.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"158 ","pages":"Article 111444"},"PeriodicalIF":7.5,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144517567","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}