2023 3rd International Symposium on Computer Technology and Information Science (ISCTIS)最新文献_第6页

Deep time series missing data from self-attention-based inference model 基于自注意推理模型的深度时间序列缺失数据

2023 3rd International Symposium on Computer Technology and Information Science (ISCTIS) Pub Date : 2023-07-07 DOI: 10.1109/ISCTIS58954.2023.10213141

Ziyu Li, Weibang Li, Xianyun Wen, Qingxi Lai

引用次数: 0

A Named Entity Recognition Method Combining Keyword and Self attention Mechanism 一种结合关键词和自关注机制的命名实体识别方法

2023 3rd International Symposium on Computer Technology and Information Science (ISCTIS) Pub Date : 2023-07-07 DOI: 10.1109/ISCTIS58954.2023.10213054

Qinwu Wang, Huifang Su, Yongwei Wang, Pengcheng Liu, Yifei Wang, Shengnan Zhou

引用次数: 0

Design and Implementation of SM4 Coprocessor Based on RISC-V 基于RISC-V的SM4协处理器的设计与实现

2023 3rd International Symposium on Computer Technology and Information Science (ISCTIS) Pub Date : 2023-07-07 DOI: 10.1109/ISCTIS58954.2023.10213118

Shangshou Wang, Lei Wang, Huaili Guo, Zheng Wang

引用次数: 0

Fuzzy ISM Application for Designing the Logical Structure of the High-speed Railway Intelligent Dispatching System 模糊ISM在高速铁路智能调度系统逻辑结构设计中的应用

2023 3rd International Symposium on Computer Technology and Information Science (ISCTIS) Pub Date : 2023-07-07 DOI: 10.1109/ISCTIS58954.2023.10213167

Liu Yang, P. Li, Sai Shao

引用次数: 0

Iterative Refinement via Adaptive Subproblem Generation Algorithm (ASGA) for Anytime Multi-Agent Path Finding 基于自适应子问题生成算法(ASGA)的迭代优化随时多智能体寻路

2023 3rd International Symposium on Computer Technology and Information Science (ISCTIS) Pub Date : 2023-07-07 DOI: 10.1109/ISCTIS58954.2023.10213139

Mengtian Li, Yingcen Xiang, Fanying Zhou, Chengshuo Zhai

{"title":"Iterative Refinement via Adaptive Subproblem Generation Algorithm (ASGA) for Anytime Multi-Agent Path Finding","authors":"Mengtian Li, Yingcen Xiang, Fanying Zhou, Chengshuo Zhai","doi":"10.1109/ISCTIS58954.2023.10213139","DOIUrl":"https://doi.org/10.1109/ISCTIS58954.2023.10213139","url":null,"abstract":"Multi-Agent Path Finding (MAPF) is the problem of finding a set of conflict-free paths from a starting location to a goal for multiple agents. Since MAPF is NP-hard, the optimal algorithm takes a long time to solve and is severely limited by the scale of the problem. The suboptimal algorithm is faster, but usually finds a low-quality solution. It is a feasible solution to use the suboptimal algorithm to quickly obtain the initial solution, and then iteratively refine the sub-problem. The key is how to select the agent subset. There are still many limitations in the current research on MAPF iterative refinement and subset selection rules. In view of the deficiencies of the existing MAPF iterative refinement scheme, this paper proposes corresponding improvement schemes:1) An adaptive sub-problem generation algorithm (ASGA) is proposed, which improves the defects of each member rule and eliminates the disadvantages of contingency and insufficiency in the combination rules of fixed order. 2) A global supervision mechanism (GSM) is proposed to solve the problem of repeated inspection of the same agent, which leads to the failure of sub-problem generation and misjudgment of the performance of the rules. 3) Using the ASGA algorithm, an anytime MAPF iterative refinement framework is proposed. Experimental results show that our algorithm outperforms other similar algorithms in refinement efficiency, solution quality improvement and scalability.","PeriodicalId":334790,"journal":{"name":"2023 3rd International Symposium on Computer Technology and Information Science (ISCTIS)","volume":"539 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127954899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research and Implementation of UAV Partial Discharge Detection Technology Based on Acoustic Array 基于声阵列的无人机局部放电检测技术研究与实现

2023 3rd International Symposium on Computer Technology and Information Science (ISCTIS) Pub Date : 2023-07-07 DOI: 10.1109/ISCTIS58954.2023.10213181

Zhezhou Li, Zhuolei Chen, Wenbin Wu, Bojian Chen, Tengfei Han, Renshu Wang

{"title":"Research and Implementation of UAV Partial Discharge Detection Technology Based on Acoustic Array","authors":"Zhezhou Li, Zhuolei Chen, Wenbin Wu, Bojian Chen, Tengfei Han, Renshu Wang","doi":"10.1109/ISCTIS58954.2023.10213181","DOIUrl":"https://doi.org/10.1109/ISCTIS58954.2023.10213181","url":null,"abstract":"With the increasing number of transmission line towers, traditional partial discharge detection methods cannot meet the needs of fast and accurate inspection. Infrared detection methods cannot identify early defects with not clear temperature rise, and ultraviolet detection methods are expensive and have high false alarms. Therefore, this article proposes to use UAV equipped with acoustic array to achieve rapid and accurate large-scale partial discharge defect inspection. The main contributions of this article are as follows: 1. Analyzing the noise characteristics of UAV. 2. Filter out noise from both hardware and software levels. 3. Studying Sound source localization algorithm. 4. Integrating the above results and developing a set of partial discharge detection device suitable for DJI M300 UAV. In order to verify our results, we conducted on-site testing on a 10kV transmission line, and the results showed that the development results can identify and accurately locate partial discharge defects on the tower, which can not be verified from infrared camera.","PeriodicalId":334790,"journal":{"name":"2023 3rd International Symposium on Computer Technology and Information Science (ISCTIS)","volume":"97 7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128001964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive Propagation Model of Network Hotspot Events Based on SEIR 基于SEIR的网络热点事件自适应传播模型

2023 3rd International Symposium on Computer Technology and Information Science (ISCTIS) Pub Date : 2023-07-07 DOI: 10.1109/ISCTIS58954.2023.10213158

Lina Li, G. Liu, Qinghe Yu, Cheng Luo, XinHang Li, Nianfeng Li

引用次数: 0

Meta-Reinforcement Learning-based Vehicle Task Offloading in Internet of Vehicles (IoV) 基于元强化学习的车联网车辆任务卸载

2023 3rd International Symposium on Computer Technology and Information Science (ISCTIS) Pub Date : 2023-07-07 DOI: 10.1109/ISCTIS58954.2023.10213055

Liyuan Wei, Yang Yang, Xiaogao Jia, Caixing Shao

引用次数: 0

Offline Handwriting Recognition of Thai Characters Using Multiple Deep Neural Networks 基于多个深度神经网络的泰文手写离线识别

2023 3rd International Symposium on Computer Technology and Information Science (ISCTIS) Pub Date : 2023-07-07 DOI: 10.1109/ISCTIS58954.2023.10213205

N. Nimsuk, Nichakorn Thumpaiboon, Wilasinee Phuangsri

引用次数: 0

Generating Talking Facial Videos Driven by Speech Using 3D Model and Motion Model 利用三维模型和运动模型生成语音驱动的说话面部视频

2023 3rd International Symposium on Computer Technology and Information Science (ISCTIS) Pub Date : 2023-07-07 DOI: 10.1109/ISCTIS58954.2023.10213134

Fei Pan, Dejun Wang, Zonghua Hu, LongYang Yu

{"title":"Generating Talking Facial Videos Driven by Speech Using 3D Model and Motion Model","authors":"Fei Pan, Dejun Wang, Zonghua Hu, LongYang Yu","doi":"10.1109/ISCTIS58954.2023.10213134","DOIUrl":"https://doi.org/10.1109/ISCTIS58954.2023.10213134","url":null,"abstract":"Facial expression is one of the most important features of a face. In previous works on generating talking facial videos driven by speech without additional driving information, existing models struggled to directly learn the mapping from speech to facial features, resulting in poor quality of generated facial expressions. In this paper, we propose a method for generating speech-driven facial videos using 3D models and motion capture. This method demonstrates good performance in terms of model robustness, adaptation to large head poses, and improvement of fine-grained facial expression details. We learn features from speech, reconstruct the face by fitting 3DMM coefficients using speech, and employ a motion-captured based generative adversarial network to ensure clear facial texture details in the generated faces. On the publicly available dataset VoxCeleb2, our method achieves scores of 31.22 in PSNR, 0.89 in SSIM, 19.4 in FID, and 1.96 in F_LMD, outperforming other methods. On the MEAD dataset, our method achieves scores of 30.65 in PSNR, 0.68 in SSIM, 20.5 in FID, 2.36 in SyncNet, and 2.45 in F_LMD, outperforming other methods. Experimental results demonstrate that our method effectively enhances the model robustness for speech-driven facial video generation without additional driving information.","PeriodicalId":334790,"journal":{"name":"2023 3rd International Symposium on Computer Technology and Information Science (ISCTIS)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116677239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0