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Titlebook: Intelligent Computing Theories and Application; 18th International C De-Shuang Huang,Kang-Hyun Jo,Abir Hussain Conference proceedings 2022

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发表于 2025-3-21 17:42:41 | 显示全部楼层 |阅读模式
书目名称Intelligent Computing Theories and Application
副标题18th International C
编辑De-Shuang Huang,Kang-Hyun Jo,Abir Hussain
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Intelligent Computing Theories and Application; 18th International C De-Shuang Huang,Kang-Hyun Jo,Abir Hussain Conference proceedings 2022
描述This two-volume set of LNCS 13393 and LNCS 13394 constitutes - in conjunction with the volume LNAI 13395 - the refereed proceedings of the 18th International Conference on Intelligent Computing, ICIC 2022, held in Xi‘an, China, in August 2022. The 209 full papers of the three proceedings volumes were carefully reviewed and selected from 449 submissions..This year, the conference concentrated mainly on the theories and methodologies as well as the emerging applications of intelligent computing. Its aim was to unify the picture of contemporary intelligent computing techniques as an integral concept that highlights the trends in advanced computational intelligence and bridges theoretical research with applications. Therefore, the theme for this conference was “Advanced Intelligent Computing Technology and Applications”. Papers focused on this theme were solicited, addressing theories, methodologies, and applications in science and technology..
出版日期Conference proceedings 2022
关键词artificial intelligence; computer networks; computer science; computer systems; computer vision; correlat
版次1
doihttps://doi.org/10.1007/978-3-031-13870-6
isbn_softcover978-3-031-13869-0
isbn_ebook978-3-031-13870-6Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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An Efficient Evaluation Mechanism for Evolutionary Reinforcement Learninghe worst scenario, it only reduces the performance marginally. We embed this mechanism into ERL, denoted as E-ERL, and compare it with original ERL and other state-of-the-art RL algorithms. Results on six continuous control problems validate the efficiency of our method.
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A New Fitness-Landscape-Driven Particle Swarm Optimizationed to select a fitter variant from these two variants. Finally, the experimental simulation is executed on 18 benchmark functions to assess the optimization performance of the proposed FLDPSO algorithm. The results show that FLDPSO can improve optimization accuracy and convergence very well.
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Research on Bearing Fault Feature Extraction Based on Graph Wavelet experimental results suggest that the graph wavelet-based bearing fault feature extraction approach has good pattern recognition and is a good method for automatic fault feature extraction and pattern identification.
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