核工业中的深度学习:一项调查

IF 7.7 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Big Data Mining and Analytics Pub Date : 2022-01-25 DOI:10.26599/BDMA.2021.9020027
Chenwei Tang;Caiyang Yu;Yi Gao;Jianming Chen;Jiaming Yang;Jiuling Lang;Chuan Liu;Ling Zhong;Zhenan He;Jiancheng Lv
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引用次数: 6

摘要

核工业作为高新技术战略性新兴综合产业,致力于核燃料的研究、生产、加工以及核能的开发利用。如今,核工业在核武器、核电、核医疗、辐射加工等应用领域取得了显著进展。随着人工智能的发展和";工业4.0;,越来越多的人工智能技术被引入核产业链,以提高生产效率,降低运营成本,提高运营安全,实现风险规避。与此同时,深度学习作为人工智能的重要技术,在核工业的理论和应用研究方面取得了惊人的进展,有力地推动了核工业信息化、数字化和智能化的发展。本文首先对核工业全产业链中的智能化需求场景进行了简单梳理和分析。然后,我们讨论了核产业链中涉及的数据类型。之后,我们调查了深度学习在核工业中不同数据类型对应的应用领域的研究现状。最后,我们讨论了深度学习在核工业中的局限性和独特挑战,以及智能核工业的未来方向。
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Deep learning in nuclear industry: A survey
As a high-tech strategic emerging comprehensive industry, the nuclear industry is committed to the research, production, and processing of nuclear fuel, as well as the development and utilization of nuclear energy Nowadays, the nuclear industry has made remarkable progress in the application fields of nuclear weapons, nuclear power, nuclear medical treatment, radiation processing, and so on. With the development of artificial intelligence and the proposal of "Industry 4.0", more and more artificial intelligence technologies are introduced into the nuclear industry chain to improve production efficiency, reduce operation cost, improve operation safety, and realize risk avoidance. Meanwhile, deep learning, as an important technology of artificial intelligence, has made amazing progress in theoretical and applied research in the nuclear industry, which vigorously promotes the development of informatization, digitization, and intelligence of the nuclear industry. In this paper, we first simply comb and analyze the intelligent demand scenarios in the whole industrial chain of the nuclear industry. Then, we discuss the data types involved in the nuclear industry chain. After that, we investigate the research status of deep learning in the application fields corresponding to different data types in the nuclear industry. Finally, we discuss the limitation and unique challenges of deep learning in the nuclear industry and the future direction of the intelligent nuclear industry.
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来源期刊
Big Data Mining and Analytics
Big Data Mining and Analytics Computer Science-Computer Science Applications
CiteScore
20.90
自引率
2.20%
发文量
84
期刊介绍: Big Data Mining and Analytics, a publication by Tsinghua University Press, presents groundbreaking research in the field of big data research and its applications. This comprehensive book delves into the exploration and analysis of vast amounts of data from diverse sources to uncover hidden patterns, correlations, insights, and knowledge. Featuring the latest developments, research issues, and solutions, this book offers valuable insights into the world of big data. It provides a deep understanding of data mining techniques, data analytics, and their practical applications. Big Data Mining and Analytics has gained significant recognition and is indexed and abstracted in esteemed platforms such as ESCI, EI, Scopus, DBLP Computer Science, Google Scholar, INSPEC, CSCD, DOAJ, CNKI, and more. With its wealth of information and its ability to transform the way we perceive and utilize data, this book is a must-read for researchers, professionals, and anyone interested in the field of big data analytics.
期刊最新文献
Contents Front Cover Incremental Data Stream Classification with Adaptive Multi-Task Multi-View Learning Attention-Based CNN Fusion Model for Emotion Recognition During Walking Using Discrete Wavelet Transform on EEG and Inertial Signals Gender-Based Analysis of User Reactions to Facebook Posts
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