Anomaly Detection of Spent Fuel Safeguards Information Based on Machine Learning : A Comparison of performance using XGBoost and OCSVM

IF 2 4区工程技术 Q4 ENERGY & FUELS

Journal of Energy Engineering Pub Date : 2023-06-30 DOI:10.5855/energy.2023.32.2.072

G. Sim, K. Cha, Moon-ghu Park

引用次数: 0

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基于机器学习的乏燃料保障信息异常检测:XGBoost与OCSVM性能比较

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Energy Engineering 工程技术-工程：土木

CiteScore

4.10

自引率

13.60%

发文量

审稿时长

3.1 months

期刊介绍： The Journal of Energy Engineering reports on the scientific and engineering knowledge in the planning, development, management, and finances of energy-related programs. The journal is dedicated to civil engineering aspects of the issues, sources, and programs that are either directly related to, or can ultimately contribute to, the production, distribution, and storage of energy. Multidisciplinary subjects are especially encouraged. Original contributions are encouraged in, but not limited to, the following areas: generation of electric power; nuclear power issues; energy planning (planning for generation capacity expansions, hydropower planning, network and transmission planning, reliability); energy policy and economics (financial and customer markets, regulatory and financial issues); energy development (solar power, renewable energy, waste-to-energy systems); energy systems operation (thermal and hydropower operation and optimization, scheduling, load forecasting, demand-side management); energy efficiency, reducing consumption of or conservation of energy; energy sustainability as related to energy and power production, distribution, and usage; waste management and environmental issues; and energy infrastructure issues (power plant safety, security of infrastructure network).