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Predictive Based Monitoring of Nuclear Plant Component Degradation Using Support Vector Regression.

机译：基于支持向量回归的核电厂组分降解预测监测。

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Agarwal, V.; Alamaniotis, M.; Tsoukalas, L. H.;
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年度 2015
页码 1-11
总页数 11
原文格式 PDF
正文语种 eng
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1. Nuclear power plant components condition monitoring by probabilistic support vector machine [J] . Jie Liu, Redouane Seraoui, Valeria Vitelli, Annals of nuclear energy . 2013,第juna期

机译：概率支持向量机在核电站部件状态监测中的应用
2. Independent component analysis-based non-Gaussian process monitoring with preselecting optimal components and support vector data description [J] . Qingchao Jiang, Xuefeng Yan, Zhaomin Lv, International Journal of Production Research . 2014,第11a12期

机译：基于独立成分分析的非高斯过程监控，并预先选择最佳成分和支持向量数据描述
3. Support vector ensemble for incipient fault diagnosis in nuclear plant components [J] . Abiodun Ayodeji, Yong-kuo Liu Nuclear engineering and technology . 2018,第8期

机译：支持向量集合用于核电站组件的早期故障诊断
4. PREDICTIVE BASED MONITORING OF NUCLEAR PLANT COMPONENT DEGRADATION USING A SUPPORT VECTOR REGRESSION APPROACH [C] . Miltiadis Alamaniotis, Lefteri H. Tsoukalas, Vivek Agarwal International topical meeting on nuclear plant instrumentation, control, and human-machine interface technologies . 2015

机译：基于支持向量回归方法的核工厂组分降解的预测监测
5. Prior-knowledge based Green's kernel for support vector regression. [D] . Farooq, Tahir. 2007

机译：基于先验知识的格林核，用于支持向量回归。
6. A semi‐supervised Support Vector Machine model for predicting the language outcomes following cochlear implantation based on pre‐implant brain fMRI imaging [O] . Lirong Tan, Scott K. Holland, Aniruddha K. Deshpande, 2015

机译：基于植入前脑fMRI成像的半监督支持向量机模型用于预测人工耳蜗植入后的语言结果
7. Nuclear Energy Research Initiative (NERI) Quarterly Progress Report Model Based Transient Control and Component Degradation Monitoring in Generation IV Nuclear Power Plants DE-FG03-02SF22612/A000 Quarter 1 Report September - December 2002 [O] . Holloway, James Paul 2003

机译：核能研究计划（NERI）季度进展报告基于模型的第四代核电厂的瞬态控制和部件退化监测DE-FG03-02sF22612 / a000第1季度报告2002年9月 - 12月

Predictive Based Monitoring of Nuclear Plant Component Degradation Using Support Vector Regression.

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