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从色谱分离到智慧医疗所涉及共同的数学物理本质:不可逆性
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作者 梁恒 罗元园 《色谱》 CAS CSCD 北大核心 2019年第4期367-375,共9页
溶质带中的众多分子在固定相-流动相环境中的色谱分离过程能够类比成疾病高危人群在多种暴露因素的疾病严重程度排序。色谱过程和机器疾病诊断-医嘱的共性在于对成分或个体疾病状态的分离或分类,二者都表现出随时间演化的不可逆性,但前... 溶质带中的众多分子在固定相-流动相环境中的色谱分离过程能够类比成疾病高危人群在多种暴露因素的疾病严重程度排序。色谱过程和机器疾病诊断-医嘱的共性在于对成分或个体疾病状态的分离或分类,二者都表现出随时间演化的不可逆性,但前者属于线性非平衡热力学而后者属于耗散结构的非线性非平衡热力学。当将科学视野从药物检测和制备扩展到循证医学、离散数学(公理集合论、概率测度)、人工智能(AI)-云计算领域时,对流-扩散方程和非平衡热力学中的不可逆性就成了色谱分离和智慧医疗两个交叉领域的共同的、核心的数学物理本质。抓住不可逆性这一学科间的共性特征,构建和发展这两个领域统一的、全覆盖的数学构架,具有深远的科学和现实意义。 展开更多
关键词 非平衡热力学分离理论 非平衡热力学 不可逆性 循证医学 人工智能 疾病状态 数学构架 综述
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Research on BP-ANN Model of Semi-rigid Connection
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作者 Jian Liu Xiangyun Huang +3 位作者 Guangen Zhou Jiping Hao Da Ren Yue Gao 《Journal of Civil Engineering and Architecture》 2012年第3期385-389,共5页
The beam-to-column semirigid connection in a steel frame structure is represented by a zero-length rotational spring at the end of the beam element. The beam-to-column semirigid connection behavior is represented by i... The beam-to-column semirigid connection in a steel frame structure is represented by a zero-length rotational spring at the end of the beam element. The beam-to-column semirigid connection behavior is represented by its moment-rotation relationship. Several traditional mathematical models have been proposed to fit the moment-rotation curves from the experimental database,but they may be more reliable within certain ranges. In this paper, the intellectualized analytical model is proposed in the semirigid connections for top and seat angles with double web angles using the feed-forward back-propagation artificial neural network (BP-ANN) technique. the intellectualized analytical model from experimental results based on BP-ANN is more reliable and it is a better choice to the moment-rotation curves for beam-to-column semirigid connection. The results are found to provide effectiveness to the experimental response that is satisfactory for use in steel structural engineering design. 展开更多
关键词 beam-to-column joint semirigid connection intellectualized analytical model artificial neural network.
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