Knowledge-Based Multiclass Support Vector Machines Applied to Vertical Two-Phase Flow

机译：基于知识的多类支持向量机在垂直两相流中的应用

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We present a knowledge-based linear multi-classification model for vertical two-phase flow regimes in pipes with the transition equations of McQuillan & Whalley used as prior knowledge. Using published experimental data for gas-liquid vertical two-phase flows, and expert domain knowledge of the two-phase flow regime transitions, the goal of the model is to identify the transition region between different flow regimes. The prior knowledge is in the form of polyhedral sets belonging to one or more classes. The resulting formulation leads to a Tikhonov regularization problem that can be solved using matrix or iterative methods.

机译：我们提出了一个基于知识的线性多分类模型，用于管道中的垂直两相流态，并以McQuillan＆Whalley的转移方程作为先验知识。利用已发布的气液垂直两相流实验数据，以及有关两相流态转换的专家知识，该模型的目标是确定不同流态之间的转换区域。先验知识是属于一个或多个类别的多面体集合的形式。所得公式导致了Tikhonov正则化问题，可以使用矩阵或迭代方法解决。

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来源
《International Conference on Computational Science(ICCS 2006) pt.1; 20060528-31; Reading(GB)》|2006年|P.188-195|共8页
会议地点 Reading(GB)
作者
Olutayo O. Oladunni; Theodore B. Trafalis; Dimitrios V. Papavassiliou;
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School of Industrial Engineering, The University of Oklahoma 202 West Boyd, CEC 124 Norman, OK 73019 USA;

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原文格式 PDF
正文语种 eng
中图分类计算技术、计算机技术;
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Knowledge-Based Multiclass Support Vector Machines Applied to Vertical Two-Phase Flow

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