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We take five parameters describing the properties of rocks (drillability, abrasive resistance, hardness, plasticity, and compressive strength) as the initial factors used for principal component analysis. Using this method, we obtained two principal components and composite scores, which we then treated using cluster analysis. These operations allow us to make a comprehensive evaluation of all 20...
The principal component related variable residual (PVR) statistic is introduced to industry process monitoring and fault diagnosis in pb-zn smelting process instead of the traditional square prediction error SPE statistic, which confines to Imperial Smelting Process(ISP) as the research background. The PVR statistical is not only able to provide more particular information about the process conditions,...
A new method based on the integration of principal component analysis (PCA) and adaptive network-based fuzzy inference system ANFIS) is put forward for selecting the real estate project. Firstly, principal component analysis (PCA) is used to reduce the evaluation index dimensions. And then, adaptive network-based fuzzy inference system (ANFIS) is used to evaluate the real estate projects. In order...
The paper presents a computer-aided decision support system for contractor selection. Based on rough set theory (RS) and principal component analysis (PCA), and, with the help of software ROSETTA and R, a computer-aid decision support system for contractor selection (CDSS-CS) is developed in this study. A case is used to demonstrate the application of this system.
The selection of engineering project manager has a decisive influence on the success of a project. Based on the analyses of the competency factors of Engineering Project Managers(EPMs), the paper constructs the framework of Engineering Project Managers' (EMPs') competencies, and, according to the framework of engineering project managers, introduces Principal Component Analysis (PCA) to select the...
Dimensionality reduction is among the keys in many fields, most of the traditional method can be categorized as local or global ones. In this paper, we consider the dimension reduction problem with prior information is available, namely, semi-supervised dimension reduction. A new dimension reduction method that can explore both the labeled and unlabeled information in the dataset is proposed. The...
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