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Study on Coal Mining Safety Evaluation Based on RS-SVM

Author: MaXingMin
Tutor: CaiZhenYu
School: Hebei University of Engineering
Course: Management Science and Engineering
Keywords: Mechanized mining face Safety Evaluation Rough Set SVM AHM
CLC: TD79
Type: Master's thesis
Year: 2011
Downloads: 78
Quote: 1
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Abstract


Coal is the basis for economic and social development . Coal in China's primary energy production and consumption structure has accounted for 70 %, and forecast to 2050 still accounted for more than 50% , so the coal for a long period of time will continue to be our main source of energy . Current rapid economic growth , the development of the coal industry has put forward higher requirements, the coal industry must ensure sustained, stable and healthy development in order to meet people's needs , coal mine production safety is particularly important. This paper analyzes the Jizhong Energy Fengfeng Group Xue Village mine production safety situation , based on management from a practical point of view, its safety production stage emerging environmental conditions are analyzed on the basis of established fully mechanized coal mining face safety evaluation index system , the application of rough set theory and support vector machine theory constructed RS-SVM evaluation model . First, the process of modeling method using rough set theory to analyze the indicator properties , eliminate redundant attributes , obtain the minimum condition attribute set , thus reducing the number of features of expression of information , thus reducing the SVM training sample input dimension ; followed by the use of SVM excellent characteristics of nonlinear regression evaluation model , introduced libsvm-mat-2.89-3 enhanced version of the tool kit of fully mechanized coal mining face safety were evaluated. Finally Xue Village ore mechanized mining face an empirical study of the actual situation , and to support vector machines to build the sample set , kernel function parameter selection and other issues were analyzed. RS-SVM combination method combines the rough set attribute reduction eliminate redundancy and SVM small sample nonlinear prediction , etc., to improve the system running speed and the prediction accuracy , to facilitate coal mining enterprises for mechanized mining face of the safety evaluation, improve the level of safety management of coal enterprises have a positive meaning .

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CLC: > Industrial Technology > Mining Engineering > Mine safety and labor protection > Labor safety
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