The application of project practice teaching in the higher vocational computer professional courses taking the simulation model of enterprise safety management evaluation for example

被引:1
作者
机构
[1] College of Information Engineering, Heilongjiang Forestry Vocation Technical College Mudanjiang, 157000, Heilongjiang
来源
Teng, Y. | 1600年 / Asian Network for Scientific Information卷 / 12期
关键词
Computer professional courses; Project practice; Safety management evaluation;
D O I
10.3923/itj.2013.3764.3768
中图分类号
学科分类号
摘要
In the higher vocational computer major course, the project practical teaching methods, is helpful to arouse the enthusiasm of students' learning and enhance the students' practical skills. In this project, the practical teaching process will be taken enterprise safety management evaluation simulation model for instance. This project takes the evaluation of enterprise safety management as the research object, combines the Kernel Principal Component Analysis (KPCA) with support vector machine (SVM) and proposes an improved KPCA-SVM model to predict the security level of enterprise. The simulation data shows: the prediction effect of KPCA-SVM model is better than that of SVM method or PCA-SVM method, which provides a new idea for the future research on the safety management evaluation. The instance is order to make student realize the combination theory with practice. © 2013 Asian Network for Scientific Information.
引用
收藏
页码:3764 / 3768
页数:4
相关论文
共 8 条
[1]  
Chen J., Cao Q.G., Li R.Z., Prediction and evaluation of personal faults in production of coal mine, Min. Saf. Environ. Prot., 34, pp. 78-81, (2007)
[2]  
Cortes C., Vapnik V., Support-vector networks, Machine Learn., 20, pp. 273-297, (1995)
[3]  
Du C.Y., Chen D.K., Du C.F., Song C.Y., Application and model of comprehensive evaluation of coal mine inherent safety management system, J. Chongqing Univ. (Nat. Sci. Edn.,), 31, pp. 197-201, (2008)
[4]  
Jin Z., Ma X.P., The analysis of coal mine safety management factors based on kernel alignment and SVM, J. Saf. Sci. Technol. China, 3, pp. 16-21, (2011)
[5]  
Li Z., Wang X.Y., Theresearchof the mechanical fault pattern recognition method based on kernel principal component analysis, J. Noise Vibr., 12, pp. 77-79, (2008)
[6]  
Qi L.P., Li D.H., The face recognition algorithm based on wavelet transform and kernel principal component analysis, Instrum. Meter Users, 11, pp. 62-64, (2012)
[7]  
Wang Q.H., Subspace Analysis Method Is Applied in a Preliminary Research of the Seismic Signal Processing, (2013)
[8]  
Zhu C.Q., The study on reliability of man-machineenvironment system of fully-mechanized face based on neural network, J. China Coal Soc, 25, pp. 268-272, (2000)