Research and Practice of Scientific Research Quality Cultivation of Undergraduates Majoring in Materials Forming and Control Engineering

Article Preview

Abstract:

In this paper, the cultivation mode of scientific research quality of undergraduates majoring in materials forming and control engineering was introduced. The goal of the major is cultivate applied talents of high quality. The authors had designed a training program_SEE Plan to cultivate the scientific research innovation consciousness and capability of undergraduates. Moreover, the authors had made full use of Beijing's academic resources such as various academic meeting of the professional to train the ability of communication of the undergraduates. Through several years cultivation, the undergraduates majoring in materials forming and control engineering had achieved considerable academic achievements, including academic papers, patents application and samples. The work is helpful of cultivating the undergraduates with strong science and technology innovation consciousness and has important theoretical meaning and practical application value in undergraduates cultivation.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

3037-3040

Citation:

Online since:

June 2013

Authors:

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2013 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] Weng Lihua, Zhang Yan, Lv Yanwen. Application of RBF network in development of undergaraduate party members. Cultural and Educational Materials. 2012, 1: 213-216

Google Scholar

[2] R. Hecht-Nielsen: Neurocomputing, 1991: 59-63, Reading, MA, Addison Wesley Publishing Co. Inc.

Google Scholar

[3] Wei You, Bingzhe Bai, Hongsheng Fang. Neural network model and prediction software design of continuous cooling transformation diagrams of steels. Heat Treatment of Metals. 2004, 29(7): 17_20

Google Scholar

[4] Ma Laikun. Application of RBF Neural Network in Comprehensive Evaluation of College Students. Manufacturing Information Engineering of China. 2010, 39(13): 64-67

Google Scholar

[5] V. Narayan, R. Abad, H.K.D.H. Bhadeshia. Estimation of Hot Torsion Stress Strain Curves in Iron Alloys using Neural Network Analysis. ISIJ International, 1999, 39(10): 999-1005

DOI: 10.2355/isijinternational.39.999

Google Scholar

[6] Sung-Sau So, Martin Karplus. Evolutionary Optimization in Quantitative Structure-Activity Relationship: An Application of Genetic Neural Networks. Journal of Medicinal Chemistry, 1996, 39(7): 1524

DOI: 10.1021/jm9507035

Google Scholar