• Structural Engineering and Mechanics
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• v.90 no.2
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• pp.189-208
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• 2024

Artificial intelligence is one of the efficient methods that can be developed to simulate nonlinear behavior and predict the response of building structures. In this regard, an adaptive method based on optimization algorithms is used to train the TSK model of the fuzzy inference system to estimate the seismic behavior of building structures based on analytical data. The optimization algorithm is implemented to determine the parameters of the TSK model based on the minimization of prediction error for the training data set. The adaptive training is designed on the feedback of the results of previous time steps, in which three training cases of 2, 5, and 10 previous time steps were used. The training data is collected from the results of nonlinear time history analysis under 100 ground motion records with different seismic properties. Also, 10 records were used to test the inference system. The performance of the proposed inference system is evaluated on two 3 and 20-story models of nonlinear steel moment frame. The results show that the inference system of the TSK model by combining the optimization method is an efficient computational method for predicting the response of nonlinear structures. Meanwhile, the multi-vers optimization (MVO) algorithm is more accurate in determining the optimal parameters of the TSK model. Also, the accuracy of the results increases significantly with increasing the number of previous steps.

• The Journal of the Korea Contents Association
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• v.12 no.4
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• pp.407-416
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• 2012

The primary purpose of this paper is to identify the development of education and training for the human resource development in the view of technology management. For this purpose, a research is developed based on the relevant literature reviews and empirical studies. Data have been collected from 110 firms in Daejeon city and tested by the frequency analysis and various statistical methods. The results of this empirical studies are summarized as follows. We classified in company competitive power as innovators, leading chasers and late chasers. The innovators, leading chasers and late chasers demanded the management of technology. They also demanded the middle manager, the CEO and R&D researchers as a important position in this demanding of the management of technology and its human resources of the market and technologies. Department of marketing and the executives and staff members find it important. We departmentalized the management of technology with the Daejeon region's strategic industries. Information technology(IT) industries thought that the middle manager, CEO and R&D department were important. Bio-industries thought that the CEO and the middle manager and R&D department were important. The high-tech parts materials industries thought that the middle manager, R&D department and marketing department considered as important.