• Journal of the Korea Society of Computer and Information
• /
• v.18 no.6
• /
• pp.81-89
• /
• 2013

Currently Internet shopping (or shopping online) is becoming the common consumption channel for Chinese, and it is more likely to continue to grow. Although E-tailers (or the Internet shopping mall) in China is rapidly growing, there are not very many shopping malls that can meet customer satisfaction. E-tailers in Korea analyze the quality evaluation and customer satisfaction of shopping malls. If the Internet shopping that is suitable for Chinese students studying in Korea is built, it is expected to strengthen international competitive power. In this paper, the comparative analysis of Customer satisfaction for Internet shopping between Chinese students studying in Korea and Korean university students is provided. Furthermore, we analyze the customer satisfaction model of Chinese students studying in Korea by using the multi lease square support vector machine that obtains the global optimal solution. Analysis of customer satisfaction of Chinese students studying in Korea are not only used for E-tailers in Korea, but it can strengthen international competitive power.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2007.11a
• /
• pp.113-116
• /
• 2007

In this paper, our proposed Nonlinear Autoregressive Exogenous (NARX) based on Least Square-Support Vector Regression (LS-SVR) is introduced and tested for producing natural sounds. This nonlinear synthesizer perfectly reproduce voiced sounds, and also conserve the naturalness such as jitter and shimmer, compared to LPC does not keep these naturalness. However, the results of some phonation are quite different from the original sounds. These results are assumed that single-band model can not afford to control and decompose the high frequency components. Therefore multi-band model with wavelet filterbank is adopted for substituting single band model. As a results, multi-band model results in improved stability. Finally, nonlinear speech modeling using NARX based on LS-SVR can successfully reconstruct synthesized sounds nearly similar to original voiced sounds.

• Journal of Electrical Engineering and Technology
• /
• v.13 no.3
• /
• pp.1089-1098
• /
• 2018

This study aims at developing and applying a hybrid model to the wind power prediction (WPP). The hybrid model for a very-short-term WPP (VSTWPP) is achieved through analytical data, multiple linear regressions and least square methods (MLR&LS). The data used in our hybrid model are based on the historical records of wind power from an offshore region. In this model, the WPP is achieved in four steps: 1) transforming historical data into ratios; 2) predicting the wind power using the ratios; 3) predicting rectification ratios by the total wind power; 4) predicting the wind power using the proposed rectification method. The proposed method includes one-step and multi-step predictions. The WPP is tested by applying different models, such as the autoregressive moving average (ARMA), support vector machine (SVM), and artificial neural network (ANN). The results of all these models confirmed the validity of the proposed hybrid model in terms of error as well as its effectiveness. Furthermore, forecasting errors are compared to depict a highly variable WPP, and the correlations between the actual and predicted wind powers are shown. Simulations are carried out to definitely prove the feasibility and excellent performance of the proposed method for the VSTWPP versus that of the SVM, ANN and ARMA models.