• 한국컴퓨터정보학회논문지
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• 제18권6호
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• pp.81-89
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• 2013

현재 인터넷 쇼핑은 중국인들의 일반적인 소비채널이 되고 있으며 앞으로도 지속적으로 성장할 가능성이 매우 높다. 중국의 인터넷 쇼핑몰 시장이 급속히 성장하고 있음에도 불구하고 중국인의 고객만족도에 맞는 인터넷 쇼핑몰은 많지 않다. 한국의 인터넷 쇼핑몰 업체들이 쇼핑몰에 대한 품질평가와 고객만족도를 분석하여 중국 유학생들의 성향에 맞는 쇼핑몰을 구축한다면 국제 경쟁력을 강화시킬 수 있을 것이다. 본 논문에서는 중국 유학생들과 한국 대학생들의 인터넷 쇼핑몰 고객만족도에 대해 비교 분석하여 어떠한 차이가 있는지 분석하고 전역적 최적의 해를 구하는 다중 LS-SVM을 이용하여 중국 유학생들의 고객만족도 모형을 분석한다. 중국 유학생들의 고객만족도 분석은 한국 인터넷 쇼핑몰 업체들에게 유익한 정보로서 활용될 수 있을 뿐 아니라 국제 경쟁력을 강화할 수 있는 방안이 될 것이다.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2007년도 추계학술발표대회
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• pp.113-116
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• 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
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• 제13권3호
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• pp.1089-1098
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• 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.