Zobrazeno 1 - 7
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pro vyhledávání: '"Heesoo Hwang"'
Publikováno v:
Journal of Social Science. 33:233-256
Autor:
Heesoo Hwang
Publikováno v:
Journal of the Korea Academia-Industrial cooperation Society. 17:190-196
Abstract This paper proposes a humanoid robot performance system for performing in public places, such as an event, exhibition, or street performance. The system of modular structures can be moved easily, and can be played by a module or a combinatio
Autor:
Heesoo Hwang
Publikováno v:
International Journal of Fuzzy Logic and Intelligent Systems. 13:39-49
This paper presents a method of improving the performance of a day-ahead 24-h load curve and peak load forecasting. The next-day load curve is forecasted using radial basis function (RBF) neural network models built using the best design parameters.
Autor:
Heesoo Hwang, Jinsung Oh
Publikováno v:
International Journal of Control, Automation and Systems. 8:857-861
In this paper, we present a new morphology-based homomorphic filtering technique for feature enhancement in medical images. The proposed method is based on decomposing an image into morphological subbands. The homomorphic filtering is performed using
Autor:
Jinsung Oh, Heesoo Hwang
Publikováno v:
International Journal of Control, Automation and Systems. 8:702-706
Predicting stock prices with traditional time series analysis has proven to be difficult. Fuzzy models have recently been used to predict stock market prices because they are capable of extracting useful information from large sets of data without an
Evolutionary Design of Morphology-Based Homomorphic Filter for Feature Enhancement of Medical Images
Autor:
Heesoo Hwang, Jinsung Oh
Publikováno v:
International Journal of Fuzzy Logic and Intelligent Systems. 9:172-177
In this paper, a new morphology-based homomorphic filtering technique is presented to enhance features in medical images. The homomorphic filtering is performed based on the morphological sub-bands, in which an image is morphologically decomposed. An
Publikováno v:
JSME International Journal Series C. 46:727-735
Design optimization has been performed for the suspension system of high speed train. Neural network and design of experiment (DOE) have been employed to build -a meta -model for the system with 29 design variables and 46 responses. A combination of