• Ecology and Resilient Infrastructure
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• v.2 no.1
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• pp.33-41
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• 2015

In this study, an optimal improvement method of stream channel structure is presented for the enhancement of fish habitat suitability by genetic algorithm. The correlation between fish habitat suitability and physical disturbance in stream is analyzed according to the changes of hydraulic characteristics by channel structure rehabilitation. Zacco koreanus which is an indicator fish of the soundness of aquatic ecosystem was selected as a restoration target species by investigating the community characteristics of fish fauna and river environments in Wonju stream. The habitat suitability is investigated by PHABSIM with the habitat suitability index of Zacco koreanus. Hydraulic analysis by HEC-RAS and physical disturbance evaluation in stream are carried out. The optimal channel width modified for the enhancement of fish habitat suitability is provided. The correlation analysis between habitat suitability and physical disturbance with the change of hydraulic characteristics by channel modification showed that the proper channel modification enhanced fish habitat suitability and mitigated physical disturbance in the stream. The improvement of physical disturbance score by the channel structure rehabilitation for the enhancement of fish habitat suitability was confirmed in this study.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.842-847
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• 2005

This paper proposes a novel real-time EMG pattern recognition for the control of a multifunction myoelectric hand from four channel EMG signals. To cope with the nonstationary signal property of the EMG, features are extracted by wavelet packet transform. For dimensionality reduction and nonlinear mapping of the features, we also propose a linear-nonlinear feature projection composed of PCA and SOFM. The dimensionality reduction by PCA simplifies the structure of the classifier, and reduces processing time for the pattern recognition. The nonlinear mapping by SOFM transforms the PCA-reduced features to a new feature space with high class separability. Finally a multilayer neural network is employed as the pattern classifier. We implement a real-time control system for a multifunction virtual hand. From experimental results, we show that all processes, including virtual hand control, are completed within 125 msec, and the proposed method is applicable to real-time myoelectric hand control without an operation time delay.

• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.472-477
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• 2006

This study aims at investigating the failure cases of the pre-cast block system in river environments which widely used nowadays and reviewing the effect and flow resistance for grass concrete structure through the physical experiments by hydraulic model test and developing application method in river slope or levee which has rigid flood resistance. Grass concrete structure has been independently tested under high velocity flow under the super critical condition, it survived the 8 m/sec maximum flow velocity. This results shows grass concrete system is also suited to use in aggressive river environments such as repairing a flood damaged embankment that had placed at risk the adjacent drainage channel with vegetation.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.42 no.2 s.302
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• pp.39-48
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• 2005

This paper proposes a novel approach to recognize nine kinds of motion for a multifunction myoelectric hand, acquiring four channel EMG signals from electrodes placed on the forearm. To analyze EMG with properties of nonstationary signal, time-frequency features are extracted by wavelet packet transform. For dimensionality reduction and nonlinear mapping of the features, we also propose a feature projection composed of PCA and SOFM. The dimensionality reduction by PCA simplifies the structure of the classifier, and reduces processing time for the pattern recognition. The nonlinear mapping by SOFM transforms the PCA-reduced features to a new feature space with high class separability. Finally a multilayer neural network is employed as the pattern classifier. From experimental results, we show that the proposed method enhances the recognition accuracy, and makes it possible to implement a real-time pattern recognition.