Neural Network Approach for Automatic Classification of Ship-Radiated Noise
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概要
- 論文の詳細を見る
Tonal signals from ship-radiated noise contain important information for a ship's design, maintenance, operating condition diagnosis, and machinery monitoring. The signals mainly consist of two components—the speed-dependent component and the speed-independent component. Therefore, some procedures are required to detect and classify the tonal signals from ship-radiated noise. We apply two neural network approaches for the detection of a tonal signal by peak extraction, and the classification of the tonal signal by pattern recognition in some numerical experiments on a simulation signal and ship-radiated noise obtained from a real ship.
- Published by the Japan Society of Applied Physics through the Institute of Pure and Applied Physicsの論文
- 2006-05-30
著者
-
Lee Phil-Ho
Agency for Defense Development
-
Park Kyu-chil
Division Of Electronic Computer And Telecommunication Engineering Pukyong National University
-
Park Jin-nam
Department Of Computer Science Kumamoto University
-
Yoon Jong-Rak
Division of Electronic, Computer and Telecommunication Engineering, Pukyong National University, Pusan 608-737, Korea
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Park Jin-Nam
Department of Multimedia Engineering, Tongmyong University, Pusan 608-711, Korea
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