확률 신경망에 의한 해저 저질의 식별
Classifying Seafloor Sediments Using a Probabilistic Neural Network
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초록
To classify seafloor sediments using a probabilistic neural network (PNN), the frequency-dependent characteristics of broadband acoustic scattering, which make it possible to qualitatively categorize seabed type, were collected from three different geographical areas in Korea. The echo data samples from three types of seafloor sediment were measured using a chirp sonar system operating over a frequency range of 20-220 kHz. The spectrum amplitudes for frequency responses of 35-75 kHz were fed into the PNN as input feature parameters. The PNN algorithm could successfully identify three seabed types: mud, mud/shell and concrete sediments. The percentage probabilities of the three seabed types being correctly classified were 86% for mud, 66% for mud/shell and 72% for concrete sediment.
Abstract
To classify seafloor sediments using a probabilistic neural network (PNN), the frequency-dependent characteristics of broadband acoustic scattering, which make it possible to qualitatively categorize seabed type, were collected from three different geographical areas in Korea. The echo data samples from three types of seafloor sediment were measured using a chirp sonar system operating over a frequency range of 20-220 kHz. The spectrum amplitudes for frequency responses of 35-75 kHz were fed into the PNN as input feature parameters. The PNN algorithm could successfully identify three seabed types: mud, mud/shell and concrete sediments. The percentage probabilities of the three seabed types being correctly classified were 86% for mud, 66% for mud/shell and 72% for concrete sediment.
참고문헌 (10)
- Kuruvilla J, "Lung cancer classification using neural networks for CT images", Comput Methods Programs Biomed, 113, pp.202~209 (2014)[DOI]
- Latha P, "Face recognition using neural networks", Sign Pro Inter J, 3, pp.153~160 (2009)
- 이대재, "인공 신경망에 의한 6개 어종의 음향학적 식별", 한국수산과학회지, 49(2), pp.224~233 (2016), 한국수산과학회
- 이대재, "어군에 의한 광대역 음향산란신호의 시간-주파수 분석을 위한 chirp 데이터수록 및 처리 시스템의 성능특성", 한국수산과학회지, 51(2), pp.178~186 (2018), 한국수산과학회[DOI]
- Parzen E, "On estimation of a probability density function and mode", Ann Math Statist, 33, pp.1065~1076 (1962)[DOI]
- Rutkowski L, "Adaptive probabilistic Neural Networks for Pattern classification in time varying environment", IEEE Trans. Neural Networks, 15, pp.811~827 (2004)[DOI]
- Saad MHM, "Classification of heart abnormalities using artificial neural network", J Appl Sci, 7, pp.820~825 (2007)[DOI]
- Selekwa MF, "Setting up a probabilistic neural network for classification of highway vehicles", Int J Comput Intell App, 5, pp.411~423 (2005)[DOI]
- Simmons EJ, "Species identification using wideband backscattering with neural network and discriminant analysis", ICES J Mar Sci, 53, pp.189~195 (1996)[DOI]
- Specht DF, "Probabilistic neural networks", Neural Networks, 3, pp.109~118 (1990)[DOI]