지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록· 키워드
오류제보하기
In this paper, a Bayesian classifier based on PCA (principle component analysis) is proposed to classify underwater transient signals using 16<SUP>th</SUP> order LPC<linear predictive coding> coefficients as feature vector. The proposed classifier is composed of two steps. The mechanical signals were separated from biological signals in the first step, and then each type of the mechanical signal was recognized in the second step. Three biological transient signals and two mechanical signals were used to condut esperiments. The classification ratios for the feature vectors of biological signals and mechanical signals were 94.75% and 97.23, respectively, when all 16 order LPC vector were used. In order to determine the effect of underwater noise on the classification performance, underwater ambient noise was added to the test signals and the classification ratio according to SNR (signal-to-noise ratio) was compared by changing dimension of feature vector using PCA. The classification ratios of the biological and mechanical signals under ocean ambient noise at 10dB SNR, were 0.51% and 100% respectively. However, the ratios were changed to 53.07%and 83.14% when the dimension of feature vector was converted to three by applying PCA. For correct, classification, it is required SNR over 10dB for three dimension feature vector and over 30dB SNR for seven dimension feature vector under ocean ambient noise environment.
목차
ABSTRACT
1. 서론
2. 수중 과도신호 및 수중 소음신호
3. 주성분 분석과 베이즈 분류기
4. 결과
5. 결론
후기
참고문헌
참고문헌 (18)
참고문헌 신청
3. [학위논문] - 임태균 / 2007 / 위그너-빌 분포함수와 멜 켑스트럼을 이용한 수중 천이신호의 특징 추출 및 식별 / 박사 / 경북대학교
4. [학술지(정기간행물)] - Boashash, B. / 1990 / A Methodology for Detection and Classification of Some Underwater Acoustic Signals using Time Frequency Analysis Techniques / IEEE Transaction on Acoustics, Speech and Signal Processing 38 (11) : 1829 ~ 1841
5. [학술지(정기간행물)] - Chen, C. H. / 1985 / Recongnition of Underwater Transient Patterns / Pattern Recognition 18 (6) : 485 ~ 490
6. [학술지(정기간행물)] - Chen, C. H. / 2000 / Classification of Underwater Signals using Neural Networks / Tamkang Journal of Science and Eng. 3 (1) : 31 ~ 48
7. [학술지(정기간행물)] - Clark, C.W. / 1996 / Distribution and Behavior of the Bowhead Whales, Balaena Mysticetus, Nased on Prelininary Anlaysis of Acoustic Data Collected During The 1993 Spring Migration off Point Barrow, Alaska / Rep. int. Whal. Commn. 46 : 541 ~ 552
4. [학술지(정기간행물)] - Boashash, B. / 1990 / A Methodology for Detection and Classification of Some Underwater Acoustic Signals using Time Frequency Analysis Techniques / IEEE Transaction on Acoustics, Speech and Signal Processing 38 (11) : 1829 ~ 1841
5. [학술지(정기간행물)] - Chen, C. H. / 1985 / Recongnition of Underwater Transient Patterns / Pattern Recognition 18 (6) : 485 ~ 490
6. [학술지(정기간행물)] - Chen, C. H. / 2000 / Classification of Underwater Signals using Neural Networks / Tamkang Journal of Science and Eng. 3 (1) : 31 ~ 48
7. [학술지(정기간행물)] - Clark, C.W. / 1996 / Distribution and Behavior of the Bowhead Whales, Balaena Mysticetus, Nased on Prelininary Anlaysis of Acoustic Data Collected During The 1993 Spring Migration off Point Barrow, Alaska / Rep. int. Whal. Commn. 46 : 541 ~ 552
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UCI(KEPA) : I410-ECN-0101-2013-559-003373298