Fig. 1. Example of CNN architecture. 그림 1. CNN구조의 예
Fig. 2. Spectrogram and gray image of active sonar signal. 그림 2. 능동소나 신호의 스펙트로그램과 이미지화 결과
Fig. 3. Data generation using data augmentation. 그림 3. 데이터확장을 적용한 데이터 생성
Fig. 4. Endpoint to define the ratio of inclusion of the target. 그림 4. 표적을 포함하는 비율을 정의하기 위한 끝점
Fig. 5. CNN model using experiment. 그림 5. 실험에 사용된 CNN 모델
Fig. 6. Error rate depending on learning epochs. 그림 6. 학습 횟수에 따른 오류율
Fig. 7. Classification results of spectrogram image. (a) Examples in which a small number of misclassified data are observed. (b) Examples in which many number of misclassified data are observed. 그림 7. 스펙트로그램이미지 식별 결과 (a) 소수의 오분류 데이터가 관측된 예 (b) 다수의 오분류 데이터가 관측된 예
Table 1. Classes according to the ratio of the target. 표 1. 표적의 비율에 따른 클래스
Table 2. Result of classification experiment. 표 2. 식별실험 결과
Table 3. Colors for classification. 표 3. 클래스를 구분하기 위한 색
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