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김승일(Seoung Il Kim),김은희(Eun Hee Kim) 한국체육교육학회 2000 한국체육교육학회지 Vol.5 No.1
The purpose of this thesis is examine empirically the influence on student`s leaning effects by the psychological condition and environmental factor before class. For the purpose, 402 undergraduates, currently majoring in Dance in the universities around Seoul and National Capital region, were selected by the meathos of stratified cluster random sampling. And, the data taken from the 402 out of that of them were used in this thesis. The method adopted here for collecting the data on the psychological condition is the questionnaire translated and standardized from the State-Trait anxiety Invertory, originally invented by Spieberger in 1972, by Jungtak Kim. And, the method adopted for measuring the environmental factor is the scale of empirica lresearch on athletes by Passer and Seese in 1981, and it is slightly revised and supplemented for the purpose of the thesis. The statistic methods used for the analysis of the data are factor analsis, descriptive analysis, one-way ANOVA, and standard multiple regression analysis. With all above mentioned research methods and procedure, the conclusion drawn from the analysis for the data on the influence on learning effects by the psychological condition and environmental factor before class is as following: First, there are significant differences in the environmental factor, and learning effects, which are set up according to changing causes, among groups of students. And, in the environmental factor, the group of older and more experienced studendts and those majoring in ballet tend to show the gap in the interpersional relationship. And, more experienced group and those graduated from the Art high school show high learning effects. Second, the psychological and environmental factor affact learning effects. Second, the psychological condition and environmental factor affect the learning effect. In the psychological condition, both positive and negative psychological conditions affect it. and in the environmental factor, the interpersonal relationship affects the learning effect. In other word, the higher the positive psychological condition is or the lower the negative psychological condition is, the higher learning effect is. Moreover, high interpersonal relationship results in the high lear
다중 주파수 대역 convolutional neural network 기반 지진 신호 검출 기법
김승일,김동현,신현학,구본화,고한석,Kim, Seung-Il,Kim, Dong-Hyun,Shin, Hyun-Hak,Ku, Bonhwa,Ko, Hanseok 한국음향학회 2019 韓國音響學會誌 Vol.38 No.1
본 논문에서는 국내에서 발생한 지진 신호를 검출 및 식별하기 위한 방법을 다루었다. 국내에서 발생한 지진 신호들을 분석해 본 결과 서로 다른 주파수 대역 신호의 특징들이 각각 분류를 위한 특징으로 적절함을 확인할 수 있었다. 이러한 분석 결과를 바탕으로 지진 신호에서 추출한 다중 주파수 대역 특징을 기반으로 하는 CNN(Convolutional Neural Network) 기법에 대해서 제안하였다. 제안하는 다중 주파수 대역 CNN 기법은 지진 신호에서 추출한 멜 스펙트럼에 대해서 각각 필터를 적용하여 서로 다른 주파수 대역(저/중/고 주파수)의 신호를 추출하였다. 추출된 신호들을 바탕으로 각각 CNN 기반 분류를 수행하였고, 수행된 결과를 융합하여 최종적으로 지진 이벤트에 대해 식별하였다. 2018년 동안 대한민국에서 발생한 실제 지진데이터를 기반으로 하는 실험을 통해 제안하는 기법에 대한 효용성을 검증하였다. In this paper, a deep learning-based detection and classification using multi-band frequency signals is presented for detecting earthquakes prevalent in Korea. Based on an analysis of the previous earthquakes in Korea, it is observed that multi-band signals are appropriate for classifying earthquake signals. Therefore, in this paper, we propose a deep CNN (Convolutional Neural Network) using multi-band signals as training data. The proposed algorithm extracts the multi-band signals (Low/Medium/High frequency) by applying band pass filters to mel-spectrum of earthquake signals. Then, we construct three CNN architecture pipelines for extracting features and classifying the earthquake signals by a late fusion of the three CNNs. We validate effectiveness of the proposed method by performing various experiments for classifying the domestic earthquake signals detected in 2018.