• 한국전자통신학회논문지
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• 제16권3호
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• pp.401-406
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• 2021

최근 들어, 음향 이벤트 검출을 위하여 CRNN(: Convolutional Recurrent Neural Network) 구조에 기반 한 평균-교사 모델이 대표적으로 사용되고 있다. 평균-교사 모델은 두 개의 병렬 형태의 CRNN을 가진 구조이며, 이들의 출력들의 일치성을 학습 기준으로 사용함으로서 약-전사 레이블(label)과 비-전사 레이블 음향 데이터에 대해서도 효과적인 학습이 가능하다. 본 연구에서는 최신의 평균-교사 모델에 로그-멜 스펙트럼에 대한 차분 특징을 추가적으로 사용함으로서 보다 나은 성능을 이루고자 하였다. DCASE 2018/2019 Challenge Task 4용 학습 및 테스트 데이터를 이용한 음향 이벤트 검출 실험에서 제안된 차분특징을 이용한 평균-교사모델은 기존의 방식에 비해서 최대 8.1%의 상대적 ER(: Error Rate)의 향상을 얻을 수 있었다.

• 한국보육지원학회지
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• 제18권2호
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• pp.39-56
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• 2022

Objective: The purpose of this study was to investigate the influence of perceived supervisor's servant leadership and teacher self-efficacy for positive teacher-child relationships on work engagement using the path model. Methods: The subjects were 210 teachers working at childcare centers in Daegu and Gyeongbuk Province. Questionnaires were used to investigate perceived supervisor's servant leadership, teacher self-efficacy for positive teacher-child relationships, and work engagement. The collected data were analyzed by Structural Equation Modeling(SEM), Bootstrapping, Pearson Correlation, AMOS 20.0, and SPSS 21.0. Results: First, supervisor's servant leadership had a positive influence on teacher self-efficacy for positive teacher-child relationships. Second, teacher self-efficacy for positive teacher-child relationships had a positive influence on work engagement. Third, supervisor's servant leadership had a positive influence on teacher's work engagement. Fourth, supervisor's servant leadership had an indirec effect on teacher's work engagement by teacher self-efficacy for positive teacher-child relationships. Conclusion/Implications: The results mean teacher self-efficacy for positive teacher-child relationships had more influence on teacher's work engagement. Therefore, it is more useful to promote teacher self-efficacy for positive teacher-child relationships in order to strengthen teacher's work engagement.

• 한국음향학회지
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• 제42권5호
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• pp.395-401
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• 2023

음향 이벤트 검출(Sound Event Detection, SED)은 음향 신호에서 관심 있는 음향의 종류와 발생 구간을 검출하는 기술로, 음향 감시 시스템 및 모니터링 시스템 등 다양한 분야에서 활용되고 있다. 최근 음향 신호 분석에 관한 국제 경연 대회(Detection and Classification of Acoustic Scenes and Events, DCASE) Task 4를 통해 다양한 방법이 소개되고 있다. 본 연구는 다양한 영역에서 성능 향상을 이끌고 있는 Dense Convolutional Networks(DenseNet)을 음향 이벤트 검출에 적용하기 위해 설계 변수에 따른 성능 변화를 비교 및 분석한다. 실험에서는 DenseNet with Bottleneck and Compression(DenseNet-BC)와 순환신경망(Recurrent Neural Network, RNN)의 한 종류인 양방향 게이트 순환 유닛(Bidirectional Gated Recurrent Unit, Bi-GRU)을 결합한 DenseRNN 모델을 설계하고, 평균 교사 모델(Mean Teacher Model)을 통해 모델을 학습한다. DCASE task4의 성능 평가 기준에 따라 이벤트 기반 f-score를 바탕으로 설계 변수에 따른 DenseRNN의 성능 변화를 분석한다. 실험 결과에서 DenseRNN의 복잡도가 높을수록 성능이 향상되지만 일정 수준에 도달하면 유사한 성능을 보임을 확인할 수 있다. 또한, 학습과정에서 중도탈락을 적용하지 않는 경우, 모델이 효과적으로 학습됨을 확인할 수 있다.

• 아동학회지
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• 제37권4호
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• pp.145-158
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• 2016

Objective: This study investigated the moderating effect of emotional dysregulation on the relationship between teacher efficacy and job stress among teachers in the Early Childhood Education and Care (ECEC). Methods: The participants included 586 ECEC teachers from 99 centers in Seoul, Gyeonggi-do, Daejeon, Chungcheong-do, Jeolla-do, and Gyeongsang-do. The data were analyzed through frequencies, percentages, and Pearson's correlations using SPSS 21.0 (IBM Co., Armonk, NY). To analyze the moderating effect, Ping's (1996) two-step approach was used via AMOS 20.0 (IBM Co., Armonk, NY). Results: The main findings are as follows. First, the mean scores of ECEC teacher efficacy and job stress showed above the average, and the mean score of emotional dysregulation was the nearly average. Second, fit statistics indicated that the proposed model, as revised, provided an acceptable fit to the sample data. This proposed model showed that the emotional dysregulation of teachers in ECEC had a moderating effect on the relationship between teacher efficacy and job stress. Conclusion: These findings imply that the ECEC teachers showed the higher level of self-trust and self-confidence than average regarding their own work, and suffered from the work overload. Also, the positive and supportive working environment would help the ECEC teachers to reduce their emotional dysregulation. In addition, there was a moderating effect of the ECEC teachers' emotional dysregulation on the relationship between teacher efficacy and job stress. These findings imply that the workshop or counselling programs need to be provided to teachers in order to help control their emotion dysregulation and reduce their job stress.

• 한국문헌정보학회지
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• 제17권
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• pp.229-259
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• 1989

The school library plays very important and central role for the fullfillment of educational objective in the process of implementing the school curriculum. And the media specialist known as teacher-librarian is. primarily responsible for the successful operation and effective management of the media center. It is true that effectiveness of the school library depends greatly on the cooperative coordination among teacher-librarian, teacher and principal in addition to the strong support and effective supervision of school district authority, specifically, supervisor of school library program. The successful program therefore is influenced by their perception of the teacher-librarian's role. The purpose of this study was to investigate the teacher-librarian's. educational role perceived by teacher-librarian, teacher, principal and school district supervisor. A standard task model describing both the management and educational functions and expectations of teacher-librarian was proposed after studying the relevant current standards of four countries. Serveral hypotheses were made regarding role perception and a survey instrument was designed based on this proposed task model and the results of the literature review to validate the hypotheses. The questionnaires were mailed out and the returned questionaires were analyzed by SPSS computer routine. The major findings are summarized as follows: 1) There was difference of the role perception among teacher-librarian, teacher, principal and district supervisor at the significance level of p<0.001 for both management and educational functions of teacher­librarian. The mean score of the teacher-librarian was highest among four groups and the score for the educational function tasks were lower than that of management function tasks. 2) The positive correlation exists at the significance level of p<0.001 between the role perception and the attitude toward changing social trends such as acceptance of new technology, need for better communication and information. 3) The positive correlation exists at the significance level of p<0.001 between the role perception and the view on the curriculum and teaching method.

• 한국가정과교육학회지
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• 제13권3호
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• pp.1-11
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• 2001

The purpose in this study was to investigate stages of concern of home economics teachers about the practical reasoning instruction(PRI) innovation configuration. levels of use. using Concerns Based Adoption Model(CBAM) The design of the study was descriptive. The Questionnaires of stages of concern. levels of use. and innovation configuration developed by Hall in 1987 were used. Data from HE teachers were collected through mailing. focus interview. and phone calls. Mean. percents. an frequencies were used to describe stages of concern, level of use. and innovation configuration. The results of the study were as follows: 1. The highest number of HE teacher respondents was stage 1. Information in March and july. 2 In terms of mean of PRI configuration. percentile. the HE teachers had implemented over 63% of the PRI elements and the cooperative learning elements in both March and July. However. of evaluation elements, 33% in March and 47% in July had been conducted. 3. The highest number of HE teacher respondents was level 3. Mechanical level in March. and July.

• 한국보육지원학회지
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• 제15권2호
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• pp.1-19
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• 2019

Objective: This study aims to examine the factors associated with teacher intention to report child abuse in child care centers. Methods: Data were collected from teachers at child care centers in the Jeonbuk region, using a self-administered questionnaire. In this study, 186 completed questionnaires were used to analyze the data. Results: The respondents reported their intention to report child abuse as follows: physical abuse(49.5%), emotional abuse(16.1%), sexual abuse(12.9%), and neglect(15.6%). The multiple hierarchical regression analyses revealed that participation at child abuse prevention training programs and awareness of reporting procedures were significantly associated with teacher intention to report child abuse at child care centers in Model 2. Also, attitudes towards reporting child abuse were significantly associated with teacher intention to report child abuse in Model 3. Conclusion/Implications: This study suggests evaluating chid abuse prevention training programs more accurately because the respondents who did not participate in the programs showed statistically significant higher mean scores of intention to report child abuse than who participated(1.83 vs .85). In addition, educational programs about child abuse for teachers in child care centers need to focus on changes in attitudes towards reporting child abuse, which in turn can change behavior.

• Journal of the Korean Data and Information Science Society
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• 제20권5호
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• pp.905-912
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• 2009

We propose a doubly penalized kernel machine (DPKM) which uses heteroscedastic location-scale model as basic model and estimates both mean and volatility functions simultaneously by kernel machines. We also present the model selection method which employs the generalized approximate cross validation techniques for choosing the hyperparameters which affect the performance of DPKM. Artificial examples are provided to indicate the usefulness of DPKM for the mean and volatility functions estimation.

• 대한임베디드공학회논문지
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• 제18권2호
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• pp.51-58
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• 2023

This paper proposes a deep learning method for estimating the heart rate from facial videos. Our proposed method estimates remote photoplethysmography (rPPG) signals to predict the heart rate. Although there have been proposed several methods for estimating rPPG signals, most previous methods can not be utilized in low-power single board computers due to their computational complexity. To address this problem, we construct a lightweight student model and employ a knowledge distillation technique to reduce the performance degradation of a deeper network model. The teacher model consists of 795k parameters, whereas the student model only contains 24k parameters, and therefore, the inference time was reduced with the factor of 10. By distilling the knowledge of the intermediate feature maps of the teacher model, we improved the accuracy of the student model for estimating the heart rate. Experiments were conducted on the UBFC-rPPG dataset to demonstrate the effectiveness of the proposed method. Moreover, we collected our own dataset to verify the accuracy and processing time of the proposed method on a real-world dataset. Experimental results on a NVIDIA Jetson Nano board demonstrate that our proposed method can infer the heart rate in real time with the mean absolute error of 2.5183 bpm.

• 한국구조물진단유지관리공학회 논문집
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• 제27권5호
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• pp.113-119
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• 2023

인프라 구조물은 대부분 경제 성장기에 완공되었다. 이러한 인프라 구조물은 최근 들어 공용연수가 점차 증가하고 있어 노후 구조물의 비중이 점차 증가하고 있다. 이러한 노후 구조물은 설계 당시의 기능과 성능이 저하될 수 있고 안전사고로까지 이어질 수 있다. 이를 예방하기 위해서는 정확한 점검과 적절한 보수가 필수적이다. 이를 위해서는 우선 미세한 균열까지 정확히 탐지할 수 있도록 컴퓨터 비전과 딥러닝 기술에 수요가 증가하고 있다. 하지만 딥러닝 알고리즘은 다수의 학습 데이터가 있어야 한다. 특히 영상 내 균열의 위치를 표시한 라벨 영상은 필수적이다. 이러한 라벨 영상을 다수 확보하기 위해서는 많은 노동력과 시간이 필요한 실정이다. 이러한 비용을 절감하고 탐지 정확도를 높이기 위해서 본 연구에서는 mean teacher 방식의 학습 구조를 제안하였다. 이 학습 구조는 900장의 라벨 영상 데이터 세트와 3000장의 비라벨 영상 데이터 세트로 훈련되었다. 학습된 균열 탐지 신경망 모델은 300여장의 실험용 데이터 세트를 통해 평가되었고 탐지 정확도는 89.23%의 mean intersection over union과 89.12%의 F1 score를 기록하였다. 이 설험을 통해 지도학습과 비교하여 탐지 성능이 향상된 것을 확인하였다. 향후에 이러한 방법은 라벨 영상을 확보하는데 필요한 비용을 절감하는데 활용될 것으로 기대한다.