• 한국콘텐츠학회논문지
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• 제4권3호
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• pp.99-105
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• 2004

이 논문은 다층퍼셉트론의 학습을 빠르게 하기 위한 최적 학습률을 제안한다. 이 학습률은 한 뉴런에 연결된 가중치들에 대한 학습률과, 중간층에 가상의 목표값을 설정하기 위한 학습률로 나타난다. 그 결과, 중간층 가중치의 최적 학습률은 가상의 중간층 목표값 할당 성분과 중간층 오차함수를 최소화 시키고자하는 성분의 곱으로 나타난다. 제안한 방법은 고립단어인식과 필기체 숫자 인식 문제의 시뮬레이션으로 효용성을 확인하였다.

• 한국정보과학회논문지:소프트웨어및응용
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• 제33권12호
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• pp.999-1007
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• 2006

역전파 학습 방법은 속도가 느리고, 지역 최소점이나 고원에 빠져 수렴에 실패하는 경우가 많다고 알려져 있다. 이제까지 알려진 역전파의 대체 방법들은 수렴 속도와 변수에 따른 수렴의 안정성 사이에서 불균형이라는 대가를 치루고 있다. 기존의 전통적인 역전파에서 발생하는 위와 같은 문제점 중, 특히 지역 최소점을 탈피하는 기능을 추가하여 적은 저장 공간으로 안정성이 보장되면서도 빠른 수렴속도를 유지하는 알고리즘을 제안한다. 이 방법은 전체 신경망을 은닉층-출력층(hidden to output)을 의미하는 상위 연결(upper connections)과 입력층-은닉층(input to hidden)을 의미하는 하위 연결(lower connections) 2개로 분리하여 번갈아 훈련을 시키는 분리 학습방법을 적용한다. 본 논문에서 제안하는 알고리즘은 다양한 classification 문제에 적용한 실험 결과에서 보듯이 전통적인 역전파 및 기타 개선된 알고리즘에 비해 계산량이 적고, 성능이 매우 좋으며 높은 신뢰성을 보장한다.

• 한국멀티미디어학회논문지
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• 제22권2호
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• pp.136-145
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• 2019

The crowded environment of a domestic pig farm is highly vulnerable to the spread of infectious diseases such as foot-and-mouth disease, and studies have been conducted to automatically analyze behavior of pigs in a crowded pig farm through a video surveillance system using a camera. Although it is required to correctly separate occluding pigs for tracking each individual pigs, extracting the boundaries of the occluding pigs fast and accurately is a challenging issue due to the complicated occlusion patterns such as X shape and T shape. In this study, we propose a fast and accurate method to separate occluding pigs not only by exploiting the characteristics (i.e., one of the fast deep learning-based object detectors) of You Only Look Once, YOLO, but also by overcoming the limitation (i.e., the bounding box-based object detector) of YOLO with the test-time data augmentation of rotation. Experimental results with two-pigs occlusion patterns show that the proposed method can provide better accuracy and processing speed than one of the state-of-the-art widely used deep learning-based segmentation techniques such as Mask R-CNN (i.e., the performance improvement over Mask R-CNN was about 11 times, in terms of the accuracy/processing speed performance metrics).

• 한국HCI학회논문지
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• 제13권4호
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• pp.25-34
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• 2018

본 연구는, 최근 국내에서 미래형 대학교육 방안으로 부상중인 플립드 러닝 적용의 실증적 사례 연구를 분석대상으로 한 문헌 연구이다. 연구의 목적은, 현재의 디지털 기반 매체 환경을 고려한 국내 대학의 플립드 러닝 적용수업에서, 학습자들의 디지털 매체 활용에 대한 탐색이다. 이를 위해, 선행 연구의 측정 변인과 통계적 유의성에 대한 고찰 및 매체 활용에 대한 분석을 진행하였다. 가장 주요한 측정 변인은 '학습 성취도'와 '수업 만족도'로, 플립드러닝 적용 수업의 효과성 측정에 대한 척도였다. 분석한 모든 연구는 매체를 활용 하였으나, 대부분의 연구가 교실수업의 효과성 검증에 치중하여, 한 편의 연구에서만 별도의 매체 활용 측정이 이루어졌고, '동영상 학습인식' 변인에 대해 통계적으로 유의한 결과가 도출되었다. 연구 별로 매체 활용 관련한 정성적인 측정 내용은 별도의 분석 결과로 제시하였다. 향후, 플립드 러닝 적용 및 디지털 매체 활용에 대한 실효적인 후속 연구를 위해서는 정확한 효과측정에 필요한 처치 기간의 확보 등 다섯 가지 주요 논점에 대한 검토가 필요하다.

• 영어어문교육
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• 제12권3호
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• pp.123-145
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• 2006

The purpose of this study is to propose an efficient vocabulary assessment model in task-based language learning and to verify the viability of this assessment model. Bachman and Palmer (1996) pointed out the fact that many language tests focus on just one of the areas of language knowledge. However, researchers suggested that it is necessary to acknowledge the needs of several analytic scales, which can provide separate ratings for different components of the language ability to be tested. Although there were many studies which tried to evaluate the various aspects of vocabulary ability, most of them measured only one or two factors. Based on previous research, this study proposed an assessment model of general construct of vocabulary ability and tried to measure vocabulary ability in four separate areas. The subjects were two classes of university level Korean EFL students. They participated in small group discussion via synchronous CMC. One class used a lexically focused task, which was proposed by Kim and Jeong (2006) and the other class used a non-lexically focused task. The results showed that the students with a lexically focused task significantly outperformed those with a non-lexically focused task in overall vocabulary ability as well as four subdivisions of vocabulary ability. In conclusion, the assessment model of separate ratings is a viable measure of vocabulary ability and this can provide elaborate interpretation of vocabulary ability.

• 한국추진공학회:학술대회논문집
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• 한국추진공학회 2006년도 제26회 춘계학술대회논문집
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• pp.209-212
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• 2006

본 논문에서는 항공기용 터보 축 엔진의 결함 진단 알고리즘을 개발하지 위해 Support Vector Machine(SVM)과 인공신경망(ANN)을 이용하였다. SVM을 이용하여 결함 위치를 판별한 후 인공신경망이 선택적으로 학습하는 분할 학습 알고리즘(SLA)을 제안하였으며 이를 고도 변화에 따른 가스 터빈 엔진의 결함 진단에 적용하여 분류 속도 및 예측 정확률 개선 가능성을 확인하였다.

• Journal of KIEE
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• 제10권1호
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• pp.7-15
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• 2000

Source separation is a statistical method, the goal of which is to separate the linear instantaneous mixtures of statistically independent sources without resorting to any prior knowledge. This paper addresses a source separation algorithm which is able to separate the mixtures of sub- and super-Gaussian sources. The nonlinear function in the proposed algorithm is derived from the generalized Gaussian distribution that is a set of distributions parameterized by a real positive number (Gaussian exponent). Based on the relationship between the kurtosis and the Gaussian exponent, we present a simple and efficient way of selecting proper nonlinear functions for source separation. Useful behavior of the proposed method is demonstrated by computer simulations.

• International Journal of Internet, Broadcasting and Communication
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• 제4권2호
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• pp.12-16
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• 2012

This paper presents a learning framework that fits the digital age - an e-Friendly PLE. The learning framework is based on the theory of connectivism which asserts that knowledge and the learning of knowledge is distributive and is not located in any given place but rather consists of the network of connections formed from experiences and interactions with a knowing community, thus, the newly empowered learner is thinking and interacting in new ways. The framework's approach to learning is based on conversation and interaction, on sharing, creation and participation, on learning not as a separate activity, but rather as embedded in meaningful activities such as games or workflows. It sees learning as an active, personal inquiry, interpretation, and construction of meaning from prior knowledge and experience with one's actual environment.

• 한국초등과학교육학회지:초등과학교육
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• 제36권3호
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• pp.219-226
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• 2017

In this study, the reliability and validity of the retrospective pre-post test were analyzed in order to solve the problem of traditional pre-post test including response shift bias. Samples of the study were 162 elementary school students who are studying at the S university gifted education center in Seoul. Before completion of the field trip, we conducted pre test of science-related attitudes. After completion of the field trip, respondents were asked to compare their responses of pre and post science-related attitudes to quantitatively analyze the commonalities and differences of the two tests. To find out more characteristics, qualitative data such as daily records and interview were also gathered and analyzed. The major results of the study are as follows. First, for the paired t-test, there was no statistically significant difference between separate pre-test scores and retrospective pre-test. There was a very high correlation between the separate pre-test scores and the retrospective pre-test. Second, there were significant differences in all seven sub-factors of science-related attitudes between the retrospective pre-test and the post-test. Third, the separate pre-test scores showed a slightly higher tendency than the retrospective pre-test scores. This suggests that the response shift bias appears when it is performed the separate pre-test in affective domain. As a result of the interview, it was found that the evaluation standards of separate pre-test did not match with those of post-test. Forth, internal consistency reliability of the retrospective pre-test was higher than that of the separate pre-test. However, there were significant differences in six factors of science-related attitudes excluding the 'social implications of science' between the separate pre-test and the post-test. Based on these results, the retrospective pre-post test design provides simplicity and convenience to both respondents and investigators, as it is done with one test. The retrospective pre-post test design can be regarded as a valid design for the self-report measurement of affective domain on a single experimental group.

• 한국멀티미디어학회논문지
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• 제21권2호
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• pp.98-107
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• 2018

In this paper, we propose a method to classify the skeletal maturity with a small amount of hand wrist X-ray image using deep learning-based meta-learning. General deep-learning techniques require large amounts of data, but in many cases, these data sets are not available for practical application. Lack of learning data is usually solved through transfer learning using pre-trained models with large data sets. However, transfer learning performance may be degraded due to over fitting for unknown new task with small data, which results in poor generalization capability. In addition, medical images require high cost resources such as a professional manpower and mcuh time to obtain labeled data. Therefore, in this paper, we use meta-learning that can classify using only a small amount of new data by pre-trained models trained with various learning tasks. First, we train the meta-model by using a separate data set composed of various learning tasks. The network learns to classify the bone maturity using the bone maturity data composed of the radiographs of the wrist. Then, we compare the results of the classification using the conventional learning algorithm with the results of the meta learning by the same number of learning data sets.