• Proceedings of the Korea Information Processing Society Conference
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• 2016.10a
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• pp.705-707
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• 2016

본 연구는 e러닝 학습 환경에서 문제 풀이에 대한 전문가와 학습자 사이의 시선 흐름을 비교 분석하여 학습자에게 보다 효율적인 학습 방법을 제시 할 수 있는 데이터를 추출하는 데 목적이 있다. 연구를 위해 빛의 투과율이 적은 장소의 PC에 웹캠을 설치하였고, 학습 화면의 해상도는 $1600{\times}900$로, 3명의 전문가와 5명의 학습자를 통하여 10문항에 대한 시선 추적으로 학습 데이터를 축적하였다. 축적한 데이터를 통하여 고득점 학습자나, 전문가의 학습 방법을 비교하여 유사도를 측정하였고, 유사도에 따라 학습 유형을 추천해 줄 수 있는 가능성을 확인하였다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.2
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• pp.157-168
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• 1993

In this paper, it is shown that neural networks trained with back propagation algorithm and dictionary can be applied to recognize handwritten English characters. To eliminate the useless data part and to minimize the variety of characters from the scanned image file, various preprocessings : that is, segmentation, centering, noise filtering, sealing and thinning are performed. After these, characteristic features are derived from thinned character pattern. The neural network is trained by using the extracted features for sample data, and all test data are classified into English alphabets according to their features through the neural network. Finally, the ways of reducing learning time and improving recognition rate, and the relationship between learning time and hidden layer nodes are considered. As a result of this study, after successful training, a high recognition rate has been obtained with this system for the trained patterns and about 93% for test patterns. Using dictionary, the recognition rate was about 97% for test pattern.

• The Transactions of the Korea Information Processing Society
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• v.7 no.11
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• pp.3356-3371
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• 2000

본 논문에서는 분산시스템 환경에서 n개의 노드가 결함일 경우, 결함을 허용해 주고, 시스템의 안정성을 유지하면서, 결함 노드의 부하를 정상 노드로 조절하기 위하여 부하 조절 알고리즘 전송정책, 위치 정책, 선정 정책을 제안하였다. 이러한 메카니즘은 부하 상태의 정보를 효과적으로 획득하고, 응답 시간을 줄이기 위하여 자기 학습 경험을 기반으로 하는 최적의 알고리즘을 선정할 수 있었다. 결과적으로 이를 기반으로 유사한 상황에서도 최적의 알고리즘을 선정할 수 있음을 알 수 있었다. 각 기법들의 효율성에 영향을 미칠 수 있는 매개변수를 적용하여 성능평가를 하였다. 성능평가 결과 작업 도착 율, 서비스 율, 노드 결합 율은 서로간에 영향을 주지 못하고, 다만 결함 수리 율과 특히 부하의 이주에 대한 통신 지연 시간의 크기에 민감한 영향을 주었다.

• The Transactions of the Korea Information Processing Society
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• v.4 no.7
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• pp.1759-1769
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• 1997

The Error BackPropagation (EBP) algorithm for multi-layered neural networks is widely used in various areas such as associative memory, speech recognition, pattern recognition and robotics, etc. Nevertheless, many researchers have continuously published papers about improvements over the original EBP algorithm. The main reason for this research activity is that EBP is exceeding slow when the number of neurons and the size of training set is large. In this study, we developed new learning speed acceleration methods using variable learning rate, variable momentum rate and variable slope for the sigmoid function. During the learning process, these parameters should be adjusted continuously according to the total error of network, and it has been shown that these methods significantly reduced learning time over the original EBP. In order to show the efficiency of the proposed methods, first we have used binary data which are made by random number generator and showed the vast improvements in terms of epoch. Also, we have applied our methods to the binary-valued Monk's data, 4, 5, 6, 7-bit parity checker and real-valued Iris data which are famous benchmark training sets for machine learning.

• Journal of The Korean Association of Information Education
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• v.23 no.5
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• pp.481-489
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• 2019

In order to explore the learning attitude of the learners and the effects of conscious learning attitudes on academic achievement in On-line education system of open high school, we analyze the log data of 2,965 first graders who studied English, Math, Integrated Society and Integrated Science during the first semester of 2018. This study examines the learning status according to the learner's background variables, and analyzes the number of lessons per hour, learning progress rate, learning period, learning start month, and formative evaluation results for each class. In addition, to verify the effects of conscious learning attitude on academic achievement, skewness and kurtosis are calculated by using learning frequency values for each class. As a result, in almost all fields, the average number of lessons per class, study duration, progress rate, and grades, women are higher than men. In addition, the older ones are, the higher they are and the Seoul area is higher than the other area. The average learning period is 2~3 months, and the longer the learning period, the higher the formative evaluation score. Lastly, even though the number of learning is lower than that of learners who concentrate on a certain period of time, the formation scores of learners who learn consciously are higher.

• Proceedings of the Korea Multimedia Society Conference
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• 2003.05b
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• pp.210-213
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• 2003

본 연구에서는 뉴우턴법의 고정점 알고리즘에 적응 조정이 가능한 학습파라미터를 이용한 신경망 기반 독립성분분석기법을 제안하였다. 이는 고정점 알고리즘의 1차 미분을 이용하는 뉴우턴법에서 역혼합행렬의 경신 상태에 따라 학습율과 모멘트가 적응조정되도록 함으로써 분리속도와 분리성능을 개선시키기 위함이다. 제안된 기법을 512$\times$512 픽셀의 10개 영상으로부터 임의의 혼합행렬에 따라 발생되는 영상들의 분리에 적용한 결과, 기존의 고정점 알고리즘에 의한 결과보다 우수한 분리성능과 빠른 분리속도가 있음을 확인하였다.

• Journal of IKEEE
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• v.26 no.4
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• pp.686-692
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• 2022

In this paper, the matching error rates of modified area-based, energy-based algorithms, and learning-based structures were compared for stereo image matching. Census transform (CT) based on region and life propagation (BP) algorithm based on energy were selected, respectively.Existing algorithms have been improved and implemented in an embedded processor environment so that they can be used for stereo image matching in mobile systems. Even in the case of the learning base to be compared, a neural network structure that utilizes small-scale parameters was adopted. To compare the error rates of the three matching methods, Middlebury's Tsukuba was selected as a test image and subdivided into non-occlusion, discontinuous, and disparity error rates for accurate comparison. As a result of the experiment, the error rate of modified CT matching improved by about 11% when compared with the existing algorithm. BP matching was about 87% better than conventional CT in the error rate. Compared to the learning base using neural networks, BP matching was about 31% superior.

• Composites Research
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• v.14 no.5
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• pp.26-37
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• 2001

After impact analysis of the composite cylindrical shells was performed. obtained outputs at 9 equally divided points of the shell were used as input patterns of the neural networks. Identification of impact loading characteristics was predicted simultaneously. Momentum backpropagation algorithm of neural networks which can modify the momentum coefficient and learning rate was developed and applied to identify the loading characteristics. Hidden layers of the backpropagation increased from 1 layer to 3 layers and trained the loading characteristics. Developed program with variable learning rate was converged close to real load characteristics under 1% error. Inverse engineering which identify the impact loading characteristics can be applicable to the composite laminated cylindrical shells with developed neural networks.

• The Journal of the Acoustical Society of Korea
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• v.23 no.3
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• pp.228-234
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• 2004

In this paper, we proposed the compensation technique that overcomes the limitations of the conventional approaches through summing up the bias terms between world's codebook and individual codebook vectors of feature parameters. But, mean compensation without condition can bring higher false acceptance. Therefore, the proposed technique compensates the channel mis-match condition by weighted bias sum using nonlinear function regarding to the distortion between speech and silence. The simulation results show that the FRR (flase reject rate) is decreased 14.95% when the proposed algorithm was applied.

• The Journal of Bigdata
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• v.4 no.2
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• pp.175-183
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• 2019

Drop-out issue is one of the challenges of cyber university. There are about 130,000 students enrolled in cyber universities, but the dropout rate is also very high. To lower the dropout rate, cyber universities invest heavily in learning analytics. Some cyber universities analyze the possibility of dropout and actively support students who are more likely to drop out. The purpose of this paper is to identify the learning data affecting the dropout prediction index. As a result of the analysis, it is confirmed that number of lessons(progress), credits, achievement and leave of absence have a significant effect on dropout rate. It is necessary to increase the accuracy of the prediction model through post-test on the student dropout prediction index.