• Journal of Korean Society of Transportation
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• v.34 no.4
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• pp.330-340
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• 2016

The main objective of study was to analyze the effectiveness of transit mall. To this end, this study compared operational status of transit malls in operation (Daegu, Seoul and Busan). For the city of Busan where the latest transit mall became operational in 2015, a discriminant model was proposed to determine user satisfaction/dissatisfaction. The outcomes of comparative study showed that each city has different operational focus - Seoul has focus on traffic demand management, Busan has focus on walking environment, and Daegu has focus on accessibility to public transportation. Also, the discriminant model indicated that market sentiment, convenience of bus service, traffic accident risk, number of pedestrian, market user number have effects on user satisfaction. These results implied that, to improve user satisfaction, measures for market revitalization such as opening events and forming consultative groups need to be prepared and traffic conditions should also be improved through expansion of curfew time, linkage with subway station.

• Journal of the Korea Society of Computer and Information
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• v.16 no.1
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• pp.31-38
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• 2011

A RAM-based Neural Net is a weightless neural network based on binary neural network. 3-D neural network using this paper is binary neural network with multiful information bits and store counts of training. Recognition method by MRD technique is based on the supervised learning. Therefore neural network by itself can not distinguish between the categories and well-separated categories of training data can achieve only through the performance. In this paper, unsupervised learning algorithm is proposed which is trained existing 3-D neural network without distinction of data, to distinguish between categories depending on the only input training patterns. The training data for proposed unsupervised learning provided by the NIST handwritten digits of MNIST which is consist of 0 to 9 multi-pattern, a randomly materials are used as training patterns. Through experiments, neural network is to determine the number of discriminator which each have an idea of the handwritten digits that can be interpreted.

• Journal of KIISE:Information Networking
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• v.36 no.3
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• pp.153-162
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• 2009

It is a challenging work for service operators to accurately classify different services, which runs on various wireless networks based upon numerous platforms. This works focuses on design and implementation of a classifier, which accurately classifies applications, which are captured horn WiBro Network. Notion of session is introduced for the classifier, instead of commonly used Flow to develop a classifier. Based on session information of given traffic, two classification algorithms are presented, Classification and Regression Tree and Support Vector Machine. Both algorithms are capable of classifying accurately and effectively with misclassification rate of 0.85%, and 0.94%, respectively. This work shows that classifier using CART provides ease of interpreting the result and implementation.

• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
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• 2002.11a
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• pp.571-574
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• 2002

Lenstra와 Verheul에 의해 주장된 XTR은 짧은 키 길이와 빠른 연산 속도의 장점을 가지고 있기 때문에 복잡한 연산에 유용하게 사용될 수 있다. 또한 본 서명자의 부재시 위임 서명자가 대신 서명할 수 있는 대리 서명은 본 서명자가 대리 서명시 사용 될 비밀 정보를 생성하여 대리 서명자에게 전송한다. 대리 서명자는 전송된 비밀 정보의 유용성을 판별한 후 유용하다면 이 정보를 사용하여 서명하게 된다. 이러한 대리 서명 과정에서 발생하는 연산들은 XTR을 이용하여 속도의 향상을 가져올 수 있고, 짧은 키 길이로 동일한 안정성을 가진다. 따라서 유선에서 뿐만 아니라 무선에서도 효율성을 가질 수 있다.

• The Journal of Korean Association of Computer Education
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• v.17 no.1
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• pp.1-12
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• 2014

Since students' behaviors during e-learning are automatically stored in LMS(Learning Management System), the LMS log data convey the valuable information of students' engagement. The purpose of this study is to develop a prediction model of e-learning course dropout by utilizing LMS log data. Log data of 578 college students who registered e-learning courses in a traditional university were used for the logistic regression analysis. The results showed that attendance and study time were significant to predict dropout, and the model classified between dropouts and completers of e-learning courses with 96% accuracy. Furthermore, the feasibility of early detection of dropouts by utilizing the model were discussed.

• Proceedings of the KIEE Conference
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• 2008.11a
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• pp.403-405
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• 2008

전통적인 FT(Fourier Transform)의 대안으로 scale과 shift에 의한 WT(Wavelet Transform)이 제안되어, 전력계통의 각 분야에 적당한 Mother Wavelet 선택에 대한 연구가 시도되고 있다. 본 논문은 발전기 고정자 사고의 경우 고장판별을 위하여 적당한 마더 웨이브릿을 선정할 수 있는 알고리즘에 관한 것이다. ATP 시뮬레이션으로부터 수집한 전류데이터를 이용하여 제안된 적당한 선정기법의 유효성을 증명하였다.

• 전기의세계
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• v.13 no.4
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• pp.1-9
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• 1964

수차발전기에서 이상진동이 발생하였을 경우 일반적인 진동판별법을 서술하기로 한다. 실제로 부디치는 진동문제를 취급하여 보면 단순한 원인에 의한 것 뿐만아니라 여러가지 원인이 중첩되어 복잡한 비선형적인 현상이 나타날 경우가 많다. 원동기측 및 피동기측의 회전자급 고정자의 각각에 진동을 발생하는 다수의 요인이 있고 또 각각의 진동원인들의 상호간의 영향력이 작용하여 복잡한 진동현상을 가지고 있어서 그 원인을 판별하기가 용이한 일이 아니다. 이러한 진동원인중 주 되는 몇가지 원인을 찾아내기 위하여는 진동특성에 관한 충분한 이해와 적절한 측정장치를 선택할 필요가 있다.

• Journal of Broadcast Engineering
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• v.19 no.2
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• pp.166-173
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• 2014

Recently, human action recognition have been developed for various broadcasting and video process. Since a video can consist of various scenes, keypoint approaches have been more attracted than template based methods for real application. Keypoint approahces tried to find regions having motion in video, and made 3-dimensional patches. Then, descriptors using histograms were computed from the patches, and a classifier based on machine learning method was applied to detect actions in video. However, a single classifier was difficult to handle various human actions. In order to improve this problem, approaches using multi classifiers were used to detect and to recognize objects. Thus, we propose a new human action recognition using decision-level fusion with support vector machine and sparse representation. The proposed method extracted descriptors based on keypoint approach from a video, and acquired results from each classifier for human action recognition. Then, we applied weights which were acquired by training stage to fuse each results from two classifiers. The experiment results in this paper show better result than a previous fusion method.

• Journal of Gifted/Talented Education
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• v.11 no.1
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• pp.99-117
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• 2001

This study looked at the validity of assessments of academic giftedness in young children, details of assessment procedures and tasks related to them. It has shown that academic giftedness in young children can be reliably observed and reported by parents and teachers through similar characteristics to older gifted children. But for young preschool children since the potentiality rather than the current performance level is more important to detect and children's abilities are not fully differentiated, more systematic and comprehensive assessments are needed. Parents'and/or teachers' referrals, behavior checklists for the gifted, tests for creativity, short or group forms of standardized tests and finally individualized tests can be used as a comprehensive battery but these methods can be used stepwise for the sake of efficiency and economy. Especially the variability of performance in young children during the rapid developmental phase as well as their vulnerability by situational factors ask for the repeated assessments as regular bases. In this study different measures for gifted young children, specialists' qualification, and procedural guidance were reviewed and the necessity for various screening tools for different programs for the gifted was discussed. Finally it was emphasized that early assessment and following educational arrangement are needed for gifted young children for their optimal development.

• Proceedings of the KIPE Conference
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• 2012.11a
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• pp.227-228
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• 2012

본 논문에서는 스위칭 주파수의 구형파 펄스 전압 주입을 이용한 센서리스 제어시 회전자 초기 위치 추정 방법을 제안하였다. 추정된 동기좌표계 d축에 스위칭 주파수의 구형파 펄스 전압을 주입하여 회전자의 위치를 추정하고, 인덕턴스 포화현상을 이용하여 회전자의 극성을 판별하였다. 제안된 방법은 실험을 통하여 그 타당성을 검증하였다.