• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1254-1259
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• 2005

In this paper, we proposed a learning algorithm for the neuro-fuzzy modeling using a learning rule to adapt clustering. The proposed algorithm includes the data partition, assigning the rule into the process of partition, and optimizing the parameters using predetermined threshold value in self-constructing algorithm. In order to improve the clustering, the learning method of neuro-fuzzy model is extended and the learning scheme has been modified such that the learning of overall model is extended based on the error-derivative learning. The effect of the proposed method is presented using simulation compare with previous ones.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제5권2호
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• pp.95-101
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• 2005

In this paper, we proposed a learning algorithm for the neuro-fuzzy modeling using a learning rule to adapt clustering. The proposed algorithm includes the data partition, assigning the rule into the process of partition, and optimizing the parameters using predetermined threshold value in self-constructing algorithm. In order to improve the clustering, the learning method of neuro-fuzzy model is extended and the learning scheme has been modified such that the learning of overall model is extended based on the error-derivative learning. The effect of the proposed method is presented using simulation compare with previous ones.

• 대한전기학회논문지
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• 제43권5호
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• pp.838-847
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• 1994

Although there is an analytical proof of modeling capability of the neural network, the convergency error in nonlinearity modeling is inevitable, since the steepest descent based practical larning algorithms do not guarantee the convergency of modeling error. Therefore, it is difficult to apply the neural network to control system in critical environments under an on-line learning scheme. Although the convergency of modeling error of a neural network is not guatranteed in the practical learning algorithms, the convergency, or boundedness of tracking error of the control system can be achieved if a proper feedback control law is combined with the neural network model to solve the problem of modeling error. In this paper, the neural network is introduced for compensating a system uncertainty to control a nonlinear dynamic system. And for suppressing inevitable modeling error of the neural network, an iterative neural network learning control algorithm is proposed as a virtual on-line realization of the Adaptive Variable Structure Controller. The efficiency of the proposed control scheme is verified from computer simulation on dynamics control of a 2 link robot manipulator.

• 한국도로학회논문집
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• 제19권4호
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• pp.1-7
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• 2017

PURPOSES : This study aims to improve complex modeling of multivariable, nonlinear, and overdispersion data with an artificial neural network that has been a problem in the civil and transport sectors. METHODS: Deep learning, which is a technique employing artificial neural networks, was applied for developing a large bus fuel consumption model as a case study. Estimation characteristics and accuracy were compared with the results of conventional multiple regression modeling. RESULTS : The deep learning model remarkably improved estimation accuracy of regression modeling, from R-sq. 18.76% to 72.22%. In addition, it was very flexible in reflecting large variance and complex relationships between dependent and independent variables. CONCLUSIONS : Deep learning could be a new alternative that solves general problems inherent in conventional statistical methods and it is highly promising in planning and optimizing issues in the civil and transport sectors. Extended applications to other fields, such as pavement management, structure safety, operation of intelligent transport systems, and traffic noise estimation are highly recommended.

• 제어로봇시스템학회논문지
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• 제13권4호
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• pp.309-314
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• 2007

This paper presents an adaptive neural computation algorithm for multi-layered neural networks which are applied to identify the characteristic function of dynamic systems. The main feature of the proposed algorithm is that the initial learning rate for the employed neural network is assigned systematically, and also the assigned learning rate can be adjusted empirically for effective neural leaning. By employing the approach, enhanced modeling of dynamic systems is possible. The effectiveness of this approach is veri tied by simulations.

• 한국정보시스템학회지:정보시스템연구
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• 제28권4호
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• pp.271-291
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• 2019

Purpose The purpose of this study is to propose a methodology to derive positive keywords and negative keywords through deep learning to classify reviews into positive reviews and negative ones, and then refine the results of topic modeling using these keywords. Design/methodology/approach In this study, we extracted topic keywords by performing LDA-based topic modeling. At the same time, we performed attention-based deep learning to identify positive and negative keywords. Finally, we refined the topic keywords using these keywords as filters. Findings We collected and analyzed about 6,000 English reviews of Gyeongbokgung, a representative tourist attraction in Korea, from Tripadvisor, a representative travel site. Experimental results show that the proposed methodology properly identifies positive and negative keywords describing major topics.

• 수산해양교육연구
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• 제24권6호
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• pp.935-949
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• 2012

The purpose of this study was to investigate the structural relationship among the self-efficacy, self-directed learning ability, school adjustment and learning flow in middle school students by the structural equation modeling analysis. The subjects of this study consisted of 553 middle school students. The data were analyzed with descriptive statistics, Pearson correlations and structural equation modeling analysis by using the SPSS 12.0 and AMOS 5.0 statistical program. The results of this study were as followed: First, there were significant correlations among the self-efficacy, self-directed learning ability, school adjustment and learning flow. Second, the self-directed learning ability and school adjustment directly affected the learning flow. Third, self-efficacy and school adjustment variables indirectly affected learning flow. The indices of the best fit model on these variable were adequate. This study shows that the self-efficacy, self-directed learning ability, school adjustment are the significant predictor for the learning flow during adolescent.

• 한국학교수학회논문집
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• 제20권2호
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• pp.91-117
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• 2017

본 논문에서는 수학적 지식을 스스로 구성하여 개념적으로 이해할 수 있도록 개발된 발생적 모델링을 활용하여 로그 단원에 대한 교수 학습 자료를 개발하고 발생적 모델링 활동을 통해 학생들이 로그 개념을 이해해 나가는 과정을 분석하고자 한다. 이를 위해 로그 단원을 3가지 소주제로 나누고 각각의 소주제별로 발생적 모델링의 교수학적 4단계인 적용, 추출, 압축, 구성 틀에 맞추어 발생적 근원 맥락을 담고 학생 스스로 개념을 구성해 나갈 수 있는 교수 학습 자료를 개발하였다. 개발된 자료를 이용하여 중하 수준 학생 2명과 중상 수준 학생 2명을 대상으로 수업을 진행하였다. 이를 통해 발생적 모델링의 교수학적 4단계를 따르는 로그 단원에 대한 개념 구성 과정을 살펴보고 van Hiele이 제시한 일반적인 수학학습수준을 바탕으로 학생들의 로그 단원에 대한 이해정도를 분석하여 몇 가지 교수학적 시사점을 제안하였다.

• 한국산학기술학회논문지
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• 제14권12호
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• pp.6455-6460
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• 2013

지능형 교육시스템을 위한 학습자 모델 구축 기술은 지능형 교육시스템의 원천 기술이라 할 수 있으며, 학습자에게 제공되는 교육 서비스가 질적으로 향상된다. 본 연구는 지능형 교육 시스템의 기반 및 원천 기술이라 할 수 있는 학습자 모델 구축 기술을 목표로 학습자 모델 생성 기술, 다양한 학습자 상태 파악을 위한 연구, 교육 데이터 마이닝 기술에 대한 체계적 연구를 실시한다.

• 대한수학교육학회지:학교수학
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• 제7권3호
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• pp.303-318
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• 2005

학생들이 자신의 문제 상황을 해결하기 위하여 수학을 이용하고, 그를 통해 수학적 지식을 학습할 수 있다면, 이것은 학생들이 수학의 유용성과 가치를 깨닫게 하는 수학교육이 될 것이다. 본 연구는 학생들이 문제해결을 통하여 수학을 학습할 수 있도록 지도하기 위해, 여러 교과에서 관심을 두고 있는 문제 중심 학습을 고찰하고 그것을 수학 교과에서 수학적 모델링으로 적용하려 시도했다. 수학적 모델링을 적용한 수업 모형을 제안하고, 학생들을 실제로 지도한 예시를 들어, 형식적이고 위계적인 학문으로서의 수학에 모델링을 도입하여 문제 중심 학습을 실현할 수 있음을 보이려 했다.