• International Journal of Contents
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• 제10권3호
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• pp.64-72
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• 2014

This study explored the elements that affect team achievements in virtual environments. In this study, consideration was given to the role of group dynamics in facilitating productive interaction. We aspired to reveal the mechanisms of group dynamics and examined how group dynamics affected team achievements in virtual environments. The empirical study was performed with undergraduate students enrolled in an e-learning course. In collaboration with other majors, students executed team projects and managed project issues in forums or chat rooms. The results of the empirical study indicated that leadership, creative friction, and group cohesion (components of group dynamics) had positive relationships with team achievements. The findings confirmed that addressing creative conflict is a method to improve team performance and that leadership is a key factor in project teams.

• 대한조선학회논문집
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• 제40권1호
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• pp.69-73
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• 2003

Neuro-Fuzzy System is to combine merits of fuzzy inference system and neural networks. The neuro-fuzzy system applies a information about given input-output data to fuzzy theories and deals these fuzzy values with neural networks, e.g. first, redefines normalized input-output data as membership functions and then executes thses fuzzy information with backpropagation neural networks. This paper describes an innovative application of the Taguchi method for the determination of these parameters to meet the training speed and accuracy requirements. Results drawn from this research show that the Taguchi method provides an effective means to enhance the performance of the neuro-fuzzy system in terms of the speed for learning and the accuracy for recall.

• 영어어문교육
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• 제11권3호
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• pp.57-69
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• 2005

There have been changes throughout the past 100 years of English education in Korea. The Ministry of Education revised the English curriculum numerous times. From the 6th national curriculum, communicative competence became an essential objective in English learning. The study is to see if E University students' TOEIC results show any significant difference between the two groups under the 5th and 6th national curriculum. Another objective of this research is to see if recruiting medical students with high scores of the standardized English tests is suitable to select the best candidates who can fulfill medical studies. For these two purposes, sophomore students' TOEIC results in 2000 & 2004 and non-resident-status students' cumulative GPA were analyzed. The study shows that there is no significant difference in the two groups. Moreover, the current recruiting policy to select the best fit medical candidates based on their high TOEIC or TOEFL scores does not seem to be an appropriate measure since such students' GPA reveals poor academic performance amid their high scores in English.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.123.2-123
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• 2001

It is difficult to determine the feeding rate of coagulant in the water treatment plant, due to nonlinearity, multivariables and slow response characteristics, etc. To deal with this difficulty, the neuro-fuzzy system and the genetic-fuzzy system were used in determining the feeding rate of the coagulant. The fuzzy system is excellently robust in multi-variables and nonlinear problems. Therefore it uses basic algorithm, but it is difficult to construct of the fuzzy parameter such as the rule table and the membership function, Therefore we made the neuro-fuzzy system and the genetic-fuzzy system with the fusion of learning algorithms and compared the performance of the two fuzzy systems. To apply these algorithms, we made the rule table, membership function from the actual operation data of the water treatment plant. We determined optimized feeding rate of coagulant using the fuzzy operation, and also compared ...

• 대한전기학회:학술대회논문집
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• 대한전기학회 2002년도 하계학술대회 논문집 D
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• pp.2120-2122
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• 2002

There are several types of control systems that use fuzzy logic controller as a essential system component. The majority of research work on fuzzy PID controller focuses on the conventional two-input PI or PD type controller. However, fuzzy PID controller design is a complex task due to the involvement of a large number of parameters in defining the fuzzy rule base. In this paper we combined conventional PI type and PD type fuzzy controller and set the initial parameters of this controller from the conventional PID controller gains obtained by Ziegler-Nichols tuning or other coarse tuning methods. After that, by replacing some of these parameters with sing1e neurons and making them to be adjusted by back-propagation learning algorithm we designed a neuro-fuzzy controller which showed good performance characteristics in both computer simulation and actual application.

• 한국수학교육학회지시리즈E:수학교육논문집
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• 제22권4호
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• pp.459-469
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• 2008

본 논문에서 행렬의 가장 큰 고유값과 그에 대응하는 고유벡터가 과학적인 의사결정과정에 어떻게 적용되는지를 살펴본다. 이를 적용한 계층분석적 의사결정(AHP) 방법에서 사용된 행렬이론을 통해서 실제로 연구과제 선정방법의 심사지표 가중치가 AHP를 이용하여 조절되는 예를 구체적으로 알아본다.

• Journal of Information Processing Systems
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• 제16권1호
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• pp.96-112
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• 2020

Solving the disclosure problem of sensitive information with the k-nearest neighbor query, location dummy technique, or interfering data in location-based services (LBSs) is a new research topic. Although they reduced security threats, previous studies will be ineffective in the case of sparse users or K-successive privacy, and additional calculations will deteriorate the performance of LBS application systems. Therefore, a model is proposed herein, which is based on geohash-encoding technology instead of latitude and longitude, memcached server cluster, encryption and decryption, and authentication. Simulation results based on PHP and MySQL show that the model offers approximately 10× speedup over the conventional approach. Two problems are solved using the model: sensitive information in LBS application is not disclosed, and the relationship between an individual and a track is not leaked.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2004년도 추계학술발표논문집(상)
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• pp.493-496
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• 2004

본 연구에서는 신경망 학습의 중요한 평가 척도로써 고려될 수 있는 일반화 성능과 학습속도를 개선시키기 위한 방안으로써 초기 가중값과 학습률과 같은 주요 인자들을 이용한 신경망 학습 영향을 살펴본다. 특히 초기 가중값과 학습률을 고정시킨 후 새롭게 조정된 계수들을 점차적으로 변화시키는 새로운 인자 결합방법을 이용하여 신경망 학습량과 학습속도를 비교해 보고 계수조정을 통한 개선된 학습 영향을 살펴본다. 그리고 단순한 예제를 이용한 실증분석을 통하여 신경망 모형의 일반화 성능과 학습 속도 개선을 위한 각 인자들의 개별 효과와 결합 효과를 살펴보고 그 개선 방안을 제시한다.

• Journal of Power Electronics
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• 제12권5호
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• pp.842-850
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• 2012

Learning Power Electronics is essential in both electrical and electronic engineering fields and the introductory courses are similar in many universities. Taking this premise into account, an educational computer-aided platform for power electronics will be presented in this paper. This educational platform includes an e-book, a set of power electronics animations, Java simulations, as well as several hands-on training sessions. The main advantages of this platform are twofold. First, all necessary teaching tools are combined on a single platform. And secondly, access to this platform is available free of charge and with no complicated registration requirements. In addition to traditional teaching techniques, the use of this platform has demonstrated an increase in student participation and has consistently improved their academic performance. Data consist of surveys, which guarantee both reliability and validity through psychometric techniques.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.29.1-29
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• 2001

Neuro-fuzzy systems are multi-layered connectionist networks that realize the elements and functions of traditional fuzzy logic control/decision systems. A trained neuro-fuzzy system is isomorphic to a fuzzy logic system, and fuzzy IF-THEN rule knowledge can be explicitly extracted from the network. This talk presents a brief introduction to self-adaptive neuro-fuzzy systems and addresses some recent research results and applications. Most of the existing neuro-fuzzy systems exhibit several major drawbacks that lead to performance degradation. These drawbacks are the curse of dimensionality (i.e., fuzzy rule explosion), inability to re-structure their internal nodes in a changing environment, and their lack of ability to extract knowledge from a given set of training data. This talk focuses on our investigation of network architectures, self-adaptation algorithms, and efficient learning algorithms that will enable existing neuro-fuzzy systems to self-adapt themselves in an unstructured and uncertain environment.