• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.04a
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• pp.271-271
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• 2007

본 내용은 첨단 정보통신 기술을 이용하여 강의자 중심의 단방향 교육(Teaching) 환경을 학생 중심의 양방향 교육(Learning) 환경으로 개선하여 보다 창의적인 인재를 양성할 수 있는 교육환경 구축에 관한 내용이다. 우리나라를 포함한 OECD 국가들은 ICT활용 수업에 대한 필요성을 공감하고 교단 선진화를 위한 연구 및 지원을 다양하게 진행하고 있다. 학생들에게 지급하는 교과서를 인쇄매체 대신에 메모리 스틱, CD-ROM 및 인터넷을 통한 전자 매체로 대체하는 방안 등이 그 예이다. 따라서 학생들이 강의실에서 멀티미디어를 이용해 강의를 듣고, 과제를 풀며 정리된 내용을 발표하고 토론할 수 있는 양방향 수업환경이 요구된다. 그러나 컴퓨터를 활용한 수업을 진행할 때의 문제점이 강의내용을 학생들에게 효율적으로 전달하기가 어렵고, 학생들의 컴퓨터를 통제할 수 없기 때문에 수업을 이탈하는 경우가 발생되는 등 교육에 역효과가 초래된다. 본 내용에서 소개하는 양방향 수업진행 장비(드림랩)는 강의자가 학생들의 컴퓨터 모니터, 키보드 및 마우스를 자유로이 통제할 수 있어서 강의자의 화면과 음성을 실시간으로 선명하게 학생들에게 전달하고, 학생들의 내용을 모니터하고 제어할 수 있으며, 개인지도 및 수준별 그룹지도가 가능하다. 또한 강의자에게 개인적으로 질문을 할 수 있고, 학생들의 내용을 자신의 자리에서 전체 학생들에게 발표할 수도 있다. 드림랩은 순수 하드웨어로 구성되어 컴퓨터 기종이나 운영체제에 영향을 받지 않으며, 컴퓨터 자원과 네트워크 자원을 사용하지 않기 때문에 컴퓨터나 네트워크의 성능을 저하시키지 않는다. 또한 사용법이 간단하고 유지관리가 쉬운 장점 등이 있다. 따라서 컴퓨터를 활용한 수업진행이 원활하여 다양한 과목에 활용 가능하고, 학생들의 자발적인 수업 참여로 강의 중심 교육에서 자기 주도적 수업환경(T2L, Teaching to Learning)으로 자연스럽게 전환되어 교육의 질적 향상과 함께 창의적인 인재를 양성할 수 있을 것으로 기대된다.

• KIISE Transactions on Computing Practices
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• v.23 no.7
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• pp.440-445
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• 2017

Due to the advance of e-commerce systems, the number of people using online shopping and products has significantly increased. Therefore, the need for an accurate recommendation system is becoming increasingly more important. Recurrent neural network is a deep-learning algorithm that utilizes sequential information in training. In this paper, an evaluation is performed on the application of recurrent neural networks to recommendation systems. We evaluated three recurrent algorithms (RNN, LSTM and GRU) and three optimal algorithms(Adagrad, RMSProp and Adam) which are commonly used. In the experiments, we used the TensorFlow open source library produced by Google and e-commerce session data from RecSys Challenge 2015. The results using the optimal hyperparameters found in this study are compared with those of RecSys Challenge 2015 participants.

• Korean Management Science Review
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• v.30 no.1
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• pp.149-165
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• 2013

This study empirically investigated the changes of performance evaluation systems under the environment of supply-chain e-commerce. The objectives of e-commerce include obtaining financial profit, internal innovation through processes integration, learning with information flow, and customer satisfaction through quick response. These objectives are generally consistent with the four evaluation measures of balanced scorecard(BSC). This study, first, demonstrated that perceived environmental uncertainty(PEU) has a significant effect on the adoptions of e-commerce and BSC, and severe competition positively influences the use of e-commerce. With cluster analysis and subgroup analysis, we also showed that under the high adoption levels of e-commerce, the high utilization of BSC can improve the supply-chain performance of a firm. In addition, it was found that the use of e-commerce indirectly and significantly affects supply-chain performance through inter-organizational information flow, and the supply-chain performance of a firm leads to the improvement of organizational performance.

• 한국경영정보학회:학술대회논문집
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• 2008.06a
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• pp.718-731
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• 2008

본 연구는 상호작용성의 속성과 이용자에게 주어진 통제력에 따라 상호작용성을 4 가지 차원으로 나누고, 이것이 어떤 경로를 거쳐 이용자 만족에 이르는지를 규명하는 것을 목적으로 한다. 이를 위해 상호작용성을 최적화하는 킬러 콘텐츠로 e 러닝을 선정하고, 상호작용성에 영향을 미치는 선행요인으로 서비스, 콘텐츠, 미디어 품질을 도출 하였으며, 상호작용성과 이용자 만족을 매개하는 변인으로 사회적 현존감과 인지적 몰입을 포함하는 연구모델을 제시하고자 한다.

• Journal of Information Processing Systems
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• v.13 no.4
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• pp.928-940
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• 2017

State-of-the-art speaker recognition systems may work better for the English language. However, if the same system is used for recognizing those who speak different languages, the systems may yield a poor performance. In this work, the decisions of a Gaussian mixture model-universal background model (GMM-UBM) and a learning vector quantization (LVQ) are combined to improve the recognition performance of a multilingual speaker identification system. The difference between these classifiers is in their modeling techniques. The former one is based on probabilistic approach and the latter one is based on the fine-tuning of neurons. Since the approaches are different, each modeling technique identifies different sets of speakers for the same database set. Therefore, the decisions of the classifiers may be used to improve the performance. In this study, multitaper mel-frequency cepstral coefficients (MFCCs) are used as the features and the monolingual and cross-lingual speaker identification studies are conducted using NIST-2003 and our own database. The experimental results show that the combined system improves the performance by nearly 10% compared with that of the individual classifier.

• Journal of Information Processing Systems
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• v.13 no.5
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• pp.1203-1212
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• 2017

Intrusion detection systems (IDSs) are crucial in this overwhelming increase of attacks on the computing infrastructure. It intelligently detects malicious and predicts future attack patterns based on the classification analysis using machine learning and data mining techniques. This paper is devoted to thoroughly evaluate classifier ensembles for IDSs in IEEE 802.11 wireless network. Two ensemble techniques, i.e. voting and stacking are employed to combine the three base classifiers, i.e. decision tree (DT), random forest (RF), and support vector machine (SVM). We use area under ROC curve (AUC) value as a performance metric. Finally, we conduct two statistical significance tests to evaluate the performance differences among classifiers.

• Journal of Korean Institute of Industrial Engineers
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• v.23 no.1
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• pp.1-22
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• 1997

Learning control refers to controllers that learn to improve their performance at executing a given task, based on experience performing this specific task. In a previous work, the authors presented a theory of indirect decentralized learning control based on use of indirect adaptive control concepts employing simultaneous identification and control. This paper extends these results to apply to the indirect repetitive control problem in which a periodic (i.e., repetitive) command is given to a control system. Decentralized indirect repetitive control algorithms are presented that have guaranteed convergence to zero tracking error under very general conditions. The original motivation of the repetitive control and learning control fields was learning in robots doing repetitive tasks such as on an assembly line. This paper starts with decentralized discrete time systems, and progresses to the robot application, modeling the robot as a time varying linear system in the neighborhood of the desired trajectory. Decentralized repetitive control is natural for this application because the feedback control for link rotations is normally implemented in a decentralized manner, treating each link as if it is independent of the other links.

• Journal of the Korean Society of Earth Science Education
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• v.3 no.3
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• pp.198-208
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• 2010

This work describes a preliminary investigation to a learning model based on cybernetics and radical constructivism. To achieve this purpose, main ideas of cybernetics, i.e., negative feedback, difference, self-regulation, equilibrium, and purpose-directed behavior was analysed under radical constructivism. Powers' model, which consists of hierarchically arranged negative feedback systems, is introduced into this work. This was based on the claim that living organisms behave to control perceptions. By adding the notion of scheme from the view of radical constructivism, a learning procedure, which consists of six steps, was suggested in this work.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.30 no.4
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• pp.133-140
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• 2007

This study aims to design and implement a learning evaluation system using .NET which is developed by Microsoft. .NET technology supports higher processing speed than ASP technology. The learning evaluation system is based on the web, consists of administrator module, questioner module and student module. The functions of the system, i.e., providing test questions, performing test, and evaluating result of test are achieving on the web in real time. Even when many users use this system, the system is stable and has a speed response time.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.1539-1542
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• 2012

고정된 컴퓨터에서 학습하는 e-learning에서 탈피하여 이동 중에도 학습이 가능한 u-learning이 필요하여 u-learning의 한부분인 스마트러닝은 급변하는 정보화시대의 교육경향이 매우 빠르게 변화하고 있는 상황을 그대로 반영해주는 결과물이라고 할 수 있다. 스마트 러닝이 학습향상에 얼마나 영향을 미치는가를 분석하고 스마트 러닝 기능을 최대한 활용하여 최대의 학습 효과를 얻을 수 있는 방법을 제시하며 스마트기기를 이용해 실제 학습하는 사례를 적용한 동영상 강의 애플리케이션의 효율적인 관리 시스템을 분석 설계한다. 각종 콘텐츠를 비롯하여 동영상강의 어플리케이션을 통한 여러 학습수단을 배경으로 전체적인 면에서 학습 환경을 살펴봄으로써 학습효과에 보다 나은 방안을 제시하고자 한다.