• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.354-359
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• 2006

The analysis of large-scale water resources systems is often complicated by the presence of multiple reservoirs and diversions, the uncertainty of unregulated inflows and demands, and conflicting objectives. Reinforcement learning is presented herein as a new approach to solving the challenging problem of stochastic optimization of multi-reservoir systems. The Q-Learning method, one of the reinforcement learning algorithms, is used for generating integrated monthly operation rules for the Keum River basin in Korea. The Q-Learning model is evaluated by comparing with implicit stochastic dynamic programming and sampling stochastic dynamic programming approaches. Evaluation of the stochastic basin-wide operational models considered several options relating to the choice of hydrologic state and discount factors as well as various stochastic dynamic programming models. The performance of Q-Learning model outperforms the other models in handling of uncertainty of inflows.

• Phonetics and Speech Sciences
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• v.3 no.3
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• pp.91-98
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• 2011

In this paper, a multi-channel speech enhancement method using the linearly constrained minimum variance (LCMV) algorithm and a variable learning rate control is proposed. To control the learning rate for adaptive filters of the LCMV algorithm, the direction of arrival (DOA) is measured for each short-time input signal and the likelihood function of the target speech presence is estimated to control the filter learning rate. Using the likelihood measure, the learning rate is increased during the pure noise interval and decreased during the target speech interval. To optimize the parameter of the mapping function between the likelihood value and the corresponding learning rate, an exhaustive search is performed using the Bark's scale distortion (BSD) as the performance index. Experimental results show that the proposed algorithm outperforms the conventional LCMV with fixed learning rate in the BSD by around 1.5 dB.

• Korean Medical Education Review
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• v.23 no.1
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• pp.3-10
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• 2021

Learning by teaching (LbT) has long been recognized as an important learning behavior that constructs meaning based on interactions between learners. This study aimed to explore the meaning of LbT as an important learning activity for future implementation in education. LbT is based on the cultural historical activity theory and sociocultural learning theory, as developed by scholars including Vygotsky. These frameworks value the construction of meaning based on language, and LbT is reported to be effective in constructing meaning. In addition, within the zone of proximal development posited by Vygotsky, learning through interaction between learners improves academic achievement, higher-order thinking, deep learning, and reflective learning. LbT also promotes students' learning presence, and strengthens various competencies such as collaboration and communication skills. Interactive behavior between learners in the form of LbT has been explored as an approach to teaching and learning, with methods including peer learning, peer tutoring, peer teaching, peer mentoring, Lernen durch Lehren, and peer-assisted learning. LbT has also been applied as a learning method. In the future, LbT has boundless potential to improve learning through activities such as flipped learning or online learning based on interactions between learners.

• International Journal of Computer Science & Network Security
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• v.24 no.1
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• pp.187-195
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• 2024

This is an extended paper explaining the role of E-learning and quality development in the current situation. Amid Covid:19, E-Learning has achieved a new miles stone in imparting education and all levels of institutions have transformed their learning platform from face to face to virtual learning. In this scenario E-Learning is facing two major challenges, first to ensure the ability of computer systems or software to exchange and make use of information on virtual platform (interoperability) and secondly, developing quality learning through e-Learning. To impart learning and teaching (L&T) through E-learning, Middle East University (MEU) has adopted Learning Management Services (LMS) through Blackboard. The university has three types of L&T methods; full online, Blended and Supportive. This research studies the concept, scope and dimensions of interoperability (InT) of E-Learning in MEU then the connection and interdependence between with quality development. In this paper we have described the support and the importance of finest standards and specifications for the objectives of InT of E-Learning and quality development in MEU. The research is based principally on secondary data observed from MEU E-Learning deanship. Also sample of 20 E-Learning experts at MEU were given closed ended as well as semi closed questionnaires for evaluating the assurance of InT of E-Learning and quality development. These experts are mainly certified online facilitators and admin staff. Results provide the verification of application and presence of InT of E-Learning and assured the quality development process in MEU.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10b
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• pp.734-739
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• 1988

For the trajectory control of dynamic systems with unidentified parameters a second-order iterative learning control method is presented. In contrast to other known methods, the proposed learning control scheme can utilize more than one error history contained in the trajectories generated at prior iterations. A convergency proof is given and it is also shown that the convergence speed can be improved in compared to conventional methods. Examples are provided to show effectiveness of the algorithm, and, via simulation, it is demonstrated that the method yields a good performance even in the presence of distubances.

• Journal of Fisheries and Marine Sciences Education
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• v.15 no.2
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• pp.184-192
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• 2003

When the present Learning System for Computer-Related Subjects Using WBI is implemented on the Web with the above characteristics to help students to study computer subjects without any limitations of time or space, they can easily attain the goals of learning, have computer-utilizing abilities or information capacity, and enhance their capabilities for self-initiative learning. This system enables the learners to carry out 'plan-do-see' for the contents of learning initiatively. The learners can study the practice part of the curriculum using multi-media, such as motion pictures, voices, images, and sound effects, vividly with a sense of actual presence. It helps the students to have an active attitude toward leaning afterward. without meeting the teacher or without any storage media, the leaners can submit their assignments or materials for performance evaluation via the Internet.

• Journal of the Korean Society of Radiology
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• v.16 no.5
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• pp.595-602
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• 2022

This study tried to suggest the most efficient learning rate for accurate and efficient automatic diagnosis of medical images for chest X-ray pneumonia images using deep learning. After setting the learning rates to 0.1, 0.01, 0.001, and 0.0001 in the Inception V3 deep learning model, respectively, deep learning modeling was performed three times. And the average accuracy and loss function value of verification modeling, and the metric of test modeling were set as performance evaluation indicators, and the performance was compared and evaluated with the average value of three times of the results obtained as a result of performing deep learning modeling. As a result of performance evaluation for deep learning verification modeling performance evaluation and test modeling metric, modeling with a learning rate of 0.001 showed the highest accuracy and excellent performance. For this reason, in this paper, it is recommended to apply a learning rate of 0.001 when classifying the presence or absence of pneumonia on chest X-ray images using a deep learning model. In addition, it was judged that when deep learning modeling through the application of the learning rate presented in this paper could play an auxiliary role in the classification of the presence or absence of pneumonia on chest X-ray images. In the future, if the study of classification for diagnosis and classification of pneumonia using deep learning continues, the contents of this thesis research can be used as basic data, and furthermore, it is expected that it will be helpful in selecting an efficient learning rate in classifying medical images using artificial intelligence.

• Journal of the Korean Chemical Society
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• v.68 no.2
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• pp.93-106
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• 2024

This study investigated differences in presence, immersion, and situational interest in small group learning using augmented reality, based on the degree of tool sharing. 84 eighth-grade students participated in small groups of four. Each group was randomly assigned to one of three environments based on marker and device sharing: the shared environment (shared marker and device usage), the mixed environment (shared marker and individual device usage), and the individual environment (individual marker and device usage). Small group learning using augmented reality was conducted for three class periods, focusing on the "Characteristics of Matter" unit. One-way ANOVA results for the dependent variables revealed that, compared to the shared environment, presence and situational interest were significantly higher in the mixed environment, while immersion and situational interest were significantly higher in the individual environment. MANOVA results for the sub-components of each dependent variable showed significant differences in realness for presence, antecedents and experiences for immersion, and instant enjoyment, novelty, and total interest for situational interest. Analysis of interviews and classroom observations indicated that students in shared and individual environments tended to use their devices individually when utilizing augmented reality. However, in mixed environments, students showed a tendency to use their devices collaboratively, leading to more active interactions. Based on these findings, environments for using tools to enhance the effectiveness of small group learning using augmented reality are discussed.

• Journal of IKEEE
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• v.26 no.4
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• pp.742-745
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• 2022

In this paper, we propose a hybrid image processing and deep learning-based method for detecting the presence of power lines in infrared images. Deep learning-based methods can learn feature vectors from a large number of data without much effort, resulting in outstanding performances in various fields. However, it is difficult to apply human intuition to the deep learning-based methods while image processing techniques can be used to apply human intuition. Based on these, we propose a method that exploits both advantages to detect the existence of power lines in infrared images. To this end, five methods have been applied and compared to find the most effective image processing technique for detecting the presence of power lines. As a result, the proposed method achieves 99.48% of accuracy which is higher than those of methods based on either image processing or deep learning.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06b
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• pp.259-261
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• 2006

"e" 시대로 대변되는 21세기는 정보화라는 짧은 과도기적인 사회변화 상태를 지나 지식이 새로운 국가 경쟁력으로 평가되는 지식기반사회로 급속하게 이전하고 있다. 이와 같은 현실은 언제(Any-Time), 어디서 (Any-Where), 누구나(Any-One) 지식을 효과적으로 활용할 수 있는 수단으로 e-learning이 교육의 새로운 패러다임으로 각광받고 있다. 하지만 현재의 e-learning은 기존의 오프라인의 교육 운영 형태를 그대로 답습함으로서 전문가에 의해 매뉴얼화 된 프로세스 정보를 매개로 학습활동이 이뤄지고 있으며 서비스제공환경의 제약으로 인해 개인의 참여와 사용자간 참여의 한계에 직면해 있다. 본 논문에서는 기존 e-learning의 문제점을 개선하고자 상호 작용성을 통한 사회적 실재감(Social Presence)을 느낄 수 있도록 차세대 Web인 Web2.0의 적용을 통해 e-learning 활성화 방안을 모색하고자 한다.