• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.652-656
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• 2002

본 연구에서는 각 수요지간의 시간이 확률적으로 주어지는 경우의 TSP(Traveling Salesman Problem)를 다루고자 한다. 현실적으로, 도심의 교통 체증 등으로 인해서 각 지점간의 걸리는 시간은 시간대별로 요일별로 심한 변화를 일으키기 마련이다. 그러나, 현재까지의 연구 결과는 수요지간의 경과시간이 확정적으로 주어지는 경우가 대부분으로, 도심물류 등에서 나타나는 현실적인 문제를 해결하는데는 많은 한계가 있다 본 연구에서는 문제의 해법으로 강화학습기법의 하나인 Q학습(Q-Learning)과 Neural Network를 활용한 효율적인 알고리즘을 제시한다.

• Journal of Dental Rehabilitation and Applied Science
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• v.33 no.2
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• pp.88-96
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• 2017

Purpose: The purpose of this study is to assess the relationship between the time spent designing custom abutments and repeated learning using dental implant computer aided design (CAD) software. Materials and Methods: The design of customized abutments was performed four stages using the 3DS CAD software and the EXO CAD software, and measured repeatedly three times by each stage. Learning effect by repetition was presented with the learning curve, and the significance of the reduction in the total time and the time at each stage spent on designing was evaluated using the Friedman test and the Wilcoxon signed rank test. The difference in the design time between groups was analyzed using the repeated measure two-way ANOVA. Statistical analysis was performed using the SPSS statistics software (P < 0.05). Results: Repeated learning of the customized abutment design displayed a significant difference according to the number of repetition and the stage (P < 0.001). The difference in the time spent designing was found to be significant (P < 0.001), and that between the CAD software programs was also significant (P = 0.006). Conclusion: Repeated learning of CAD software shortened the time spent designing. While less design time on average was spent with the 3DS CAD than with the EXO CAD, the EXO CAD showed better results in terms of learning rate according to learning effect.

• Journal of Korea Multimedia Society
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• v.11 no.3
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• pp.353-363
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• 2008

A multi-agent system has a longer learning period and larger state-spaces than a sin91e agent system. In this paper, we suggest a new method to reduce the learning time of Nash Q-learning in a multi-agent environment. We apply Macro-actions to Nash Q-learning to improve the teaming speed. In the Nash Q-teaming scheme, when agents select actions, rewards are accumulated like Macro-actions. In the experiments, we compare Nash Q-learning using Macro-actions with general Nash Q-learning. First, we observed how many times the agents achieve their goals. The results of this experiment show that agents using Nash Q-learning and 4 Macro-actions have 9.46% better performance than Nash Q-learning using only 4 primitive actions. Second, when agents use Macro-actions, Q-values are accumulated 2.6 times more. Finally, agents using Macro-actions select less actions about 44%. As a result, agents select fewer actions and Macro-actions improve the Q-value's update. It the agents' learning speeds improve.

• Journal of Korea Society of Industrial Information Systems
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• v.26 no.4
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• pp.11-18
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• 2021

We propose a method that combines learning in a virtual environment and a real environment for indoor autonomous driving through reinforcement learning. In case of learning only in the real environment, it takes about 80 hours, but in case of learning in both the real and virtual environments, it takes 40 hours. There is an advantage in that it is possible to obtain optimized parameters through various experiments through fast learning while learning in a virtual environment and a real environment in parallel. After configuring a virtual environment using indoor hallway images, prior learning was carried out on the desktop, and learning in the real environment was conducted by connecting various sensors based on Jetson Xavier. In addition, in order to solve the accuracy problem according to the repeated texture of the indoor corridor environment, it was possible to determine the corridor wall object and increase the accuracy by learning the feature point detection that emphasizes the lower line of the corridor wall. As the learning progresses, the experimental vehicle drives based on the center of the corridor in an indoor corridor environment and moves through an average of 70 steering commands.

• 한국정보교육학회:학술대회논문집
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• 2010.01a
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• pp.101-107
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• 2010

컴퓨터와 통신 기술이 발달한 정보화 사회의 현실에서 웹 기반의 프로그램 및 모바일은 중요한 매체로서 자리잡아가고 있다. 읽기는 정보 획득에서 가장 중요한 학습능력이나 학습장애아의 대부분이 읽기능력에서 문제를 가지고 있다. 오늘날 컴퓨터의 발달은 아동의 언어교육 특히 읽기 교육을 위해서 시간과 공간의 제약을 넘어 자주적으로 학습할 수 있는 모바일 활용 학습을 가능하게 하고 있다. 본 논문의 목적은 학습장애아들의 읽기 능력을 향상하기 위한 PREP 기반의 모바일 시스템을 설계하는 데 있다. 본 시스템을 통해 첫째, 학습장애아들의 읽기 능력 프로그램이 특수교사, 일반교사, 학생과 상호협력 할 수 있는 프로그램을 제공함으로써 학습장애아의 읽기 능력에 대한 진전도를 점검하고 학습능력을 향상시킬 수 있도록 설계하였다. 둘째, 학습자의 개별적 수준에 따라 수준별 학습을 제공하여 다양한 학생의 개별적 수준을 충족할 수 있도록 설계함으로써 학습장애아동의 읽기능력 향상에 기여할 수 있다. 셋째, 모바일 기기를 통해 공간적, 시간적 제약을 벗어남으로써 기존의 PREP 프로그램을 적용하는 것보다 효과적으로 읽기 능력을 향상시킬 수 있다.

• Journal of Digital Convergence
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• v.10 no.8
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• pp.341-346
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• 2012

This paper reports the findings of research into the effect of flow experience and internet addiction on e-learning participation. The total number of access, playing time of contents and lag time (time between content upload and first access) were investigated as e-learning participation by analyzing web log data. The paper also examines the relationship between academic achievement and e-learning participation. The results suggest that the flow experience significantly correlates with all variables related with e-learning participation and internet addiction significantly correlates with lag time. It is found that female students significantly access more, longer and have shorter lag time than their male counterparts. It also suggest total number of access, lag time and gender difference have significant effect on academic achievement.

• Journal of The Korean Association of Information Education
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• v.15 no.1
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• pp.137-146
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• 2011

The elementary school English Education Course consists of four integrated aspects of language (Listen, Speak, Read, Write) used to develop skills for daily communication in English. yet the 6th Grade English Education Curriculum focuses almost exclusively on sentence completion through copying and "fill-in-the-blank" exercises. Further, these activities are insufficient time to develop literacy skills. Additionally, the curriculum's emphasis on memorization within the written component is very time consuming for students, leading many to develop negative opinions of the written aspect of a comprehensive understanding of English. This thesis attempts to address each these problems through development of a web-based Learning Model for Cooperative Writing in English. The study resulted in three observations. First, this model overcame limitations of the current teaching model in schools. Second, students expressed more interest in the experimental model than in the current curriculum and standard pedagogical methods. Finally, the study demonstrated that improvement of English literacy is indeed possible using the model presented here.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.8
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• pp.66-74
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• 2014

Recently a variety of study of Korean parsing system is carried out by many software engineers and linguists. The parsing system mainly uses the method of machine learning or symbol processing paradigm. But the parsing system using machine learning has long training time because the data of Korean sentence is very big. And the system shows the limited recognition rate because the data has self error. In this thesis we design system using feature module which can reduce training time and analyze the recognized rate each the number of training sentences and repetition times. The designed system uses the separated modules and sorted table for binary search. We use the refined 36,090 sentences which is extracted by Sejong Corpus. The training time is decreased about three hours and the comparison of recognized rate is the highest as 84.54% when 10,000 sentences is trained 50 times. When all training sentence(32,481) is trained 10 times, the recognition rate is 82.99%. As a result it is more efficient that the system is used the refined data and is repeated the training until it became the steady state.

• Journal of the Korea Society of Computer and Information
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• v.16 no.8
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• pp.39-47
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• 2011

Ergezinger's method, one of the layer-by-layer algorithms used for multilyer perceptrons, consists of an output node and can make premature saturations in the output's weight because of using linear least squared method in the output layer. These saturations are obstacles to learning time and covergence. Therefore, this paper expands Ergezinger's method to be able to use an output vector instead of an output node and introduces a learning rate to improve learning time and convergence. The learning rate is a variable rate that reflects the correlation coefficient between new weight and previous weight while updating hidden's weight. To compare the proposed method with Ergezinger's method, we tested iris recognition and nonlinear approximation. It was found that the proposed method showed better results than Ergezinger's method in learning convergence. In the CPU time considering correlation coefficient computation, the proposed method saved about 35% time than the previous method.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.10a
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• pp.269-279
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• 1999

본 연구는 Web상에서 원격 협력 학습을 위한 수준별 학습자 모집 스케줄링 에이전트의 설계와 구현에 관해 제안한다. 본 시스템의 구조는 원격 교사 모듈과 여러 명의 학습자, 그리고 이를 연결해 주는 스케줄링 Agents, 학습자를 진단할 수 있는 진단 Agent로 구성된다. 컴퓨터가 분산환경으로 발전됨에 따라서 교육의 변화도 가속화되었고, 지식의 공유와 정보의 공유가 원격 협력학습에 의하여 절실히 필요하게 되었다. 원격 협력 학습에서의 학습자는 동일한 과목과 주제에 흥미를 느끼는 여러 명의 아동이 동시에 학습할 수 있는 상황이 필요하며, 선행 지식 또한 비슷한 수준이어야 동일한 주제로 학습의 효과가 있다. 이런 학습자를 판단하기 위해서 진단 Agent가 학습자를 진단하며 스케줄링 Agents의 학습자 지식에 추가한 후 스케줄링 Agents가 학습자의 기본 사항과 요구 내용을 추론하여 비슷한 수준의 학습자를 연결한다. 교사 모듈은 전통적인 ITS의 구조의 교수 학습 모듈, 전문가모듈로 구성되어 교수 학습을 할 수 있다. 이렇게 여러 명의 학습자를 연결하여 협력학습을 하기 위해서는 학습자간의 요구사항과 지식 수준 그리고 학습 가능한 시간이 같아야 하는데 이를 위해 시간을 자원으로 하는 동적 자원 스케줄링(Dynamic Resource Scheduling)으로 모델링 하였다. 본 연구에서 도형학습을 기반으로 하는 실험을 통해 구현한 원격 협력학습을 위한 지능형 스케줄링 에이전트를 평가하였다.