• The Journal of Korean Association of Computer Education
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• v.7 no.2
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• pp.35-46
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• 2004

In this paper, I analyze error cases a learner made during learning using ICT, set up error types, and search for effective treatment methods in order to enhance the effects of ICT education. I search for error case to use the methodology of the study which is observation, interviews, and survey. I set up the error types which is the error type of confusion with functions, that of confusion with concepts, that of barriers in interface interpretation, that caused by psychological anxiety, that according to learner personality patterns, and habitual error type. The biggest frequency of errors was found in the error type of confusion with function and that of confusion with concepts, whose treatment methods were searched for using the web-based Q&A learning. Also, I apply the error treatment methods on the classroom and prove the effect.

• The Journal of Korean Association of Computer Education
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• v.4 no.1
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• pp.87-97
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• 2001

With coming information society and universal use of internet, we have been concerned about the educational utilization of the internet. Recognizing the growing need for the use of the internet in education, there are many research of WBI According to behaviorism learning theory, learning effects are extremely improved when immediate feedback is offered to learner. In this paper, we designed and implemented feedback BBS that offers immediately reponses to learner. The feedback BBS was implemented using PHP and MySQL. And we evaluated implemented feedback BBS by questionnaire.

• Smart Media Journal
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• v.9 no.2
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• pp.92-98
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• 2020

This paper proposes a vehicle license plate recognition system using light weight deep learning models without high-end server. The proposed license plate recognition system consists of 3 steps: [license plate detection]-[character area segmentation]-[character recognition]. SSD-Mobilenet was used for license plate detection, ResNet with localization was used for character area segmentation, ResNet was used for character recognition. Experiemnts using Samsung Galaxy S7 and LG Q9, accuracy showed 85.3% accuracy and around 1.1 second running time.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2007.11a
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• pp.284-293
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• 2007

본 논문에서는 실시간 동정 환경에 효과적인 L-CAA 에이전트 구조를 제안한다. L-CAA 에이전트 구조는 변화하는 환경에 대한 적응성을 높이기 위해, 선행 연구를 통해 개발된 행위 기반 에이전트 구조인 CAA에 강화 학습 기능을 추가하여 확장한 것이다. 안정적인 성능을 위해 L-CAA에서 행위 선택 메커니즘은 크게 두 단계로 나뉜다. 첫 번째 단계에서는 사용자가 미리 정의한 각 행위의 수행 가능 조건과 효용성을 검사함으로써 행위 라이브러리로부터 실행할 행위들을 추출한다. 하지만 첫 번째 단계에서 다수의 행위가 추출되면, 두 번째 단계에서는 강화 학습의 도움을 받아 이들 중에서 실행할 하나의 행위를 선택한다. 즉, 강화 학습을 통해 갱신된 각 행위들의 Q 함수 값을 서로 비교함으로써, 가장 큰 기대 보상 값을 가진 행위를 선택하여 실행한다. 또한 L-CAA에서는 실행 중인 행위의 유지 가능 조건을 지속적으로 검사하여 환경의 동적 변화로 인해 일부 조건이 만족되지 않는 경우가 발생하면 현재 행위의 실행을 즉시 종료할 수 있다. 그 뿐 아니라, L-CAA는 행위 실행 중에도 효용성이 더 높은 다른 행위가 발생하면 현재의 행위를 일시 정지하였다가 복귀하는 기능도 제공한다. 본 논문에서는 L-CAA 구조의 효과를 분석하기 위해, 대표적인 동적 가상환경인 Unreal Tournament 게임에서 자율적을 동작하는 L-CAA기반의 UTBot 들을 구현하고, 이들을 이용하여 성능실험을 전개해본다.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.3
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• pp.40-48
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• 2022

An automated material handling system (AMHS) has been emerging as an important factor in the semiconductor wafer manufacturing industry. In general, an automated guided vehicle (AGV) in the Fab's AMHS travels hundreds of miles on guided paths to transport a lot through hundreds of operations. The AMHS aims to transfer wafers while ensuring a short delivery time and high operational reliability. Many linear and analytic approaches have evaluated and improved the performance of the AMHS under a deterministic environment. However, the analytic approaches cannot consider a non-linear, non-convex, and black-box performance measurement of the AMHS owing to the AMHS's complexity and uncertainty. Unexpected vehicle congestion increases the delivery time and deteriorates the Fab's production efficiency. In this study, we propose a Q-Learning based dynamic routing algorithm considering vehicle congestion to reduce the delivery time. The proposed algorithm captures time-variant vehicle traffic and decreases vehicle congestion. Through simulation experiments, we confirm that the proposed algorithm finds an efficient path for the vehicles compared to benchmark algorithms with a reduced mean and decreased standard deviation of the delivery time in the Fab's AMHS.

• Journal of Information Processing Systems
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• v.18 no.3
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• pp.443-456
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• 2022

An efficient and reasonable resource allocation strategy can greatly improve the service quality of Internet of Vehicles (IoV). However, most of the current allocation methods have overestimation problem, and it is difficult to provide high-performance IoV network services. To solve this problem, this paper proposes a network resource allocation strategy based on deep learning network model DDQN. Firstly, the method implements the refined modeling of IoV model, including communication model, user layer computing model, edge layer offloading model, mobile model, etc., similar to the actual complex IoV application scenario. Then, the DDQN network model is used to calculate and solve the mathematical model of resource allocation. By decoupling the selection of target Q value action and the calculation of target Q value, the phenomenon of overestimation is avoided. It can provide higher-quality network services and ensure superior computing and processing performance in actual complex scenarios. Finally, simulation results show that the proposed method can maintain the network delay within 65 ms and show excellent network performance in high concurrency and complex scenes with task data volume of 500 kbits.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.11a
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• pp.369-370
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• 2022

최근 자율 운항 선박에 대한 관심이 높아지고 있다. 특히, MUNIN (Maritime Unmanned Navigation through Intelligence in Networks) 프로젝트를 계기로 자율 운항 선박에 대한 개발과 연구가 활발히 진행되고 있다. 또한 국제해사기구 IMO는 자율 운항 선박 시대에 대응하기 위해 자율 선박을 MASS (Maritime Autonomous Surface Ship)라 정의하고 선박 자율화 정도에 따라 4단계 등급을 제시하고 있다. 완전한 자율 운항 선박에 대한 요구조건을 만족하기 위해서는 항로 결정과 제어기술이 필수적이다. 본 연구에서는 여러 가지 기술 중 선박의 최적경로를 생성하는 기법을 다룬다. 기존에 최적항로를 생성하기 위한 방법으로는 A^*, Dijkstra와 같은 알고리즘들이 주로 사용되었다. 그러나 이와 같은 알고리즘은 섬이나 육지에 대한 충돌 회피는 고려하고 있지만 수심 및 연안 선박에 대한 규정들은 고려하지 않고 있어 실제로 적용하기에는 한계점이 있다. 따라서 본 연구에서는 안전을 위해 선박의 선저 여유 수심과, 해도에 규정되어 있는 선박 운항에 대한 여러 규정들을 반영하여 최적 항로를 생성하고자 한다. 최적 항로를 생성하기 위한 알고리즘으로는 강화학습 기반의 Q-learning 알고리즘을 적용하였다.

• Journal of IKEEE
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• v.28 no.2
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• pp.180-186
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• 2024

This paper proposes an efficient fault diagnosis method for ITSC(Inter-Turn Short Circuit) in three-phase induction motors using CNN. By utilizing only the D-axis component of the D-Q synchronous coordinate system, it compares SWM(Slide Window Method) and GAF(Gramian Angular Field) methods for image encoding. Results show GAF achieving ~74% accuracy, while SWM achieves ~65%, indicating GAF's superiority by 9%. Learning time (~14.74s) remains consistent, particularly with epochs ≤ 100, showcasing faster learning.

• Journal of Practical Engineering Education
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• v.13 no.2
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• pp.283-292
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• 2021

As universities have operated non-face-to-face semesters due to COVID-19, although instructors applying flipped learning to their classes also have changed it into non-face-to-face ways, there is still a lack of exploratory research on effectiveness of the new form of flipped learning. In this study, we explored the effectiveness of the non-face-to-face flipped learning by analyzing students' learning experiences throughout FGI and survey. By doing so, we sought to provide in-depth insights for successful implications of non-face-to-face flipped learning classes ultimately. The findings showed that many learners positively evaluated non-face-to-face flipped learning in terms of interactions, including quizzes, team activities, and interpersonal interactions (e.g., Q&A, feedback) with professors in non-face-to-face flipped learning classes. The result of the survey also showed significant differences in the pre-post test regarding learner's perceived learning achievement. Based on these findings, the implications were discussed.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.1
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• pp.79-85
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• 2006

This paper presents a strategy and tactic for robot soccer using furry logic mediator that determines robot action depending on the positions and the roles of adjacent two robots. Conventional Q-learning algorithm, where the number of states increases exponentially with the number of robots, is not suitable for a robot soccer system, because it needs so much calculation that processing cannot be accomplished in real time. A modular Q-teaming algorithm reduces a number of states by partitioning the concerned area, where mediator algorithm for cooperation of robots is used additionally. The proposed scheme implements the mediator algorithm among robots by fuzzy logic system, where simple fuzzy rules make the calculation easy and hence proper for robot soccer system. The simulation of MiroSot shows the feasibility of the proposed scheme.