• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.8
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• pp.1011-1017
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• 2019

This paper proposes reinforcement learning to solve the node-disjoint path problem which establishes multipath for reliable data transmission in wireless ad-hoc networks. The node-disjoint path problem is a problem of determining a plurality of paths so that the intermediate nodes do not overlap between the source and the destination. In this paper, we propose an optimization method considering transmission distance in a large-scale wireless ad-hoc network using Q-learning in reinforcement learning, one of machine learning. Especially, in order to solve the node-disjoint path problem in a large-scale wireless ad-hoc network, a large amount of computation is required, but the proposed reinforcement learning efficiently obtains appropriate results by learning the path. The performance of the proposed reinforcement learning is evaluated from the viewpoint of transmission distance to establish two node-disjoint paths. From the evaluation results, it showed better performance in the transmission distance compared with the conventional simulated annealing.

• The Journal of the Korea Contents Association
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• v.20 no.5
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• pp.327-337
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• 2020

This study analyzed subjective cognitive types of culinary majors by applying TBL of cooking practice subjects, and applied Q methodology to multifaceted analysis of subjective cognitive types of learners. For the analysis of the study, interviews were conducted for college students majoring in cooking, and the survey was conducted in the order of constructing the Q population, selecting P-samples, classifying Q, interpreting the results, conclusions, and discussion. A total of four types were derived from the type analysis, and each was named according to its specificity as follows. Type 1 (N = 8): Cooperative Learning Effect Types, Type 2 (N = 8): Problem Solving Ability Effect Types, Type 3 (N = 6): Self Directed Learning Effect Type, Type 4 (N = 6): Individual Practice Preference Type analyzed for each unique feature type. It is expected that through the results of the study, it is expected to provide important implications that can help in the study of similar teaching methods in the future by fostering talents who can increase the needs of the industry and social stress.

• Journal of Korean Tunnelling and Underground Space Association
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• v.22 no.3
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• pp.239-248
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• 2020

Exact rock classification helps suitable support patterns to be installed. Face mapping is usually conducted to classify the rock mass using RMR (Rock Mass Ration) or Q values. There have been several attempts to predict the grade of rock mass using mechanical data of jumbo drills or probe drills and photographs of excavation surfaces by using deep learning. However, they took long time, or had a limitation that it is impossible to grasp the rock grade in ahead of the tunnel surface. In this study, a method to predict the Q value ahead of excavation surface is developed using recurrent neural network (RNN) technique and it is compared with the Q values from face mapping for verification. Among Q values from over 4,600 tunnel faces, 70% of data was used for learning, and the rests were used for verification. Repeated learnings were performed in different number of learning and number of previous excavation surfaces utilized for learning. The coincidence between the predicted and actual Q values was compared with the root mean square error (RMSE). RMSE value from 600 times repeated learning with 2 prior excavation faces gives a lowest values. The results from this study can vary with the input data sets, the results can help to understand how the past ground conditions affect the future ground conditions and to predict the Q value ahead of the tunnel excavation face.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.11
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• pp.4174-4181
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• 2010

This paper presents an adaptive scheduling algorithm for manufacturing processes with non-stationary rework probabilities. The adaptive scheduling scheme named by hybrid Q-learning algorithm is proposed in this paper making use of the non-stationary rework probability and coupling with artificial neural networks. The proposed algorithm is measured by mean tardiness and the extensive computational results show that the presented algorithm gives very efficient schedules superior to the existing dispatching algorithms.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.3
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• pp.441-446
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• 2010

Each robot decides and behaviors themselves surrounding circumstances in the swarm robot system. Robots have to conduct tasks allowed through cooperation with other robots. Therefore each robot should have the ability to learn and evolve in order to adapt to a changing environment. In this paper, we proposed learning based on Q-learning algorithm and evolutionary using Harmony Search algorithm and are trying to improve the accuracy using Harmony Search Algorithm, not the Genetic Algorithm. We verify that swarm robot has improved the ability to perform the task.

• The Journal of the Korea Contents Association
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• v.20 no.1
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• pp.562-572
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• 2020

This study analyzed subjectivity of underachievers on Peer Assisted Learning(PAS) in culinary skills related subject for providing better educational environment through consideration of educational efficiency of particular teaching method. Q Methodology was employed for analysing of responses of a small group of the students. The research found that three types of distinctive structures of responses of the students' subjectivity. The first one was Increase learning effectiveness type(Type1, N=8), the second one was Development of lesson materials for passive students(Type2, N=8), and the last one was Practical self-directed learning needs development(Type3, N=6). From the result, PAS was an effective teaching method for underachievers for encouraging participation of study program, helping to rise self-confidence in subject's tasks, and awareness of self directed learning and additional study on subjects matters. The study, however, found that students could consider themselves as an interruption to other students' study progress, and could feel other students' awareness as a burden. At last, forming a class by deeper consideration on the learning levels of each students and providing additional educational contents for encouraging self directed learning are necessary for the better efficiency for the future.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.5
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• pp.502-506
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• 2008

The recently proposed "Potential-based" reinforcement learning (RL) method made it possible to combine multiple learnings and expert advices as supervised knowledge within an RL framework. The effectiveness of the approach has been established by a theoretical convergence guarantee to an optimal policy. In this paper, the potential-based RL method is applied to a dynamic channel assignment (DCA) problem in a cellular networks. It is empirically shown that the potential-based RL assigns channels more efficiently than fixed channel assignment, Maxavail, and Q-learning-based DCA, and it converges to an optimal policy more rapidly than other RL algorithms, SARSA(0) and PRQ-learning.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.723-724
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• 2022

지난 코로나 상황 동안 비대면 수업을 진행했고, 학생들은 빠르게 적응했다. 온라인 수업은 학습자가 이해될 때까지 반복 학습이 가능하고, 시간과 공간의 제약 없이 자기 주도적으로 학습할 수 있다는 장점이 있지만, 온라인상이라는 특징 때문에 교수자와 학습자 간 상호작용이 부족하다는 한계점이 존재한다. 하지만 이점은 차후에 비대면 수업의 지속적인 활용 및 확대를 제한하는 요인이 될 수 있다. 본 논문에서는 상호작용을 높일 수 있는 웹 기반 에듀테크 시스템을 제안한다. 온라인 수업의 강의 영상을 세부적인 내용을 나누는 Section을 통해 다른 학생들이 질문했던 Q&A 데이터를 모아서 생성된 Section-FAQ를 열람할 수 있고, 그 Q&A에 반응해서 상호작용이 가능하다. 또한 교수자에게 Q&A를 보낼 때 영상의 Section 정보와 강의시간 정보를 같이 전송하여 강의 영상을 확인하지 않고, 빠른 답변이 가능하도록 설계했다. 본 논문에서 제안하는 온라인 수업의 상호작용 향상을 위한 웹 기반 에듀테크 시스템을 통해 온라인상에서 교수자의 역할을 대신해주고 비대면 수업의 단점을 해소해주면서, 교수자과 학습자 간의 상호작용을 높여 수업의 이해도를 높이고 학습자들의 학업성취를 높일 수 있을 것이다.

• Journal of The Korean Association of Information Education
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• v.22 no.2
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• pp.177-193
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• 2018

The purpose of this paper is to explore the types of subjectivity on edutainment features of software education implemented in elementary school. Q-method is used to seek individual learners' subjectivity type. Three types of subjectivity are found: implementation type, intellectual-fun type, and relationship type. Implementation type learners show positive attitude toward making their thinking into realization, intellectual-fun type learners show positive attitude toward solving problems that require intellectual activities, and relationship type learners show positive toward other persons' attention and consideration. These results imply software education will be more enhanced with these three types considered for implementing software education in elementary schools. This study is expected to contribute to further following research and practices.

• The Journal of the Korea Contents Association
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• v.5 no.5
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• pp.1-9
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• 2005

In this paper, we suggest multi colony interaction ant reinforcement learning model. This method is a hybrid of multi colony interaction by elite strategy and reinforcement teaming applying Temporal Difference(TD) learning to Ant-Q loaming. Proposed model is consisted of some independent AS colonies, and interaction achieves search according to elite strategy(Intensification, Diversification strategy) between the colonies. Intensification strategy enables to select of good path to use heuristic information of other agent colony. This makes to select the high frequency of the visit of a edge by agents through positive interaction of between the colonies. Diversification strategy makes to escape selection of the high frequency of the visit of a edge by agents achieve negative interaction by search information of other agent colony. Through this strategies, we could know that proposed reinforcement loaming method converges faster to optimal solution than original ACS and Ant-Q.