• 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.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.65-70
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• 2001

We consider a hyper-redundant system that consists of many uniform units. The hyper-redundant system has many degrees of freedom and it can accomplish various tasks. Applysing the reinforcement learning to the hyper-redundant system is very attractive because it is possible to acquire various behaviors for various tasks automatically. In this paper we present a new reinforcement learning algorithm "Q-learning with propagation of motion". The algorithm is designed for the multi-agent systems that have strong connections. The proposed algorithm needs only one small Q-table even for a large scale system. So using the proposed algorithm, it is possible for the hyper-redundant system to learn the effective behavior. In this algorithm, only one leader agent learns the own behavior using its local information and the motion of the leader is propagated to another agents with time delay. The reward of the leader agent is given by using the whole system information. And the effective behavior of the leader is learned and the effective behavior of the system is acquired. We apply the proposed algorithm to a snake-like hyper-redundant robot. The necessary condition of the system to be Markov decision process is discussed. And the computer simulation of learning the locomotion is demonstrated. From the simulation results we find that the task of the locomotion of the robot to the desired point is learned and the winding motion is acquired. We can conclude that our proposed system and our analysis of the condition, that the system is Markov decision process, is valid.

• Journal of Energy Engineering
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• v.27 no.2
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• pp.26-31
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• 2018

This paper introduces a study to improve the thermal efficiency of the district heating user control facility based on reinforcement learning. As an example, it is proposed a general method of constructing a deep Q learning network(DQN) using deep Q learning, which is a reinforcement learning algorithm that does not specify a model. In addition, it is also introduced the big data platform system and the integrated heat management system which are specialized in energy field applied in processing huge amount of data processing from IoT sensor installed in many thermal energy control facilities.

• Journal of the Korean Society of Industry Convergence
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• v.24 no.1
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• pp.19-29
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• 2021

In the Port Logistics system, Container Stowage planning is an important issue for cost-effective efficiency improvements. At present, Planners are mainly carrying out Stowage planning by manual or semi-automatically. However, as the trend of super-large container ships continues, it is difficult to calculate an efficient Stowage plan with manpower. With the recent rapid development of artificial intelligence-related technologies, many studies have been conducted to apply enhanced learning to optimization problems. Accordingly, in this paper, we intend to develop and present a Deep Q-Learning Network model for the Master Stowage planning of Container ships.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2660-2662
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• 2001

Q-학습은 최근에 연구되는 강화학습으로서 환경에 대한 정의가 필요 없어 자율이동로봇의 행동학습에 적합한 방법이다. 그러나 다개체 시스템의 학습처럼 환경이 복잡해짐에 따라 개체의 입출력 변수는 늘어나게 되고 Q함수의 계산량은 기하급수적으로 증가하게 된다. 따라서 이러한 문제를 해결하기 위해 다개체 시스템의 Q-학습에 적합한 연속적인 Q-학습 알고리즘을 제안하였다. 연속적인 Q-학습 알고리즘은 개체가 가질 수 있는 모든 상태-행동 쌍을 하나의 Q함수에 표현하는 방법으로서 계산량 및 복잡성을 줄임으로써 동적으로 변하는 환경에 능동적으로 대처하도록 하였다. 제안한 연속적인 Q-학습 알고리즘을 벽으로 막힌 공간에서 두 포식자와 한 먹이로 구성되는 먹이-포식자 문제에 적용하여 먹이개체의 효율적인 회피능력을 검증하였다.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.125-131
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• 2021

Detecting fraudulent insurance claims is difficult due to small and unbalanced data. Some research has been carried out to better cope with various types of fraudulent claims. Nowadays, technology for detecting fraudulent insurance claims has been increasingly utilized in insurance and technology fields, thanks to the use of artificial intelligence (AI) methods in addition to traditional statistical detection and rule-based methods. This study obtained meaningful results for a fraudulent insurance claim detection model based on machine learning (ML) and deep learning (DL) technologies, using fraudulent insurance claim data from previous research. In our search for a method to enhance the detection of fraudulent insurance claims, we investigated the reinforcement learning (RL) method. We examined how we could apply the RL method to the detection of fraudulent insurance claims. There are limited previous cases of applying the RL method. Thus, we first had to define the RL essential elements based on previous research on detecting anomalies. We applied the deep Q-network (DQN) and double deep Q-network (DDQN) in the learning fraudulent insurance claim detection model. By doing so, we confirmed that our model demonstrated better performance than previous machine learning models.

• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.145-156
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• 2022

Nowadays, the complexity of object tracking models among hardware applications has become a more in-demand duty to complete in various indeterminable environment tracking situations with multifunctional algorithm skills. In this paper, we propose a virtual city environment using AirSim (Aerial Informatics and Robotics Simulation - AirSim, CityEnvironment) and use the DQN (Deep Q-Learning) model of deep reinforcement learning model in the virtual environment. The proposed object tracking DQN network observes the environment using a deep reinforcement learning model that receives continuous images taken by a virtual environment simulation system as input to control the operation of a virtual drone. The deep reinforcement learning model is pre-trained using various existing continuous image sets. Since the existing various continuous image sets are image data of real environments and objects, it is implemented in 3D to track virtual environments and moving objects in them.

• Journal of the Korean Chemical Society
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• v.56 no.1
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• pp.137-143
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• 2012

This study analyzed the questions posted on the chemistry Q&A board by students and teachers in a chemistry education homepage, in order to understand the difficulties in learning and teaching chemistry. The different tendencies were found in the contents and the motivations of questions by students and teachers. In Chemistry I, students raised many questions in the 'Water' unit, while teachers raised many ones in the 'Chemical compound in our life' unit. In Chemistry II, students asked many questions in the 'Gas, liquid, solid' unit, while teachers did in 'Chemical reaction and energy' unit. Students' motivations of questioning were 'Explanation of unclear concept', and 'Problem solving', while teachers' motivations were 'Searching information', and 'Question in experiment'. The Q&A board provided a field in exchanging informations needed in learning and teaching chemistry. Educational implications were discussed on the use of Q&A board in chemistry education.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.944-947
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• 1996

Realization of autonomous agents that organize their own internal structure in order to behave adequately with respect to their goals and the world is the ultimate goal of AI and Robotics. Reinforcement learning gas recently been receiving increased attention as a method for robot learning with little or no a priori knowledge and higher capability of reactive and adaptive behaviors. In this paper, we present a method of reinforcement learning by which a multi robots learn to move to goal. The results of computer simulations are given.

• The Journal of the Korea Contents Association
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• v.21 no.4
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• pp.454-462
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• 2021

The purpose of this study was to identify learners with poor learning in basic cooking practice subjects for college students majoring in cooking, operate a basic practical ability improvement program, and apply an action learning teaching method. We tried to analyze the subjective perception of learners using the Q methodology. In addition, it was intended to improve the major competencies for the operation of practical programs related to cooking training, field training, and employment of learners. The survey was conducted from May 1st to 20th, 2020 for first-year students in C cuisine major. As a result of the analysis, a total of three types were derived. Type 1 (N=7): Self-directed learning effect type, Type 2 (N=8): Problem Solving Effect Types, Type 3 (N=6): Peer learning effect type, each unique feature type Was analyzed as. Through the progress of this study and the derivation of implications, it is expected that it will be useful data for the application of teaching and learning methods related to practical work and program operation in cooking-related departments.