• Journal of the Korea Computer Graphics Society
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• v.27 no.1
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• pp.9-17
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• 2021

In this paper, we present a simple and fast supervised learning framework based on model predictive control so as to learn motion controllers for a physic-based character to track given example motions. The proposed framework is composed of two components: training data generation and offline learning. Given an example motion, the former component stochastically controls the character motion with an optimal controller while repeatedly updating the controller for tracking the example motion through model predictive control over a time window from the current state of the character to a near future state. The repeated update of the optimal controller and the stochastic control make it possible to effectively explore various states that the character may have while mimicking the example motion and collect useful training data for supervised learning. Once all the training data is generated, the latter component normalizes the data to remove the disparity for magnitude and units inherent in the data and trains an artificial neural network with a simple architecture for a controller. The experimental results for walking and running motions demonstrate how effectively and fast the proposed framework produces physics-based motion controllers.

• The Journal of Korean Association of Computer Education
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• v.5 no.3
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• pp.79-87
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• 2002

Theorema system is built on top of Mathematica to automate deductive reasoning processes as the combination of computer algebra system and deductive system. Theorema system that was developed at RISC in Austria provides three modes based on rewriting techniques; computation, problem solving and proving. This paper shows how the computation mode of Theorema can simulate Derive part for TI-92 graphic calculators to produce TI-92 manuals. Moreover, it also describes how such manuals can be converted to web-based learning materials via Java servlet.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.5
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• pp.395-400
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• 2000

In this paper, we propose a new neuro-fuzzy controller based on reinforcement learning. The proposed system is composed of neuro-fuzzy controller which decides the behaviors of an agent, and dynamic recurrent neural networks(DRNNs) which criticise the result of the behaviors. Neuro-fuzzy controller is learned by reinforcement learning. Also, DRNNs are evolved by genetic algorithms and make internal reinforcement signal based on external reinforcement signal from environments and internal states. This output(internal reinforcement signal) is used as a teaching signal of neuro-fuzzy controller and keeps the controller on learning. The proposed system will be applied to controller optimization and adaptation with unknown environment. In order to verifY the effectiveness of the proposed system, it is applied to collision avoidance of an autonomous mobile robot on computer simulation.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.1
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• pp.19-28
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• 2021

Recently, in the field of education, various attempts have been made to apply virtual reality technology to an educational field and use it as an educational medium. Accordingly, in the science subject area, it is necessary to simulate science experiments that can make various and active experiments out of various limitations such as space and situation by using virtual reality environment construction technology. In this study, after selecting a physics course from a science subject, an experimental simulation using a parabolic motion formula, one of physical phenomena, is implemented in a virtual space, and then used in actual physics education based on the learning standards of the STEAM theory. Prove this is possible. Through this, it was confirmed that a specific educational model using virtual reality space can be designed, and it shows that education can be conducted with more effective educational methods in various subjects of education through the combination of traditional educational model and modern technology. Regarding the results of the research, it suggests the possibility of future research plans and practical use in the educational field.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.5
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• pp.47-52
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• 2021

Recently, demand for artificial intelligence solutions for production process management has been increasing in the manufacturing industry. However, through the application of AI solutions in the manufacturing industry, there are limitations to legacy smart factory solutions such as POP and MES.Therefore, in order to overcome this, this paper aims to improve production management efficiency by applying guidance, an artificial intelligence concept, to image recognition systems. In the system flow, As_is To be separated and actual work flow was applied, and the process was improved for overall productivity efficiency. The pre-processing plan for AI guidance learning was established and the relevant AI model was designed, developed, and simulated, resulting in a 97% recognition rate.

• Proceedings of KOSOMES biannual meeting
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• 2003.05a
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• pp.105-110
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• 2003

현대의 선박운항에 있어서 선내 노동의 경감과 운항의 안정성 및 경제성 확보를 위하여 선박 자동차 및 원격 조종화가 이루어지고 있다. 또한, 선박조종 및 작업제어를 위하여 컴퓨터를 이용한 통합제어 시스템에 관한 연구도 활발하게 진행되고 있다. 그러나 소형선박 및 어선과 같은 환경에서의 지능형 시스템에 관한 연구는 많이 부족한 실정이다. 본 논문에서는 지능형 선박을 구현하기 위한 기반 연구로서 선박 운항자의 원격지시로 선박의 조타기를 제어하는 지능형 조타제어시스템을 구축하여 실용화를 위한 방안을 모색하였다. 구체적인 연구결과로는 지능형 학습 기법을 바탕으로 하는 퍼지 조타수 조작 모델을 구축하여 조타제어 시스템을 구현하였고. 모형선박(Miniature Ship)의 조타 시스템을 PC 상에서 원격으로 제어하기 위한 시뮬레이션 시스템을 구축하여 그 유효성을 확인하였다.

• Journal of the Semiconductor & Display Technology
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• v.16 no.3
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• pp.127-130
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• 2017

In this research, we present a path planning method for an autonomous flight of unmanned aerial vehicles (UAVs) through reinforcement learning under simulated environment. We design the simulator for reinforcement learning of uav. Also we implement interface for compatibility of Deep Q-Network(DQN) and simulator. In this paper, we perform reinforcement learning through the simulator and DQN, and use Q-learning algorithm, which is a kind of reinforcement learning algorithms. Through experimentation, we verify performance of DQN-simulator. Finally, we evaluated the learning results and suggest path planning strategy using reinforcement learning.

• Journal of Convergence for Information Technology
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• v.12 no.3
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• pp.286-293
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• 2022

Recently, the importance of elemental technologies constituting smart factories is increasing due to the 4th Industrial Revolution, and simulation is widely used as a tool to learn these technologies. In particular, PID control is an automatic control technique used in various fields, and most of them analyze mathematical models in certain situations or research on application development with built-in controllers. In actual educational environment requires PID simulator training as well as PID control principles. In this paper, we propose a model that enables education and practice of various PID controls through 3D simulation. The proposed model implemented virtual balls and Fan and implemented PID control by configuring a system so that the force can be lifted by the air pressure generated in the Fan. At this time, the height of the ball was expressed in a graph according to each gain value of the PID controller and then compared with the actual system, and through this, satisfactory results sufficiently applicable to the actual class were confirmed. Through the proposed model, it is expected that the rapidly increasing elemental technology of smart factories can be used in various ways in a remote classroom environment.

• The Journal of Korean Association of Computer Education
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• v.13 no.6
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• pp.9-21
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• 2010

Concurrent processes come into conflict with each other when they are competing for the use of the same resources. In the case of competing processes three control problems must be faced: mutual exclusion, deadlock, and starvation. The concurrency is a subject rather difficult to understand. In addition, because concurrent programs included in most of OS texts are described by pseudocode and are not being able to execute directly, almost learners are difficult to understand behaviour of concurrent programs. The purpose of this study is to propose instruction method for mutual exclusion using SDL and MSC base on graphic. In order to verify the effectiveness of the proposed materials, we compare with materials based on pseudocode. The results indicated that the proposed materials is more effective than materials based on pseudocode in teaching-learning for mutual exclusion mechanisms.

• The Journal of Korean Institute for Practical Engineering Education
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• v.3 no.2
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• pp.18-26
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• 2011

Computational chemistry is one of the chemistry fields that deals with the theoretical chemistry problem using computer calculations and can be described as the chemistry lab moved on computer space. In line with recent enhancement of processing capability of computers, utilization of high performance computer cannot be overemphasized in the field of computational chemistry in performing complex calculation of huge molecular structure and simulation. While they have to use commands and consoles for high performance computer to execute complex calculation of huge molecular structure and simulation, most of students in natural science and engineering, who are not experts in computer technically, are likely to be unaware of UNIX. Under the circumstances, web-based educational support system for computational chemistry is needed to enable them to practice computational chemistry, even not knowing UNIX command. In this study, e-Chem, one of such educational support systems, is developed by using Liferay portal platform, which is a Java open source more oriented to standard and outstanding in its content management and collaboration function than other web portals. By using this system, even students who are not familiar with computer, are expected to take part in lab classes and save time learning Unix command and also enhance the learning efficiency by using familiar interface.