• Journal of The Korean Association For Science Education
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• v.38 no.2
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• pp.147-160
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• 2018

The purpose of this study is to construct a Teachers' Learning Community (TLC) with three teachers from the same school and to develop a joint teaching plan for students through the TLC. The conclusions from this study are as follows: The TLC with the same grade helps teachers to implement STEAM classes, where teachers overcame difficulties of STEAM lessons and successfully implemented them. Teachers in this study expressed difficulties of STEAM lessons including lack of time, difficulties of STEAM lesson implementation, and difficulties of developing a good STEAM lesson. Teachers worked together to develop a common teaching plan, to overcome the burden of teaching, and to plan better lessons through discussions and cross-checking. In addition, teachers newly discovered difficulties of lesson implementation as they watch each other teaching using a joint lesson plan. Teachers will conduct a better lesson as they improve these difficulties, where a better lesson means having students reach learning goals and learn from the lesson. Teachers in TLC felt that their lesson improved and they themselves growing through a series of courses of watching and learning each other's lessons.

• Journal of KIISE:Software and Applications
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• v.29 no.11
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• pp.785-794
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• 2002

Automatic stock trading systems should be able to solve various kinds of optimization problems such as market trend prediction, stock selection, and trading strategies, in a unified framework. But most of the previous trading systems based on supervised learning have a limit in the ultimate performance, because they are not mainly concerned in the integration of those subproblems. This paper proposes a stock trading system, called R-Trader, based on reinforcement teaming, regarding the process of stock price changes as Markov decision process (MDP). Reinforcement learning is suitable for Joint optimization of predictions and trading strategies. R-Trader adopts two popular reinforcement learning algorithms, temporal-difference (TD) and Q, for selecting stocks and optimizing other trading parameters respectively. Technical analysis is also adopted to devise the input features of the system and value functions are approximated by feedforward neural networks. Experimental results on the Korea stock market show that the proposed system outperforms the market average and also a simple trading system trained by supervised learning both in profit and risk management.

• Journal of Contemporary Eastern Asia
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• v.16 no.1
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• pp.69-81
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• 2017

This study examines how South Korean governments responded to the outbreak of Middle East Respiratory Syndrome Coronavirus (MERS) using the adaptive governance framework. As of November 24, 2015, the MERS outbreak in South Korea resulted in the quarantine of about 17,000 people, 186 cases confirmed, and a death of 38. Although the national government had overall responsibility for MERS response, there is no clear understanding of how the ministries, agencies, and subnational governments take an adaptive response to the public health crisis. The paper uses the adaptive governance framework to understand how South Korean governments respond to the unexpected event regarding the following aspects: responsiveness, public learning, scientific learning, and representativeness of the decision mechanisms. The framework helps understand how joint efforts of the national and subnational governments were coordinated to the unexpected conditions. The study highlights the importance of adaptive governance for an effective response to a public-health related extreme event.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.58-61
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• 2003

This paper proposes e-Learning environments far digital circuit experiment. The e-Learning environments are implemented as a WBT system that includes the circuits monitoring system and the students management system. In the WBT client-server system, the instructor represents the server and students represent clients. The client computers are equipped with a digital circuit training board and connected to the server on the World Wide Web. The training board consists of a Programmable Logic Device (PLD) and measuring instruments. The instructor can reconfigure the PLD with various circuit designs from the server so that students can investigate signals from the training board. The instructor can monitor the progress of the students using Joint Test Action Grouo(JTAG) technology. We implement the WBT system and a courseware fo digital circuits and evaluation the environments.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.04a
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• pp.244-248
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• 1996

Nowadays, most shop floors using industrial robots have many problems such as constructing robot workcell, generating robot arm moving trajectory, etc.. In the case of programming robot-arms for a specific task, shop operator commonly use the teach pendant to record the target position and determine the moving trajectory. However, such a teaching process may result in an inefficient trajectory in the sense of moving distance and joint angle fluctuation. Moreover, shop operators who have little knowledge about robot programming process need a lot of learning time and cost. The purpose of this paper is to propose a user friendly robot programming method to program robot-arms easily and efficiently for shop operator so that the programming time is reduced and a short and stable trajectory is obtained.

• The Transactions of the Korea Information Processing Society
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• v.7 no.5
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• pp.1351-1360
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• 2000

The hand acupuncture, called Sujichim, is mostly conducted at home. Many people try to learn and apply the Sujichim treatment to the health care of their family. This paper proposes a mixed reality scheme for the hand acupuncture supporting system. Using our system, a novice can easily perform the acupuncture by himself to improve his body condition. Our system has two major phases: learning phase and operation phase. In learning phase, we extract the finger joint lines from the real world image, where the lines are very important for finding the acupuncture spots on a hand. In operation mode, user can easily make Sujichim treatment by the knowledge provided by the computer. According to the experiments, our system is proved to be very effective and easy to use.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.431-431
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• 2000

The modeling of 5-bar linkage robot manipulator dynamics by means of a mathematical and neural architecture is presented. Such a model is applicable to the design of a feedforward controller or adjustment of controller parameters. The inverse model consists of two parts: a mathematical part and a compensation part. In the mathematical part, the subsystems of a 5-bar linkage robot manipulator are constructed by applying Kawato's Feedback-Error-Learning method, and trained by given training data. In the compensation part, MLP backpropagation algorithm is used to compensate the unmodeled dynamics. The forward model is realized from the inverse model using the inverse of inertia matrix and the compensation torque is decoupled in the input torque of the forward model. This scheme can use tile mathematical knowledge of the robot manipulator and analogize the robot characteristics. It is shown that the model is reasonable to be used for design and initial gain tuning of a controller.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.1
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• pp.41-47
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• 2000

A better tracking performance can be achieved, if visual sensors such as CCD cameras are used in controling a robot manipulator, than when only relative sensors such as encoders are used. However, for precise visual servoing of a robot manipulator, an expensive vision system which has fast sampling rate must be used. Moreover, even if a fast vision system is implemented for visual servoing, one cannot get a reliable performance without use of robust and stable inner joint servo-loop. In this paper, we propose a dynamic control scheme for robot manipulators with eye-in-hand camera configuration, where a dynamic learning controller is designed to improve the tracking performance of robotic system. The proposed control scheme is implemented for tasks of tracking moving objects and shown to be robust to parameter uncertainty, disturbances, low sampling rate, etc.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.7
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• pp.1007-1013
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• 2012

Modeling of photovoltaic power systems is significant to analytically predict its dynamics in practical applications. This paper presents a novel modeling algorithm of such system by using probability and statistic theories. We first establish a linear model basically composed of Fourier parameter sets for mapping the input/output variable of photovoltaic systems. The proposed model includes solar irradiation and ambient temperature of photovoltaic modules as an input vector and the inverter power output is estimated sequentially. We deal with these measurements as random variables and derive a parameter learning algorithm of the model in terms of statistics. Our learning algorithm requires computation of an expectation and joint expectation against solar irradiation and ambient temperature, which are analytically solved from the integral calculus. For testing the proposed modeling algorithm, we utilize realistic measurement data sets obtained from the Seokwang Solar power plant in Youngcheon, Korea. We demonstrate reliability and superiority of the proposed photovoltaic system model by observing error signals between a practical system output and its estimation.

• Steel and Composite Structures
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• v.46 no.1
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• pp.107-114
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• 2023

A new intelligent adaptive control scheme was proposed that combines observer disturbance-based adaptive control and fuzzy adaptive control for a composite structure with a mass-adjustable damper. The most important advantage is that the control structures do not need to know the uncertainty limits and the interference effect is eliminated. Three adjustable parameters in LMI are used to control the gain of the 2D fuzzy control. Binary performance indices with weighted matrices are constructed to separately evaluate validation and training performance using the revalidation learning function. Determining the appropriate weight matrix balances control and learning efficiency and prevents large gains in control. It is proved that the stability of the control system can be ensured by a linear matrix theory of equality based on Lyapunov's theory. Simulation results show that the multilevel simulation approach combines accuracy with high computational efficiency. The M-TMD system, by slightly reducing critical joint load amplitudes, can significantly improve the overall response of an uncontrolled structure.