• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.12
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• pp.35-44
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• 1994

In this paper, we propose a new modification method of the look-up table in self-organizing fuzzy control using look-up table. This method has the property that look-up table is modified to have fast response property. Its principle is that the controller forces the trajectory to go into the fast respose region which the error change amount is larger than the error at initial time whenever the reference or disturbance change. Also we introduce the variable learning speed coefficient which is proportional to distance from switching curve. And to demonstrate the applicability of the proposed method, we had simulation study for some examples and esecuted pole balance experiments with inverted pendulum.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.9
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• pp.929-938
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• 2009

This paper presents a novel control methodology for communication network based nonlinear systems with time delay nature. We construct a nominal nonlinear control law for representing a linear model and a reset control system which is aimed for corrective control strategy to compensate system error due to uncertain time delay through wireless communication network. Next, online neural control approach is proposed for overcoming nonstationary statistical nature in the network topology. Additionally, DBN (Dynamic Bayesian Network) technique is accomplished for modeling of its dynamics in terms of casuality, which is then utilized for estimating prediction of system output. We evaluate superiority and reliability of the proposed control approach through numerical simulation example in which a nonlinear inverted pendulum model is employed as a networked control system.

• Journal of the Korea Convergence Society
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• v.9 no.3
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• pp.173-179
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• 2018

The purpose of this study was to explore college students' use of learning strategies in a flipped learning class, and to examine the students' use of learning strategies in relation to their achievement levels. The participants were 33 college students who took an introduction to English education course. The study used three data collection procedures: (1) students' performance score; (2) a pre-and post-survey of student learning strategies; (3) a survey of student perception towards the flipped learning experience. Data were analyzed by using paired samples t-test and ANOVA. Results showed that the students used different learning strategies in the beginning and the end of the course, depending on their achievement levels. In particular, significant differences were found among three groups in terms of time management, concentration, selecting main idea, self-testing, and test strategies. The result indicates that learning strategies can be effectively trained and developed in the flipped learning environment with the consideration of students' levels.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.142-145
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• 2003

Many control applications using Neural Network need a priori information about the objective system. But it is impossible to get exact information about the objective system in real world. To solve this problem, several control methods were proposed. Reinforcement learning control using neural network is one of them. Basically reinforcement learning control doesn't need a priori information of objective system. This method uses reinforcement signal from interaction of objective system and environment and observable states of objective system as input data. But many methods take too much time to apply to real-world. So we focus on faster learning to apply reinforcement learning control to real-world. Two data types are used for reinforcement learning. One is reinforcement signal data. It has only two fixed scalar values that are assigned for each success and fail state. The other is observable state data. There are infinitive states in real-world system. So the number of observable state data is also infinitive. This requires too much learning time for applying to real-world. So we try to reduce the number of observable states by classification of states with Self-Organizing Map. We also use neural dynamic programming for controller design. An inverted pendulum on the cart system is simulated. Failure signal is used for reinforcement signal. The failure signal occurs when the pendulum angle or cart position deviate from the defined control range. The control objective is to maintain the balanced pole and centered cart. And four states that is, position and velocity of cart, angle and angular velocity of pole are used for state signal. Learning controller is composed of serial connection of Self-Organizing Map and two Multi-layer Feed-Forward Neural Networks.

• Journal of The Korean Association of Information Education
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• v.21 no.5
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• pp.487-495
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• 2017

The flipped learning is an inverted teaching model where students learn the basic concepts using short videos at home and then come to class to enable effective practice and interactions among teachers and students. However, due to the students' lack of self-regulated competence, most students have difficulties of comprehending the instructional materials out of class by themselves. In this paper, we develop an interactive app for active flipped learning in the mathematics courses in the elementary schools. We examine the effectiveness of the active flipped learning on learners groups with different achievement levels in learning 4th grade mathematics concepts in the elementary schools. The pretest and posttest survey results show that the proposed flipped learning approach has better performance compared to the traditional flipped learning approach.

• Journal of Advanced Marine Engineering and Technology
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• v.28 no.2
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• pp.228-236
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• 2004

In this paper. we present a neuro-fuzzy controller which unifies both fuzzy logic and multi-layered feed forward neural networks. Fuzzy logic provides a means for converting linguistic control knowledge into control actions. On the other hand. feed forward neural networks provide salient features. such as learning and parallelism. In the proposed neuro-fuzzy controller. the parameters of membership functions in the antecedent part of fuzzy inference rules are identified by using the error back propagation algorithm as a learning rule. while the coefficients of the linear combination of input variables in the consequent part are determined by using the least square estimation method. Finally. the effectiveness of the proposed controller is verified through computer simulation for an inverted pole system.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.10
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• pp.888-894
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• 2005

In this paper, experimental studies of balancing a pendulum mounted on a wheeled drive mobile robot and its position control are presented. Main PID controllers are compensated by a neural network. Neural network learning algorithm is embedded on a DSP board and neural network controls the angle of the pendulum and the position of the mobile robot along with PID controllers. Uncertainties in system dynamics are compensated by a neural network in on-line fashion. Experimental results show that the performance of balancing of the pendulum and position tracking of the mobile robot is good.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.139-142
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• 2018

최근, Industry과 IoT 기기의 보급으로 인하여 수많은 센서와 액추에이터, 모바일 기기 등이 Cyber-Physical System을 통해 네트워크와 연결되며, 더 효율적인 시스템을 요규한다. 이를 위하여, EdgeX와 SDN을 활용하여 빠르고 효율적인 네트워크 서비스를 제공한다. 따라서 본 논문에서는 CPS 기반의 Reinforcement Learning을 활용한 Rotary Inverted Pendulum System을 통해 실시간으로 빠르고 안전한 네트워크 서비스를 제공할 수 CPS 아키텍처를 구현한다.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2261-2263
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• 2001

Fuzzy controller design consists of intuition, and any other information about how to control system, into a set of rules. These rules can then be applied to the system. It is very important to decide parameters of IF-THEN rules. Because fuzzy controller can make more adequate force to the plant by means of parameter optimization, which is accomplished by learning procedure. In this paper, we apply fuzzy controller designed to the Swing-UP Inverted pendulum.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2031-2048
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• 2021

Not only can air pollution reduce the overall competitiveness of tourist destinations, but also changes tourists' travel decisions, thereby affecting the tourism flows. The study presents a machine learning method to analyze how the haze pollution puts spatial effect on tourism flows in China from 2001 to 2018, and reveals the regional differences in heterogeneity among eastern, central, and western China. Our investigation reveals three interesting observations. First, the Environmental Kuznets Curve of the impact of haze pollution on tourism flows is not significant. In the eastern and western regions, the interaction between haze pollution and domestic tourism flows as well as inbound tourism flows shows an inverted U-shaped curve respectively. Second, there is an significantly positive spillover effect of tourism flows in all of the eastern, central, and western regions. As to the intensity of spillover, domestic tourism flows is higher than that of the inbound tourism flows. Both of the above figures are greatest in the eastern. Third, the Chinese haze pollution mainly reduces the inbound tourism flows, and only imposes significantly negative direct effects on the domestic tourism flows in the central region. In the central and eastern regions, significantly negative direct effects and spillover effects are exerted on inbound tourism.