• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.169.3-169
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• 2001

This paper introduces a new approach to analysis of error convergence for a class of iterative learning control systems. First, a nonlinear plant is represented using a Takagi-Sugeno(T-S) fuzzy model. Then each iterative learning controller is designed for each linear plant in the T-S fuzzy model. From the view point of two-dimensional(2-D) system theory, we transform the proposed learning systems to a 2-D error equation, which is also established in the form of T-S fuzzy model. We analysis the error convergence in the sense of induced 2 L -norm, where the effects of disturbances and initial conditions on 2-D error are considered. The iterative learning controller design problem to guarantee the error convergence can be reduced to linear matrix inequality problems. In comparison with others, our learning algorithm ...

• English Language & Literature Teaching
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• v.17 no.3
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• pp.141-159
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• 2011

This study examined the effects of collocation-based instruction on L2 vocabulary acquisition and learners' interests in it. Fifty one students were randomly assigned to the experimental group (collocation-based instruction group) and to the control group. The participants' English vocabulary capacity was checked through pre and post tests, and two surveys were used to probe the learners' vocabulary learning behaviors and their interests in English vocabulary learning respectively. To better understand the participants' opinions and feelings on the collocation-based learning, follow-up interviews were also carried out. The results showed that second language (L2) learners' vocabulary capacity was significantly improved through collocation-based instruction. However, the participants' degree of interest in vocabulary learning did not reach our expectation partly because of external factors such as the Test for the College Scholastic Ability Test (CSAT) and lack of familiarity of collocations. Such results indicate that the high school students' rooted perception of putting importance on test-based language learning could not be easily changed since it is closely related to their immediate needs. Based on the results, this study suggested how to implement collocations into L2 classrooms effectively.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.7
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• pp.1411-1417
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• 2009

In this paper, we consider the design of $H_{\infty}$ control of linear discrete-time systems having no mathematical model. The basic approach is to use Q-learning which is a reinforcement learning method based on actor-critic structure. The model-free control design is to use not the mathematical model of the system but the informations on states and inputs. As a result, the derived iterative algorithm is expressed as linear matrix inequalities(LMI) of measured data from system states and inputs. It is shown that, for a sufficiently rich enough disturbance, this algorithm converges to the standard $H_{\infty}$ control solution obtained using the exact system model. A simple numerical example is given to show the usefulness of our result on practical application.

• International Journal of Advanced Culture Technology
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• v.8 no.4
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• pp.51-57
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• 2020

A propeller-based seesaw system is a system that can represent one of axis of four propeller drones and its stabilization has been replaced by intelligent control system instead of often used control methods such as PID and state space. Today, robots are increasingly use machine learning methods to adapt to their environment and learn to perform the right actions. In this article, we propose a Q-learning-based approach to control the stability of a seesaw system with a propeller. From the experimental results that it is possible to fully learn the balance control of a seesaw system by correctly defining the state of the system, the actions to be performed, and the reward functions. Our proposed method solves the seesaw stabilization.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2002.11a
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• pp.596-602
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• 2002

An easy elevator for learning originated is opened to compare the existed learning equipment, and it had a high studying efficient that the sequence control circuit can opens and closes with the wire. The structure of equipment to be controlled from the first floor to the fifth floors is demonstrated a constructive apparatus by a lamp atc to express the function of the open-close of the door according to the cage moving with a mechanical actuation of the forward-reverse breaker and the motor of load and a mechanical actuation of hand-operation control components of push-button S/W and L/S and relay etc. These components let connects each other in order to control of the elevator function with the auto program and the designed sequence control circuit. Consequent1y the process of these functions of 1~5steps could operates the cage with an auto program of the elevator and the sequence control circuit. The sequence control circuit is controlled by the step of forward and reverse to follow as that the sensor function of the L/S1~L/S5 let posit with the control switchs of S/W1~S/W5 of PLC testing panel and switchs of S/W1~S/W5 installed on the transparent acryl plate of the frame. In here, improved apparatus is a hand-auto operation combined learning equipment to study the principle and a technique of the originated sequence control circuit and the auto program of PLC.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2002.07b
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• pp.1107-1112
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• 2002

An easy elevator originated is an opened system to compare an existing equipment, and learning efficient is high as a wiring that the sequence control circuit is on and off. The structure of an equipment to be controled from the first floor to the fifth floor is constructed by a lamp to express the function of the open-close of the door according to the cage moving, to express the mechanical actuation of the forward-reverse break and motor of load and of hand-worked control component of Push-Button S/W, L/S and Relay. In order to act of the elevator function that these components connected, designed the auto program and the sequence control circuit. Consequently the process that these(1~5steps) operated the cage with an auto program of the elevator and the sequence control circuit is controled by the step of forward and reverse that the L/S1~L/S5 of sensor adjust function let posit, by the adjustable S/W1~S/W5 of PLC testing panel and the S/W1~S/W5 which installed on the transparent acryl plate of a frame. In here, improved apparatus is the learning equipment of combined use to study the principle and the technique of the originated sequence control circuit and the auto program of PLC.

• Journal of Korean Academy of Nursing
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• v.48 no.6
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• pp.743-753
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• 2018

Purpose: This study aimed to explore the effects of a community-based first and third Intergenerational Exchange Program (IGEP) on older adults' health-related quality of life (HRQoL), loneliness, depression, and walking speed, and on 4~5-year-old preschool children's learning-related social skills. Methods: This study employed a non-equivalent control group pre-post-test design. The experimental group included 42 older adults and 42 children who participated in the IGEP for 8 weeks, and the control group included 39 older adults. The experimental group participated in the IGEP once a week for 8 weeks. It comprised a traditional play program based on the intergroup contact theory. Results: Compared to the control group, there was a significant increase in scores on the HRQoL-Visual analogue scale (VAS) and a decrease in loneliness and depression in older adults in the experimental group (p<.05). Children who participated in the IGEP showed an improvement in their learning-related social skills (p<.001). Conclusion: These results confirm that the IGEP is an effective intervention to improve HRQoL-VAS, loneliness, and depression among older adults and learning-related social skills among preschool children in the community.

• Electronics and Telecommunications Trends
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• v.34 no.6
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• pp.87-99
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• 2019

Reinforcement learning is a type of machine learning paradigm that forces agents to repeat the observation-action-reward process to assess and predict the values of possible future action sequences. This allows the agents to incrementally reinforce the desired behavior for a given observation. Thanks to the recent advancements of deep learning, reinforcement learning has evolved into deep reinforcement learning that introduces promising results in various control and optimization domains, such as games, robotics, autonomous vehicles, computing, industrial control, and so on. In addition to this trend, a number of programming libraries have been developed for importing deep reinforcement learning into a variety of applications. In this article, we briefly review and summarize 10 representative deep reinforcement learning libraries and compare them from a development project perspective.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.920-924
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• 2005

This paper presents adaptive learning data of evolvable neural networks (ENNs) for time series prediction of nonlinear dynamic systems. ENNs are a special class of neural networks that adopt the concept of biological evolution as a mechanism of adaptation or learning. ENNs can adapt to an environment as well as changes in the environment. ENNs used in this paper are L-system and DNA coding based ENNs. The ENNs adopt the evolution of simultaneous network architecture and weights using indirect encoding. In general just previous data are used for training the predictor that predicts future data. However the characteristics of data and appropriate size of learning data are usually unknown. Therefore we propose adaptive change of learning data size to predict the future data effectively. In order to verify the effectiveness of our scheme, we apply it to chaotic time series predictions of Mackey-Glass data.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.804-806
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• 1995

This paper discusses Self-organized Distributed Networks(SODN) as identifier of nonlinear dynamical systems. The structure of system identification employs series-parallel model. The identification procedure is based on a discrete-time formulation. The learning with the proposed SODN is fast and precise. Such properties arc caused from the local learning mechanism. Each local networks learns only data in a subregion. Large number of memory requirements and low generalization capability for the untrained region, which are drawbacks of conventional local network learning, are overcomed in the SODN. Through extensive simulation, SODN is shown to be effective for identification of nonlinear dynamical systems.