• International Journal of Precision Engineering and Manufacturing
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• v.4 no.4
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• pp.13-18
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• 2003

This paper presents the in-process compensation to control cutter ronout and to improve the machined surface quality. Cutter ronout compensation system consists of the micro-positioning servo system with piezoelectric actuator which is embeded in the sliding table to manipulate radial depth of cut in real-time. Cutting force feedback control was proposed in the angle domain based upon repetitive learning control strategy to eliminate chip load variation in end milling process. Micro-positioning control due to adaptive actuation force response improves the machined surface quality by cutter ronout compensation.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.4
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• pp.330-336
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• 2005

An intelligent control method is proposed for control of rigid robot manipulators which achieves exponential tracking of repetitive robot trajectory under uncertain operating conditions such as parameter uncertainty and unknown deterministic disturbance. In the learning controller, exponentially stable learning algorithms are combined with stabilizing computed error feedforward and feedback inputs. It is shown that all the error signals in the learning system are bounded and the repetitive robot motion converges to the desired one exponentially fast with guaranteed convergence rate. An engineering workstation based control system is built to verify the effectiveness of the proposed control scheme.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.3
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• pp.137-143
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• 2002

This paper presents the In-process compensation to control cutter runout and improve the machined surface quality. Cutter runout compensation system consists of the micro-positioning servo system with piezoelectric actuator which is embeded in the sliding table to manipulate radial depth of cut in real-time. Cutting force feedback control was proposed in the angle domain based upon repetitive learning control strategy to eliminate chip load variation in end milling process. Micro-positioning control due to adaptive actuation force response improves the machined surface quality by compensation runout effect induced cutting force variation. This result will provide lots of information to build-up the preciswion machining technology.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1573-1576
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• 1997

In this paper we present an associative menory network(AMN) controller for learning of robot trajectories. We use AMN controller in order to improve the performance of conventional learning control, e.g. RCL, which had studied by Sadegh et al. Computer simulations show the feasibility and effectiveness of the proposed AMN controller.

• Journal of the Korea Convergence Society
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• v.7 no.4
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• pp.133-140
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• 2016

This paper suggests designing how to acquire English vocabularies on the smart devices based on the research that a ground-breaking English Vocabulary Learning Contents needs developing. The method makes it possible to develop the contents which helps the learners to master English vocabularies effectively on the smart phone. The core idea of this paper is as in the following: 1) English learners learn 30 vocabularies for three minutes 10 times (one is for a new learning and the other nine ones are for reviews about the first learning) a day. 2) Considering Ebbinghaus Forgetting Curve, the reflection study proposes to provide the learners with three times' reviews: one day, 10days, and 30days later from which they learn the first 30 vocabularies. This contents is mainly made up of 5 developing sections (1)to generate App ID, (2)to access App, (3)to set up Alarm, (4)to process Word learning, and (5)to monitor the result of learning. This proposed idea is optimized to enhance the memory by Ebbinghaus Periodic Repetitive Method, which makes the learners satisfied with their English vocabulary learning.

• Journal of the Korean Operations Research and Management Science Society
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• v.30 no.3
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• pp.137-149
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• 2005

This study proposes a framework enhancing the accuracy of estimation for project duration by combining linear Bayesian updating scheme with the learning curve effect. Activities in a particular project might share resources in various forms and might be affected by risk factors such as weather Statistical dependence stemming from such resource or risk sharing might help us learn about the duration of upcoming activities in the Bayesian model. We illustrate, using a Monte Carlo simulation, that for partially repetitive projects a higher degree of statistical dependence among activity duration results in more variation in estimating the project duration in total, although more accurate forecasting Is achievable for the duration of an individual activity.

• The Research Journal of the Costume Culture
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• v.30 no.5
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• pp.717-731
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• 2022

The purpose of this study is to suggest effective online pedagogical strategies for a fashion design CAD course to enhance student learning and satisfaction. The study investigated student experience of online learning and compared online learning with a face-to-face learning experience. Student concentration, participation, perceptions of effectiveness of teaching, utilization of learning materials, and satisfaction were analyzed using a 5-point Likert scale. Advantages and disadvantages of online learning as well as advantages of face-to-face learning were also analyzed both quantitatively and qualitatively. Student concentration, participation, and perception of effectiveness of teaching were greater for face-to-face learning with significantly higher concentration on individual practice. Students utilized video recording of synchronous online lectures more actively than PDF lecture notes. The advantages of face-to-face learning were plentiful communication and feedback and easy questioning process as well as high levels of understanding and concentration. Meanwhile, major disadvantages of online learning were the speed of the lecture, lower levels of understanding and concentration, limited peer interaction, and technical problems. Major advantages of online learning were flexibility and convenience, repetitive learning through videos, and instant communication and feedback. Students preferred a blended learning approach for the fashion design CAD course. For effective online learning, it is suggested that instructors frequently question and check student practice through screen share in a private online meeting room and engage activities that are demanding of student interaction. The video recording of synchronous online lectures is also suggested as a supplemental learning material for repetitive learning.

• Journal of the Korea Society of Computer and Information
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• v.16 no.10
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• pp.211-218
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• 2011

Although the disaster management of Korea such as mitigation of disasters, preparedness for them and recovery from them. It should be considered based on the failures of the disaster management and the past experimental knowledge, it is believed that the repetitive occurrence of similar disasters is caused by absence of learning of disaster management organizational. That is, non-learning of the management organs due to experimental errors indicates that the organization themselves are not able to adjust to environment and the same kinds of disasters may happen in the future. Therefore, this study identifies repetitive failures by analysing reasons of the failures in terms of organizational learning in order to prevent from repetition of similar failures, and presents suggestions on the policy of disaster management. For the purposes, it carries both bibliographical analysis and case analysis. this study targets Daegu Subway Fire in 2003.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10a
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• pp.326-331
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• 1988

In this paper, an iterative leaning control algorithm for the linear discrete system is proposed. Based upon the parameter estimation method, the learning for good tracking control is acqured through a sequence of repetitive operations. A series of simulation are performed to show the validity of this algorithm.

• Proceedings of the KIEE Conference
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• 2007.10a
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• pp.297-298
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• 2007

Repetitive systems stand for a kind of systems that perform a simple task on a fixed pattern repetitively and are widely spread in industrial fields. Hence, those systems have been of much interests by many researchers, especially in the field of iterative learning control (ILC). In this paper, we propose a finite-horizon tracking control scheme for linear time-varying repetitive systems with uncertain initial conditions. The scheme is derived both analytically and numerically for state-feedback systems and only numerically for output-feedback systems. Then, it is extended to stable systems with input constraints. All numerical schemes are developed in the forms of linear matrix inequalities. A distinguished feature of the proposed scheme from the existing iterative learning control is that the scheme guarantees the tracking performance exactly even under uncertain initial conditions. The simulation results demonstrate the good performance of the proposed scheme.