• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1990년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 26-27 Oct. 1990
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• pp.1277-1283
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• 1990

The iterative learning control synthesized in the frequency domain has been utilized for temperature control of a batch reactor. For this purpose, a feedback-assisted generalized learning control scheme was constructed first, and the convergence and robustness analyses were conducted in the frequency domain. The feedback-assisted learning operation was then implemented in a bench scale batch reactor where reaction heat is simulated using an electric heater. As a result, progressive reduction of temperature control error could be obviously observed as batch operation is repeated.

• 영어어문교육
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• 제15권4호
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• pp.237-264
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• 2009

The present study explores patterns of teachers' corrective feedback and learners' uptake in Korean EFL undergraduate classroom setting. It also examines consistencies and discrepancies in the perception of corrective feedback by teachers and learners. Teachers' and learners' preferences and perception of corrective feedback are further analyzed to determine whether or not those differ from actual practices in English language learning classrooms. The results of the study are as follows. First of all, teachers' corrective feedback type varied according to the learners' error type and English proficiency level. There was a lack of consistency between the teachers' feedback practices and the learners' error types. Second, for the phonological errors, learners' data witnessed the most frequent uptake on recast. For the other error types, however, the learners' uptake rates were high for the explicit corrective feedback. Third, the teachers' explicit knowledge of corrective feedback was rather low and the preferences differed from teacher to teacher. The teachers' feedback perception and preferences did not consistently reflect their actual practices. Finally, patterns of the learners' expectations of corrective feedback varied according to learners' proficiency level. Teachers' and learners' expectations of corrective feedback were also compared and some mismatches were detected.

• 한국산학기술학회논문지
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• 제20권2호
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• pp.336-345
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• 2019

본 연구의 목적은 초등학생을 대상으로 학습 환경적인 측면의 교실성취목표구조 및 피드백 유형과 교실 내 사회적 관계 측면의 교사 및 친구 관계가 실수에 대한 인식에 어떤 영향을 미치는지 살펴보는 것이었다. 이를 위해 초등학교 4, 5, 6학년 316명의 데이터를 활용하였으며, 분석 방법으로는 중다회귀분석을 실시하였다. 분석 결과, 첫째, 교실성취목표구조, 피드백 유형, 교사관계, 친구관계는 실수로부터 학습할 수 있다는 생각의 총 변량의 19%를 설명하며, 친구관계(${\beta}=.35$), 교사관계(${\beta}=.16$), 피드백 유형(${\beta}=-.11$) 순으로 상대적인 영향력을 가지는 것으로 확인되었다. 둘째, 네 개 예측변인은 실수에 대한 도전적인 태도의 총 변량의 20%를 설명하며, 친구관계(${\beta}=.32$), 교사관계(${\beta}=.21$), 교실성취목표구조(${\beta}=.11$) 순으로 상대적인 영향력을 가지는 것으로 확인되었다. 셋째, 네 개 예측변인은 실수에 대한 고찰의 총 변량의 12%를 설명하는 것으로 나타났으며, 친구관계(${\beta}=.27$), 교사관계(${\beta}=.13$) 순으로 상대적인 영향력을 가지는 것으로 확인되었다. 넷째, 네 개 예측변인은 실수에 대한 부담을 설명하지 못하는 것으로 확인되었다. 본 연구 결과는 초등학생의 실수에 대한 긍정적 인식을 높이기 위해 어떻게 교실 분위기를 조성해야 하는지에 대한 정보를 제공한다는 의의가 있다.

• 제어로봇시스템학회논문지
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• 제4권5호
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• pp.608-615
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• 1998

In this paper a second-order iterative learning control algorithm with feedback is proposed for the trajectory-tracking control of nonlinear dynamic systems with unidentified parameters. In contrast to other known methods, the proposed teaming control scheme utilize more than one past error history contained in the trajectories generated at prior iterations, and a feedback term is added in the learning control scheme for the enhancement of convergence speed and robustness to disturbances or system parameter variations. The convergence proof of the proposed algorithm is given in detail, and the sufficient condition for the convergence of the algorithm is provided. We also discuss the convergence performance of the algorithm when the initial condition at the beginning of each iteration differs from the previous value of the initial condition. The effectiveness of the proposed algorithm is shown by computer simulation result. It is shown that, by adding a feedback term in teaming control algorithm, convergence speed, robustness to disturbances and robustness to unmatched initial conditions can be improved.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1990년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 26-27 Oct. 1990
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• pp.30-35
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• 1990

Learning control of a robot manipulator is proposed using the backpropagation neural network. The learning controller is composed of both a linear feedback controller and a neural network-based feedforward controller. The stability analysis of the learning controller is presented. Three energy functions are selected in teaching the neural network controller : 1/2.SIGMA.vertical bar torque error vertical bar $^{2}$, 1/2.SIGMA..alpha. vertical bar position error vertical bar $^{2}$ + .betha. vertical bar velocity error vertical bar $^{2}$ + .gamma. vertical bar acceleration error vertical bar $^{2}$ and learning methods are presented. Simulation results show that the learning controller which is learned to minimize the third energy function performs better than the others in tracking problems. Some properties of the learning controller are discussed with simulation results.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1990년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 26-27 Oct. 1990
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• pp.234-239
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• 1990

A neural controller for process control was proposed that combines a simple feedback controller with a neural network. This control was applied to distillation control. The feedback error learning technique was used for on-line learning. Important characteristics on neural controller were analyzed. The proposed neural controller can cope well with strong interactions, significant time delays, sudden changes in process dynamics without any prior knowledge of the process. It was shown that the neural controller has good features such as fault tolerance, interpolation effect and random learning capability

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.346-346
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• 2000

We proposed a method of second-order iterative learning control with feedback, which shows an enhancement of convergence speed and robustness to the disturbances in our previous study. In this paper, we show that the proposed second-order iterative learning control algorithm with feedback is more effective and has better convergence performance than the algorithm without feedback in the case of the existence of initial condition errors. And the convergence woof of the proposed algorithm in the case of the existence of initial condition error is given in detail, and the effectiveness of the Proposed algorithm is shown by simulation results.

• International Journal of Advanced Culture Technology
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• 제9권1호
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• pp.203-209
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• 2021

When carrying out computer programming, the process of checking and correcting errors in the source code is essential work for the completion of the program. Non-computer majors who are learning programming for the first time receive feedback from instructors to correct errors that occur when writing the source code. However, in a learning environment where the time for the learner to practice alone is long, such as an online learning environment, the learner starts to feel many difficulties in solving program errors by himself/herself. Therefore, training on how to check and correct errors after writing the program source code is necessary. In this paper, various types of errors that can occur in a Python program were described, the errors were classified into simple errors and complex errors according to the characteristics of the errors, and the distributions of errors by Python grammar category were analyzed. In addition, a coding learning process to refer error lists was designed to present a coding learning method that enables learners to solve program errors by themselves.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 19-21 Oct. 1992
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• pp.632-637
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• 1992

Recently many dynamics control algorithms using robot dynamic equation have been proposed. One of them, Kawato's feedback error learning scheme requires neither an accurate model nor parameter estimation and makes the robot motion closer to the desired trajectory by repeating operation. In this paper, the feedback error learning algorithm is implemented to control a robot system, 5 DOF revolute type movemaster. For this purpose, an actuator dynamic model is constructed considering equivalent robot dynamics model with respect to actuator as well as friction model. The command input acquired from the actuator dynamic model is the sum of products of unknown parameters and known functions. To compute the control algorithm, a parallel processing computer, transputer, is used and real-time computing is achieved. The experiment is done for the three major link of movemaster and its result is presented.

• International journal of advanced smart convergence
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• 제9권2호
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• pp.90-104
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• 2020

It is difficult for non-experts to build machine learning (ML) models at the level that satisfies their needs. Deep learning models are even more challenging because it is unclear how to improve the model, and a trial-and-error approach is not feasible since training these models are time-consuming. To assist these novice users, we examined how interactive and explainable feedback while training a deep learning network can contribute to model performance and users' satisfaction, focusing on the data preparation process. We conducted a user study with 31 participants without expertise, where they were asked to improve the accuracy of a deep learning model, varying feedback conditions. While no significant performance gain was observed, we identified potential barriers during the process and found that interactive and explainable feedback provide complementary benefits for improving users' understanding of ML. We conclude with implications for designing an interface for building ML models for novice users.