• International Journal of Control, Automation, and Systems
• /
• 제6권2호
• /
• pp.194-203
• /
• 2008

Iterative learning control (ILC) is a simple and effective method for the control of systems that perform the same task repetitively. ILC algorithm uses the repetitiveness of the task to track the desired trajectory. In this paper, we propose a PID (proportional plus integral and derivative) type ILC update law for control discrete-time single input single-output (SISO) linear time-invariant (LTI) systems, performing repetitive tasks. In this approach, the input of controlled system in current cycle is modified by applying the PID strategy on the error achieved between the system output and the desired trajectory in a last previous iteration. The convergence of the presented scheme is analyzed and its convergence condition is obtained in terms of the PID coefficients. An optimal design method is proposed to determine the PID coefficients. It is also shown that under some given conditions, this optimal iterative learning controller can guarantee the monotonic convergence. An illustrative example is given to demonstrate the effectiveness of the proposed technique.

• International Journal of Control, Automation, and Systems
• /
• 제6권2호
• /
• pp.171-179
• /
• 2008

This paper addresses the synthesis of an iterative learning controller for a class of linear systems with norm-bounded parameter uncertainties. We take into account an iterative learning algorithm with current cycle feedback in order to achieve both robust convergence and robust stability. The synthesis problem of the developed iterative learning control (ILC) system is reformulated as the ${\gamma}$-suboptimal $H_{\infty}$ control problem via the linear fractional transformation (LFT). A sufficient convergence condition of the ILC system is presented in terms of linear matrix inequalities (LMIs). Furthermore, the ILC system with fast convergence rate is constructed using a convex optimization technique with LMI constraints. The simulation results demonstrate the effectiveness of the proposed method.

• 한국수학교육학회지시리즈D:수학교육연구
• /
• 제22권2호
• /
• pp.135-152
• /
• 2019

This conceptual paper proposes a conceptualization regarding teacher candidates' experiences as learners during instructional activities implemented by teacher educators in practice-based teacher education programs. We argue that the current learning cycle framework for teacher candidates to engage in core teaching practices does not fully address teacher candidates' own learning experiences as learners. To provide a rationale for our proposal, we examine the current conceptualization of learning to enact core practices and suggest the need for integrating teacher candidates' experiences into the current conceptualization. We also draw on research on figured worlds as an effort to conceptualize teacher candidates' experiences coming from multiple figured world. We present some examples from our own mathematics methods courses to illustrate how this newly proposed framework can be used in practice and share remaining questions for future research.

• 로봇학회논문지
• /
• 제16권2호
• /
• pp.130-136
• /
• 2021

In recent years, much study has been conducted in robotic grasping. The grasping algorithms based on deep learning have shown better grasping performance than the traditional ones. However, deep learning-based algorithms require a lot of data and time for training. In this study, a grasping algorithm using an artificial neural network-based graspability estimator is proposed. This graspability estimator can be trained with a small number of data by using a neural network based on the residual blocks and point clouds containing the shapes of objects, not RGB images containing various features. The trained graspability estimator can measures graspability of objects and choose the best one to grasp. It was experimentally shown that the proposed algorithm has a success rate of 90% and a cycle time of 12 sec for one grasp, which indicates that it is an efficient grasping algorithm.

• 한국컴퓨터정보학회:학술대회논문집
• /
• 한국컴퓨터정보학회 2021년도 제63차 동계학술대회논문집 29권1호
• /
• pp.337-339
• /
• 2021

본 연구는 최근 펜데믹 위기에서 교육의 변화하는 모습을 점검하고 미래의 학습에 대한 모습들을 예측하기 위해 이러닝과 학습분석에 대한 신흥기술의 동향을 살펴보고자 한다. 연구방법으로 신흥기술의 '하이프 사이클'과 '이러닝 예측 하이프 커버'를 기반으로 하여 각 단계별 기술들을 점검하고 펜데믹 위기에서 더 공고히 된 이러닝과 학습 관련 기술들이 무엇인지 살펴본다. 또한 하이프 사이클의 5단계인 기술촉발 단계, 부풀려진 기대의 정점 단계, 환멸 단계, 계몽 단계, 생산성 안정 단계인 각 단계별 학습과 관련된 기술들은 어떤 것이 있으며, 그 기술들이 이러닝과 학습분석에 어떠한 영향을 미칠 것인지 예측해 본다. 향후 연구로는 본 연구를 기반으로 인공지능이 이러닝과 학습분석에서의 역할을 알아보고자 한다.

• 컴퓨터교육학회논문지
• /
• 제6권3호
• /
• pp.99-108
• /
• 2003

본 논문은 VOD(Video On Demand) 기반 온라인 교육 시스템의 한계점들을 분석하고 이에 대한 대안으로 e- Learning 표준에 기초한 EOD(Education On Demand) 시스템의 디자인과 구현에 대해 기술한다. EOD 시스템은 XML로 표현되는 메타 정보와 컴포넌트 기술을 사용하여 학습 컨텐츠 라이프사이클 전반에 적용되는 저작 도구, 컨텐츠 서버, 학습 정책 시스템 그리고 뷰어로 구성된다. 구성요소간의 모든 정보 교환은 SCORM 표준에 기반한 메타 정보로 표현되어 효과적인 컨텐츠 관리와 자동화를 가능하게 한다. 또한 학습자의 상호작용 및 피드백 정보를 통합 관리하여 학습 정책 시스템을 통해 개별 학습자에게 맞추어진 학습 지도를 제공 할 수 있다. 이러한 EOD 시스템을 통해 단순한 컨텐츠 제공을 넘어선 발전된 형태의 온라인 교육 시스템에 대해 고찰해 본다.

• 한국가스학회지
• /
• 제24권6호
• /
• pp.91-97
• /
• 2020

리튬이온 배터리(LIB)는 다른 배터리에 비해 수명이 길고, 에너지 밀도가 높으며, 자체 방전율이 낮아, 에너지 저장장치(ESS)로 선호되고 있다. 하지만, 2017~2019년 기간 동안 국내에서만도 28건의 화재사고가 발생하였으며, LIB의 운영 중 안전성 및 신뢰성을 보장하기 위해 LIB의 정확한 용량추정은 필수요소이다. 본 연구에서는 LIB의 충방전 cycle에 따른 용량변화를 예측하는 기계학습 기반 모델의 설계에 있어 중요한 요소인 최적 머신러닝 모델의 선정을 위해, Decision Tree, 앙상블학습법, Support Vector Regression, Gaussian Process Regression (GPR) 각각을 이용한 예측모델을 구현하고 성능비교를 실시하였다. 학습을 위해 NASA에서 제공하는 시험데이터를 사용하였으며, GPR이 가장 좋은 예측성능을 보였다. 이를 바탕으로 추가 시험데이터 학습을 통해 개선된 LIB 용량예측과 잔여 수명추정 모델을 개발하여, 운영 중 이상 감지 및 모니터링 성능을 높여, 보다 안전하고 안정된 ESS 운용에 활용하고자 한다.

• Industrial Engineering and Management Systems
• /
• 제15권2호
• /
• pp.148-155
• /
• 2016

Existing power plants may consume significant amounts of fuel and require high operating costs, partly because of poor electrical power output estimates. This paper suggests a continuous conditional random field (C-CRF) model to predict more precisely the full-load electrical power output of a base load operated combined cycle power plant. We introduce three feature functions to model association potential and one feature function to model interaction potential. Together, these functions compose the C-CRF model, and the model is transformed into a multivariate Gaussian distribution with which the operation parameters can be modeled more efficiently. The performance of our model in estimating power output was evaluated by means of a real dataset and our model outperformed existing methods. Moreover, our model can be used to estimate confidence intervals of the predicted output and calculate several probabilities.

• 대한안전경영과학회지
• /
• 제19권3호
• /
• pp.97-107
• /
• 2017

This research studies on the demand forecasting for service parts considering parts life cycle, that gets relatively less attentions in the field of forecasting. Our goal is to develop forecasting method robust across many situations, not necessarily optimal for a limited number of specific situations. For this purpose, we first extensively analyze the drawbacks of the existing forecasting methods, then we propose the new demand forecasting method by using these findings and reinforcement leaning technique. Using simulation experiments, we proved that the proposed forecasting method is better than the existing methods under various experimental environments.

• 한국산업정보학회:학술대회논문집
• /
• 한국산업정보학회 2000년도 추계공동학술대회논문집
• /
• pp.131-167
• /
• 2000

Top CEO Priorities 1. Increasing Globalization(94%) 2. Improving Knowledge Management(88%) 3. Reducing Cost and Cycle Time(79%) 4. Improving Supply Chains Globally(78%). "Personally, I believe that future leadership companies and future leadership institutions of all kinds will be those that know how to compete and win on the basis of knowledge-learning, adapting and improving this vital asset we know as information."(omitted)