• Nuclear Engineering and Technology
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• 제54권9호
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• pp.3283-3292
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• 2022

Based on the Deep Q-Network(DQN) algorithm of reinforcement learning, an active fault-tolerance method with incremental action is proposed for the control system with sensor faults of the once-through steam generator(OTSG). In this paper, we first establish the OTSG model as the interaction environment for the agent of reinforcement learning. The reinforcement learning agent chooses an action according to the system state obtained by the pressure sensor, the incremental action can gradually approach the optimal strategy for the current fault, and then the agent updates the network by different rewards obtained in the interaction process. In this way, we can transform the active fault tolerant control process of the OTSG to the reinforcement learning agent's decision-making process. The comparison experiments compared with the traditional reinforcement learning algorithm(RL) with fixed strategies show that the active fault-tolerant controller designed in this paper can accurately and rapidly control under sensor faults so that the pressure of the OTSG can be stabilized near the set-point value, and the OTSG can run normally and stably.

• 한국수학교육학회지시리즈A:수학교육
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• 제53권1호
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• pp.75-92
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• 2014

In this paper, we summarize briefly some of the most salient features of Repkina & Zaika's theory of learning action formation levels. We concretize Repkina & Zaika's theory by comparing various points of view of Uoo, Polya, Krutetskii, and Davydov et al. In this study we are able to diagnose students' learning action formation levels in the process of mathematics problem solving. In addition we use interview method to collect various information about students' levels. As a result we suggest data related with each level of learning action formation, and characteristics of students who belong to each level of learning action formation.

• Human Ecology Research
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• 제58권4호
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• pp.561-583
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• 2020

This study developed and implemented a teaching · learning process plan for Home Economics in middle school by converging Habermas's three systems of action. It also examined the effect of the class through the evaluation of students and teachers who participated in the class. This study developed 10 sessions for a teaching and learning process plan by converging three systems of action and reconstructing learning elements related to 'balanced meal plan' and 'food choice' according to the practical action teaching model. After class, we surveyed the degree of help for students, analyzed the learning activity sheets, and analyzed the reflection journals of teachers to evaluate the effects of the class. This class was found to be the most helpful in practicing the healthy dietary life of students, expanding their thoughts, understanding learning contents, and helping them change their lives. As a result of analyzing the learning activity sheet, students gained enlightenment by reflecting and evaluating their action through the class; in addition, changes in interest, awareness, and action appeared. Through the convergence of three systems of action, teachers who practiced the class criticized and realized the act that students were unconsciously accepted. In addition, it confirmed the possibility that students could change their lives, family and society by promoting optimal nutrition and health for a good life that pursues the best good.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제8권3호
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• pp.220-224
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• 2008

This paper presents the polygon-based Q-leaning and Parallel SVM algorithm for object search with multiple robots. We organized an experimental environment with one hundred mobile robots, two hundred obstacles, and ten objects. Then we sent the robots to a hallway, where some obstacles were lying about, to search for a hidden object. In experiment, we used four different control methods: a random search, a fusion model with Distance-based action making (DBAM) and Area-based action making (ABAM) process to determine the next action of the robots, and hexagon-based Q-learning, and dodecagon-based Q-learning and parallel SVM algorithm to enhance the fusion model with Distance-based action making (DBAM) and Area-based action making (ABAM) process. In this paper, the result show that dodecagon-based Q-learning and parallel SVM algorithm is better than the other algorithm to tracking for object.

• 벤처창업연구
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• 제10권6호
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• pp.99-106
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• 2015

본 연구는 신발제조업체에 속하는 B산업을 대상으로 액션러닝을 이용한 학습조직에 관한 사례조사를 한다. 구체적인 학습조직 활동의 촉진을 위한 컨설팅 스킬로 액션러닝(action learning)기법을 적용한다. 액션러닝의 경우, 기업의 당면한 문제도 해결하고 프로그램에 참여하는 동안 구성원의 역량도 함께 강화되기 때문에 경영혁신도구로 활용도가 높으며, 불확실성이 높은 환경극복에 좋은 방법론으로 알려져 있다. 사례분석에서 컨설팅에서 기대한 효과가 학습조직의 회의특성에서 긍정적인 변화로 나타나고 있다. 따라서 중소기업의 학습조직 구축에 중요한 시사점을 액션러닝 방식의 컨설팅과정에서 도출할 수 있었다. 연구결과는 기업 내 학습조직의 지속적 운영을 위해서는 기업 환경과 전략을 고려한 학습조직 방향의 수립이 중요하며 새로운 학습조직 운영방법을 터득하여 실천하는 과정의 반복이 필요하다는 점을 제시한다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제16권4호
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• pp.270-275
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• 2016

We present an approach for acceleration actor-critic algorithm for reinforcement learning with continuous action space. Actor-critic algorithm has already proved its robustness to the infinitely large action spaces in various high dimensional environments. Despite that success, the main problem of the actor-critic algorithm remains the same-speed of convergence to the optimal policy. In high dimensional state and action space, a searching for the correct action in each state takes enormously long time. Therefore, in this paper we suggest a search accelerating function that allows to leverage speed of algorithm convergence and reach optimal policy faster. In our method, we assume that actions may have their own distribution of preference, that independent on the state. Since in the beginning of learning agent act randomly in the environment, it would be more efficient if actions were taken according to the some heuristic function. We demonstrate that heuristically-accelerated actor-critic algorithm learns optimal policy faster, using Educational Process Mining dataset with records of students' course learning process and their grades.

• 한국콘텐츠학회논문지
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• 제18권5호
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• pp.525-538
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• 2018

액션러닝은 실제 문제를 찾아 해결책을 찾는 과정을 통해 학습이 이루어지는 교수방법으로 최근 대학 수업에서도 그 사례 연구가 수행되고 있다. 본 연구의 목적은 서울에 위치한 S 대학교의 아동청소년학과에 개설된 '아동미디어교육' 수업에서 액션러닝 교수법을 실시하고 그 효과성 여부를 조사하는 것이다. Marquardt(2000)가 제안한 액션러닝의 구성 요소들과 과정에 따라 학생들은 아동의 뉴스리터러시 교육이라는 과제를 도출하였으며 초등 6학년을 위한 뉴스리터러시 교육 목적과 목표, 내용을 설정하고 교수학습방법을 개발하여 교육현장에 적용하였다. 액션러닝 수업에 대한 대학생들의 반응과 평가는 설문지와 구두로 수집되었다. 그들은 액션러닝 적용 수업 방식을 통한 능동적이며 자발적인 문제 탐구와 현장에서의 적용이 아동미디어교육 수업 목표를 달성하는데 도움을 주었다고 답하였다. 마지막으로, 액션러닝 수업방식의 필요 조건으로서 교육 현장과의 긴밀한 협조와 현장 적용 시 적절한 피드백의 중요성이 논의되었다.

• 한국멀티미디어학회논문지
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• 제13권12호
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• pp.1820-1831
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• 2010

When interacting with unknown environments, an autonomous agent needs to decide which action or action order can result in a good state and determine the transition probability based on the current state and the action taken. The traditional multiple sequential learning model requires predefined probability of the states' transition. This paper proposes a multiple sequential learning and prediction system with definition of autonomous states to enhance the automatic performance of existing AI algorithms. In sequence learning process, the sensed states are classified into several group by a set of proposed motivation filters to reduce the learning computation. In prediction process, the learning agent makes a decision based on the estimation of each state's cost to get a high payoff from the given environment. The proposed learning and prediction algorithms heightens the automatic planning of the autonomous agent for interacting with the dynamic unknown environment. This model was tested in a virtual library.

• 한국지능시스템학회논문지
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• 제18권3호
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• pp.291-296
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• 2008

본 논문에서는 군집로봇시스템에서 목표물 추적을 위하여 SVM을 이용한 12각형 기반의 Q-learning 알고리즘을 제안한다. 제안한 알고리즘의 유효성을 보이기 위해 본 논문에서는 여러 대의 로봇과 장애물 그리고 하나의 목표물로 정하고, 각각의 로봇이 숨겨진 목표물을 찾아내는 실험을 가정하여 무작위, DBAM과 AMAB의 융합 모델, 마지막으로는 본 논문에서 제안한 SVM과 12각형 기반의 Q-learning 알고리즘을 이용하여 실험을 수행하고, 이 3가지 방법을 비교하여 본 논문의 유효성을 검증하였다.

• 한국콘텐츠학회논문지
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• 제16권12호
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• pp.35-42
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• 2016

본 연구는 액션러닝을 적용한 간호과정 학습프로그램이 간호학과 학생들의 문제해결능력과 자기주도적 학습능력에 미치는 효과를 규명하기 위한 비동등성 대조군 전후설계 실험연구이다. 연구대상은 G시와 J도에 소재한 4년제 간호학과 2학년 중 실험군 53명, 대조군 52명으로 총 105명이었다. 주 2회, 4주간 프로그램을 실시하였으며 실시 전과 후에 문제해결능력과 자기주도적 학습능력를 측정하였다. 측정된 결과는 ${\chi}^2$-test와 Chi-Square test, t-test, paired t-test로 분석하였다. 연구결과 액션러닝 학습프로그램은 문제명료화, 원인분석, 대안개발, 계획/실행, 수행평가로 구성된 학습자의 문제해결능력을 증진시켰고 학습계획, 학습실행, 학습평가로 구성된 자기주도적 학습능력을 증진시켰다. 그러므로 본 연구는 학습방법으로의 액션러닝이 간호학과 학생들의 간호과정 수업을 하는데 효과적임을 파악한 점에서 연구의 의의를 지니며, 향후 간호관련 전공학문 분야의 효과적인 교수법으로 확대적용 할 수 있을 것으로 기대한다.