• 한국수학교육학회지시리즈C:초등수학교육
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• 제2권2호
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• pp.133-144
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• 1998

We examined two kinds of problem posing, 'problem making' and 'problem modifying' to find which one is more effective for improving mathematical problem solving ability according to the student's learning-levels and sexes. The results showed that 'problem making' is more effective for high and middle-level groups than 'problem modifying'. There was no big difference according to the sexes. These facts implies that making a problem when a situation was presented is more effective to develop problem solving ability than modifying a problem ： modifying some conditions and contents of given problem.

• 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.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2023년도 학술발표회
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• pp.165-165
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• 2023

Streamflow prediction is a critical task in water resources management and essential for planning and decision-making purposes. However, the streamflow prediction is challenging due to the complexity and non-linear nature of hydrological processes. The transfer learning is a powerful technique that enables a model to transfer knowledge from a source domain to a target domain, improving model performance with limited data in the target domain. In this study, we apply the transfer learning using the Informer model, which is a state-of-the-art deep learning model for streamflow prediction. The model was trained on a large-scale hydrological dataset in the source basin and then fine-tuned using a smaller dataset available in the target basin to predict the streamflow in the target basin. The results demonstrate that transfer learning using the Informer model significantly outperforms the traditional machine learning models and even other deep learning models for streamflow prediction, especially when the target domain has limited data. Moreover, the results indicate the effectiveness of streamflow prediction when knowledge transfer is used to improve the generalizability of hydrologic models in data-sparse regions.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 1994년도 춘계학술발표회
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• pp.55-67
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• 1994

Studies on the ship's automatic navigation & berthing control have been continued by way of solving the ship's mathematical model but the results of such studies have not reached to our satisfactory level due to its non-linear characteristics ar low speed. In this paper the authors propose a new berthing control system which can evaluate as closely as captain's decision-making by using the FNN(Fuzzy Neural Network) controller which can simulate captain's decision-making by using the FNN(Fuzzy neural Network) controller which can simulate captain's knowledge. This berthing controller consists of the navigation subsystem FNN controller and the berthing subsystem FNN controller. The learning data are drawn from Ship Handling Simulator (NavSim NMS90 MK III) and represent the ship motion characteristics internally According to learning procedure both FNN controllers can tune membership functions and identify fuzzy control rules automatically The verified results show the FNN controllers effective to incorporate captain's knowledge and experience of berthing.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제6권3호
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• pp.264-269
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• 2006

This paper presents the strategy for searching a hidden object in an unknown area for using by multiple distributed autonomous robotic systems (DARS). To search the target in Markovian space, DARS should recognize th ε ir surrounding at where they are located and generate some rules to act upon by themselves. First of all, DARS obtain 6-distances from itself to environment by infrared sensor which are hexagonally allocated around itself. Second, it calculates 6-areas with those distances then take an action, i.e., turn and move toward where the widest space will be guaranteed. After the action is taken, the value of Q will be updated by relative formula at the state. We set up an experimental environment with five small mobile robots, obstacles, and a target object, and tried to research for a target object while navigating in a un known hallway where some obstacles were placed. In the end of this paper, we present the results of three algorithms - a random search, an area-based action making process to determine the next action of the robot and hexagon-based Q-learning to enhance the area-based action making process.

• 한국경영과학회지
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• 제29권4호
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• pp.117-134
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• 2004

This study suggests a data driven optimization approach, which simulates the models of human learning processes from cognitive sciences. It shows how the human learning processes can be simulated and applied to solving combinatorial optimization problems. The main advantage of using this method is in applying it into problems, which are very difficult to simulate. 'Undecidable' problems are considered as best possible application areas for this suggested approach. The concept of an 'undecidable' problem is redefined. The learning models in human learning and decision-making related to combinatorial optimization in cognitive and neural sciences are designed, simulated, and implemented to solve an optimization problem. We call this approach 'SLO : simulated learning for optimization.' Two different versions of SLO have been designed: SLO with position & link matrix, and SLO with decomposition algorithm. The methods are tested for traveling salespersons problems to show how these approaches derive new solution empirically. The tests show that simulated learning for optimization produces new solutions with better performance empirically. Its performance, compared to other hill-climbing type methods, is relatively good.

• International Journal of Computer Science & Network Security
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• 제21권11호
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• pp.23-30
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• 2021

Sentiment Analysis has become very important field of research because posting of reviews is becoming a trend. Supervised, unsupervised and semi supervised machine learning methods done lot of work to mine this data. Feature engineering is complex and technical part of machine learning. Deep learning is a new trend, where this laborious work can be done automatically. Many researchers have done many works on Deep learning Convolutional Neural Network (CNN) and Long Shor Term Memory (LSTM) Neural Network. These requires high processing speed and memory. Here author suggested two models simple & bidirectional deep leaning, which can work on text data with normal processing speed. At end both models are compared and found bidirectional model is best, because simple model achieve 50% accuracy and bidirectional deep learning model achieve 99% accuracy on trained data while 78% accuracy on test data. But this is based on 10-epochs and 40-batch size. This accuracy can also be increased by making different attempts on epochs and batch size.

• 한국지능시스템학회논문지
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• 제22권6호
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• pp.715-721
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• 2012

위협평가는 전장상황을 인지하여 아군의 자산을 보호하고 무장 할당의 효율적 의사결정에 도움을 줄 수 있는 기술로서, 교전을 실시할 트랙들에 대한 우선순위를 결정하는 알고리즘이다. 즉, 다 표적 교전상황에서의 신속한 의사결정을 도와 아군의 피해를 최소한으로 하고 적군에 대한 공격을 최대한으로 하는 것을 목적으로 한다. 위협평가에 이용되는 위협치 산출은 전장에서 발생하는 센서 데이터들을 통해 연산된다. 그러나 전장상황은 예측 불허하고 다양한 변수가 일어날 가능성이 높으므로 이러한 데이터들의 변질 및 유실은 위협평가를 통한 의사결정에 혼란을 더할 수 있다. 그러므로 본 논문에서는 불완전한 몇 몇 데이터만을 가지고도 신뢰도 높은 결과를 산출하는 데 유리한 베이지안 네트워크의 추론기능과 전장 환경변화에 네트워크의 적응을 가능하게 해주는 학습기능을 위협평가 분야에 적용하여 보다 견고한 위협치를 산출할 것을 제안하여 실험을 통해 이에 대한 성능을 입증하였다.

• 대한간호학회지
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• 제35권1호
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• pp.65-76
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• 2005

Objective: The aim of this study is to explore the variation in perceptions about problem-based learning(PBL) according to the level of academic achievement and learning attitude in the nursing students of a junior college (3-year program). Method: Students (n=39) learned the respiratory and cardiac system with seven PBL packages and group-based learning for a semester in 2002. Students were asked to write reflective journals that focused on their learning perception after an experience with each learning package. A total of 208 journals were used for analysis. Result: Students positively perceived that PBL making them increase their sense of responsibility for learning and felt satisfaction with the learning process, and had a confidence in the use of clinical nursing interventions. On the other hand, they negatively perceived that PBL was a burden because it took more time than traditional learning tasks, and they experienced an anxiety about regular tests and felt conflicts and diffidences in the learning process. The negative perceptions were expressed more often from students with a low academic achievement and low learning attitude compared to others. Conclusion: Students perceived the PBL as effective in understanding the learning concepts in the clinical practice environment. PBL need to be supplemented by feedback-based lecture and facilitative strategies for academically low-achieved students.Objective: The aim of this study is to explore the variation in perceptions about problem-based learning(PBL) according to the level of academic achievement and learning attitude in the nursing students of a junior college (3-year program). Method: Students (n=39) learned the respiratory and cardiac system with seven PBL packages and group-based learning for a semester in 2002. Students were asked to write reflective journals that focused on their learning perception after an experience with each learning package. A total of 208 journals were used for analysis. Result: Students positively perceived that PBL making them increase their sense of responsibility for learning and felt satisfaction with the learning process, and had a confidence in the use of clinical nursing interventions. On the other hand, they negatively perceived that PBL was a burden because it took more time than traditional learning tasks, and they experienced an anxiety about regular tests and felt conflicts and diffidences in the learning process. The negative perceptions were expressed more often from students with a low academic achievement and low learning attitude compared to others. Conclusion: Students perceived the PBL as effective in understanding the learning concepts in the clinical practice environment. PBL need to be supplemented by feedback-based lecture and facilitative strategies for academically low-achieved students.

• 한국콘텐츠학회논문지
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• 제5권2호
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• pp.19-28
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• 2005

웹 기반 교수-학습 시스템은 학습자 중심의 학습 환경으로 지속적인 변화를 지향해왔으며, 특히 자기 주도적이고 적극적인 학습 형태인 자기조절 학습은 이상적인 학습 형태로서 이에 대한 관심이 증가하고 있다. 본 연구에서는 학습자가 시스템과의 계약과정을 거쳐 자신의 요구와 학습 수준에 따른 개인화된 코스웨어를 구성할 수 있다. 시스템에서 분석된 결과를 통해 자신의 학습 진행과 결과를 인지하고, 학습전략을 수립하여 효과적으로 학습목표에 도달할 수 있는 자기조절 학습 시스템을 설계 구현하였다. 제안된 시스템은 학습자에게 개인의 특성을 고려한 차별화되고 유연성 있는 개인화된 학습 서비스를 자기 주도적으로 진행할 수 있는 학습자 위주의 학습 환경을 제공한다.