• Education of Primary School Mathematics
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• v.2 no.2
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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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• v.54 no.9
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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.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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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.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 1994.04a
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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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• v.6 no.3
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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.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.715-721
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• 2012

A threat evaluation is the technique which decides order of priority about tracks engaging with enemy by recognizing battlefield situation and making it efficient decision making. That is, in battle situation of multiple target it makes expeditious decision making and then aims at minimizing asset's damage and maximizing attack to targets. Threat value computation used in threat evaluation is calculated by sensor data which generated in battle space. Because Battle situation is unpredictable and there are various possibilities generating potential events, the damage or loss of data can make confuse decision making. Therefore, in this paper we suggest that substantial threat value calculation using learning bayesian network which makes it adapt to the varying battle situation to gain reliable results under given incomplete data and then verify this system's performance.

• Journal of the Korean Operations Research and Management Science Society
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• v.29 no.4
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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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• v.21 no.11
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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.

• Journal of Korean Academy of Nursing
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• v.35 no.1
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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.

• The Journal of the Korea Contents Association
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• v.5 no.2
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• pp.19-28
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• 2005

A web-based instructor-learner system has changed the form into a learner-centered environment. Especially a self-regulated learning which is a self-leading and a positive learning, is an ideal learning, and the interest on it is more increasing. In this research, learners can organize the individualized course based on the learner's demand and learning level after making a contract process with the system, The self-regulated learning system which can recognize a learning status and result by analyzed data, and which can lead to a learning goal effectively by establishing a learning strategy, is designed and implemented. The proposed system provides the learner-centered learning environment which can process the differentiated and flexible individualized-teaming service considering an individual characteristic.