• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권9호
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• pp.2942-2960
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• 2022

The core of cognitive radio is the problem concerning intelligent decision-making for communication parameters, the objective of which is to find the most appropriate parameter configuration to optimize transmission performance. The current algorithms have the disadvantages of high dependence on prior knowledge, large amount of calculation, and high complexity. We propose a new decision-making model by making full use of the interactivity of reinforcement learning (RL) and applying the Q-learning algorithm. By simplifying the decision-making process, we avoid large-scale RL, reduce complexity and improve timeliness. The proposed model is able to find the optimal waveform parameter configuration for the communication system in complex channels without prior knowledge. Moreover, this model is more flexible than previous decision-making models. The simulation results demonstrate the effectiveness of our model. The model not only exhibits better decision-making performance in the AWGN channels than the traditional method, but also make reasonable decisions in the fading channels.

• 한국식생활문화학회지
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• 제35권2호
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• pp.173-180
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• 2020

This study evaluated methods of improving sustained learning participation by examining the structural relationship of school support consisting of professor support, friend-senior support and educational environment support, career decisionmaking self-efficacy, school satisfaction, and learning persistence depending on the characteristics of college students majoring in culinary art and food service. The study findings were as follows. First, the general characteristics of college students majoring in culinary art and food service were perceived significantly more by female students than by male students. Second, school support directly influenced the career decision-making self-efficacy and school satisfaction, but did not directly influence the learning persistence. Instead, school support influenced school satisfaction and learning persistence indirectly by the medium of career decision-making self-efficacy. Third, career decision-making self-efficacy directly influenced school satisfaction and learning persistence and indirectly influenced learning persistence by the medium of school satisfaction. Lastly, school satisfaction directly influenced the learning persistence, implying that school satisfaction is an important factor for the learning persistence of college students majoring in culinary art and food service. These results show that, because school members and environmental support cannot exclusively make learning persistence, diverse systems and programs must be developed and applied to improve the career decision-making self-efficacy and school satisfaction of college students majoring in culinary art and food service.

• 동의신경정신과학회지
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• 제20권4호
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• pp.115-126
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• 2009

Objectives : This study was performed to investigate the relationship between decision making style and learning style according to Sasang constitution. Methods : The subjects were 213 nursing students of K college in Jeonbuk, and the period of data gathering was limited from 1 Sep. 2009 to 7 Sep. 2009. The instrument tools included QSCC II, decision making style, and learning style. The collected data were analyzed by SPSS-PC programme. Results : 1. Decision making style: Soeumin group had significantly high score in rational score compared with Soyangin(F=7.174 p=.001), and in dependent score compared with Taeumin and Soyangin (F=3.414, p=.035). 2. Learning style: Soyangin group had significantly high score in cooperation score compared with Taeumin(F=5.688 p=.004), and Taeumin group had significantly high score in emulous score compared with Soeumin and Soyangin (F=.148, p=.002). Conclusions : In conclusion, it was found that decision making style and learning style are significantly different according to Sasang constitution. Therefore, these results suggest that nursing educational program needs to be developed considering Sasang constitution.

• 아동학회지
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• 제33권6호
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• pp.71-84
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• 2012

The purpose of this study is to analyze the relationship between decision making abilities young children and their self-directed learning abilities. A survey was carried out using 160 young children in the J region. The collected data were analyzed by Pearson correlation and multiple regression techniques using the SPSS statistics program. The conclusions are as follows : First, decision making abilities in young children exhibited a positive correlation with their self-directed learning abilities. Second, decision making abilities in young children were an influential variable in terms of their self-directed learning abilities. As a result, decision making abilities in young children were an important variable in predicting their self-directed learning abilities.

• 인터넷정보학회논문지
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• 제25권4호
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• pp.7-21
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• 2024

There are various machine learning techniques such as Reinforcement Learning, Deep Learning, Neural Network Learning, and so on. In recent, Large Language Models (LLMs) are popularly used for Generative AI based on Reinforcement Learning. It makes decisions with the most optimal rewards through the fine tuning process in a particular situation. Unfortunately, LLMs can not provide any explanation for how they reach the goal because the training is based on learning of black-box AI. Reinforcement Learning as black-box AI is based on graph-evolving structure for deriving enhanced solution through adjustment by human feedback or reinforced data. In this research, for mutually exclusive decision-making, Mutually Exclusive Learning (MEL) is proposed to provide explanations of the chosen goals that are achieved by a decision on both ends with specified conditions. In MEL, decision-making process is based on the tree-based structure that can provide processes of pruning branches that are used as explanations of how to achieve the goals. The goal can be reached by trade-off among mutually exclusive alternatives according to the specific contextual conditions. Therefore, the tree-based structure is adopted to provide feasible solutions with the explanations based on the pruning branches. The sequence of pruning processes can be used to provide the explanations of the inferences and ways to reach the goals, as Explainable AI (XAI). The learning process is based on the pruning branches according to the multi-dimensional contextual conditions. To deep-dive the search, they are composed of time window to determine the temporal perspective, depth of phases for lookahead and decision criteria to prune branches. The goal depends on the policy of the pruning branches, which can be dynamically changed by configured situation with the specific multi-dimensional contextual conditions at a particular moment. The explanation is represented by the chosen episode among the decision alternatives according to configured situations. In this research, MEL adopts the tree-based learning model to provide explanation for the goal derived with specific conditions. Therefore, as an example of mutually exclusive problems, employment process is proposed to demonstrate the decision-making process of how to reach the goal and explanation by the pruning branches. Finally, further study is discussed to verify the effectiveness of MEL with experiments.

• 한국IT서비스학회지
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• 제15권4호
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• pp.85-95
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• 2016

Recently, deep learning and machine learning have attracted considerable attention and many supporting frameworks appeared. In artificial intelligence field, a large body of research is underway to apply the relevant knowledge for complex problem-solving, necessitating the application of various learning algorithms and training methods to artificial intelligence systems. In addition, there is a dearth of performance evaluation of decision making agents. The decision making agent that can find optimal solutions by using reinforcement learning methods designed through this research can collect raw pixel data observed from dynamic environments and make decisions by itself based on the data. The decision making agent uses convolutional neural networks to classify situations it confronts, and the data observed from the environment undergoes preprocessing before being used. This research represents how the convolutional neural networks and the decision making agent are configured, analyzes learning performance through a value-based algorithm and a policy-based algorithm : a Deep Q-Networks and a Policy Gradient, sets forth their differences and demonstrates how the convolutional neural networks affect entire learning performance when using pixel data. This research is expected to contribute to the improvement of artificial intelligence systems which can efficiently find optimal solutions by using features extracted from raw pixel data.

• 대한간호학회지
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• 제40권6호
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• pp.863-871
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• 2010

Purpose: The purpose of this study was to examine the effects of case-base learning (CBL) using video on clinical decision-making and learning motivation. Methods: This research was conducted between June 2009 and April 2010 as a nonequivalent control group non-synchronized design. The study population was 44 third year nursing students who enrolled in a college of nursing, A University in Korea. The nursing students were divided into the CBL and the control group. The intervention was the CBL with three cases using video. The controls attended a traditional live lecture on the same topics. With questionnaires objective clinical decision-making, subjective clinical decision-making, and learning motivation were measured before the intervention, and 10 weeks after the intervention. Results: Significant group differences were observed in clinical decision-making and learning motivation. The post-test scores of clinical decision-making in the CBL group were statistically higher than the control group. Learning motivation was also significantly higher in the CBL group than in the control group. Conclusion: These results indicate that CBL using video is effective in enhancing clinical decision-making and motivating students to learn by encouraging self-directed learning and creating more interest and curiosity in learning.

• 한국시스템다이내믹스연구
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• 제9권1호
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• pp.57-71
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• 2008

This research paper describes a complex and vague settings in which organization makes a decision and explains a role of decision maker's learning process. The original paper, written by Cohen, March, Olsen in 1972, said that all members of organization depended on the technology taken through trials and errors, which is the 'learning' process literally. But they intended to exclude the learning process in their simulation model because their PORTRAN model couldn't replicate the learning concept. As a result, they couldn't explain how all agents of garbage can simulation model resolve the problem dynamically. To overcome this original paper's limitations, we try to rebuild a learning process simulation model using by system dynamics approach that can capture the linkage between organization leanings and agents-based decision-makings. Our learning simulation results reveal two points. First, decision maker's leanings process improves the efficiency of decision making in complex situation. Second, group learning shows a superior efficiency to an individual learning because group members share organizational memory and energy.

• 문화기술의 융합
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• 제10권4호
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• pp.339-345
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• 2024

본 연구는 병리학 수업에서 교과 외 활동으로 이루어진 하브루타-문제만들기 후 간호대학생의 학습태도, 학습몰입, 자기주도적학습능력의 차이를 확인하고자 하였다. M대학교 간호대학생 84명을 대상으로 2023년 8월 25일~12월 23일까지 설문지를 수집하고 분석하였다. 수집된 자료는 SPSS/WIN 20 프로그램을 이용하여 Paired t-test를 실시하였다. 연구결과 하르부타-문제만들기 적용으로 학습태도(t=-2.00, p=.046). 학습몰입(t=-1.54, p=.124), 자기주도적학습능력(t=-.63, p=.529)이 통계적으로 유의하게 향상 되었다. 하브루타-문제만들기가 간호대학생들에게 효과적인 교수법으로 확인되었으므로, 학년간의 차이를 확인하는 연구를 제언한다. 또한 대학생들 학습태도를 측정한 연구가 부족하여 반복하는 연구가 필요하다.

• 정보교육학회논문지
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• 제16권1호
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• pp.33-40
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• 2012

본 연구는 게임제작 활동을 통한 학습에서 학업적 자기효능감, 학습동기, 컴퓨터 학습태도의 관계를 알아보는데 있다. 이를 통해 디지털게임을 통한 학습에서 학습자들의 참여와 학습목표 달성을 위한 변인들의 구조적 관계를 확인하였다. 연구결과 과제난이도, 자기조절효능감, 자신감의 하위요인으로 이루어진 학업적 자기효능감이 학습동기와 컴퓨터 학습태도에 유의한 영향을 미치는 구조방정식모형을 검증하였다. 학습자의 신념, 과제 수용태도, 학습과정을 조절하는 요인이 활동중심 학습이 중심이 되는 디지털 게임을 통한 학습에서 중요한 요인이었다.