• The Korean Journal of Applied Statistics
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• v.29 no.7
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• pp.1165-1172
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• 2016

In this paper, a surplus process with investments is introduced. Whenever the level of the surplus reaches a target value V > 0, amount S($0{\leq}S{\leq}V$) is invested into other business. After assigning three costs to the surplus process, a reward per unit amount of the investment, a penalty of the surplus being empty and the keeping (opportunity) cost per unit amount of the surplus per unit time, we obtain the long-run average cost per unit time to manage the surplus. We prove that there exists a unique value of S minimizing the long-run average cost per unit time for a given value of V, and also that there exists a unique value of V minimizing the long-run average cost per unit time for a given value of S. These two facts show that an optimal investment policy of the surplus exists when we manage the surplus in the long-run.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.40 no.3
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• pp.215-221
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• 2012

In this paper, we introduce an optimization method to improve pursuit performance of a pursuer in a pursuit-evasion game (PEG). Pursuers build a probability map and employ a hybrid pursuit policy which combines the merits of local-max and global-max pursuit policies to search and capture evaders as soon as possible in a 2-dimensional space. We propose an episodic parameter optimization (EPO) algorithm to learn good values for the weighting parameters of a hybrid pursuit policy. The EPO algorithm is performed while many episodes of the PEG are run repeatedly and the reward of each episode is accumulated using reinforcement learning, and the candidate weighting parameter is selected in a way that maximizes the total averaged reward by using the golden section search method. We found the best pursuit policy in various situations which are the different number of evaders and the different size of spaces and analyzed results.

• Journal of Information Processing Systems
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• v.11 no.4
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• pp.616-629
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• 2015

A mobile terminal will expect a number of handoffs within its call duration. In the event of a mobile call, when a mobile node moves from one cell to another, it should connect to another access point within its range. In case there is a lack of support of its own network, it must changeover to another base station. In the event of moving on to another network, quality of service parameters need to be considered. In our study we have used the Markov decision process approach for a seamless handoff as it gives the optimum results for selecting a network when compared to other multiple attribute decision making processes. We have used the network cost function for selecting the network for handoff and the connection reward function, which is based on the values of the quality of service parameters. We have also examined the constant bit rate and transmission control protocol packet delivery ratio. We used the policy iteration algorithm for determining the optimal policy. Our enhanced handoff algorithm outperforms other previous multiple attribute decision making methods.

• Journal of Digital Convergence
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• v.20 no.5
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• pp.253-260
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• 2022

Creativity, one of the core competencies of the 21st century, is required as an essential item for members of society. Emphasizes its ability in terms of personality that allows it to be used in the desired direction. However, creativity is considered to contribute to positive change in the organization, not only in creating new ideas or products, but also in adapting to a changing environment and solving problems. Accordingly, by reviewing previous studies, it was concluded that rewards can promote or hinder creativity, which may vary depending on the nature of rewards, the concept of creativity possessed by the researcher, individual differences, and external environment. We also proposed that rewards may influence creativity through motivational, cognitive, and synthetic functions. Based on the analysis, a specific model was proposed for the effect of reward on creativity. This study is based on existing research and analyzed various factors and mechanisms acting in the process of influencing creativity based on comparison of which extrinsic and intrinsic motivations have what kind of relationship. Next, it appears that rewards differ from person to person according to the way they are given in environmental circumstances. Finally, by rewarding various types of creative tasks, an active reward role can be secured.

• Journal of Digital Convergence
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• v.10 no.6
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• pp.23-31
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• 2012

The Entrepreneurship education are contributes to creation of new business or national economy. The increasing of job-loss recovery, youth unemployment and 1 person creative company entrepreneurship follows the increasing to Entrepreneurship failure. We identify entrepreneurship education factors which motivate individuals' self-leadership and entrepreneurship. Research results suggests that understanding level and creativity level of entrepreneurship education significant effects on the reward self-leadership. And reward self-leadership positive significant effects on the entrepreneurship.

• Journal of Digital Convergence
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• v.9 no.3
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• pp.117-131
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• 2011

This study aims to examine the impact of Self-leadership(behavior- focused strategies, natural reward and constructive thought pattern to Technology Innovation. Hypotheses were tested by surveying 306 employees at Gumi Industrial Complex. The findings are as follows : First, two factors of Self-leadership had a significant positive effect on the product innovation except constructive thought pattern strategies. Second, two factors of Self-leadership had a significant positive effect on the process innovation except constructive thought pattern strategies.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.59-64
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• 2001

The robot soccer simulation game is a dynamic multi-agent environment. In this paper we suggest a new reinforcement learning approach to each agent's dynamic positioning in such dynamic environment. Reinforcement learning is the machine learning in which an agent learns from indirect, delayed reward an optimal policy to chose sequences of actions that produce the greatest cumulative reward. Therefore the reinforcement learning is different from supervised learning in the sense that there is no presentation of input pairs as training examples. Furthermore, model-free reinforcement learning algorithms like Q-learning do not require defining or learning any models of the surrounding environment. Nevertheless it can learn the optimal policy if the agent can visit every state- action pair infinitely. However, the biggest problem of monolithic reinforcement learning is that its straightforward applications do not successfully scale up to more complex environments due to the intractable large space of states. In order to address this problem. we suggest Adaptive Mediation-based Modular Q-Learning (AMMQL)as an improvement of the existing Modular Q-Learning (MQL). While simple modular Q-learning combines the results from each learning module in a fixed way, AMMQL combines them in a more flexible way by assigning different weight to each module according to its contribution to rewards. Therefore in addition to resolving the problem of large state effectively, AMMQL can show higher adaptability to environmental changes than pure MQL. This paper introduces the concept of AMMQL and presents details of its application into dynamic positioning of robot soccer agents.

• Proceedings of the Korea Society for Simulation Conference
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• 2001.10a
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• pp.321-324
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• 2001

The robot soccer simulation game is a dynamic multi-agent environment. In this paper we suggest a new reinforcement learning approach to each agent's dynamic positioning in such dynamic environment. Reinforcement Beaming is the machine learning in which an agent learns from indirect, delayed reward an optimal policy to choose sequences of actions that produce the greatest cumulative reward. Therefore the reinforcement loaming is different from supervised teaming in the sense that there is no presentation of input-output pairs as training examples. Furthermore, model-free reinforcement loaming algorithms like Q-learning do not require defining or loaming any models of the surrounding environment. Nevertheless it can learn the optimal policy if the agent can visit every state-action pair infinitely. However, the biggest problem of monolithic reinforcement learning is that its straightforward applications do not successfully scale up to more complex environments due to the intractable large space of states. In order to address this problem, we suggest Adaptive Mediation-based Modular Q-Learning(AMMQL) as an improvement of the existing Modular Q-Learning(MQL). While simple modular Q-learning combines the results from each learning module in a fixed way, AMMQL combines them in a more flexible way by assigning different weight to each module according to its contribution to rewards. Therefore in addition to resolving the problem of large state space effectively, AMMQL can show higher adaptability to environmental changes than pure MQL. This paper introduces the concept of AMMQL and presents details of its application into dynamic positioning of robot soccer agents.

• Health Policy and Management
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• v.18 no.4
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• pp.125-147
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• 2008

The purpose of this study is to propose the factors affecting collaboration between community nurses and social workers in Korea. Data that is used in this study, were collected from 295 provider respondents by questionnaire, additionally from telephone survey and secondary data review. This study focuses on the working relationship between professionals in the field of health and social care. Based on the literature review, this study proposes a conceptual framework for collaboration between nurses and social workers in community health and social care. The dependent variable in this study is collaboration. It reveals whether or not the inter-professional works and shows the level of collaboration. The independent variables are categorized in: the client characteristics (frailty of client, client-provider relationship); the provider characteristics (specialization, perception of interdependence, perception of the other professional); the organization characteristics (closeness of the other professional, autonomy, on-the-job training, evaluation-reward); and the community characteristics (urbanization, capacity of resources). Major findings are as follows: First, the factors that appear to have the strongest impact on whether or not inter-professional working of respondent sampled are: the perception of other professional; the perception of interdependence; closeness of, the, other professional; and the frailty of client. Secondly, the factors that found to have the most significant effect on level of cooperation are: the perception of, the other professional; on-the-job training; evaluation-reward; and the closeness of the other professional.

• Health Policy and Management
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• v.18 no.1
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• pp.63-84
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• 2008

The aim of this study was to analyze the correlation between professional autonomy and ideology among Korean physicians and to investigate how these factors affect job satisfaction like social status satisfaction and economic reward satisfaction. This study utilized a self-administered questionnaire survey and collected data nationwide between July and August, 2003. 211 responses were used for final analysis. SPSS 12.0 was used for a chi-square test, one-way ANOVA, Pearson correlation analysis, independent t-test and hierarchical multiple regression analysis. The results of this study were as follows. First, many variables of ideological factor were related to job satisfaction. Second, physicians expecting the change of political influence has patient-centered attitude. Third, there were many relationships between professional autonomy and ideology variables. Fourth, physicians expecting the change of political influence and customer-centered healthcare system showed more job satisfaction. In conclusion, professional autonomy is related to ideology, and in order to enhance job satisfaction, ideological factor needs more development. In addition, market-oriented healthcare system would contribute to enhance the job satisfaction of physicians expecting the change of political influence and customer-centered healthcare system.