• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1300-1304
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• 2003

The following paper deals with a control problem of a mobile robot in home network environment. The home network causes the mobile robot to communicate with sensors to get the sensor measurements and to be adapted to the environment changes. To get the improved performance of control of a mobile robot in spite of the change in home network environment, we use the fuzzy inference system with multiple reward reinforcement learning. The multiple reward reinforcement learning enables the mobile robot to consider the multiple control objectives and adapt itself to the change in home network environment. Multiple reward fuzzy Q-learning method is proposed for the multiple reward reinforcement learning. Multiple Q-values are considered and max-min optimization is applied to get the improved fuzzy rule. To show the effectiveness of the proposed method, some simulation results are given, which are performed in home network environment, i.e., LAN, wireless LAN, etc.

• Journal of the Korean Home Economics Association
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• v.38 no.8
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• pp.29-51
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• 2000

The purpose of this study is to examine how midlife working women's psychological well-being is associated with their reward/cost of family role and work role according to their kinds of job. For empirical research, 627 married working women living in Seoul, aged between 40-55 answered the structured questionnaire. The subjects consisted of 301 professional working women and 326 non-professional working women. The data were analysed by the frequencies, mean, oneway ANOVA, and multiple regression. The major findings were as follows 1) Two sub areas of midlife working women's psychological well-being-self esteem and life satisfaction-were higher than an average level. 2) The more midlife working women performed family role and work role, they perceived reward more than cost. 3) For the professional working women, the more they perceived the reward of family role and work role, the higher their psychological well-being was. The more they perceived the cost of family role and work role, the lower their psychological well-being was. These consequencies applied to not only general reward/cost of family role and work role but also interrole reward/cost between family role and work role. For the non-professional working women, general and interrole reward of family role and work role had the positive effects on psychological well-being. Their general cost of spouse role, general and interrole cost of mother role, general cost of work role had the negative erects on psychological well-being. However interrole cost between spouse role and work role did not have a significant effect on psychological well-being. Finally, the result of multiple regression analysis showed that general reward of work role had the largest positive effect on midwife working women's self-esteem. General reward/cost of spouse role had the largest effect on their life satisfaction.

• Korean Journal of Child Studies
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• v.17 no.2
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• pp.221-233
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• 1996

The purpose of the present study was to investigate the effects of age, sex, cognitive style and reward presentation on delay of gratification. The subjects of this study were 120 children 4, 6 and 8 years of age attending preschool and an elementary school in Pusan. They were identified as impulsive or reflective according to their performance on Kagan's Matching Familiar Figures Test. The levels of reward presentation consisted of the reward which was presented physically and the reward which wasn't presented physically. Length of waiting time was recorded as the measure of maintenance of delay of gratification. The data of this study were analyzed with Two-way ANOVA, Duncan's Multiple Range Test. The major finding were as follows: (1) Delay time increased with age. (2) No sex difference is found in delay time. (3) Reflective children delayed longer than impulsive children in all age groups. (4) The reward which wasn't physically presented produced loner delay time than the reward which was physically presented in all age groups.

• Korean Journal of Occupational Health Nursing
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• v.31 no.2
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• pp.77-85
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• 2022

Purpose: This study aimed to identify the influence of effort-reward imbalance and job satisfaction on turnover intention among hospital nurses. Methods: Data were collected from January 28 to February 10, 2022, from 237 nurses from five hospitals including clinics, general hospitals, and tertiary care hospitals located in B city. The collected data were analyzed using descriptive statistics, t-test, ANOVA, the Scheffe test, Pearson's correlation coefficients, and multiple linear regression analysis, using SPSS/WIN 26.0. Results: The average of the effort-reward ratio, an indicator of effort-reward imbalance, was 1.67±0.66, and 86.5% of the participants had a value of 1 or more. The mean job satisfaction and turnover intention were 3.32±0.48 and 3.69±0.89 on a 5-point scale, respectively. Multiple regression revealed that factors affecting turnover intention among hospital nurses included effort-reward imbalance (β=.30, p<.001) and job satisfaction (β=-.32, p<.001), and these variables explained 29.0% of turnover intention. Conclusion: These findings indicate that effort-reward imbalance and job satisfaction are associated with turnover intention. Therefore, to decrease the turnover intention of hospital nurses, interventions and policies should be prepared to resolve the nurse's effort-reward imbalance and increase job satisfaction at regional or national level hospitals.

• ETRI Journal
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• v.44 no.2
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• pp.194-207
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• 2022

In the last decade, substantial progress has been achieved in intelligent traffic control technologies to overcome consistent difficulties of traffic congestion and its adverse effect on smart cities. Edge computing is one such advanced progress facilitating real-time data transmission among vehicles and roadside units to mitigate congestion. An edge computing-based deep reinforcement learning system is demonstrated in this study that appropriately designs a multiobjective reward function for optimizing different objectives. The system seeks to overcome the challenge of evaluating actions with a simple numerical reward. The selection of reward functions has a significant impact on agents' ability to acquire the ideal behavior for managing multiple traffic signals in a large-scale road network. To ascertain effective reward functions, the agent is trained withusing the proximal policy optimization method in several deep neural network models, including the state-of-the-art transformer network. The system is verified using both hypothetical scenarios and real-world traffic maps. The comprehensive simulation outcomes demonstrate the potency of the suggested reward functions.

• Journal of information and communication convergence engineering
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• v.20 no.1
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• pp.22-30
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• 2022

The reinforcement learning algorithm has proven its potential in solving sequential decision-making problems under uncertainties, such as finding paths to route data packets in wireless sensor networks. With reinforcement learning, the computation of the optimum path requires careful definition of the so-called reward function, which is defined as a linear function that aggregates multiple objective functions into a single objective to compute a numerical value (reward) to be maximized. In a typical defined linear reward function, the multiple objectives to be optimized are integrated in the form of a weighted sum with fixed weighting factors for all learning agents. This study proposes a reinforcement learning -based routing protocol for wireless sensor network, where different learning agents prioritize different objective goals by assigning weighting factors to the aggregated objectives of the reward function. We assign appropriate weighting factors to the objectives in the reward function of a sensor node according to its hop-count distance to the sink node. We expect this approach to enhance the effectiveness of multi-objective reinforcement learning for wireless sensor networks with a balanced trade-off among competing parameters. Furthermore, we propose SDN (Software Defined Networks) architecture with multiple controllers for constant network monitoring to allow learning agents to adapt according to the dynamics of the network conditions. Simulation results show that our proposed scheme enhances the performance of wireless sensor network under varied conditions, such as the node density and traffic intensity, with a good trade-off among competing performance metrics.

• Journal of Families and Better Life
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• v.24 no.3 s.81
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• pp.15-25
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• 2006

This study has been performed to identify intelligent ability, reward choice in assumed situation of delay of gratification, and delay of gratification ability. The subjects for this study were 100 preschoolers between the ages of 4 and 5, their mothers, and 15 teachers of three day-care centers in Seoul. T-test, F-test, Correlation analysis and multiple regression analysis were used for data analysis. The main results of this study were as follows: First, preschoolers' delay of gratification ability by mothers' educational background was significant and delay of gratification ability by sex was significant. This means that mothers who had a higher educational background were positively related to preschoolers' delay of gratification ability. Second, in an assumed situation of delay of gratification, preschoolers' delay of gratification ability by reward choice was not significant. Third, delay of gratification by intelligent ability was significant. Fourth, the correlation among intelligent ability, reward choice in assumed situation of delay of gratification and delay of gratification ability were significant. Finally, preschoolers' delay of gratification ability was significantly influenced by two factors: reward choice in assumed situation of delay of gratification and preschoolers' intelligent ability.

• Journal of the Korean Society of Physical Medicine
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• v.12 no.1
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• pp.113-120
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• 2017

PURPOSE: The purpose of this study is to provide the basic data for developing the self-leadership program by identifying the effect of self-leadership on intrinsic motivation among physical therapy students. METHODS: One hundred physical therapy students in E university of Gyeonggido were recruited by convenience sampling from October 4 to 14, 2016. Of them, 89% were chosen and 79% were analyzed after excluding the cases with wrong answers. The survey, using Likert's five scales was conducted with fifteen items of intrinsic motivation (Cronbach's ${\alpha}$, .84) and thirty-five items of self-leadership (Cronbach's ${\alpha}$, .90). Frequency analysis, correlation analysis regression diagnostics, and multiple regression analysis were done with SPSS 20.0 Statistics program (IBM, Korea). RESULTS: The total score of Self-leadership was 3.61 and of substrategies was 4.05 (Natural reward strategy), 3.38 (Behavior-focus strategy), and 3.43 (Constructive thought pattern strategy), respectively. The score of intrinsic motivation was 3.43. The substrategy of Self-leadership indicated positive correlation with intrinsic motivation. The correlation values in higher order were .75 (Natural reward strategy), .66 (Behavior-focus strategy), and .61 (Constructive thought pattern strategy). The Constructive thought pattern strategy (t=5.18, p=.00) and Natural reward strategy (t=2.10, p=.38), except Behavior-focus strategy were effective on intrinsic motivation, according to the multiple regression analysis. CONCLUSION: Before stepping up to the next level of being a physical therapist, students must go through the educational program to improve the Constructive thought pattern strategy and Natural reward strategy.

• Journal of Korean Academy of Nursing
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• v.39 no.3
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• pp.329-337
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• 2009

Purpose: The purpose of this study was to investigate the determinants of job satisfaction of hospital nurses. The focus was on work rewards. A causal model of job satisfaction of hospital nurses was constructed based on situational perspectives. Methods: The sample for this study consisted of 505 nurses from 2 general hospitals located in Seoul and Kyeonggi Province, Korea. Data were collected with self-administrated questionnaires and analyzed by hierarchical multiple regression. Results: All variables except workload were positively correlated with job satisfaction. It was found that three task reward variables(workload, meaning, and participation), two organizational reward variables(security and promotional chances) and one social reward variable(family support) had significant influence on nurses' job satisfaction. The explained variance for job satisfaction was 41.4%. The data further indicate that task rewards were the most significant determinants of nurse job satisfaction. Conclusion: Theses findings provide strong empirical evidence for importance of task, organizational and social reward variables in explaining job satisfaction of nurses. The model used for this study will be useful for predicting nurse job satisfaction.

• Journal of information and communication convergence engineering
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• v.20 no.3
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• pp.181-188
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• 2022

An intelligent distributed multi-agent system (IDMS) using reinforcement learning (RL) is a challenging and intricate problem in which single or multiple agent(s) aim to achieve their specific goals (sub-goal and final goal), where they move their states in a complex and cluttered environment. The environment provided by the IDMS provides a cumulative optimal reward for each action based on the policy of the learning process. Most actions involve interacting with a given IDMS environment; therefore, it can provide the following elements: a starting agent state, multiple obstacles, agent goals, and a cluttered index. The reward in the environment is also reflected by RL-based agents, in which agents can move randomly or intelligently to reach their respective goals, to improve the agent learning performance. We extend different cases of intelligent multi-agent systems from our previous works: (a) a proposed environment-clutter-based-index for agent sub-goal selection and analysis of its effect, and (b) a newly proposed RL reward scheme based on the environmental clutter-index to identify and analyze the prerequisites and conditions for improving the overall system.