• Journal of Industrial Convergence
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• v.21 no.1
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• pp.77-86
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• 2023

The purpose of this study is to seek a more progressive promotion of employment plan for peer support activities of people with mentally disabled. Therefore, a focus group interview (FGI) was conducted with vocational rehabilitation professionals in charge of peer support activity, leadership development and job creation project for people with mentally disabled. As a result of the study, the research participants had expectations for capacity strengthening of ability through the project, and recognized the role of peer support workers as emotional support for peers, planning and implementation of programs, operation of self-help meeting, promotion of project and facility, assistant support for colleagues etc. In addition, they saw that they could be more motivated if they were given financial rewards and meaningful role performance, feeling hard but rewarded and taking efforts for improvement of one's specialty through participation in the project. Based on the results, this study discussed about and provided practical suggestion for promoting employment of peer support workers for people with mentally disabled.

• Smart Media Journal
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• v.11 no.9
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• pp.9-20
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• 2022

Deep reinforcement learning(RL) is an end-to-end data-driven control method that is widely used in the autonomous driving domain. However, conventional RL approaches have difficulties in applying it to autonomous driving tasks due to problems such as inefficiency, instability, and uncertainty. These issues play an important role in the autonomous driving domain. Although recent studies have attempted to solve these problems, they are computationally expensive and rely on special assumptions. In this paper, we propose a new algorithm MCDT that considers inefficiency, instability, and uncertainty by introducing a method called uncertainty sequence modeling to autonomous driving domain. The sequence modeling method, which views reinforcement learning as a decision making generation problem to obtain high rewards, avoids the disadvantages of exiting studies and guarantees efficiency, stability and also considers safety by integrating uncertainty estimation techniques. The proposed method was tested in the OpenAI Gym CarRacing environment, and the experimental results show that the MCDT algorithm provides efficient, stable and safe performance compared to the existing reinforcement learning method.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.1
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• pp.265-275
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• 2023

The purpose of this paper is to devise an optimal route allocation algorithm for automated vehicle(AV) in monitoring quality of road infrastructure to support the road safety. The tasks of an AV in this paper include visiting node-links at least once during its operation and checking status of road infrastructure, and coming back to its depot.. In selecting optimal route, its priority goal is visiting the node-links with higher risks while reducing costs caused by operation. To deal with the problem, authors devised reward maximizing algorithm for AVs. To check its validity, the authors developed simple toy network that mimic node-link networks and assigned costs and rewards for each node-link. With the toy network, the reward maximizing algorithm worked well as it visited the node-link with higher risks earlier then chinese postman route algorithm (Eiselt, Gendreau, Laporte, 1995). For further research, the reward maximizing algorithm should be tested its validity in a more complex network that mimic the real-life.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.4
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• pp.1123-1146
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• 2023

In South Korea, there have been many studies on efficient building-energy management using renewable energy facilities in single zero-energy houses or buildings. However, such management was limited due to spatial and economic problems. To realize a smart zero-energy city, studying efficient energy integration for the entire city, not just for a single house or building, is necessary. Therefore, this study was conducted in the eco-friendly energy town of Chungbuk Innovation City. Chungbuk successfully realized energy independence by converging new and renewable energy facilities for the first time in South Korea. This study analyzes energy data collected from public buildings in that town every minute for a year. We propose a smart city building-energy management model based on the results that combine various renewable energy sources with grid power. Supervised learning can determine when it is best to sell surplus electricity, or unsupervised learning can be used if there is a particular pattern or rule for energy use. However, it is more appropriate to use reinforcement learning to maximize rewards in an environment with numerous variables that change every moment. Therefore, we propose a power distribution algorithm based on reinforcement learning that considers the sales of Energy Storage System power from surplus renewable energy. Finally, we confirm through economic analysis that a 10% saving is possible from this efficiency.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.2
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• pp.31-45
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• 2023

This study deals with exploring qualitative steps and paths that appear as YouTube users' usage time increases quantitatively. For the study, I applied theories from psychology and neuroscience, subdivided the interval between the personalization algorithm of the recommendation system, and active use and analyzed the relationship between variables in this process. According to the theory behavioral model theory (FBM), variable reward, and dopamine addiction were applied. Personalization algorithms easy clicks as triggers according to associated content presentation functions in behavioral model theory (FBM). Variable rewards increase motivational effectiveness with unpredictability of the content you search, and dopamine nation is summarized as stimulating the dopaminergic nerve to continuously and actively consume content. This study is expected to make an academic and practical contribution in that it divides the purpose of use of content in the personalization algorithm and active use section into four stages from a psychological perspective: first use, reuse, continuous use, and active use, and analyzes the path.

• Journal of Convergence for Information Technology
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• v.12 no.3
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• pp.11-16
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• 2022

Although the number of foreign students is increasing with the development of internationalization, international students are often unfamiliar to the campus environment in the early stages of their school visits. This research aims to solve the problems of foreign students' unfamiliarity with the campus and the inconvenience of study and life after enrollment and to design and produce an AR campus guide application based on gamification. The application built are designed according to the targets, missions, and rewards of different places. Through the 'A Survey on the Awareness of Kongju University's Buildings' questionnaire survey of international students at National Kongju University, six place were selected as POI (Point of Interest). Missions and questions suitable for users were designed. Through this application, it is hoped that users can learn about important places of the school interestingly and learn about the use of related convenience facilities.

• The Korean Journal of Franchise Management
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• v.7 no.2
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• pp.37-45
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• 2016

Purpose - This study aims to examine the impact of leadership on organizational trust and innovation performance, and to identify whether organizational trust plays a mediating role in the relationship between leadership and innovation performance. Also, this study attempts to find out how to improve organizational efficiency and effectiveness based on leadership-based or trust-based strategies. And, this research proposed that organizational trust plays a core mediating role in the relationship between transactional and transformational leadership and innovation performance. Research design, data, and methodology - In order to test the hypotheses of this study, the survey was conducted towards franchise coffee shop employees between November 7 and 18, 2016. We contacted top executives of coffee shop franchise headquarters and explained the purpose of this study. Among 150 questionnaires distributed, 123 were collected. Of these collected questionnaires, 102 questionnaires were coded and analyzed for further analysis. In order to test the unidimensionality and reliability of the factors, factor analysis and reliability test were performed using SPSS/PC+ 22.0. And, the hypotheses were tested using hierarchical mediated regression analysis. Result - The results are as follows. First, transactional leadership, and intellectual stimulation, motivation of transformational leadership had significant impacts on organizational trust. Second, organizational trust, transactional leadership, and influence of transformational leadership had significant impacts on innovation performance. Third, the mediating test of organizational trust showed that transactional leadership plays a partial mediator, and intellectual of transformational leadership plays a full mediator in the relationship between leadership and innovation performance. Conclusions - The implications of this study are as follows. First, the top management should provide their organizational members incentives or rewards based on their performance. Second, top management should identify and express a clear vision and desirable organizational goals for the future, present an idealized vision, and communicate to organizational members that the vision is achievable, also have organizational members to think creatively and find optimal solutions to difficult problems. In sum, this study revealed the important role of leadership in embedding organizational trust in and improving innovation performance of coffee shop employees and the mediating role of organizational trust in the influence of leadership on innovation performance.

• The Journal of Bigdata
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• v.7 no.1
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• pp.1-14
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• 2022

The proof-of-work consensus algorithm used by most blockchains is causing a massive waste of computing resources in the form of mining. A useful proof-of-work consensus algorithm has been studied to reduce the waste of computing resources in proof-of-work, but there are still resource waste and mining centralization problems when creating blocks. In this paper, the problem of resource waste in block generation was solved by replacing the relatively inefficient computation process for block generation with distributed artificial intelligence model learning. In addition, by providing fair rewards to nodes participating in the learning process, nodes with weak computing power were motivated to participate, and performance similar to the existing centralized AI learning method was maintained. To show the validity of the proposed methodology, we implemented a blockchain network capable of distributed AI learning and experimented with reward distribution through resource verification, and compared the results of the existing centralized learning method and the blockchain distributed AI learning method. In addition, as a future study, the thesis was concluded by suggesting problems and development directions that may occur when expanding the blockchain main network and artificial intelligence model.

• Journal of the Korean School Mathematics Society
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• v.26 no.2
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• pp.159-184
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• 2023

In this study, after defining motivating efficacy operationally, we developed a draft of the Motivating Efficacy Scale for Mathematics Teachers (MESMT), a measure of mathematics teachers' motivating efficacy, through the literature review and an expert Delphi survey, and conducted the exploratory factor analysis using online survey responses from 347 elementary and secondary mathematics teachers across the country to explore the factor structure of the measure and to test its validity and reliability. The exploratory factor analysis resulted in the deletion of 17 items from the initial 42 items developed through the literature review and expert Delphi survey and the identification of four factors (Providing successful experiences, Eliciting attention and engagement, Creating mathematics case-based relevance, and Providing extrinsic rewards), resulting in a final MESMT of 25 items. The MESMT developed in this study is a valid and reliable measure of mathematics teachers' motivating efficacy, and is expected to serve as a starting point for many subsequent studies to understand mathematics teachers' motivating efficacy and improve mathematics teachers' ability to motivate students' mathematics learning.

• Journal of Korea Entertainment Industry Association
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• v.5 no.2
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• pp.77-86
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• 2011

This paper is aimed at analyzing the relationship between users' actions in relation to a SNG (Social Network Game), which mainly targets communities, and the motivations that give rise to such selective actions. The subjects of existing researches on game area have rarely dealt with game users but mainly focused on the studies and utilization of game production technologies; and, in cases of studies on games users, their subjects have been hardly more than observations of users' behaviors in relation to the performance to achieve certain goals or themes of a game; for example, upgrading a character's level or obtaining rewards through "defeat". Therefore, it is necessary to analyze the actions of SNS game users from the perspective of behavioral selections caused by various motivations of human beings rather than approaching from the perspective of problem solving methods. In order to accomplish this goal, fist of all, Lazzaro's People Fun model and motivation theory of SNS users will be analyzed. Secondly, relevant materials from 13 SNG cases will be collected. Games' events and the functional actions of users will be classified. Lastly, the primary actions of SNG users will be classified into 8 different types and motivations - action patterns will be analyzed based on the classified materials.