• The Korean Journal of Food & Health Convergence
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• v.6 no.3
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• pp.1-4
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• 2020

This study analyzed in nutrient contents changes of adult's favorite foods between March 2019 and July 2020 after policy implementation nutrient-poor foods based on special act on safety control of adult's dietary life in Korea. Among adult's favorite foods manufactured or sold in 2020 as well as 2019, calories and key nutrients in breads, ice creams and pizzas were improved in comparison to those in the other food groups. However, most of the changes in calories or key nutrient contents exist. The newly introduced candies, breads showed slightly greater improvements in calories and key nutrient contents than in 2019. On the other hand, some negative changes were found in newly introduced chocolates in comparison to previous ones. Overall, policy implementation on foods seemed to induce changes in nutrient contents of adult's favorite foods. In particular, nutrition education is reported to have a positive impact on adult's frequency and preference for processed foods, and more systematic and continuous nutrition education measures should be devised to help adult as consumers selectively purchase healthy foods. This research is meaningful in that it is the first study to analyze the quality changes of adult's favorite foods since the high-calorie and low-nutrient food management policy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.6
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• pp.1877-1891
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• 2022

As a part of cloud computing technology, algorithms for cloud task scheduling place an important influence on the area of cloud computing in data centers. In our earlier work, we proposed DeepEnergyJS, which was designed based on the original version of the policy gradient and reinforcement learning algorithm. We verified its effectiveness through simulation experiments. In this study, we used the Proximal Policy Optimization (PPO) algorithm to update DeepEnergyJS to DeepEnergyJSV2.0. First, we verify the convergence of the PPO algorithm on the dataset of Alibaba Cluster Data V2018. Then we contrast it with reinforcement learning algorithm in terms of convergence rate, converged value, and stability. The results indicate that PPO performed better in training and test data sets compared with reinforcement learning algorithm, as well as other general heuristic algorithms, such as First Fit, Random, and Tetris. DeepEnergyJSV2.0 achieves better energy efficiency than DeepEnergyJS by about 7.814%.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.6_3
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• pp.1303-1313
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• 2022

Modern cities need to revitalize the downtown area, which is declining due to population decline, economic recession, and deterioration of the residential environment, economically, socially, and physically by introducing and creating new functions. In addition, the hollowing out of the existing city center is getting worse due to the development of the outskirts of the city. Therefore, the discussion for the development of urban regeneration is the core task of modern cities. This study analyzed based on a basic understanding of urban regeneration projects, and through this, the problems of domestic urban regeneration projects were derived. In addition, the problem factors and major improvement plans of the urban regeneration project were analyzed from the expert's point of view using the AHP analysis technique. Based on this, the purpose is to present policy alternatives for the future development of urban regeneration projects. The problems derived to present the policy alternatives and improvement directions of the urban regeneration project were classified into problems related to goal achievement, problems related to the business itself, and problems related to project results. It was subdivided into sub-categories. This study analyzed the problem factors and major improvements from the expert's point of view by using the AHP analysis technique for the problems of the urban regeneration project. Based on the AHP analysis results and experts' opinions, five policy alternatives for the development of urban regeneration projects were presented.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.858-861
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• 2020

로봇이 사람과 같이 다양하고 복잡한 사물 조작을 하기 위해서 휴먼형 로봇손의 사물 파지 작업이 필수적이다. 자유도 (Degree of Freedom, DoF)가 높은 휴먼형(anthropomorphic) 로봇손을 학습시키기 위하여 사람 데모(human demonstration)가 결합된 강화학습 최적화 방법이 제안되었다. 본 연구에서는 강화학습 최적화 방법에 사람 데모가 결합된 Demonstration Augmented Natural Policy Gradient(DA-NPG)와 NPG 의 성능 비교를 통하여 행동 복제의 효율성을 확인하고, DA-NPG, DA-Trust Region Policy Optimization (DA-TRPO), DA-Proximal Policy Optimization (DA-PPO)의 최적화 방법의 성능 평가를 위하여 6 종의 물체에 대한 휴먼형 로봇손의 사물 조작 작업을 수행한다. 그 결과, DA-NPG 와 NPG를 비교한 결과를 통해 휴먼형 로봇손의 사물 조작 강화학습에 행동 복제가 효율적임을 증명하였다. 또한, DA-NPG 는 DA-TRPO 와 유사한 성능을 보이면서 모든 물체에 대한 사물 파지에 성공하여 가장 안정적이었다. 반면, DA-TRPO 와 DA-PPO 는 사물 조작에 실패한 물체가 존재하여 불안정한 성능을 보였다. 본 연구에서 제안하는 방법은 향후 실제 휴먼형 로봇에 적용하여 휴먼형 로봇 손의 사물조작 지능 개발에 유용할 것으로 전망된다.

• Journal of Biomedical Engineering Research
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• v.38 no.4
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• pp.205-210
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• 2017

The convergence healthcare industry in Korea has been stalled due to conflicts between stakeholders as well as a supplier-centered industry structure. This situation is caused by the structural contradiction in which the Korean industry has a prolonged conflict structure among stakeholders due to a strong regulation and an institutional inertia from the viewpoint of the sociotechnical system. Therefore, it is necessary to identify new system management plan that enhances social acceptability such as laws, customs and ideas while reducing conflicts between stakeholders. In this study, mainly adapting the stakeholder-oriented autopoiesis and focusing on publicness of healthcare, we propose the rationale and direction for policy making to harmonize various systems within the convergence healthcare industry.

• Journal of the Korea Convergence Society
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• v.6 no.2
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• pp.31-36
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• 2015

This paper was studied in export policy Revitalization of SMEs mainly in various countries of Indochina, especially in Laos, which is emerging as a new market after China. Laos is a socialist country, but in 2014, led by the current active open-door policy, education, social, cultural and people-to-people exchanges are going briskly, light industry and the expansion of social infrastructure such as laying the foundation for economic development. This paper presents a plan to dominate the emerging frontier markets are public enterprises and government agencies that do not respond quickly. Already pioneered by building a network in local development staff for this purpose, such as volunteers, missionaries, professors and configure the network of exploitation and human development personnel look for Revitalization of SMEs in urban and regional growth.

• Convergence Security Journal
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• v.18 no.2
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• pp.3-10
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• 2018

The existing smart grid, which is centered on the power grid, is rapidly spreading to new energy and renewable energy such as heat and gas, which are expressed as smart energy. Smart Energy interacts with electric energy and is connected to wired / wireless network based on IoT sensor based on energy analysis using AI to rapidly expand ecosystem with various energy carriers and customers. However, smart energy based on IoT is lacking in technological and institutional preparation for security compared to efforts to activate the market according to the interests of government and business operators. In this study, we will present Smart Energy 's privacy policy in terms of value system(CPND) of convergence ICT.

• Journal of Digital Convergence
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• v.11 no.12
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• pp.697-708
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• 2013

This study suggests the need of 'policy design' that aims to develop visual contents with competitiveness in a global market by comparing and analyzing the programs of promoting the visual content industry of each local government. Moreover it focuses to derive a policy implication so that the local government in its role could develop and promote programs of the industry to enhance its competitiveness. The scope of the study covers local governments of the national capital region and the five metropolitan cities of Korea. The support programs on the visual content industry have been compared and analyzed in 2012. A case study of the policy program of the UK, USA and France has been conducted. In conclusion, we must recognize visual industry as regional strategic industry. The policy orientation, organizations and the scale of support should be designed to make the most of regional characteristics and lead to sustainable growth in response to a rapidly changing IT convergence technology and the new digital environment.

• Journal of Korea Multimedia Society
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• v.23 no.7
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• pp.830-839
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• 2020

As a popular mathematical framework for modeling decision making, Markov decision process (MDP) has been widely used to solve problem in many engineering fields. MDP consists of a set of discrete states, a finite set of actions, and rewards received after reaching a new state by taking action from the previous state. The objective of MDP is to find an optimal policy, that is, to find the best action to be taken in each state to maximize the expected discounted reward of policy (EDR). In practice, MDP is typically unknown, so simulation-based policy improvement (SBPI), which improves a given base policy sequentially by selecting the best action in each state depending on rewards observed via simulation, can be a practical way to find the optimal policy. However, the efficiency of SBPI is still a concern since many simulation samples are required to precisely estimate EDR for each action in each state. In this paper, we propose a method to select the best action accurately in each state using a small number of simulation samples, thereby improving the efficiency of SBPI. The proposed method accumulates the simulation samples observed in the previous states, so it is possible to precisely estimate EDR even with a small number of samples in the current state. The results of comparative experiments on the existing method demonstrate that the proposed method can improve the efficiency of SBPI.

• Journal of the Korea Convergence Society
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• v.9 no.9
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• pp.69-81
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• 2018

The ultimate intention of this research is to identify the factors that have a significant effect on the perceived importance of information security as the antecedent of intention to information security policy compliance. We found that the effectiveness of information security training program did not have statistically significant effect on the perceived cost of policy compliance. Second, the effectiveness of information security policy has significant influence on the perceived cost of policy compliance. Third, perceived vulnerability has a significant effect on the perceived cost of policy compliance. Fourth, perceived cost of policy compliance has a significant effect on perceived importance of information security. Fifth, supervisor's attitude toward information security silence has a significant effect on employee silent behavior towards information security. Sixth, communication opportunities towards information security has a significant influence on employee silent behavior towards information security. Finally, it was shown that employee silent behavior towards information security had a significant influence on the perceived importance of information security.