• Proceedings of KOSOMES biannual meeting
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• 2017.04a
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• pp.167-167
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• 2017

• Journal of Korean Society on Water Environment
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• v.22 no.3
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• pp.383-385
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• 2006

• Korean Business Review
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• v.22 no.2
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• pp.1-34
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• 2009

We should have a new perspective on the words, green and growth, to have a full understanding of them and consider the environment itself as leading to the nation's growth. The green growth policy is to convert the paradigm of economic growth into one with a good circle of environment and growth. However, as each country has a different view of the green growth policy, we will see how the countries carry out the policy and how the companies and people accept it. To do this, we will employ an actual analysis and propose a green growth strategy for the nation. The following are from the actual analysis in the article: 1. The GG policy is not just limited on the environmental problems but related to the nation's well-being as well. 2. Energy policy should be defined as the core thing of the GG policy and energy effectiveness among others things should be carried out on a short term basis. 3. Developing a strategy using IT is necessary for the GG policy. 4. Very careful approach should be taken to build a master plan for the nation bearing effective outcome of the policy. 5. The GG policy should be regarded as a social reformative one motivating the nation's much interest in it.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.4
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• pp.96-114
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• 2022

This study aimed to present a policy direction that could preemptively respond to urban development by analyzing past trends and predicting future changes in urban logistics in South Korea. In particular, the scope of the study was centered on changes in the national and regional logistics master plans presented in a framework that acted on logistics policy. So, the study examined the policy changes related to urban logistics from the past to the present based on the national logistics master plan and identified a national policy base to respond to the expected changes in the logistics environment in the future. Moreover, by combining this base with the contents of the urban logistics master plan of Seoul, we proposed a logistics policy that should be continued or newly added to the urban logistics master plan. Policy proposals were also presented by dividing the future logistics environment changes into policy, social, and technological changes. In particular, the policies were proposed in urban planning (10 policies), transportation planning and ITS (8 policies), logistics technology and ITS (6 policies), and laws and systems (8 policies). Regarding policy continuity, 15 policies needed to be continued, and 17 policies were to be introduced later.

• The Journal of Korea Robotics Society
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• v.15 no.4
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• pp.398-403
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• 2020

Robot's throwing control is difficult to accurately calculate because of air resistance and rotational inertia, etc. This complexity can be solved by using machine learning. Reinforcement learning using reward function puts limit on adapting to new environment for robots. Therefore, this paper applied deep reinforcement learning using neural network without reward function. Throwing is evaluated as a success or failure. AI network learns by taking the target position and control policy as input and yielding the evaluation as output. Then, the task is carried out by predicting the success probability according to the target location and control policy and searching the policy with the highest probability. Repeating this task can result in performance improvements as data accumulates. And this model can even predict tasks that were not previously attempted which means it is an universally applicable learning model for any new environment. According to the data results from 520 experiments, this learning model guarantees 75% success rate.

• Journal of Digital Convergence
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• v.11 no.4
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• pp.35-44
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• 2013

This article is exploring on the concept and realization conditions of 'Smart Education' including the propulsion environment and problems about 'Smart Education'. Based on the concept and realization conditions of 'Smart Education', this paper will review various aspects, such as the technology, infrastructure, school teachers' preparation situation, ecosystem and distribution system, and propose further policy alternatives of 'Smart Education'.

• Asian Journal of Innovation and Policy
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• v.11 no.2
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• pp.222-237
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• 2022

As the COVID-19 pandemic crisis left developing countries with economic setbacks, it is high time to highlight that innovative technologies lead the digital economy. The big powers including the United States and China are already implementing industrial policies that involve large-scale fiscal expenditures to secure the lives and safety of their people. To prepare for the future up to 2025, this paper reflects opinions of industry-academia-research experts regarding changes in the external environment and industry trends. By reflecting results of focus group interviews and changes in the external environment and industry trends, a new high-level 5X strategy (Digital Transformation, Energy Transformation, Bio Health Transformation, Supply Chain Transformation, and Research Transformation) to solve national tasks required for the existing ten policy demand fields and ten agenda during lower-level policy implementation stages were derived.

• KDI Journal of Economic Policy
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• v.34 no.1
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• pp.1-27
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• 2012

This paper attempts to define, construct a policy framework, and analyze interactions with monetary policy of macroprudential policy. The available pieces of evidence suggest that the effects of the LTV and DTI regulations for financial stability are rather unclear in Korea. It also shows that when financial markets exhibit instability in a stable inflationary environment, macroprudential policy could run into conflict with monetary policy. This paper proposes an appropriate modality of macroprudential policy to minimize the potential conflict with monetary policy.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.9
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• pp.409-418
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• 2015

In order to delivery of the correct information in IoT environment, it is important to deduce collected information according to a user's situation and to create a new information. In this paper, we propose a control access scheme of information through context-aware to protect sensitive information in IoT environment. It focuses on the access rights management to grant access in consideration of the user's situation, and constrains(access control policy) the access of the data stored in network of unauthorized users. To this end, after analysis of the existing research 'CP-ABE-based on context information access control scheme', then include dynamic conditions in the range of status information, finally we propose a access control policy reflecting the extended multi-dimensional context attribute. Proposed in this paper, access control policy considering the dynamic conditions is designed to suit for IoT sensor fusion environment. Therefore, comparing the existing studies, there are advantages it make a possible to ensure the variety and accuracy of data, and to extend the existing context properties.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.4
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• pp.105-112
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• 2022

Recently, many studies using reinforcement learning-based autoscaling have been performed to make autoscaling policies that are adaptive to changes in the environment and meet specific purposes. However, training the reinforcement learning-based Horizontal Pod Autoscaler(HPA) policy in a real environment requires a lot of money and time. And it is not practical to retrain the reinforcement learning-based HPA policy from scratch every time in a real environment. In this paper, we implement a reinforcement learning-based HPA in Kubernetes, and propose a transfer leanring technique using a queuing model-based simulation to accelerate the training of a reinforcement learning-based HPA policy. Pre-training using simulation enabled training the policy through simulation experience without consuming time and resources in the real environment, and by using the transfer learning technique, the cost was reduced by about 42.6% compared to the case without transfer learning technique.