• KDI Journal of Economic Policy
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• v.45 no.2
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• pp.21-50
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• 2023

In this paper, I empirically estimate the relationship between digital technology and climate technology using the United States Patent and Trademark Office's patent database. I find that innovation in digital technology increases the number of patents for climate technology by 17.3% on average, with digital data-processing technology and machine-learning-related technologies especially playing a key role in this relationship. Designing and implementing detailed policies that take into account the relationship between the two technologies will help us reduce the time required to achieve carbon neutrality and shift to the digital economy.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.5
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• pp.76-87
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• 2014

Today the energy consumption of ICT networks is about 10% of the worldwide power consumption and is predicted to increase remarkably in the near future. For this reason, this paper studies energy saving strategies assuring the network-level QoS. In the strategies, the energy consumption of NIC(network interface card) on both endpoint of links decreases by selecting links and making them sleep when the total traffic volume of the IP network is lower than a threshold. In this paper, we propose a heuristic routing algorithm based on so-called delegating/delegated routers, and evaluate its characteristics using computer simulation considering network-level QoS. The selection of sleep links is determined in terms of the number of traffic paths (called min_used path) or the amount of traffics(called min_used traffic) through those kinks. To our experiment, the min_used traffic method shows a little better energy saving but the increased path length compared to the min_used path method. Those two methods have better energy saving characteristics than the random method. This paper confirms that the delegating/delegated router-based routing algorithm results in energy saving effects and sustains network-level QoS in IP networks.

• ETRI Journal
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• v.45 no.4
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• pp.650-665
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• 2023

A novel smart metering technique capable of anomaly detection was proposed for real-time home power management system. Smart meter data generated in real-time were obtained from 900 households of single apartments. To detect outliers and missing values in smart meter data, a deep learning model, the autoencoder, consisting of a graph convolutional network and bidirectional long short-term memory network, was applied to the smart metering technique. Power management based on the smart metering technique was executed by multi-objective optimization in the presence of a battery storage system and an electric vehicle. The results of the power management employing the proposed smart metering technique indicate a reduction in electricity cost and amount of power supplied by the grid compared to the results of power management without anomaly detection.

• Journal of the Korea Society of Computer and Information
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• v.21 no.2
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• pp.51-60
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• 2016

This paper proposes a hierarchical routing algorithm for enhancing survivability of sensor nodes on WSAN. Proposed algorithm has two important parts. The first is a clustering algorithm that uses distance between sensor and actor, and remaining energy of sensor nodes for selecting cluster head. It will induce uniform energy consumption, and this has a beneficial effect on network lifetime. The second is an enhanced routing algorithm that uses the shortest path tree. The energy efficient routing is very important in WSAN which has energy limitation. As a result, proposed algorithm extends network and nodes lifetime through consuming energy efficiently. Simulation results show that the proposed clustering algorithm outperforms conventional routing algorithms such as VDSPT in terms of node and network life time, delay, fairness, and data transmission ratio to BS.

• KIEAE Journal
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• v.17 no.4
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• pp.41-48
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• 2017

Purpose : Currently, 25% of the domestic energy consumption structure is used as building energy, and more than 18% of this energy is consumed in the residential. Accordingly, various efforts and policies that can save energy of the building is being performed. The various researchers are conducting research to diagnose the thermal performance of existing buildings. This study is to apply in the field of precision thermal insulation performance diagnostic method for thermal performance analysis of existing detached house in Seoul, Gangreung, Gyeongju, Pohang. And this paper is analyzed quantitatively measure the existing detached house energy performance. Method: Research methodology analyzed the thermal performance over the Heat Flow Meter method by applying the measurement process and method by applying the criteria of ISO 9869-1 & ASTR method. In this study, the surface heat transfer coefficient was calibrated by applying indoor surface heat transfer resistance with reference to ISO 6946 standard. The measurement error rate between the HFM diagnosis method and the ASTR diagnosis method was reduced and the measurement reliability was obtained through measurement method error verification. Result : As a result of the study, the thermal performance vulnerable parts of the building were quantitatively analyzed, and presented for methods which can be improved capable of efficient energy use buildings.

• Journal of Digital Convergence
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• v.19 no.12
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• pp.331-337
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• 2021

The adaptation of smartfarm technology that converges ICT is increasing productivity and competitiveness in the agriculture. Technologies have been developed that enable environmental monitoring through various sensors and automatic control of the cultivation environment, and researches are underway to advance smartfarm technology using data generated from smartfarms. In this paper, an environmental control method to reduce the energy consumption of a smartfarm by using the environment and control data of the smartfarm is proposed. It was confirmed that energy consumption could be reduced compared to an independent environmental control method by creating an environmental prediction model using accumulated environmental data and selecting a control method to minimize energy consumption in a given situation by considering multiple environmental factors. In the future, research is needed to obtain higher energy efficiency through the advancement of the predictive model and the improvement of the complex control algorithms.

• Annual Conference of KIPS
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• 2016.10a
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• pp.309-312
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• 2016

Green Internet of Things (IoT) is the study and practice of eco-friendly sustainable computing. The basic goal of green computing is to reduce the use of materials and maximize energy efficiency with reliable and secure communications. The paper presents various technologies and issues regarding green IoT. It also studies the green Information and Communication Technology (ICT) such as green M2M, green Cloud Computing (CC), and green Data Center (DC). In addition, this paper mentions about the reliability in IoT Communication and and issues to achieve green IoT communication by applying efficient activity scheduling technique for energy saving. Finally, we propose the green IoT-Home Service (GIHS) model which provides efficient energy management in home automation system.

• Journal of Digital Contents Society
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• v.17 no.6
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• pp.499-507
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• 2016

The era of IoT, which figures exchanging data from the internet between things is coming. Recently, former electric power energy policy paradigm, namely Supply side paradigm, is changing, because electric power energy consumption is rapidly increasing. As new paradigm for this limit, convergence of existing electric power grid and ICT(Information and Communication Technology) will accelerate intellectualization of electric power device, its operation system. This change brought opened electric power grid. Consequently, attacks to the national electric power grid are increasing. On this paper, we will analyze security threats of existing IoT, discuss security weakness on electric power industry IoT and suggest needed security requirements, security technology.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.5
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• pp.119-125
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• 2021

As the electricity grid systems has been intelligent with the development of ICT technology, power consumption information of users connected to the grid is available to acquired and analyzed for the power utilities. In this paper, the energy theft problem is solved by feature selection methods, which is emerging as the main cause of economic loss in smart grid. The data preprocessing steps of the proposed system consists of five steps. In the feature selection step, features are selected using analysis of variance and mutual information (MI) based method, which are filtering-based feature selection methods. According to the simulation results, the performance of support vector machine classifier is higher than the case of using all the input features of the input data for the case of the MI based feature selection method.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.5
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• pp.851-858
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• 2021

Due to the increase in renewable energy and distributed resources, not only traditional data but also various energy-related data are being generated in the new energy industry. In other words, there are various renewable energy facilities and power generation data, system operation data, metering and rate-related data, as well as weather and energy efficiency data necessary for new services and analysis. Energy big data processing technology can systematically analyze and diagnose data generated in the first half of the power production and consumption infrastructure, including distributed resources, systems, and AMI. Through this, it will be a technology that supports the creation of new businesses in convergence between the ICT industry and the energy industry. To this end, research on the data analysis system, such as itemized characteristic analysis of the collected data, correlation sampling, categorization of each feature, and element definition, is needed. In addition, research on data purification technology for data loss and abnormal state processing should be conducted. In addition, it is necessary to develop and structure NIFI, Spark, and HDFS systems so that energy data can be stored and managed in real time. In this study, the overall energy data processing technology and system for various power transactions as described above were proposed.