• Journal of Navigation and Port Research
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• v.36 no.6
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• pp.501-506
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• 2012

To serve basic data for the design of capacity and management of Distributed(or On-site) Power Generation System using renewable energies, this study measured the electric power consumption(hereafter abbreviated as EPC) of 5 families of fishing village located at island in southeastern area of Korea. The results are as following. The maximum monthly average EPC occurred in December or January. Although the total monthly EPC of H family is 2~3 times more than J family, individual monthly EPC of J family is 10~30 % more than H family. Hourly EPC pattern shows that the maximum EPC occurred between 20~24 o'clock in summer season, but it occurred between 18~24 o'clock in winter season. Compared to summer, the height of fluctuation through a day is small. And the EPC patterns of weekdays and weekend estimated as very similar.

• JSTS:Journal of Semiconductor Technology and Science
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• v.16 no.4
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• pp.481-487
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• 2016

A new two-line based inversion driving method is introduced for low power display-driver ICs. By inserting a timing offset between the chopper stabilization and the alternation of LCD polarity, we can reduce power consumption without noticeable degradation in the display quality. By applying the proposed scheme to 12" LCD applications, we achieved 7.5% and 27% power saving in the display-driver IC with white and black patterns, respectively.

• Smart Media Journal
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• v.13 no.1
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• pp.18-23
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• 2024

Power efficiency management in smart farms is important due to its link to climate change. As climate change negatively impacts agriculture, future agriculture is expected to utilize smart farms to minimize climate impacts, but smart farms' power consumption may exacerbate the climate crisis due to the current electricity production system. Therefore, it is essential to efficiently manage and optimize the power usage of smart farms. In this study, we propose a system that monitors the power usage of smart farm equipment in real time and predicts the power usage one hour later using GRU. CT sensors are installed to collect power usage data, which are analyzed to detect and prevent abnormal patterns, and combined with IoT technology to efficiently manage and monitor the overall power usage. This helps to optimize power usage, improve energy efficiency, and reduce carbon emissions. The system is expected to improve not only the energy management of smart farms, but also the overall efficiency of energy use.

• Journal of the Korean Society for Geothermal and Hydrothermal Energy
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• v.16 no.4
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• pp.31-38
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• 2020

The investment in the technology of using a combined heat source is insufficient, which utilizes the advantages of various heat sources to maximize the potential energy and at the same time increases the performance of the heat pump. In this study, as basic data for the development of a high-efficiency hybrid heat pump system that actively connects and uses various heat sources, simulations were conducted for the heat pumps in two cases where geothermal and hydrothermal heat were applied respectively. In May, COP increased by about 27.3% compared to geothermal heat. In February, the COP percentage decrease of hydrothermal heat compared to geothermal heat is -6.9%. In May, total energy consumption can be reduced by 21.1% when hydrothermal is applied compared to geothermal heat. In February, the total energy consumption increases by 3.4%.

• Journal of Electrical Engineering and Technology
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• v.12 no.4
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• pp.1427-1434
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• 2017

Since the late 2000s, there has been growing preparation in South Korea for a sudden reunification of South and North Korea. Particularly in the power industry field, thorough preparations for the construction of a power infrastructure after reunification are necessary. The first step is to estimate the peak load demand. In this paper, we suggest a new peak demand estimation methodology by integrating existing correlation analysis methods between economic indicators and power generation quantities with a power supply interruption model in consideration of power consumption patterns. Through this, the potential peak demand and actual peak demand of the Nation, which experiences power supply interruption can be estimated. For case studies on North Korea after reunification, the potential peak demand in 2015 was estimated at 5,189 MW, while the actual peak demand within the same year was recorded as 2,461 MW. The estimated potential peak demand can be utilized as an important factor when planning the construction of power system facilities in preparation for reunification.

• KIPS Transactions on Computer and Communication Systems
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• v.5 no.10
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• pp.293-302
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• 2016

Since the electricity is produced and consumed simultaneously, predicting the electric load and securing affordable electric power are necessary for reliable electric power supply. In particular, a university campus is one of the highest power consuming institutions and tends to have a wide variation of electric load depending on time and environment. For these reasons, an accurate electric load forecasting method that can predict power consumption in real-time is required for efficient power supply and management. Even though various influencing factors of power consumption have been discovered for the educational institutions by analyzing power consumption patterns and usage cases, further studies are required for the quantitative prediction of electric load. In this paper, we build an electric load forecasting model by implementing and evaluating various machine learning algorithms. To do that, we consider three building clusters in a campus and collect their power consumption every 15 minutes for more than one year. In the preprocessing, features are represented by considering periodic characteristic of the data and principal component analysis is performed for the features. In order to train the electric load forecasting model, we employ both artificial neural network and support vector machine. We evaluate the prediction performance of each forecasting model by 5-fold cross-validation and compare the prediction result to real electric load.

• JSTS:Journal of Semiconductor Technology and Science
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• v.16 no.5
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• pp.582-594
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• 2016

Various test data compression techniques have been developed to reduce the test costs of system-on-a-chips. In this paper, a scan chain reordering algorithm for code-based test data compression techniques is proposed. Scan cells within an acceptable relocation distance are ranked to reduce the number of conflicts in all test patterns and rearranged by a positioning algorithm to minimize the routing overhead. The proposed method is demonstrated on ISCAS '89 benchmark circuits with their physical layout by using a 180 nm CMOS process library. Significant improvements are observed in compression ratio and test power consumption with minor routing overhead.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.3A
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• pp.221-229
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• 2007

In this paper, we propose and analyze the optimized sleep mode for a mobile node (MN) in IEEE 802.16e wireless metropolitan area networks. Because a MN in a sleep mode specified in 802.16e specification should maintain state information with the base station currently attached, it must renew sleep state with a new base station after handover which leads to unnecessary waste of battery power. Noting that the mobility pattern of a MN is independent of call arrival patterns, we propose an optimized sleep mode to eliminate unnecessary standby period of a MN in sleep state after handover. We also propose an analytical model for the proposed scheme in terms of power consumption and the initial call response time. Simulation studies that compare the performance between the sleep mode and the optimized sleep mode show that our scheme marginally increases initial call response delay with the huge reduction in power consumption.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.11
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• pp.1581-1587
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• 2021

As embedded system are used widely and variously, multi-edge system, which multiple edges gather and perform complex operations together, is actively operating. In a multi-edge system, it often occurs that an abnormal operation at one edge is transferred to another edge or the entire system goes down. It is necessary to determine and control edge anomalies in order to prevent system down, but this can be a heavy burden on the resource-limited edge. As a solution to this, we use power consumption data to check the state of the edge device and transmit it based on a QRcode to check and control errors at the server. The architecture proposed in this paper is implemented using 'chip-whisperer' to measure the power consumption of the edge and 'Raspberry Pi 3' to implement the server. As a result, the proposed architecture server showed successful data transmission and error determination without additional load appearing at the edge.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.8
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• pp.1701-1710
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• 2012

Recently, it has been found that it is important to use a smart grid to reduce greenhouse-gas emissions worldwide. A smart grid is a digitally enabled electrical grid that gathers, distributes, and acts on information regarding the behavior of all participants (suppliers and consumers) to improve the efficiency, importance, reliability, economics, and sustainability of electricity services. The smart grid technology uses two-way communication, where users can monitor and limit the electricity consumption of their home appliances in real time. Likewise, power companies can monitor and limit the electricity consumption of home appliances for stabilization of the electricity supply. However, if information regarding the measured electricity consumption of a user is leaked, serious privacy issues may arise, as such information may be used as a source of data mining of the electricity consumption patterns or life cycles of home residents. In this paper, we propose a data transaction protocol for privacy protection in a smart grid. In addition, a power company cannot decrypt an encrypted home appliance ID without the user's password.