• Journal of the Korea Society of Computer and Information
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• v.14 no.8
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• pp.89-96
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• 2009

In the future energy environment, a power information system will meet the real-time capability to process the emergency events, unexpected blackouts or over-load, and the high performance to provide the consumer service events such as remote meter reading. In addition to, it must have facility which is able to process a large information occurred on system effectively. In this paper, we developed a distributed power information system based on event with metadata processing technique which was both load balancing and decreased hot spot using XML that was efficient for information exchange. In order to experiment, we made a reduced future power system with controling power device using wireless communications and we could do experiments through it.

• The Journal of the Korea Contents Association
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• v.14 no.2
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• pp.482-493
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• 2014

Many kinds of carbon emission monitoring systems or integrated systems have been developed so far. However, despite of the development of related techniques, they tend to be lack of statistic processing functions for feedback and policy-making data for users. In this study, a new CEMS (Carbon Emission Monitoring System) has been suggested and implemented in Sejong City, Korea. This system adapted automatic remote reading system from the site management agency as data hub to collect the electricity, gas and water usage of each household. The CEMS is consisted of 6 parts; carbon emission measurement, carbon emission standard setup and management, statistic analysis and the incentives. CEMS is distinguished with other systems for its UIs for users and the administrators. Also, data sharing with urban information system(UIS) of local government to produce information for users and policy-makers. This system makes it possible to investigate the change of energy consumption patterns, especially depending on the family structure and the housing characteristics. Furthermore, analyzing their correlation with carbon emission, it is expected to provide basic data used to establish urban environmental policies.

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.377-379
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• 2007

To reduce the chattering errors of reed switch sensors used for automatic remote measurement of water supply system, a reed switch sensor was analyzed and improved. The operation of reed switch sensors can be described as a mechanical contact by approximation of permanent magnet piece to generate an electrical pulse. The reed switch sensors are used in measurement application by detecting the rotational or translational displacement. To apply for flow measurement devices, the reed switch sensors should keep high reliability. They are applied for the electronic digital type of water flow meters. The reed switch sensor is just installed simply on the mechanical type flow meter. A small magnet is attached on a pointer of the water meter counter rotor. Inside the reed sensor, two steel leaf springs make mechanical contact and apart as rotation of flow meter counter. The counting electrical contact pulses can be converted as the water flow amount. The MCU sends the digital flow rate data to the server using the wireless communication network. But it occurs data difference or errors by chattering noise. The reed switch sensor contains chattering error by it self at the force equivalent position. The vibrations such as passing car near to the switch sensor installed location. In order to reduce chattering error, most system uses just software methods for example using filter and also statistical calibration methods. The chattering errors were reduced by changing leaf spring structure using mechanical hysteresis characteristics.

• Environmental and Resource Economics Review
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• v.25 no.2
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• pp.141-178
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• 2016

This paper, using AMR (Automatic Meter Reading) electricity data accurately measured in real time, analyses the characteristics and patterns of temperature effect on the industrial electricity usage. For this goal, the paper constructs and estimates a model which captures the properties of AMR time series including long-term trends, mid-term temperature effects, and short-term special day effects. Based on the estimated temperature response function and the temperature effect, we categorize the whole industry into two groups: one group with sharp temperature effect and the other with weak temperature effect. Furthermore, the industry group with sharp temperature effect is classified into a summer peak industry group and a winter peak industry group, based on the estimates of the temperature response function. These empirical results carry practical policy implications on the real time electricity demand management.

• Journal of Astronomy and Space Sciences
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• v.13 no.2
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• pp.105-116
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• 1996

This study is based upon the thermal modeling, analysis and operational results of KITSAT-1 and KITSAT-2 microsatellites launched on August 11, 1992 and Septermber 26, 1993, respectively. As KITSAT-1/2 was designed to be launched as an auxiliary payload of ARIANE launcher, the constraints on volume, power consumption, and mass were required to adopt passive thermal control method controlling absorptivity, emissivity, and conductivities among adjacent modules. The main of KITSAT was to take Earth images using CCD cameras positioned at the bottom of spacecraft, in which the cameras were always pointing to the center of Earth. This study is concerned with orbital analysis, thermal modeling, simulation results, and its verification by utilizing in-orbit telemetry data of KITSAT-2. The results of telemetry analysis show that the thermal modeling is matched to actual temperature data within 10 degrees of error range in average.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.1
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• pp.71-77
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• 2014

In this paper, we propose an improved numeric character recognition method which can recognize numeric characters well under low-illuminated and shade-illuminated environment. The LN(Local Normalization) preprocessing method is used in order to enhance low-illuminated and shade-illuminated image quality. The reading area is detected using line segment information extracted from the illumination-normalized meter images, and then the three-phase procedures are performed for segmentation of numeric characters in the reading area. Finally, an efficient hybrid classifier is used to classify the segmented numeric characters. The proposed numeric character classifier is a combination of multi-layered feedforward neural network and template matching module. Robust heuristic rules are applied to classify the numeric characters. Experiments using meter image database were conducted. Meter image database was made using various kinds of meters under low-illuminated and shade-illuminated environment. The experimental results indicates the superiority of the proposed numeric character recognition method.

• Journal of the Korean Institute of Gas
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• v.12 no.2
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• pp.63-68
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• 2008

The ubiquitous technology is capable of innovating various area to constructing intelligent and network to all objects. This technology can construct network infrastructure to facilities for applying legacy industry and need to develop intelligent service that analyze context-aware through network. This paper derive and propose the introduction range of ubiquitous technology, which should be testified and analyzed network, and studied the detection and analysis of information for managing gas facilities. We investigate the current status of gas facilities and automatic reading system, testing and analyzing wireless personal area network and mobile communication by scalability, self-configuration, connection in underground. Furthermore, we propose modelization method for intelligent service and derive the introduction range for applying ubiquitous technologies for managing gas facilities.

• Journal of IKEEE
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• v.23 no.1
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• pp.120-126
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• 2019

Recently, accurate prediction of power consumption based on machine learning techniques in Internet of Energy (IoE) has been actively studied using the large amount of electricity data acquired from advanced metering infrastructure (AMI). In this paper, we propose a deep learning model based on Gated Recurrent Unit (GRU) as an artificial intelligence (AI) network that can effectively perform pattern recognition of time series data such as the power consumption, and analyze performance of the prediction based on real household power usage data. In the performance analysis, performance comparison between the proposed GRU-based learning model and the conventional learning model of Long Short Term Memory (LSTM) is described. In the simulation results, mean squared error (MSE), mean absolute error (MAE), forecast skill score, normalized root mean square error (RMSE), and normalized mean bias error (NMBE) are used as performance evaluation indexes, and we confirm that the performance of the prediction of the proposed GRU-based learning model is greatly improved.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.4
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• pp.125-136
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• 2011

The vulnerabilities existed in Korean electricity control systems is unexposed because it is being operated in a closed network with superior security. The threat will become greater once the closed network develops into a smart grid environment with superior intelligence. Security will have a greater impact once each household will be connected to the power plant via the smart meter. This research focuses on stable data transfer in harsh external environment and whole-nation coverage network, and suggested standardized and optimized Virtual Private Network (VPN) Gateway architecture to support Power Line Communication (PLC). The functionality and stability of the prototype has been verified with field tests. For implementation of outdoor type VPN device for smart grid, we adopted PLC low voltage remote-meter-net for data communication. Also, IPSec type tunneling and ARIA algorithm based encryption of data collected by PLC low voltage remote meter is transmitted.

• The KIPS Transactions:PartD
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• v.16D no.3
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• pp.307-316
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• 2009

In this paper, the spatiotemporal data mining methodology for detecting a cycle of power consumption pattern with the change of time and spatial was proposed, and applied to the power consumption data collected by GIS-AMR system with an aim to use its resulting knowledge in real world applications. First, partial clustering method was applied for cluster analysis concerned with the aim of customer's power consumption. Second, the patterns of customer's power consumption data which contain time and spatial attribute were detected by 3D cube mining method. Third, using the calendar pattern mining method for detection of cyclic patterns in the various time domains, the meanings and relationships of time attribute which is previously detected patterns were analyzed and predicted. For the evaluation of the proposed spatiotemporal data mining, we analyzed and predicted the power consumption patterns included the cycle of time and spatial feature from total 266,426 data of 3,256 customers with high power consumption from Jan. 2007 to Apr. 2007 supported by the GIS-AMR system in KEPRI. As a result of applying the proposed analysis methodology, cyclic patterns of each representative profiles of a group is identified on time and location.