• Journal of IKEEE
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• v.11 no.4
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• pp.205-212
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• 2007

The ubiquitous sensor network consists of micro sensors with wireless communication capabilities. Compared to wired communication, wireless communication is more subject to eavesdropping as well as data variation and manipulation. Accordingly, there must be efforts to secure the information delivered over the sensor network. Providing security to the sensor network, however, requires additional energy consumption, which is an important issue since energy transformation is difficult to implement in a sensor network. This paper proposes a routing mechanism based on the energy-efficient cluster that features security functions capable of safely processing the data acquired from the sensor network. The proposed algorithm reduces energy consumption by fixing the clusters formed at the initial stage and using the pre-distribution scheme so that the cluster and node keys generated and exchanged at the initial stage are not re-generated or re-exchanged. Simulation experiments confirmed that the proposed approach reduces energy consumption compared to implementing security measures to the conventional cluster-based routing mechanism.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.6
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• pp.1013-1024
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• 2022

In addition to the assessment measure of electric quality levels, load loss are also a factor in hindering the financial profits of electrical sales companies. Therefore, accurate analysis of load losses generated from distributed power networks is very important. The accurate calculation of load losses in the distribution line has been carried out for a long time in many research institutes as well as power utilities around the world. But it is increasingly difficult to calculate the exact amount of loss due to the increase in the congestion of distribution power network due to the linkage of distributed energy resources(DER). In this paper, we develop smart grid big data infrastructure in order to accurately analyze the load loss of the distribution power network due to the connection of DERs. Through the preprocess of data selected from the smart grid big data, we develop a load loss analysis model that eliminated 'veracity' which is one of the characteristics of smart grid big data. Our analysis results can be used for facility investment plans or network operation plans to maintain stable supply reliability and power quality.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.6
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• pp.1447-1452
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• 2010

Ubiquitous society to build basic infrastructure in the power supply and power equipment safety is important. u-City in order to prevent the disaster of u-IT Power Equipment Performance Module and the security for the safety of the u-City is necessary. In this paper, the power unit of u-IT module, temperature sensor, humidity sensor, equipped with sensors arranged throughout the fire, and home distribution boards, Home Network Wall-Pad, Blocker, MPNP black boxes, arc detection, arc safety equipment, outlet of the modular performance evaluation methods and security methods will be studied. u-IT power devices and sensors to analyze the information conveyed by proactive risk and ensure safety, access control, authentication, security safeguards, such as u-IT integrated management system for electrical safety and strengthen the security, safety and security with a u-City will contribute to the construction and operation.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.5
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• pp.730-737
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• 2016

Electric utilities may face new threats with increase in electric vehicles (EVs) in the personal automobile market. The peak demand will increase which may stress the distribution network equipment. The focus of this paper is on an adaptive control of smart household appliances by using an intelligent load management system (ILMS). The main objectives are to accomplish consumer needs and prevent overloading of power grid. The stress from the network is released by limiting the peak demand of a house when it exceeds a certain point. In the proposed strategy, for each smart appliance, the customers will set its order/rank according to their own preferences and then system will control the household loads intelligently for consumer reliability. The load order can be changed at any time by the customer. The difference between the set and actual value for each load's specific parameter will help the utility to estimate the acceptance of this intelligent load management system by the customers.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5568-5587
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• 2018

This research uses artificial intelligence methods for computer network intrusion detection system modeling. Primary classification is done using self-organized maps (SOM) in two levels, while the secondary classification of ambiguous data is done using Sugeno type Fuzzy Inference System (FIS). FIS is created by using Adaptive Neuro-Fuzzy Inference System (ANFIS). The main challenge for this system was to successfully detect attacks that are either unknown or that are represented by very small percentage of samples in training dataset. Improved algorithm for SOMs in second layer and for the FIS creation is developed for this purpose. Number of clusters in the second SOM layer is optimized by using our improved algorithm to minimize amount of ambiguous data forwarded to FIS. FIS is created using ANFIS that was built on ambiguous training dataset clustered by another SOM (which size is determined dynamically). Proposed hybrid model is created and tested using NSL KDD dataset. For our research, NSL KDD is especially interesting in terms of class distribution (overlapping). Objectives of this research were: to successfully detect intrusions represented in data with small percentage of the total traffic during early detection stages, to successfully deal with overlapping data (separate ambiguous data), to maximize detection rate (DR) and minimize false alarm rate (FAR). Proposed hybrid model with test data achieved acceptable DR value 0.8883 and FAR value 0.2415. The objectives were successfully achieved as it is presented (compared with the similar researches on NSL KDD dataset). Proposed model can be used not only in further research related to this domain, but also in other research areas.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.23 no.2
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• pp.244-250
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• 2012

The compact dipole antenna elements and array structure is proposed. The array structure is designed for applicator in regional hyperthermia treatment to enhance the uniformity of the heat distribution which makes the treatment effective and prevents overheating. The compact dipole is designed with branched dipole and matching network to have small size and symmetric shape. The temperature simulation with specific absorption rate(SAR) and bio-heat equation is performed to have heat distribution. The applicator is designed, fabricated, and measured with multi-channel thermometer in 30 and 60 minutes. The simulation and measurement results showed agreement and the simulation in body circumstance has proper temperature result for hyperthermia therapy.

• Journal of Electrical Engineering and Technology
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• v.13 no.3
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• pp.1069-1078
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• 2018

Demand response (DR) programs give opportunity to consumers to manage their electricity bills. Besides, distribution system operator (DSO) is interested in using DR programs to obtain technical and economic benefits for distribution network. Since small consumers have difficulties to individually take part in the electricity market, an entity named demand response provider (DRP) has been recently defined to aggregate the DR of small consumers. However, implementing DR programs face challenges to fairly allocate benefits and payments between DRP and DSO. This paper presents a procedure for modeling the interaction between DRP and DSO based on a bilevel programming model. Both DSO and DRP behave from their own viewpoint with different objective functions. On the one hand, DRP bids the potential of DR programs, which are load shifting and load curtailment, to maximize its expected profit and on the other hand, DSO purchases electric power from either the electricity market or DRP to supply its consumers by minimizing its overall cost. In the proposed bilevel programming approach, the upper level problem represents the DRP decisions, while the lower level problem represents the DSO behavior. The obtained bilevel programming problem (BPP) is converted into a single level optimizing problem using its Karush-Kuhn-Tucker (KKT) optimality conditions. Furthermore, point estimate method (PEM) is employed to model the uncertainties of the power demands and the electricity market prices. The efficiency of the presented model is verified through the case studies and analysis of the obtained results.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.19 no.2
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• pp.162-168
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• 2006

Recently, diagnosis techniques have been investigated to detect a Partial Discharge associated with a dielectric material defect in a high voltage electrical apparatus, However, the properties of detection technique of Partial Discharge aren't completely understood because the physical process of Partial Discharge. Therefore, this paper analyzes the process on surface discharge of polymer insulator using wavelet transform. Wavelet transform provides a direct quantitative measure of spectral content in the time~frequency domain. As it is important to develop a non-contact method for detecting the kaolin contamination degree, this research analyzes the electromagnetic waves emitted from Partial Discharge using wavelet transform. This result experimentally shows the process of Partial Discharge as a two-dimensional distribution in the time-frequency domain. Feature extraction parameter namely, maximum and average of wavelet coefficients values, wavelet coefficients value at the point of $95\%$ in a histogram and number of maximum wavelet coefficient have used electromagnetic wave signals as input signals in the preprocessing process of neural networks in order to identify kaolin contamination rates. As result, root sum square error was produced by the test with a learning of neural networks obtained 0.00828.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.53 no.8
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• pp.509-516
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• 2004

The concerns of distributed generations including photovoltaic(PV) system have been increased around the world since PV system is becoming widespread as a clean and gentle energy source for earth. In the future high density grid-connected PV systems will be interconnected with distribution network. As a result, the stability and long-term reliability of PV systems have become more important issues in this area. Grid-connected PV systems have been installed and monitored at field demonstration test center(FDTC) and also data acquisition system(DAS) has been constructed for measuring and analyzing performance of PV system to observe the overall effect of environmental conditions on their operation characteristics. The performance of PV system has been evaluated and analyzed for component perspective (PV array and power conditioning system) and global perspective (system efficiency, capacity factor, and electrical power energy) by field test and loss factors of PV system.

• KIEE International Transactions on Power Engineering
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• v.5A no.1
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• pp.79-84
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• 2005

In many countries, the electric power industry is undergoing significant changes known as deregulation and restructuring. These alterations introduce competition in generation and retail and require open access to the transmission network. The competition of the electric power industry causes many issues to surface. Among them, unbundling of the transmission service is probably the most complicated as it is a single and integrated sector and the transmission revenue requirement must be allocated to market participants in a fair way. In these situations, it is valuable to research the methodologies to allocate transmission usage. The power tracing method offers useful information such as which generators supply a particular load or how much each generator (load) uses a particular transmission line. With this information, we can allocate required transmission revenue to market participants. Recently, several algorithms were proposed for tracing power flow but there is no dominant power tracing method. This paper proposes a power tracing method based on graph theory and complex-current distribution. For practicability, the proposed method for transmission usage allocation is applied to IEEE 30 buses and compared with the method proposed by Felix F.Wu.