• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.2
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• pp.267-281
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• 2012

SCADA system is a computer system that monitors and controls the national infrastructure or industrial process including transportation facilities, water treatment and distribution, electrical power transmission and distribution, and gas pipelines. The SCADA system has been operated in a closed network, but it changes to open network as information and communication technology is developed rapidly. As the way of connecting with outside user extends, the possibility of exploitation of vulnerability of SCADA system gets high. The methodology to protect the possible huge damage caused by malicious user should be developed. In this paper, we proposed anomaly detection based intrusion detection methodology by estimating self-similarity of SCADA system.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.11a
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• pp.383-387
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• 2006

자기유사(self-similar)과정은 인터넷 트래픽을 보다 정확하게 분석하는데 꼭 필요한 확률과정이다. 본 연구는 계산이 간편하고 다양한 시간범위의 종속성을 반영할 수 있는 M/G/${\infty}$에 모형을 기반으로 하여 자기유사과정을 생성하는 방법을 채택하고 G를 파레토 분포로 표준화하여 적용 가능성을 다양하게 실험한다. 시뮬레이션에서 이산화를 매 단위시점으로 설정하지 않고 대기열에서의 도착, 이탈시점으로 설정하여 시뮬레이션의 속도를 높이고 보다 정확한 성능측정이 이루어지도록 시도한다.

• The KIPS Transactions:PartD
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• v.8D no.5
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• pp.507-512
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• 2001

Feature-based similarity has become an important research issue in multimedia database systems. The features of multimedia data are useful for discriminating between multimedia objects. the performance of conventional multidimensional data structures tends to deteriorate as the number of dimensions of feature vectors increase. The $R^{*}-Tree$ is the most successful variant of the R-Tree. In this paper, we propose a SOM-based $R^{*}-Tree$ as a new indexing method for high-dimensional feature vectors. The SOM-based $R^{*}-Tree$ combines SOM and $R^{*}-Tree$ to achieve search performance more scalable to high-dimensionalties. Self-Organizingf Maps (SOMs) provide mapping from high-dimensional feature vectors onto a two-dimensional space. The map is called a topological feature map, and preserves the mutual relationships (similarity) in the feature spaces of input data, clustering mutually similar feature vectors in neighboring nodes. Each node of the topological feature map holds a codebook vector. We experimentally compare the retrieval time cost of a SOM-based $R^{*}-Tree$ with of an SOM and $R^{*}-Tree$ using color feature vectors extracted from 40,000 images. The results show that the SOM-based $R^{*}-Tree$ outperform both the SOM and $R^{*}-Tree$ due to reduction of the number of nodes to build $R^{*}-Tree$ and retrieval time cost.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.20 no.42
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• pp.143-149
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• 1997

먼저 Cantor dust 의 성질 및 유사성, 축소인자, 불변집합, $\delta$ - covering, Box counting 차원 등에 대한 정의를 하였다, {f_i}{\;}{{\infty}\atop{i=1}}$ 를 $R^n$ 상에서 개집합 조건을 만족시키는 축소인 자 $C_i$에 대한 유사성 이라하자. F를｛{f_i}{\;}{{\infty}\atop {i=1}}$ 에 대한 $R^n$상의 불변집합, 즉, F = $\bigcup_{i=0}^\infty{\;}f_1(F)$를 만족시키는 집합이라 하자. 이때, $\sum\limits_{n=0}^\infty{\;}C^s_i{\;}=1,{\;}0{\;}<{\;}C_1{\;}<{\;}1$ 일 때, $dim{\;}_H{\;}F{\;}={\;}dim{\;}_B{\;}F{\;}={\;}s$ 임을 보임으로서, 자기동형집합의 후랙탈 차원에 대하여 논의 하고자 한다.

• The KIPS Transactions:PartB
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• v.8B no.5
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• pp.515-522
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• 2001

Feature-based similarity retrieval become an important research issue in image database systems. The features of image data are useful to discrimination of images. In this paper, we propose the highspeed k-Nearest Neighbor search algorithm based on Self-Organizing Maps. Self-Organizing Map(SOM) provides a mapping from high dimensional feature vectors onto a two-dimensional space. A topological feature map preserves the mutual relations (similarity) in feature spaces of input data, and clusters mutually similar feature vectors in a neighboring nodes. Each node of the topological feature map holds a node vector and similar images that is closest to each node vector. We implemented about k-NN search for similar image classification as to (1) access to topological feature map, and (2) apply to pruning strategy of high speed search. We experiment on the performance of our algorithm using color feature vectors extracted from images. Promising results have been obtained in experiments.

• The KIPS Transactions:PartC
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• v.10C no.1
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• pp.51-60
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• 2003

One of the most important matters in designing network and realizing service, is to grip on the traffic characteristics. Conventional traffic prediction and analysis used the models which based on the Poisson or Markovian. Recently, experimental research on the LAN, WAN and VBR traffic properties have been pointed rut that they weren't able to display actual real traffic specificities because the models based on the Poisson assumption had been underestimated the long range dependency of network traffic and self-similar peculiarities, it has been lately presented that the new approach method using self-similarity characteristics as similar as the real traffic models. Therefore, in this paper, we generated self-similar data traffic like real traffic as background load. On the existing ABR congestion control algorithm, we analyzed by classify into ACR, buffer utilization. cell drop rate, transmission throughput with the representative EFCI, ERICA, EPRCA and NIST twitch algorithm to show the efficient reaction about the burst traffic.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.3
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• pp.416-420
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• 2002

Traditional queueing analyses are very useful for designing a network's capacity and predicting there performances, however most of the predicted results from the queueing analyses are quite different from the realistic measured performance. And recent empirical studies on LAN, WAN and VBR traffic characteristics have indicated that the models used in the traditional Poisson assumption can't properly predict the real traffic properties due to under estimation of the long range dependence of network traffic and self-similarity In this parer self-similar characteristics over statistical approaches and real time network traffic measurements are estimated It is also shown that the self- similar traffic reflects network traffic characteristics by comparing source model.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.5
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• pp.1-10
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• 2007

Most of natural images have a special property, what is called self-similarity, which is the basis of fractal image coding. Even though an image has local stationarity in several homogeneous regions, it is generally non-stationarysignal, especially in edge region. This is the main reason that poor results are induced in linear techniques. In order to overcome the difficulty we propose a non-linear technique using self-similarity in the image. In our work, an image is classified into stationary and non-stationary region with respect to sample variance. In case of stationary region, do-noising is performed as simply averaging of its neighborhoods. However, if the region is non-stationary region, stationalization is conducted as make a set of center pixels by similarity matching with respect to bMSE(block Mean Square Error). And then do-nosing is performed by Gaussian weighted averaging of center pixels of similar blocks, because the set of center pixels of similar blocks can be regarded as nearly stationary. The true image value is estimated by weighted average of the elements of the set. The experimental results show that our method has better performance and smaller variance than other methods as estimator.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1112-1113
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• 2007

능동형 자기베어링과 보(Beam)와 피봇으로 구성되는 시소 (SISO)는 제어적 관점에서 보면 많은 유사성이 있다. 본 논문에서는 Carl R. Knospe 등이 제안한 보와 피봇을 이용한 1자유도 자기베어링 시뮬레이터를 실제 제작하여, 비레 미분 적분 제어 알고리즘을 적용한 디지털 제어기와, 큰 힘을 발생시키기 위해서는 필연적인 전자석 코일의 큰 인덕턴스에 의해 제한되는 Bandwidth(BW)를 보상하기 위해 앞섬 보상기를 가지는 전류제어기를 구현하였다. 복잡한 자기베어링 시스템을 기구적으로 간단한 보와 피봇 시스템으로 상사시킴으로써, 자기베어링 제어 알고리즘 개발 및 파라미터 튜닝의 목적을 달성할 수 있었다.

• Journal of KIISE:Databases
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• v.29 no.5
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• pp.358-366
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• 2002

Feature-based similarity retrieval become an important research issue in image database systems. The features of image data are useful to discrimination of images. In this paper, we propose the high speed k-Nearest Neighbor search algorithm based on Self-Organizing Maps. Self-Organizing Maps(SOM) provides a mapping from high dimensional feature vectors onto a two-dimensional space and generates a topological feature map. A topological feature map preserves the mutual relations (similarities) in feature spaces of input data, and clusters mutually similar feature vectors in a neighboring nodes. Therefore each node of the topological feature map holds a node vector and similar images that is closest to each node vector. We implemented a k-NN search for similar image classification as to (1) access to topological feature map, and (2) apply to pruning strategy of high speed search. We experiment on the performance of our algorithm using color feature vectors extracted from images. Promising results have been obtained in experiments.