• Journal of Korea Multimedia Society
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• v.10 no.10
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• pp.1284-1294
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• 2007

In this paper, a blind nonlinear channel equalization is implemented by using a Modified Fuzzy C-Means (MFCM) algorithm. The proposed MFCM searches the optimal channel output states of a nonlinear channel from the received symbols, based on the Bayesian likelihood fitness function instead of a conventional Euclidean distance measure. Next, the desired channel states of a nonlinear channel are constructed with the elements of estimated channel output states, and placed at the center of a Radial Basis Function (RBF) equalizer to reconstruct transmitted symbols. In the simulations, binary signals are generated at random with Gaussian noise. The performance of the proposed method is compared with that of a hybrid genetic algorithm (GA merged with simulated annealing (SA): GASA), and the relatively high accuracy and fast searching speed are achieved.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2007.05a
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• pp.382-388
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• 2007

In this paper, a modified Fuzzy C-Means (MFCM) algorithm is presented for nonlinear blind channel equalization. The proposed MFCM searches the optimal channel output states of a nonlinear channel from the received symbols, based on the Bayesian likelihood fitness function instead of a conventional Euclidean distance measure. Next, the desired channel states of a nonlinear channel are constructed with the elements of estimated channel output states, and placed at the center of a Radial Basis Function (RBF) equalizer to reconstruct transmitted symbols. In the simulations, binary signals are generated at random with Gaussian noise. The performance of the proposed method is compared with that of a hybrid genetic algorithm (GA merged with simulated annealing (SA): GASA), and the relatively high accuracy and fast searching speed are achieved.

• Journal of KIISE:Computer Systems and Theory
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• v.31 no.1_2
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• pp.78-85
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• 2004

Given a set S of n points in the plane, a minimum-diameter spanning tree(MDST) for the set might have a degree up to n-1. This might cause the degradation of the network performance because the node with high degree should handle much more requests than others relatively. Thus it is important to construct a spanning tree network with small degree and diameter. This paper presents an algorithm to construct a spanning tree for S satisfying the following four conditions: (1) the degree is controled as an input, (2) the tree diameter is no more than constant times the diameter of MDST, (3) the tree is monotone (even if arbitrary point is fixed as a root of the tree) in the sense that the Euclidean distance from the root to any node on the path to any leaf node is not decreasing, and (4) there are no crossings between edges of the tree. The monotone property will play a role as an aesthetic criterion in visualizing the tree in the plane.

• Journal of Intelligence and Information Systems
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• v.15 no.4
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• pp.23-36
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• 2009

Network intrusion detection have been continuously improved by using data mining techniques. There are two kinds of methods in intrusion detection using data mining-supervised learning with class label and unsupervised learning without class label. In this paper we have studied the way of improving network intrusion detection accuracy by using LBG clustering algorithm which is one of unsupervised learning methods. The K-means method, that starts with random initial centroids and performs clustering based on the Euclidean distance, is vulnerable to noisy data and outliers. The nonuniform binary split algorithm uses binary decomposition without assigning initial values, and it is relatively fast. In this paper we applied the EM(Expectation Maximization) based LBG algorithm that incorporates the strength of two algorithms to intrusion detection. The experimental results using the KDD cup dataset showed that the accuracy of detection can be improved by using the LBG algorithm.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.4
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• pp.431-437
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• 2004

This paper propose a facial recognition method based on an ubiquitous computing that is one of next generation intelligence technology fields. The facial images are acquired by a mobile device so-called cellular phone camera. We consider a mobile security using facial feature extraction and recognition process. Facial recognition is performed by the PCA and fuzzy LDA algorithm. Applying the discrete wavelet based on multi-resolution analysis, we compress the image data for mobile system environment. Euclidean metric is applied to measure the similarity among acquired features and then obtain the recognition rate. Finally we use the mobile equipment to show the efficiency of method. From various experiments, we find that our proposed method shows better results, even though the resolution of mobile camera is lower than conventional camera.

• Journal of Korea Spatial Information System Society
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• v.9 no.3
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• pp.67-80
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• 2007

Recently, many studies on query processing algorithms has been done for spatial networks, such as roads and railways, instead of Euclidean spaces, in order to efficiently support LBS(location-based service) and Telematics applications. However, both a closest pairs query and an e-distance join query require a very high cost in query processing because they can be answered by processing a set of POIs, instead of a single POI. Nevertheless, the query processing cost for closest pairs and e-distance join queries is rapidly increased as the number of k (or the length of radius) is increased. Therefore, we propose both a closest pairs query processing algorithm and an e-distance join query processing algorithm using a POI-based materialization technique so that we can process closest pairs and e-distance join queries in an efficient way. In addition, we show the retrieval efficiency of the proposed algorithms by making a performance comparison of the conventional algorithms.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.8C
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• pp.675-680
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• 2007

This paper presents a new method for classification of underwater transient signals, which employs frame-based decision with Mel Frequency Cepstral Coefficients(MFCC). The MFCC feature vector is extracted frame-by-frame basis for an input signal that is detected as a transient signal, and Euclidean distances are calculated between this and all MFCC feature. vectors in the reference database. Then each frame of the detected input signal is mapped to the class having minimum Euclidean distance in the reference database. Finally the input signal is classified as the class that has maximum mapping rate in the reference database. Experimental results demonstrate that the proposed method is very promising for classification of underwater transient signals.

• Journal of Internet Computing and Services
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• v.8 no.6
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• pp.43-53
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• 2007

Intrusion detection system(IDS) has recently evolved while combining signature-based detection approach with anomaly detection approach. Although signature-based IDS tools have been commonly used by utilizing machine learning algorithms, they only detect network intrusions with already known patterns, Ideal IDS tools should always keep the signature database of your detection system up-to-date. The system needs to generate the signatures to detect new possible attacks while monitoring and analyzing incoming network data. In this paper, we propose a new outlier cluster detection algorithm with density (or influence) function, Our method assumes that an outlier is a kind of cluster with similar instances instead of a single object in the context of network intrusion, Through extensive experiments using KDD 1999 Cup Intrusion Detection dataset. we show that the proposed method outperform the conventional outlier detection method using Euclidean distance function, specially when attacks occurs frequently.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.6
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• pp.1323-1329
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• 2014

In this paper, we analyzed efficient decoding scheme with FTN (Faster than Nyquist) method that is transmission method faster than Nyquist theory and increase the throughput. we proposed viterbi equalizer model to minimize ISI (Inter-Symbol Interference) when FTN signal is transmitted. the proposed model utilized interference as branch information. In this paper, to decode FTN singal, we used turbo equalization algorithms that iteratively exchange probabilistic information between soft Viterbi equalizer (BCJR method) and LDPC decoder. By changing the trellis diagram in order to maximize Euclidean distance, we confirmed that performance was improved compared to conventional methods as increasing throughput of FTN signal.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.3A
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• pp.223-231
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• 2011

It has been shown that the performance of multiple-input multiple-output (MIMO) spatial multiplexing systems is significantly degraded when spatial correlation exists between transmit and receive antenna pairs. In this paper, we investigate designs of a new statistical precoder for spatial multiplexing systems with maximum likelihood (ML) receiver which requires only correlation statistics at the transmitter. Two kinds of closed-form solution precoders based on rotation and power allocation are proposed by means of maximizing the minimum E tlidean distance of joint symbol constellations. In addition, we extend our results to linear receivers for correlated channels. We provide a method which yields the same profits from the proposed precoders based on a simple zero-forcing (ZF) receiver. The simulation shows that 2dB and 8dB gains are achieved for ML and ZF systems with two transmit antennas, respectively, compared to the conventional systems.