• Journal of the Institute of Convergence Signal Processing
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• v.6 no.1
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• pp.15-22
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• 2005

In this paper, we propose a face recognition system by using the CCD color image. We first get the face candidate image by using YCbCr color model and adaptive skin color information. And we use it initial curve of active contour model to extract face region. We use the Eye map and mouth map using color information for extracting facial feature from the face image. To obtain center point of Log-polar image, we use extracted facial feature from the face image. In order to obtain feature vectors, we use extracted coefficients from DCT and wavelet transform. To show the validity of the proposed method, we performed a face recognition using neural network with BP learning algorithm. Experimental results show that the proposed method is robuster with higher recogntion rate than the conventional method for the rotation and scale variant.

• 한국정보통신설비학회:학술대회논문집
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• 2009.08a
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• pp.190-196
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• 2009

In this paper, performance of a turbo-coded OFDM system is analyzed and simulated in a power line communication channel. Since the power line communication system typically operates in a hostile environment, turbo code has been employed to enhance reliability of transmitted data. The performance is evaluated in terms of bit error probability. As turbo decoding algorithms, MAP (maximum a posteriori), Max-Log-MAP, and SOVA (soft decision Viterbi output) algorithms are chosen and their performances are compared. From simulation results, it is demonstrated that Max-Log-MAP algorithm is promising in terms of performance and complexity. It is shown that performance is substantially improved by increasing the number of iterations and interleaver length of a turbo encoder. The results in this paper can be applied to OFDM-based high-speed power line communication systems.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.11C
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• pp.1045-1051
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• 2003

The SOVA, which produces the soft decision value, can be used as a sub-optimum solution for concatenated codes such as turbo codes, since it is computationally efficient compared with the optimum MAP algorithm. In this paper, we propose an efficient implementation of the SOVA used for decoding turbo codes, by reducing the number of calculations for soft decision values and trace-back operations. In order to utilize the memory efficiently, the whole block of turbo codes is divided into several sub-blocks in the proposed algorithm. It is demonstrated that the proposed algorithm requires less computation than the conventional algorithm, while providing the same overall performance.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.6A
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• pp.611-616
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• 2006

In this paper, we propose a practical implementation method of a soft bit decision expression for an R-QAM (Gray coded Rectangular Quadrature Amplitude Modulation) signal based on the Max-Log-MAP algorithm. The parameters of the soft decision expression for the practical implementation can be obtained with simple arithmetic functions associated with some deterministic parameters such as a received value, distances between symbols, and the order of modulation on a signal space. Also, we analyze the performance of an iterative decoding scheme for the QAM signal with I/Q phase unbalance. The unbalance results from the non-ideal characteristic of components such as a phase shifter between in-phase and quadrature paths for quadrature modulator/demondulator.

• Proceedings of the Korea Information Processing Society Conference
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• 2002.04b
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• pp.1615-1618
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• 2002

본 논문은 MAP(Maximum A Posteriori) 복호 알고리즘을 이용한 MAP Decoder의 설계에 관해 다룬다. 채널코딩기법은 채널을 통해서 디지털 정보를 전송할 때 신뢰성을 제공하기 위해서 사용되어 진다. 즉 수신단에서 수신된 정보의 오류를 검사하고 수정하기 위한 목적으로 송신단에서는 디지털 정보에 부가 정보를 첨가해서 전송하게 된다. 그래서 무선 이동 통신에서 성능이 우수한 채널코딩기법은 우수한 통신 품질을 위해서는 필수적이라고 할 수 있다. 최근에 Shannon의 한계에 매우 근접한 성능으로 많이 알려진 오류정정부호로 터보코드가 발표되었고 많은 연구가 진행되고 있다. 터보코드의 부호기로는 RSC(recursive systematic convolutional)코드가 사용되며 디코딩 알고리즘으로는 주로 MAP 복호 알고리즘을 사용한다. 본 논문에서 제안된 MAP 복호기는 하드웨어로 구현하기 위해서 변형된 LOG-MAP 복호 알고리즘을 이용하였고 터보디코더의 반복 복호에 이용할 수 있다.

• Journal of the Korea Society of Computer and Information
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• v.25 no.12
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• pp.165-172
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• 2020

Recently, due to exponential growth of access information on the web, the importance of predicting a user's next web page use has been increasing. One of the methods that can be used for predicting user's next web page is deep learning. To predict next web page, web logs are analyzed by data preprocessing and then a user's next web page is predicted on the output of the analyzed web logs using a deep learning algorithm. In this paper, we propose a framework for web page prediction that includes methods for web log preprocessing followed by deep learning techniques for web prediction. To increase the speed of preprocessing of large web log, a Hadoop based MapReduce programming model is used. In addition, we present a web prediction system that uses an efficient deep learning technique on the output of web log preprocessing for training and prediction. Through experiment, we show the performance improvement of our proposed method over traditional methods. We also show the accuracy of our prediction.

• The Journal of the Korea Contents Association
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• v.8 no.5
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• pp.9-18
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• 2008

Web-services have originally been devised to share information as open services. In connection with it, hacking incidents have surged. Currently, Web-log analysis plays a crucial clue role in detecting Web-hacking. A growing number of cases are really related to perceiving and improving the weakness of Web-services based on Web-log analysis. Such as this, Web-log analysis plays a central role in finding out problems that Web has. Hence, Our research thesis suggests Web-DoS-hacking detective technique In the process of detecting such problems through SOM algorithm, the emergence frequency of BMU(Best Matching Unit) was studied, assuming the unit with the highest emergence frequency, as abnormal, and the problem- detection technique was recommended through the comparison of what's called BMU as input data.

• Journal of Internet Computing and Services
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• v.20 no.1
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• pp.87-96
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• 2019

In this paper, we propose a MapReduce-supported clustering technique for collecting and classifying distributed workflow enactment event logs as a preprocessing tool. Especially, we would call the distributed workflow enactment event logs as Workflow BIG-Logs, because they are satisfied with as well as well-fitted to the 5V properties of BIG-Data like Volume, Velocity, Variety, Veracity and Value. The clustering technique we develop in this paper is intentionally devised for the preprocessing phase of a specific workflow process mining and analysis algorithm based upon the workflow BIG-Logs. In other words, It uses the Map-Reduce framework as a Workflow BIG-Logs processing platform, it supports the IEEE XES standard data format, and it is eventually dedicated for the preprocessing phase of the ${\rho}$-Algorithm that is a typical workflow process mining algorithm based on the structured information control nets. More precisely, The Workflow BIG-Logs can be classified into two types: of activity-based clustering patterns and performer-based clustering patterns, and we try to implement an activity-based clustering pattern algorithm based upon the Map-Reduce framework. Finally, we try to verify the proposed clustering technique by carrying out an experimental study on the workflow enactment event log dataset released by the BPI Challenges.

• The KIPS Transactions:PartB
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• v.8B no.6
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• pp.651-656
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• 2001

In this paper, we propose Improved Self-Organized Graph(Improved SOG：$SOG^*$)thinning method, which maintains the excellent thinning results of Self-organized graph(SOG) built from Self-Organizing features map and improves the performance of modified SOG using a new incremental learning method of Kohonen features map. In the experiments, this method shows the thinning results equal to those of SOG and the time complexity O((logM)3) superior to it. Therefore, the proposed method is useful for the feature extraction from digits and characters in the preprocessing step.

• Journal of KIISE
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• v.43 no.2
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• pp.229-236
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• 2016

Theta join is one of the essential and important types of queries in database systems. As the amount of data needs to be processed increases, processing theta joins with a single machine becomes impractical. Therefore, theta join algorithms using distributed computing frameworks have been studied widely. Although one of the state-of-the-art theta-join algorithms uses M-Bucket-I heuristic, it is hard to use since running time of M-Bucket-I heuristic, which computes a mapping from a record to a reducer (i.e., reducer mapping), is O(n) where n is the size of input data. In this paper, we propose MBI-I algorithm which reduces the running time of M-Bucket-I heuristic to $O(r_{max}log\;n)$ and gives the same result as M-Bucket-I heuristic does. We also conducted several experiments to show algorithm and confirmed that our algorithm can improve the performance of a theta join by 10%.