• Proceedings of the IEEK Conference
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• 2003.07a
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• pp.537-540
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• 2003

본 논문에서는 터보 디코더에 사용되는 MAP 알고리즘의 저전력 구조를 제안한다. 터보 디코더 알고리즘 중 하나인 MAP 알고리즘은 많은 메모리 사이즈와 복잡한 연산량을 가진다. 본 논문에서는 메모리 사이즈를 줄이기 위하여 두 번의 상태 천이(branch metric) 과정을 하나로 통합 계산하는 방식을 제안하였다. 제안된 방식으로 구한 상태 천이 값을 이용해서 FSM(Forward State Metric)값을 구하면 BM(branch metric)값이 다음 상태의 FSM에 포함되어지므로 APP(A Posteriori Probability)를 계산할 때 BM부분이 빠져 LLR(Log Likelihood Ratio)의 연산량을 줄일 수 있다. 실험결과 기존의 MAP 알고리즘과 동일 성능을 가지면서 MAP 알고리즘을 개선한 Pietrobon 알고리즘을 log-MAP 알고리즘에 적용하여 LLR 연산량을 비교했을 때 덧셈 연산을 반으로 줄일 수 있음을 확인하였다.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.2
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• pp.397-404
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• 2018

As the usage of internet is increasing, a lot of user behavior are written in a log file and the researches and industries using the log files are getting activated recently. This paper uses the Hadoop based on open source distributed computing platform and proposes a customized tourism system by analyzing user behaviors in the log files. The proposed system uses Google Analytics to get user's log files from the website that users visit, and stores search terms extracted by MapReduce to HDFS. Also it gathers features about the sight-seeing places or cities which travelers want to tour from travel guide websites by Octopus application. It suggests the customized cities by matching the search terms and city features. NBP(next bit permutation) algorithm to rearrange the search terms and city features is used to increase the probability of matching. Some customized cities are suggested by analyzing log files for 39 users to show the performance of the proposed system.

• 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.

• ETRI Journal
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• v.41 no.3
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• pp.308-315
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• 2019

Due to its high spectrum efficiency, 64-amplitude phase-shift keying (64-APSK) is one of the primary technologies used in deep space communications and digital video broadcasting through satellite-second generation. However, 64-APSK suffers from considerable computational complexity because of the de-mapping method that it employs. In this study, a low-complexity de-mapping method for (4 + 12 + 20 + 28) 64-APSK is proposed in which we take full advantage of the symmetric characteristics of each symbol mapping. Moreover, we map the detected symbol to the first quadrant and then divide the region in this first quadrant into several partitions to simplify the formula. Theoretical analysis shows that the proposed method requires no operation of exponents and logarithms and involves only multiplication, addition, subtraction, and judgment. Simulation results validate that the time consumption is dramatically decreased with limited degradation of bit error rate performance.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.388-389
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• 2008

이중 이진 (double binary) 터보 디코더는 오류 정정 코드 중 하나로써, 높은 오류 정정 성능으로 인해 IEEE 802.16 표준 (WiMAX)에서 사용되고 있다. Maximum ${\alpha}$ posteriori probability (MAP) 디코딩 블록은 이중 이진 터보 디코더의 가장 핵심적인 블록으로, 본 논문은 이를 구현하기 위한 알고리즘 중 하나인 improved Max-Log-MAP 알고리즘에 대한 효율적인 하드웨어 구조를 제안한다. 제안하는 하드웨어 구조는 기존의 하드웨어 구조와 비교하였을 때, 오류 정정 성능은 동일만 반면, 구떤 복잡도는 감소한다. 0.13um 공정에서 입력 비트폭을 8비트로 가정하고 시뮬레이션 한 결과, 속도와 칩 면적, 그리고 소비전력 측면에서 각각 8.92%, 1845%, 그리고 29.93%의 향상을 보인다. 제안하는 구조를 WIMAX 설계에 적용하여 성능 개선을 이끌어낼 수 있다.

• The Journal of Bigdata
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• v.1 no.2
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• pp.1-8
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• 2016

User activity log includes lots of hidden information, however it is not structured and too massive to process data, so there are lots of parts uncovered yet. Especially, it includes time series data. We can reveal lots of parts using it. But we cannot use log data directly to analyze users' behaviors. In order to analyze user activity model, it needs transformation process through extra framework. Due to these things, we need to figure out user activity model analysis framework first and access to data. In this paper, we suggest a novel framework model in order to analyze user activity model effectively. This model includes MapReduce process for analyzing massive data quickly in the distributed environment and data architecture design for analyzing user activity model. Also we explained data model in detail based on real online service log design. Through this process, we describe which analysis model is fit for specific data model. It raises understanding of processing massive log and designing analysis model.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.42 no.9 s.339
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• pp.73-80
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• 2005

The Turbo decoders based on Log-MAP decoding algorithm inherently requires large amount of memory and intensive complexity of hardware due to iterative decoding, despite of excellent decoding efficiency. To decrease the large amount of memory and reduce hardware complexity, the result of previous research. And this paper design the Turbo decoder applicable to the 3G W-CDMA systems. Through the result of previous research, we decided 5-bits for the received data 6-bits for a priori information, and 7-bits for the quantization state metrics. The error correction term for $MAX^{*}$ operation which is the main function of Log-MAP decoding algorithm is implemented with very small hardware overhead. The proposed Turbo decoder is synthesized in $0.35\mu$m Hynix CMOS technology. The synthesized result for the Turbo decoder shows that it supports a maximum 9Mbps data rate, and a BER of $10^{-6}$ is achieved(Eb/No=1.0dB, 5 iterations, and the interleaver size $\geq$ 2000).

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.3
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• pp.523-533
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• 2014

It has been spent too much time to figure out the incident route when we are facing computer security incident. The incident often recurs moreover the damage is expanded because critical clues are lost while we are wasting time with hesitation. This paper suggests to build a Digital Evidence Map (DEM) in order to find out the incident cause speedy and accurately. The DEM is consist of the log chain which is a mesh relationship between machine data. And the DEM should be managed constantly because the log chain is vulnerable to various external facts. It could help handle the incident quickly and cost-effectively by acquainting it before incident. Thus we can prevent recurrence of incident by removing the root cause of it. Since the DEM has adopted artifacts in data as well as log, we could make effective response to APT attack and Anti-Forensic.

• Journal of KIISE:Databases
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• v.31 no.3
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• pp.274-286
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• 2004

In mobile client-server environments, mobile clients usually are disconnected with their server because of high cost of wireless communication and keep their own local copies to provide efficient updating the cached map. The update of the server database leads to invalidation of the cached map in the client side. To solve the issues of invalidation of the cached map, it is not efficient to resend part of the updated server database to clients whenever the updating of the server database occurs. This paper proposes a log-based update propagation method to propagate the server's update into its relevant clients by using only update logs. Too many logs increasingly accumulate as the sever database is updated several times. The sequential search of the relevant log data for a specific client is time-consuming. Sending of unnecessary logs should be avoided for reducing the overhead of communication.'re solve these problems, we first define unnecessary logs and then suggest log reduction methods to avoid or cancel creating unnecessary logs. The update log index is used for quickly retrieving relevant logs.

• 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.