• Journal of the Institute of Electronics Engineers of Korea SD
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• v.46 no.11
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• pp.92-100
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• 2009

In this paper, we analyze the hardware architecture of Low Density Parity Check (LDPC) decoder using Log Likelihood Ration-Belief Propagation (LLR-BP) decoding algorithm. Various design issues that affect the decoding performance and the hardware complexity are discussed and the tradeoffs between the hardware complexity and the performance are analyzed. The message data for passing error probability is quantized to 7 bits and among them the fractional part is 4 bits. To maintain the decoding performance, the integer and fractional parts for the intrinsic information is 2 bits and 4 bits respectively. We discuss the alternate implementation of $\Psi$(x) function using piecewise linear approximation. Also, we improve the hardware complexity and the decoding time by applying overlapped scheduling.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.2
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• pp.53-64
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• 2011

In this paper, we propose a channel estimation of side information method based received motion vectors for distributed video coding. The proposed decoder estimates motion vectors of side information and transmits it to the encoder. As the proposed encoder generates side information which is the same to one in the decoder with received motion vectors, accuracy of side information of the decoder is assessed and it is transmitted to decoder. The proposed decoder can also estimate accurate crossover probability with received error information. As the proposed method conducts correct belief propagation, computational complexity of the channel decoder decreases and error correction capability is significantly improved with the smaller amount of parity bits. Experimental results show that the proposed algorithm is better in rate-distortion performance and it is faster than several conventional distributed video coding methods.

• 한국태양에너지학회:학술대회논문집
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• 2012.03a
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• pp.419-424
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• 2012

This paper discusses the state-of-the-art techniques in real-time state estimation for the Smart Microgrids. The most popular method used in traditional power system state estimation is a Weighted Least Square(WLS) algorithm which is based on Maximum Likelihood(ML) estimation under the assumption of static system state being a set of deterministic variables. In this paper, we present a survey of dynamic state estimation techniques for Smart Microgrids based on Belief Propagation (BP) when the system state is a set of stochastic variables. The measurements are often too sparse to fulfill the system observability in the distribution network of microgrids. The BP algorithm calculates posterior distributions of the state variables for real-time sparse measurements. Smart Microgrids are modeled as a factor graph suitable for characterizing the linear correlations among the state variables. The state estimator performs the BP algorithm on the factor graph based the stochastic model. The factor graph model can integrate new models for solar and wind correlation. It provides the Smart Microgrids with a way of integrating the distributed renewable energy generation. Our study on Smart Microgrid state estimation can be extended to the estimation of unbalanced three phase distribution systems as well as the optimal placement of smart meters.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.1A
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• pp.57-63
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• 2012

In this paper, we propose a real-time view interpolation method using virtual viewpoint disparity estimation and convergence check. For the real-time process, we estimate a disparity map at the virtual viewpoint from stereo images using the belief propagation method. This method needs only one disparity map, compared to the conventional methods that need two disparity maps. In the view synthesis part, we warp pixels from the reference images to the virtual viewpoint image using the disparity map at the virtual viewpoint. For real-time acceleration, we utilize a high speed GPU parallel programming, called CUDA. As a result, we can interpolate virtual viewpoint images in real-time.

• Science of Emotion and Sensibility
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• v.20 no.2
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• pp.161-170
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• 2017

Emotion affects many parts of human life such as learning ability, behavior and judgment. It is important to understand human nature. Emotion can only be inferred from facial expressions or gestures, what it actually is. In particular, emotion is difficult to classify not only because individuals feel differently about emotion but also because visually induced emotion does not sustain during whole testing period. To solve the problem, we acquired bio-signals and extracted features from those signals, which offer objective information about emotion stimulus. The emotion pattern classifier was composed of unsupervised learning algorithm with hidden nodes and feature vectors. Restricted Boltzmann machine (RBM) based on probability estimation was used in the unsupervised learning and maps emotion features to transformed dimensions. The emotion was characterized by non-linear classifiers with hidden nodes of a multi layer neural network, named deep belief network (DBN). The accuracy of DBN (about 94 %) was better than that of back-propagation neural network (about 40 %). The DBN showed good performance as the emotion pattern classifier.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.3
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• pp.418-424
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• 2018

Detection and classification of underwater targets is an important issue for both military and non-military purposes. Recently, many performance improvements are being reported in the field of pattern recognition with the development of deep learning technology. Among the results, DBN showed good performance when used for pre-training of DNN. In this paper, DBN was used for the classification of underwater targets using active sonar, and the results are compared with that of the conventional BPNN. We synthesized active sonar target signals using 3-dimensional highlight model. Then, features were extracted based on FrFT. In the single aspect based experiment, the classification result using DBN was improved about 3.83% compared with the BPNN. In the case of multi-aspect based experiment, a performance of 95% or more is obtained when the number of observation sequence exceeds three.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.1
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• pp.7-12
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• 2005

This paper deals with the fuzzy modeling for the complex and uncertain nonlinear systems, in which conventional and mathematical models may fail to give satisfactory results. Finally, we provide numerical examples to evaluate the feasibility and generality of the proposed method in this paper. The expert system which introduces fuzzy logic in order to process uncertainties is called fuzzy expert system. The fuzzy expert system, however, has a potential problem which may lead to inappropriate results due to the ignorance of some information by applying fuzzy logic in reasoning process in addition to the knowledge acquisition problem. In order to overcome these problems, We construct fuzzy inference network by extending the concept of reasoning network in this paper. In the fuzzy inference network, the propositions which form fuzzy rules are represented by nodes. And these nodes have the truth values representing the belief values of each proposition. The logical operators between propositions of rules are represented by links. And the traditional propagation rule is modified.

• The Transactions of the Korea Information Processing Society
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• v.3 no.5
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• pp.1138-1146
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• 1996

Fuzzy logic ignores some informations in the reasoning process. Neural networks are powerful tools for the pattern processing. However, to model human knowledges, besides pattern processing capability, the logical reasoning capability is equally important. Another new neural network called neural logic network is able to do the logical reasoning. Because the fuzzy logical reasoning, we construct fuzzy inference net-work based on the neural logic network, extending the existing rule-inferencing network. And the traditional propagation rule is modified. For the search strategies to find out the belief value of a conclusion in the fuzzy inference network, we conduct a simulation to evaluate the search cost for searching sequentially and searching by means of priorities.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2010.11a
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• pp.285-287
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• 2010

본 논문에서는 기존 오류정정부호의 복호 과정에 사용되는 Belief propagation (BP) 알고리즘을 이용한 저밀도 양자 오류정정 부호의 복호 기법에 대해 기술한다. Depolarizing 채널 가정하에 기존 오류정정부호와 다르게 양자 오류정정 부호가 갖는 초기 채널 오류 확률에 의한 성능 열화를 개선하기 위해 초기 채널 오류 확률 정보를 개선하는 기법을 적용하였다. 테너 그래프를 바탕으로 각 체크 노드의 신드롬과 노드의 연결 상태를 고려하여 오류가 발생한 위치를 추적하고 BP 알고리즘에 입력되는 초기 채널 오류 확률 정보를 수정하여 반복 복호 시 발생할 수 있는 성능 열화를 개선하였다.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.3
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• pp.175-182
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• 2017

In this paper, we present a multi-depth generation method using a time-of-flight (ToF) fusion camera system. Multi-view color cameras in the parallel type and ToF depth sensors are used for 3D scene capturing. Although each ToF depth sensor can measure the depth information of the scene in real-time, it has several problems to overcome. Therefore, after we capture low-resolution depth images by ToF depth sensors, we perform a post-processing to solve the problems. Then, the depth information of the depth sensor is warped to color image positions and used as initial disparity values. In addition, the warped depth data is used to generate a depth-discontinuity map for efficient stereo matching. By applying the stereo matching using belief propagation with the depth-discontinuity map and the initial disparity information, we have obtained more accurate and stable multi-view disparity maps in reduced time.