• The KIPS Transactions:PartA
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• v.14A no.3 s.107
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• pp.151-158
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• 2007

Quadtree, which is a hierarchical data structure, is a very important data structure to represent images. The linear quadtree representation as a way to store a quadtree is efficient to save space compared with other representations. Therefore, it has been widely studied to develop efficient algorithms to execute operations related with quadtrees. The computation of block location is one of important geometry operations in image processing, which extracts a component completely including a given block. In this paper, we present a constant time algorithm to compute the block location of images represented by quadtrees, using three-dimensional $n\times n\times n$ processors on RMESH(Reconfigurable MESH). This algorithm has constant-time complexity by using efficient basic operations to deal with the locational codes of quardtree on the hierarchical structure of $n\times n\times n$ RMESH.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.735-740
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• 2012

In this paper, we introduce design methodologies of polynomial radial basis function neural network classifier with the aid of Principal Component Analysis(PCA) and Linear Discriminant Analysis(LDA). By minimizing the information loss of given data, Feature data is obtained through preprocessing of PCA and LDA and then this data is used as input data of RBFNNs. The hidden layer of RBFNNs is built up by Fuzzy C-Mean(FCM) clustering algorithm instead of receptive fields and linear polynomial function is used as connection weights between hidden and output layer. In order to design optimized classifier, the structural and parametric values such as the number of eigenvectors of PCA and LDA, and fuzzification coefficient of FCM algorithm are optimized by Artificial Bee Colony(ABC) optimization algorithm. The proposed classifier is applied to some machine learning datasets and its result is compared with some other classifiers.

• Journal of the Society of Naval Architects of Korea
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• v.35 no.4
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• pp.118-125
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• 1998

This paper presents the hybrid curve approximation with geometric boundary conditions as position vector and tangent vector of start and end point using a B-spline approximation and a genetic algorithm First, H-spline approximation generates control points to fit B-spline curries through specified data points. Second, these control points are modified by genetic algorithm(with floating point representation) under geometric boundary conditions. This method would be able to execute the efficient design work without fairing.

• Journal of the Korean Data and Information Science Society
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• v.9 no.1
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• pp.29-36
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• 1998

A Simple, closed form expression is derived for a linear smoothing spline by Eubank (1994, 1997). We introduced his estimater and studied how well examples are fitted to this estimator with the values of minimum generalized cross validation.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2012.07a
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• pp.373-376
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• 2012

Multiple-Input Multiple-Output (MIMO) 시스템의 복잡도를 감소시키는 방식은 실생활에서 MIMO 시스템을 활용하는 데에 있어 중요한 부분이다. 널리 사용되는 Maximum Likelihood (ML) 복호기의 경우 낮은 에러오율 (BER) 을 보여주지만 복잡도가 높다. 실생활에 활용하기 위하여 ML 복호기의 복잡도를 감소시킬 필요가 있고 이에 Sphere Decoding Algorithm (SDA) 이 제안되었다. 이를 발전시킨 List Sphere Decoding(LSD) 은 여러 종류가 있다. 그 중에 넓이 우선 탐색 방식인 K-Best LSD 알고리즘은 각 레이어에서 리스트의 크기가 복잡도와 밀접한 연관이 있다. 본 논문에서는 기존의 K-Best LSD 알고리즘에 기반하여 초기 반지름 설정 및 선형적으로 리스트 크기를 감소시키는 방식으로 K-Best LSD 알고리즘의 복잡도를 기존 알고리즘에 비해 크게 낮추면서도 비트 오율 성능 열화가 적은 알고리즘을 제안하고 전산 실험을 통해 이를 검증한다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.03a
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• pp.99-102
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• 1998

본 논문에서는 복잡하고 비선형적인 시스템의 최적 모델링을 우해서 지능형 퍼지-뉴럴네트워크의 최적 모델 구축을 위한 방법을 제안한다. 기본 모델은 퍼지 추론 시스템의 언어적인 규칙생성의 장점과 뉴럴 네트워크의 학습기능을 결합한 FNNs 모델을 사용한다. FNNs 모델의 퍼지 추론부는 간략추론이 사용되고, 학습은 요류 역전파 알고리즘을 사용하여 다른 모델들에 비해 학습속도가 빠르고 수렴능력이 우수하다. 그러나 기본 모델은 주어진 시스템에 대하여 퍼지 공간을 균등하게 분할하여 퍼지 소속을 정의한다. 이것은 비선형 시스템의 모델링에 있어어서 성능을 저하시켜 최적의 모델을 얻기가 어렵다. 논문에서는 주어진 데이터의 특성을 부여한 공간을 설정하기 위하여 클러스터링 알고리즘을 사용한다. 클러스터링 알고리즘은 주어진 시스템에 대하여 상호 연관성이 있는 데이터들끼리 특성을 나누어 몇 개의 클래스를 이룬다. 클러스터링 알고리즘을 사용하여 초기 FNNs 모델의 퍼지 공간을 나누고 소속함수를 정의한다. 또한, 최적화 기법중의 하나로 자연선택과 자연계의 유전자 메카니즘에 바탕을 둔 탐색 알고리즘인 유전자 알고리즘을 사용하여 주\ulcorner 진 모델에 대하여 최적화를 수행한다. 또한 본 연구에서는 학습 및 테스트 데이터의 성능 결과의 상호 균형을 얻기 위한 하중값을 가긴 성능지수가 제시된다.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.12
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• pp.205-209
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• 2013

A conventional acoustic echo suppressor (AES) considering only room impulse response between a loudspeaker and a microphone eliminates acoustic echo from the microphone input. However, in a nonlinear acoustic echo environment, the conventional AES degraded because of a nonlinearity of the loudspeaker. In this paper, we adopt AES based on the frequency-domain second-order Volterra filter using Least Square method. For comparing performances, we conduct objective tests including Echo Return Loss Enhancement (ERLE) and Speech Attenuation (SA). The proposed algorithm shows better performance than the conventional in both linear and nonlinear acoustic echo environments.

• Journal of the Korean earth science society
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• v.38 no.4
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• pp.263-268
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• 2017

The determination algorithm of the location of a line source with strike and dip using the gravity gradient tensor on a single profile is proposed. We already proposed the determination of strike and dip in the previous paper and then, now we improved the algorithm to locate a line source after determining strike and dip. The strike and dip of the line source can be determined by rotating the gravity gradient tensor matrix as reducing 2 independent components. Using the ratio of remaining 2 components, the location can be determined by the least square manner of the pointing vectors on each observation point. A synthetic model is tested for proving the usefulness of the proposed algorithm.

• Journal of Digital Convergence
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• v.14 no.1
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• pp.203-209
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• 2016

In this paper, we introduce a fuzzy inference systems for nonlinear inference using fuzzy cluster. Typically, the generation of fuzzy rules for nonlinear inference causes the problem that the number of fuzzy rules increases exponentially if the input vectors increase. To handle this problem, the fuzzy rules of fuzzy model are designed by dividing the input vector space in the scatter form using fuzzy clustering algorithm which expresses fuzzy cluster. From this method, complex nonlinear process can be modeled. The premise part of the fuzzy rules is determined by means of FCM clustering algorithm with fuzzy clusters. The consequence part of the fuzzy rules have four kinds of polynomial functions and the coefficient parameters of each rule are estimated by using the standard least-squares method. And we use the data widely used in nonlinear process for the performance and the nonlinear characteristics of the nonlinear process. Experimental results show that the non-linear inference is possible.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.9 no.3
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• pp.291-297
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• 2016

In this paper, we are comparative analysis of exit algorithm and proposal algorithm for desired target direction of arrival estimation in correlation signal system. Proposed algorithm in this paper is to decrease target direction of arrival an estimation error probability using bayesian, maximum posterior, and MUSIC algorithm in order to decrease direction of arrival error probability as optimize and use linear constrained minimum variance to update weight value. Through simulation, we were comparative analysis proposed algorithm and exit MUSIC algorithm. In case SNR is 10dB and antenna element arrays are 9 and 12, We show the superior performance of the proposed method relative to the class method to decrease of signal estimation error probability about 11% and 13%, respectively.