• Journal of the Korea Society of Computer and Information
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• v.17 no.9
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• pp.9-16
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• 2012

In this paper, we propose the two dimensional variable-length vector storage format which can be used for efficient storage of sparse matrix in the FEM (finite element method). The proposed storage format is the method storing only actual needed non-zero values of each row on upper triangular matrix with the total rows N, by using two dimensional variable-length vector instead of $N{\times}N$ large sparse matrix of entire equation of finite elements. This method only needs storage spaces of the number of minimum 1 to maximum 5 in 2D grid structure and the number of minimum 1 to maximum 14 in 3D grid structure of analysis target. The number doesn't excess two times although involving index number. From the experimental result, we can find out that the proposed storage format can reduce the memory space more effectively, as the total number of nodes increases, than the existing skyline storage format storing maximum column height.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.1
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• pp.1-9
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• 1993

In recent days high perfermance graphic operation is needed, since computer graphics is widely used for computer-aided design and simulator using high resolution graphic card. In this paper a graphic accelerator is designd with the functions of graphic primitives generation and geometrical transformations. 1-D Systolic Array Processor for Matris Vector operation is designed and used in main ALU of a graphic accelerator, since these graphic algorithms have comonon operation of Matris Vector. Conclusively, in case that the resolution of graphic domain is 800$\times$600, and 33.3nsec operator is used in a graphic accelerator, 29732 lines per second and approximately 6244 circles per second is generated.

• Speech Sciences
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• v.11 no.1
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• pp.7-20
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• 2004

In this paper, we propose a two-step noise compensation algorithm in feature extraction for achieving robust speech recognition. The proposed method frees us from requiring a priori information on noisy environments and is simple to implement. First, in frequency domain, the Harmonics-based Spectral Subtraction (HSS) is applied so that it reduces the additive background noise and makes the shape of harmonics in speech spectrum more pronounced. We then apply a judiciously weighted variance Feature Vector Normalization (FVN) to compensate for both the channel distortion and additive noise. The weighted variance FVN compensates for the variance mismatch in both the speech and the non-speech regions respectively. Representative performance evaluation using Aurora 2 database shows that the proposed method yields 27.18% relative improvement in accuracy under a multi-noise training task and 57.94% relative improvement under a clean training task.

• Proceedings of the Korean Society of Computer Information Conference
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• 2009.01a
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• pp.49-54
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• 2009

A fuzzy weighted mean method on a 2D shape recognition system is introduced in this paper. The bispectrum based on third order cumulant is applied to the contour sequence of each image for the extraction of a feature vector. This bispectral feature vector, which is invariant to shape translation, rotation and scale, represents a 2D planar image. However, to obtain the best performance, it should be considered certain criterion on the calculation of weights for the fuzzy weighted mean method. Therefore, a new method to calculate weights using means by differences of feature values and their variances with the maximum distance from differences of feature values. is developed. In the experiments, the recognition results with fifteen dimensional bispectral feature vectors, which are extracted from 11.808 aircraft images based on eight different styles of reference images, are compared and analyzed.

• Proceedings of the Korean Information Science Society Conference
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• 2006.10c
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• pp.222-225
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• 2006

한국은 다른 나라에 비해 많은 인터넷 사용자를 가지고 있다. 이에 비례해서 한국의 인터넷 유저들은 Spam Mail에 대해 많은 불편함을 호소하고 있다. 이러한 문제를 해결하기 위해 본 논문은 다양한 Feature Weighting, Feature Selection 그리고 문서 분류 알고리즘들을 이용한 한국어 스팸 문서 Filtering연구에 대해 기술한다. 그리고 한국어 문서(Spam/Non-Spam 문서)로부터 영사를 추출하고 이를 각 분류 알고리즘의 Input Feature로써 이용한다. 그리고 우리는 Feature weighting 에 대해 기존의 전통적인 방법이 아니라 각 Feature에 대해 Variance 값을 구하고 Global Feature를 선택하기 위해 Max Value Selection 방법에 적용 후에 전통적인 Feature Selection 방법인 MI, IG, CHI 들을 적용하여 Feature들을 추출한다. 이렇게 추출된 Feature들을 Naive Bayes, Support Vector Machine과 같은 분류 알고리즘에 적용한다. Vector Space Model의 경우에는 전통적인 방법 그대로 사용한다. 그 결과 우리는 Support Vector Machine Classifier, TF-IDF Variance Weighting(Combined Max Value Selection), CHI Feature Selection 방법을 사용할 경우 Recall(99.4%), Precision(97.4%), F-Measure(98.39%)의 성능을 보였다.

• IEIE Transactions on Smart Processing and Computing
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• v.3 no.6
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• pp.388-392
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• 2014

This paper proposes an early termination method of a block vector search for fast encoding of high efficiency video coding (HEVC) screen content coding (SCC). In the proposed algorithm, two blocks indicated by two block vector predictors (BVPs) were first employed as an intra block copy (IBC) search. If the sum of absolute difference (SAD) value of the block is less than a threshold defined empirically, an IBC BV search is terminated early. The initial threshold for early termination is derived by statistical analysis and it can be modified adaptively based on a quantization parameter (QP). The proposed algorithm is evaluated on SCM-2.0 under all intra (AI) coding configurations. Experimental results show that the proposed algorithm reduces IBC BV search time by 29.23% on average while the average BD-rate loss is 0.41% under the HEVC SCC common test conditions (CTC).

• The Transactions of the Korean Institute of Power Electronics
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• v.21 no.6
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• pp.538-541
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• 2016

This paper proposes an LCL-filter design for space vector pulse width modulation (SVM) in grid-connected three-phase inverter systems. Although there are a several studies in progress, the existing methods are erroneous because they do not focus on the other switching methods. This paper presents the design methodology for an LCL-filter that is optimized for SVM switching operations. The design procedure for the LCL-filter is presented step-by-step. The inverter-side inductor was determined by an analysis of the ripple components, mathematically. Based on the reactive power absorption ratio, the filter capacitor was determined. The grid-side inductor was determined by the ripple attenuation factor of the output current. Experimental results verify the validity of the design method for the LCL-filter.

• Journal of Communications and Networks
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• v.11 no.4
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• pp.350-358
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• 2009

In a wireless sensor network (WSN) setting, this paper presents a distributed decision-making framework and illustrates its application in an online structural health monitoring (SHM) system. The objective is to recover a damage severity vector, which identifies, localizes, and quantifies damages in a structure, via distributive and collaborative decision-making among wireless sensors. Observing the fact that damages are generally scarce in a structure, this paper develops a nonlinear 0-norm minimization formulation to recover the sparse damage severity vector, then relaxes it to a linear and distributively tractable one. An optimal algorithm based on the alternating direction method of multipliers (ADMM) and a heuristic distributed linear programming (DLP) algorithm are proposed to estimate the damage severity vector distributively. By limiting sensors to exchange information among neighboring sensors, the distributed decision-making algorithms reduce communication costs, thus alleviate the channel interference and prolong the network lifetime. Simulation results in monitoring a steel frame structure prove the effectiveness of the proposed algorithms.

• ETRI Journal
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• v.33 no.6
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• pp.924-934
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• 2011

Sensor networks play an important role in making the dream of ubiquitous computing a reality. With a variety of applications, sensor networks have the potential to influence everyone's life in the near future. However, there are a number of issues in deployment and exploitation of these networks that must be dealt with for sensor network applications to realize such potential. Localization of the sensor nodes, which is the subject of this paper, is one of the basic problems that must be solved for sensor networks to be effectively used. This paper proposes a probabilistic support vector machine (SVM)-based method to gain a fairly accurate localization of sensor nodes. As opposed to many existing methods, our method assumes almost no extra equipment on the sensor nodes. Our experiments demonstrate that the probabilistic SVM method (PSVM) provides a significant improvement over existing localization methods, particularly in sparse networks and rough environments. In addition, a post processing step for PSVM, called attractive/repulsive potential field localization, is proposed, which provides even more improvement on the accuracy of the sensor node locations.

• The Pure and Applied Mathematics
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• v.12 no.4 s.30
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• pp.275-287
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• 2005

Geometric invariants are basic tools for geometric processing and computer vision. In this paper, we give a linear approximation for the differentiation of the binormal vector field of a space curve by using the forward and backward differences of discrete binormal vectors. Two kind of discrete torsion, say, back-ward torsion $T_b$ and forward torsion $T_f$ can be defined by the dot product of the (backward and forward) discrete differentiation of binormal vectors that are linear approximations of torsion. Using Frenet formula and Taylor series expansion, we give error estimations for the discrete torsions. We also give numerical tests for a curve. Notably the average of $T_b$ and $T_f$ looks more stable in errors.