• Korean Journal of Computational Design and Engineering
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• v.11 no.1
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• pp.1-10
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• 2006

This paper addresses B-spline curve approximation of a set of ordered points to a specified toterance. The important issue in this problem is to reduce the number of control points while keeping the desired accuracy in the resulting B-spline curve. In this paper we propose a new method for error-bounded B-spline curve approximation based on adaptive selection of dominant points. The method first selects from the given points initial dominant points that govern the overall shape of the point set. It then computes a knot vector using the dominant points and performs B-spline curve fitting to all the given points. If the fitted B-spline curve cannot approximate the points within the tolerance, the method selects more points as dominant points and repeats the curve fitting process. The knots are determined in each step by averaging the parameters of the dominant points. The resulting curve is a piecewise B-spline curve of order (degree+1) p with $C^{(p-2)}$ continuity at each knot. The shape index of a point set is introduced to facilitate the dominant point selection during the iterative curve fitting process. Compared with previous methods for error-bounded B-spline curve approximation, the proposed method requires much less control points to approximate the given point set with the desired shape fidelity. Some experimental results demonstrate its usefulness and quality.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.4
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• pp.1063-1075
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• 2012

To locate an object accurately in the wireless sensor networks, the distance measure based on time-delay plays an important role. In this paper, we propose a maximum likelihood (ML) time-delay estimation algorithm in multi-path wireless propagation channel. We get the joint probability density function after sampling the frequency domain response of the multi-path channel, which could be obtained by the vector network analyzer. Based on the ML criterion, the time-delay values of different paths are estimated. Considering the ML function is non-linear with respect to the multi-path time-delays, we first obtain the coarse values of different paths using the subspace fitting algorithm, then take them as an initial point, and finally get the ML time-delay estimation values with the pattern searching optimization method. The simulation results show that although the ML estimation variance could not reach the Cramer-Rao lower bounds (CRLB), its performance is superior to that of subspace fitting algorithm, and could be seen as a fine algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.6C
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• pp.551-559
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• 2005

Estimation of unknown signal parameters with sensor array measurements has been investigated quite extensively. Also, there has been in recent years an explosive increase in the number of mobile users in wireless cellular systems, thus contributing to growing levels of multi-user interference. To overcome this problem, application of adaptive antenna array techniques to further increase the channel capacity has been discussed. In this paper, a new model of locally scattered signals in the vicinity of mobiles is proposed by defining the mean steering vector and is manipulated mathematically for several distributions. Under this model an estimation method of the angle of arrival(AOA) is investigated based on a weighted subspace fitting(WSF) technique. Statistical analysis and simulations are also considered.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2004.11a
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• pp.251-256
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• 2004

The image classification is one of the most important studies in the remote sensing. In general, the MLC(Maximum Likelihood Classification) classification that in consideration of distribution of training information is the most effective way but it produces a bad result when we apply it to actual hyperspectral image with the same classification technique. The purpose of this research is to reveal that which one is the most effective and suitable way of the classification algorithms iii the hyperspectral image classification. To confirm this matter, we apply the MLC classification algorithm which has distribution information and SAM(Spectral Angle Mapper), SFF(Spectral Feature Fitting) algorithm which use average information of the training class to both multispectral image and hyperspectral image. I conclude this result through quantitative and visual analysis using confusion matrix could confirm that SAM and SFF algorithm using of spectral pattern in vector domain is more effective way in the hyperspectral image classification than MLC which considered distribution.

• Communications for Statistical Applications and Methods
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• v.28 no.3
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• pp.233-250
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• 2021

This paper studies if the accuracies of mortality models (LC model vs. 4-parametric model) are aggravated if a mortality structure changes due to the impact of COVID-19. LC model (LCM) uses dimension reduction for fitting to the log mortality matrix so that the performance of the dimension reduction method may not be good when the matrix structure changes. On the other hand, 4-parametric factor model (4-PFM) is designed to use factors for fitting to log mortality data by age groups so that it would be less affected by the change of the mortality structure. In fact, the forecast accuracies of LCM are better than those of 4-PFM when life-tables are used whereas those of 4-PFM are better when the mortality structure changes. Thus this result shows that 4-PFM is more reliable in performance to the structural changes of the mortality. To support the accuracy changes of LCM the functional aspect is explained by computing eigenvalues produced by singular vector decomposition

• IE interfaces
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• v.22 no.3
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• pp.252-262
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• 2009

It is crucial to prevent low yields in the semiconductor industry. Since many factors affect variation in yield and they are deeply related, preventing low yield is difficult. There have been substantial researches in the field of yield prediction. Many researchers had used the statistical methods. Many studies have shown that artificial neural network (ANN) achieved better performance than traditional statistical methods. However, despite ANN's superior performance some problems such as over-fitting and poor explanatory power arise. In order to overcome these limitations, a relatively new machine learning technique, support vector machine (SVM), is introduced to classify the yield. SVM is simple enough to be analyzed mathematically, and it leads to high performances in practical applications. This study presents a new efficient classification methodology, Stepwise-SVM (SSVM), for detecting high and low yields. SSVM is step-by-step adjustment of parameters to be precisely the classification for actual high and low yield lot. The objective of this paper is to examine the feasibility of SVM and SSVM in the yield classification. The experimental results show that SVM and SSVM provides a promising alternative to yield classification for the field data.

• Journal of Power Electronics
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• v.17 no.6
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• pp.1500-1511
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• 2017

The purpose of this paper is to study a high-performance control scheme for neutral-point-clamping three-level (NPC-3L) inverter fed dual three-phase permanent magnet synchronous motor (PMSM) drives by considering some asymmetric factors such as the non-identical parameters in phase windings. To implement this, the system model is analyzed for dual three-phase PMSM drives with asymmetric factors based on the vector space decomposition (VSD) principle. Based on the equivalent circuits, PI controllers with feedforward compensation are used in the d-q subspace for regulating torque, where the cut-off frequency of the PI controllers are set at the twice the fundamental frequency for compensating both the additional DC component and the second order component caused by asymmetry. Meanwhile, proportional resonant (PR) controllers are proposed in the x-y subspace for suppressing the possible unbalanced currents in the phase windings. A dual three-phase space vector modulation (DT-SVM) is designed for the drive, and the balancing factor is designed based on the numerical fitting surface for balancing the DC link capacitor voltages. Experimental results are given to demonstrate the validity of the theoretical analysis and the proposed control scheme.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.05a
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• pp.274-274
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• 2004

조향성능은 완성된 자동차를 평가하는 아주 중요한 요소이며, 운전자에게 직접적인 영향을 가지는 까닭으로 우선적으로 해결되어야 할 문제이다. 자동차의 조향성능과 관련된 자동차 요소로는 프런트 샤시 모듈이 있으며, 프런트 샤시 모듈의 자세는 구성 요소인 서브프레임의 조립자세에 의하여 결정된다. 서브프레임은 4개의 원통형 지지부로 이루어져 있으며, 차체와의 조립시 지지부에 물리적 접촉이 발생한다. 즉 공간상에서 서브프레임의 자세는 지지부의 조립위치에 의하여 결정이 되며, 서브프레임의 자세를 결정하기 위해서는 지지부에 대한 적절한 해석이 필요하다.(중략)

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1997.11a
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• pp.123-126
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• 1997

최근에는 영역(혹은 객체)를 이용하여 비디오 시퀀스를 표현하거나 부호화하는 기법들이 많이 연구되고 있다. 이러한 부호화 기법에서는 형태정보를 효율적으로 이용하는 것이 중요함에도 불구하고, 현재 사용되고 있는 대부분의 기법에서는 기존의 블록 기반 부호화 알고리즘에서처럼 오직 PSNR 만을 고려하여 움직임 벡터를 추정하고 있다. 따라서, 형태 정보를 다루는 효율적 움직임 추정 알고리즘이 필요하다. 본 논문에서는 각 영역의 경계(contour)를 잘 피팅(fitting)시키는 움직임 추정 방법을 생각해 본다. 이를 위하여 PSNR과 영역의 모양을 함께 고려하는 비용함수를 제안하고 이를 이용한 움직임 벡터 추정을 고려해 본다.

• Journal of electromagnetic engineering and science
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• v.7 no.1
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• pp.7-11
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• 2007

PCB Power-Bus (comprising power/ground planes) impedance and fields are evaluated by an efficient series expansion method that is suggested in this paper. It is used to investigate the structure's radiated emission(RE) levels and find acceptable ways of loading the power/ground planes such as decoupling capcitor(DeCap)s, balanced feeding and slits, in order to reduce the interferences. Also, the calculations and measurements of a proposed geometry are verified by vector fitting as a analysis model to check the behavior of the slit.