• Proceedings of the Korean Information Science Society Conference
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• 2004.04b
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• pp.805-807
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• 2004

본 논문에서는 노이즈 모델에 기반한 훼손된 얼굴 영상의 인증하는 방법을 제안한다. 제안된 방법은 먼저 학습 단계에서 노이즈 파라미터의 변화에 의해 훼손된 영상을 생성한다. 그 훼손된 영상과 노이즈 파라미터는 PCA에 의해 훼손된 영상과 노이즈 파라미터들의 선형 조합으로 표현된다. 테스트 단계에서는 훼손된 영상으로 LSM(Least-square minimization)방법을 적용하여 훼손된 영상의 노이즌 파라미터를 추정한다. 그리고 추정된 노이즈 파라미터를 가지고 원본 영상으로부터 합성된 영상을 생성하고, 그것을 테스트 영상과 인증한다. 실험 결과는 제안된 방법이 노이즈 파라미터를 정확하게 추정하여 얼굴 인증의 성능 개선 가능성을 보여준다.

• Proceedings of the KIPE Conference
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• 2010.07a
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• pp.55-56
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• 2010

다상 유도 전동기(Multi phase induction motor)의 고성능 제어를 수행하기 위해서는 정확한 파라미터 계산이 필수적이다. 특히 전동기의 벡터제어(FOC, Field oriented control)나 직접토크제어(DTC, Direct torque control)와 같은 고성능 제어 시스템의 경우, 슬립 계산이나 자속관측기 그리고 PI 제어기 게인 추정에서 전동기 상수들이 필수적으로 사용된다. 본 논문에서는 실험용으로 집중권(Concentrated winding) 구조를 가지는 2kW, 5상 유도전동기를 제작하였으며, 5상 유도전동기 파라미터 추정에 대한 방법을 제시하였다. 일반적으로 다상 유도전동기의 경우 1차 공간 고조파(1st space harmonic) 성분에 대한 파라미터만을 추정하여 제어에 사용하지만, 본 논문에서는 1차 공간 고조파 성분과 3차 공간 고조파(3rd space harmonic) 성분에 대한 파라미터 추정 방법을 제시한다. 결과적으로 제안된 파라미터 추정 방법의 타당성을 확인하기 위해서 설계값과 실험값을 비교하였다.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.24 no.8
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• pp.84-93
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• 2010

This paper describes a method of visualization and fast estimation of parameter in continuous time signal. The parameter estimation method of this paper directly estimate the parameters on the basis of the discrete Fourier transform. Also, this paper present to efficient visualization method of dominant parameters obtained in continuous time signal. The proposed methods are applied to test functions with three dominant modes. The results show that the proposed methods are highly applicable to parameter estimation and visualization in continuous time signal.

• Journal of Broadcast Engineering
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• v.3 no.1
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• pp.50-60
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• 1998

This paper addresses a problem of extraction of parameteric motion estimation and structural motion segmentation for compact image sequence representation and object-based generic video coding. In order to extract meaningful motion structure from image sequences, a direct parameteric motion estimation based on a pre-segmentation is proposed. The pre-segmentation which considers the motion of the moving objects is canied out based on probabilistic clustering with mixture models using optical flow and image intensities. Parametric motion segmentation can be obtained by iterated estimation of motion model parameters and region reassignment according to a criterion using Gauss-Newton iterative optimization algorithm. The efficiency of the proposed methoo is verified with computer simulation using elF real image sequences.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.217-218
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• 2008

이 논문은 이산신호에 고속푸리에 변환을 적용하여 신호에 포함되어 있는 저주파수의 진동 파라미터를 추정하는 새로운 방법에 대해서 기술하고 있다. 제안한 방법은 지수감쇠 정현파함수의 푸리에 변환에 기초를 두고 푸리에스펙트럼으로부터 직접 파라미터를 추정하는 방법이다. 푸리에스펙트럼의 첨두치와 첨두주파수 사이에 일정한 수학적 관계에서 모드를 추정하고 추정한 모드를 이용하여 모드의 크기와 위상을 추정하는 방법을 제안하고 있다. 이 논문에서 제안한 파라미터 추정방법은 수식에 기반을 둔 매우 단순한 알고리즘으로 계산속도가 매우 빠르고 작은 기억장소를 필요로 하므로 DSP 수준의 실시간 연산에 매우 적합한 알고리즘이다. 제안한 알고리즘을 간단한 시험함수에 적용한 결과, 정확하게 파라미터를 추정하여 알고리즘의 정확성을 검증하였다.

• The KIPS Transactions:PartB
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• v.11B no.6
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• pp.653-660
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• 2004

Object-oriented analysis-synthesis coding (OOASC) subdivides each image of a sequence into a number of moving objects and estimates and compensates the motion of each object. It employs a motion parameter technique for estimating motion information of each object. The motion parameter technique employing gradient operators requires a high computational load. The main objective of this paper is to present efficient motion parameter estimation techniques using the hierarchical structure in object-oriented analysis-synthesis coding. In order to achieve this goal, this paper proposes two algorithms : hybrid motion parameter estimation method (HMPEM) and adaptive motion parameter estimation method (AMPEM) using the hierarchical structure. HMPEM uses the proposed hierarchical structure, in which six or eight motion parameters are estimated by a parameter verification process in a low-resolution image, whose size is equal to one fourth of that of an original image. AMPEM uses the same hierarchical structure with the motion detection criterion that measures the amount of motion based on the temporal co-occurrence matrices for adaptive estimation of the motion parameters. This method is fast and easily implemented using parallel processing techniques. Theoretical analysis and computer simulation show that the peak signal to noise ratio (PSNR) of the image reconstructed by the proposed method lies between those of images reconstructed by the conventional 6- and 8-parameter estimation methods with a greatly reduced computational load by a factor of about four.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.35 no.10
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• pp.912-917
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• 2007

In this paper system identification of a small UAV via neural networks is tried and the estimated parameters are then compared to those obtained by Fourier Transform Regression and Maximum Likelihood Estimation Techniques. With the estimated parameters a linear system is constructed and simulated to compare to the flight data. The results show that parameter identification using neural networks is comparable to the existing techniques

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.2
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• pp.772-778
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• 2012

In this paper, the states and the parameters in the dynamic system are simultaneously estimated by applying the UKF(Unscented Kalman Filter), which is widely used for estimating the state of non-linear systems. Estimating the parameter is very important in various fields, such as system control, modeling, analysis of performance, and prediction. Most of the dynamic systems which are dealt with in engineering have non-linearity as well as some noise. Therefore, the parameter estimation is difficult. This paper estimates the states and the parameters applying to the UKF, which is a non-linear filter and has strong noise. The augmented equation is used by including the addition of the parameter factors to the original state equation of the system. Moreover, it is simulated by applying to a 2-DOF(Degree of Freedom) dynamic system composed of the pendulum and the slide. The measurement noise of the dynamic equation is assumed to be a Gaussian distribution. As the simulation results show, the proposed parameter estimation performs better than the LSM(Least Square Method). Furthermore, the estimation errors and convergence time are within three percent and 0.1 second, respectively. Consequentially, the UKF is able to estimate the system states and the parameters for the system, despite having measurement data with noise.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2004.04a
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• pp.305-309
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• 2004

The problem for parameters estimation of the received signals impinging on array sensors has long been of great research interest in a great variety of applications, such as radar, sonar, and land mobile communications systems. Conventional subspace-based algorithms, such as MUSIC and ESPRIT, require an extensive computation of inverse matrix and eigen-decomposition. In this paper, we propose a new parameters estimation algorithm via nonlinear minimization, which is simplified computationally and estimates signal parameters simultaneously.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.49 no.2
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• pp.10-16
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• 2012

Currently, the majority of systems which are non-linear are in need of the correct system equations for controlling and monitoring. Therefore, the correct estimation of parameters is crucial. Generally, parameters are changed due to system deterioration or sudden environmental alterations. Given the limitations of system monitoring unstable controls can arise. In the following paper, the parameter estimation method is proposed using software filters to combat these system instabilities. For dynamic instances, a powerful particle filter is used to control the nonlinear and noisy environments in which they take place. Using a setup simulation comprised of a slider and pendulum, the state variable of noise is obtained. After collecting the data, the proposed algorithm is used to estimate both the state variable and its parameters. Finally, these results are checked with correct parameter estimations to evaluate and verify the algorithms performance.