• Journal of applied mathematics & informatics
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• 제8권2호
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• pp.345-360
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• 2001

Affine invariant sufficient conditions are given for two local convergence theorems involving inexact Newton-like methods. The first uses conditions on the first Frechet-derivative whereas the second theorem employs hypotheses on the mth(m≥2 an integer). Radius of convergence as well as rate of convergence results are derived. Results involving superlinear convergence and known to be true for inexact Newton methods are extended here. Moreover, we show that under hypotheses on the mth Frechet-derivative our radius of convergence can sometimes be larger than the corresponding one in [10]. This allows a wider choice for the initial guess. A numerical example is also provided to show that our radius of convergence is larger than the one in [10].

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권9호
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• pp.3782-3796
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• 2020

A three-dimensional (3D) reconstruction is an important research area in computer vision. The ability to detect and match features across multiple views of a scene is a critical initial step. The tracking matrix W obtained from a 3D reconstruction can be applied to structure from motion (SFM) algorithms for 3D modeling. We often fail to generate an acceptable number of features when processing face or medical images because such images typically contain large homogeneous regions with minimal variation in intensity. In this study, we seek to locate sufficient matching points not only in general images but also in face and medical images, where it is difficult to determine the feature points. The algorithm is implemented on an adaptive threshold value, a scale invariant feature transform (SIFT), affine SIFT, speeded up robust features (SURF), and affine SURF. By applying the algorithm to face and general images and studying the geometric errors, we can achieve quasi-dense matching points that satisfy well-functioning geometric constraints. We also demonstrate a 3D reconstruction with a respectable performance by applying a column space fitting algorithm, which is an SFM algorithm.

• 한국산업융합학회 논문집
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• 제7권2호
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• pp.215-220
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• 2004

In this paper, some algorithms for robust stabilization of linerar time - invariant single - input - multi output (SIMO) systems subject to parameter perturbatations are presented. At first, the determination algorithm of the largest stable hypersphere in the parameter space of a given characteristic polynomial with its coefficient perturbations near some stable nominal values is presented. These algorithms iteratively enlarge the stability hypersph ere in plant parameter space and can be used to design a controller to stabilize a plant subject to givien range of parameter ecxursions.

• 대한전자공학회논문지SP
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• 제45권2호
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• pp.90-94
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• 2008

적응 필터의 입력 신호의 상관도 (correlation)가 클 경우 LMS 알고리즘의 수렴 속도는 상당히 느려지게 된다. 본 논문에서는 입력 신호의 상관도가 높은 상황에서 수렴 속도를 향상시킬 수 있는 적응 필터링 알고리즘을 제안한다. 입력 신호에 대하여 직교성을 가지도록 변환을 인위적으로 가하여 LMS 알고리즘의 한계를 극복한다. 제안한 알고리즘의 성능 향상은 시스템식별 모델을 통하여 그 수렴 속도의 개선을 확인하며 또한 시변 환경 하에서 적응 필터의 시변 추적 능력을 통해 보여 진다.

• 인터넷정보학회논문지
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• 제6권6호
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• pp.187-197
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• 2005

본 논문에서는 불변 점 특징에 기반한 카메라 동작인수 측정방법을 제안한다. 일반적으로 영상의 특징정보는 카메라 뷰포인트에 따라 변하는 단점이 있어 시간이 지나면 정보량이 증가하게 된다. 또한 카메라 외부인수 산출을 위한 비선형 최소제곱 측정을 이용한 LM 방법은 초기값에 따라 최소점에 근접하는 반복회수가 다르고 지역 최소점에 빠질 경우 수렴시간이 증가하는 단점이 있다. 본 논문에서는 이러한 문제를 개선하기 위해 첫째, 기하학의 불변 벡터를 사용하여 특징 모델을 구성하는 것을 제안하였다. 둘째, 2D 호모그래피와 LM 방법을 이용하여 정확도와 수렴도를 향상시키는 2단계 측정 방법을 제안하였다. 실험에서는 제안한 알고리즘의 우수성을 입증하기 위해 기존방법과 제안한 방법을 비교 분석하였다.

• 제어로봇시스템학회논문지
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• 제2권2호
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• pp.108-114
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• 1996

We present proofs of the stability and convergence of Self-organizing feature map (SOFM) neural network with time-invarient learning rate and binary reinforcement function. One of the major problems in Self-organizing feature map neural network concerns with learning rate-"Kalman Filter" gain in stochsatic control field which is monotone decreasing function and converges to 0 for satisfying minimum variance property. In this paper, we show that the stability and convergence of Self-organizing feature map neural network with time-invariant learning rate. The analysis of the proposed algorithm shows that the stability and convergence is guranteed with exponentially stable and weak convergence properties as well.s as well.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2001년도 제14회 신호처리 합동 학술대회 논문집
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• pp.73-76
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• 2001

It is well-known that the convergence rate gets worse when an input signal to an adaptive filter is correlated. In this paper we propose a new adaptive filtering algorithm that makes the convergence rate highly improved even for highly correlated input signals. By introducing an orthogonal constraint between successive input signal vectors, we overcome the slow convergence problem caused by the correlated input signal. Simulation results show that the proposed algorithm yields highly improved convergence speed and excellent tracking capability under both time-invariant and time varying environments, while keeping both computation and implementation simple.

• 융합신호처리학회논문지
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• 제10권2호
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• pp.112-119
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• 2009

본 논문에서는 옥외 환경에 강인한 영상 감시알고리듬을 구현하는 과정을 기술하였다. 옥외 감시시스템의 어려운 처리 과정들 중 하나는 배경화면을 효과적으로 갱신하는 것이다. 배경 영상에는 건물, 나무들, 이동하는 구름 및 기타 다른 물체들의 그림자를 포함하기 때문에. 시간과 조명광에 따라 변화한다. 이는 옥외에서의 감시시스템의 성능을 저하시킨다. 따라서 본 논문에서는 배경 영상을 효과적으로 갱신하기 위해 적응 혼합 가우시안 필터와 컬러불변성을 화소레벨에서 적용하여 옥외에서도 강인한 영상 감시알고리듬을 제안하였다. 그 결과, 다양한 그림자가 있는 옥외에서 움직이는 대상 물체를 검출할 수 있음을 확인하였다.

• International Journal of Control, Automation, and Systems
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• 제6권2호
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• pp.194-203
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• 2008

Iterative learning control (ILC) is a simple and effective method for the control of systems that perform the same task repetitively. ILC algorithm uses the repetitiveness of the task to track the desired trajectory. In this paper, we propose a PID (proportional plus integral and derivative) type ILC update law for control discrete-time single input single-output (SISO) linear time-invariant (LTI) systems, performing repetitive tasks. In this approach, the input of controlled system in current cycle is modified by applying the PID strategy on the error achieved between the system output and the desired trajectory in a last previous iteration. The convergence of the presented scheme is analyzed and its convergence condition is obtained in terms of the PID coefficients. An optimal design method is proposed to determine the PID coefficients. It is also shown that under some given conditions, this optimal iterative learning controller can guarantee the monotonic convergence. An illustrative example is given to demonstrate the effectiveness of the proposed technique.

• 한국수학교육학회지시리즈B:순수및응용수학
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• 제17권1호
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• pp.1-27
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• 2010

Wu and Zhao [17] provided a semilocal convergence analysis for a Newton-type method on a Banach space setting following the ideas of Frontini and Sormani [9]-[11]. In this study first: we point out inconsistencies between the hypotheses of Theorem 1 and the two examples given in [17], and then, we provide the proof in affine invariant form for this result. Then, we also establish new convergence results with the following advantages over the ones in [17]: weaker hypotheses, and finer error estimates on the distances involved. A numerical example is also provided to show that our results apply in cases other fail [17].