• Title/Summary/Keyword: least squares problem

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Self-Tuning Adaptive Control Using State Observer (상태 관측기를 이용한 자기-동조 적응 제어)

  • Kim, Yoon-Ho;Yoon, Byung-Do;Oh, Gi-Hong
    • Proceedings of the KIEE Conference
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    • 1991.11a
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    • pp.223-226
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    • 1991
  • In this paper, the problem of designing on adaptive controller for dc drives using state observers, which is operated under varying load conditions, is addressed. A robust self-tuning controller that can track a constant reference and reject constant load disturbances is also studied. This scheme is very attractive since the estimates of system parameters are available in real time. Parameter estimation is based on the recursive least squares method and the control algorithm of the pole placement technique. Also, state observer systems are applied. State observer systems are required to estimate the states quickly and exactly without being affected by the disturbances.

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Optimal Minimum Bias Designs for Model Discrimination

  • Park, Joong-Yang
    • Communications for Statistical Applications and Methods
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    • v.5 no.2
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    • pp.339-351
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    • 1998
  • Designs for discriminating between two linear regression models are studied under $\Lambda$-type optimalities maximizing the measure for the lack of fit for the designs with fixed model inadequacy. The problem of selecting an appropriate $\Lambda$-type optimalities is shown to be closely related to the estimation method. $\Lambda$-type optimalities for the least squares and minimum bias estimation methods are considered. The minimum bias designs are suggested for the designs invariant with respect to the two estimation methods. First order minimum bias designs optimal under $\Lambda$-type optimalities are then derived. Finally for the case where the lack of fit test is significant, an approach to the construction of a second order design accommodating the optimal first order minimum bias design is illustrated.

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Improvement of Alignment Accuracy in Electron Tomography

  • Jou, Hyeong-Tae;Lee, Sujeong;Kim, Han-Joon
    • Applied Microscopy
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    • v.43 no.1
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    • pp.1-8
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    • 2013
  • We developed an improved method for tilt series alignment with fiducial markers in electron tomography. Based on previous works regarding alignment, we adapted the Levenberg-Marquardt method to solve the nonlinear least squares problem by incorporating a new formula for the alignment model. We also suggested a new method to estimate the initial value for inversion with higher accuracy. The proposed approach was applied to geopolymers. A better alignment of the tilt series was achieved than that by IMOD S/W. The initial value estimation provided both stability and a good rate of convergence since the new method uses all marker positions, including those partly covering the tilt images.

Feedforward Active Shock Response Control of a Flexible Beam (유연빔의 피드포워드 능동 충격응답 제어)

  • Pyo, Sang-Ho;Lee, Young-Sup;Shin, Ki-Hong
    • Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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    • 2005.05a
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    • pp.213-216
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    • 2005
  • Active control method is applied to a flexible beam excited by a shock impulse by focusing on reducing the residual vibrations after the shock input. It is assumed that the shock input can be measured and is always occurred on the same point of the beam. If the system is well identified and the corresponding inverse system is designed reliably, it has shown that a very simple feed-forward active control method may be applied to suppress the residual vibrations without using an error sensor and adaptive algorithm. Both numerical simulation and experimental result show a promising possibility of applying to a practical problem.

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A robust test for the parallelism of two regression lines (두 회귀직선의 평행성에 대한 로버스트 검정)

  • 남호수;송문섭;신봉섭
    • The Korean Journal of Applied Statistics
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    • v.8 no.2
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    • pp.77-86
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    • 1995
  • For the problem of testing the parallelism of two regression lines, a robust procedure is proposed and examined. The proposed test statistic is based on the one-step GM-estimators of slope parameters proposed by Song et al. (1994b). These GM-estimators used the Least Trimmed Squares estimates as an initial values so as to obtain high breakdown point. Through a small-sample Monte Carlo simulation the empirical levels and powers of the proposed test are compared with other tests under various error distributions.

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Subtype classification of Human Breast Cancer via Kernel methods and Pattern Analysis of Clinical Outcome over the feature space (Kernel Methods를 이용한 Human Breast Cancer의 subtype의 분류 및 Feature space에서 Clinical Outcome의 pattern 분석)

  • Kim, Hey-Jin;Park, Seungjin;Bang, Sung-Uang
    • Proceedings of the Korean Information Science Society Conference
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    • 2003.04c
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    • pp.175-177
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    • 2003
  • This paper addresses a problem of classifying human breast cancer into its subtypes. A main ingredient in our approach is kernel machines such as support vector machine (SVM). kernel principal component analysis (KPCA). and kernel partial least squares (KPLS). In the task of breast cancer classification, we employ both SVM and KPLS and compare their results. In addition to this classification. we also analyze the patterns of clinical outcomes in the feature space. In order to visualize the clinical outcomes in low-dimensional space, both KPCA and KPLS are used. It turns out that these methods are useful to identify correlations between clinical outcomes and the nonlinearly protected expression profiles in low-dimensional feature space.

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A Study on Power System State Estimation and bad data detection Using PSO (PSO기법을 이용한 전력계통의 상태추정해법과 불량정보처리에 관한 연구)

  • Ryu, Seung-Oh;Jeong, Hee-Myung;Park, June-Ho;Lee, Hwa-Seok
    • Proceedings of the KIEE Conference
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    • 2008.11a
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    • pp.261-263
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    • 2008
  • In power systems operation, state estimation takes an important role in security control. For the state estimation problem, the weighted least squares(WLS) method and the fast decoupled method have been widely used at present. But these algorithms have disadvantage of converging local optimal solution. In these days, a modern heuristic optimization method such as Particle Swarm Optimization(PSO), are introduced to overcome the problems of classical optimization. In this paper, we proposed particle swarm optimization (PSO) to search an optimal solution of state estimation in power systems. To demonstrate the usefulness of the proposed method, PSO algorithm was tested in the IEEE-57 bus systems. From the simulation results, we can find that the PSO algorithm is applicable for power system state estimation.

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On the Local Identifiability of Load Model Parameters in Measurement-based Approach

  • Choi, Byoung-Kon;Chiang, Hsiao-Dong
    • Journal of Electrical Engineering and Technology
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    • v.4 no.2
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    • pp.149-158
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    • 2009
  • It is important to derive reliable parameter values in the measurement-based load model development of electric power systems. However parameter estimation tasks, in practice, often face the parameter identifiability issue; whether or not the model parameters can be estimated with a given input-output data set in reliable manner. This paper introduces concepts and practical definitions of the local identifiability of model parameters. A posteriori local identifiability is defined in the sense of nonlinear least squares. As numerical examples, local identifiability of third-order induction motor (IM) model and a Z-induction motor (Z-IM) model is studied. It is shown that parameter ill-conditioning can significantly affect on reliable parameter estimation task. Numerical studies show that local identifiability can be quite sensitive to input data and a given local solution. Finally, several countermeasures are proposed to overcome ill-conditioning problem in measurement-based load modeling.

The Structural Equation Model with Ordinal Data (순서형 자료로 측정된 구조방정식모형 분석)

  • 윤상운;박정선;이태섭
    • Journal of Korean Society for Quality Management
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    • v.30 no.3
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    • pp.38-52
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    • 2002
  • This paper is concerned with the analysis of structural equation model(SEM) with the ordinal data such as Likert scale. The SEM is misused when the arbitrary scores allocated to the Likert scale are treated as quantitative data. The underlying distribution approaches have been studied to solve this problem, and the partial least squares(PLS) Is also tried. In this paper the quantification methods for the Likert scale are proposed to analyze the SEM. We assume that the Likert scale is an observation of the interval of the continuous underlying distribution, and the respondents have their own patterns in the response of some questions. Normal and beta distributions as the response patterns are considered to quantify the Likert scale. To compare the efficiency of the proposed method the bootstrap simulations are tried.

Dual Generalized Maximum Entropy Estimation for Panel Data Regression Models

  • Lee, Jaejun;Cheon, Sooyoung
    • Communications for Statistical Applications and Methods
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    • v.21 no.5
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    • pp.395-409
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    • 2014
  • Data limited, partial, or incomplete are known as an ill-posed problem. If the data with ill-posed problems are analyzed by traditional statistical methods, the results obviously are not reliable and lead to erroneous interpretations. To overcome these problems, we propose a dual generalized maximum entropy (dual GME) estimator for panel data regression models based on an unconstrained dual Lagrange multiplier method. Monte Carlo simulations for panel data regression models with exogeneity, endogeneity, or/and collinearity show that the dual GME estimator outperforms several other estimators such as using least squares and instruments even in small samples. We believe that our dual GME procedure developed for the panel data regression framework will be useful to analyze ill-posed and endogenous data sets.