• Title/Summary/Keyword: Least Square

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A New Least Mean Square Algorithm Using a Running Average Process for Speech Enhancement

  • Lee, Soo-Jeong;Ahn, Chan-Sik;Yun, Jong-Mu;Kim, Soon-Hyob
    • The Journal of the Acoustical Society of Korea
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    • v.25 no.3E
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    • pp.123-130
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    • 2006
  • The adaptive echo canceller (AEC) has become an important component in speech communication systems, including mobile station. In these applications, the acoustic echo path has a long impulse response. We propose a running-average least mean square (RALMS) algorithm with a detection method for acoustic echo cancellation. Using colored input models, the result clearly shows that the RALMS detection algorithm has a convergence performance superior to the least mean square (LMS) detection algorithm alone. The computational complexity of the new RALMS algorithm is only slightly greater than that of the standard LMS detection algorithm but confers a major improvement in stability.

Parameter Estimations in the Complementary Weibull Reliability Model

  • Sarhan Ammar M.;El-Gohary Awad
    • International Journal of Reliability and Applications
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    • v.6 no.1
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    • pp.41-51
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    • 2005
  • The Bayes estimators of the parameters included in the complementary Weibull reliability model are obtained. In the process of deriving Bayes estimators, the scale and shape parameters of the complementary Weibull distribution are considered to be independent random variables having prior exponential distributions. The maximum likelihood estimators of the desired parameters are derived. Further, the least square estimators are obtained in closed forms. Simulation study is made using Monte Carlo method to make a comparison among the obtained estimators. The comparison is made by computing the root mean squared errors associated to each point estimation. Based on the numerical study, the Bayes procedure seems better than the maximum likelihood and least square procedures in the sense of having smaller root mean squared errors.

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Development of an AOA Location Method Using Self-tuning Weighted Least Square (자기동조 가중최소자승법을 이용한 AOA 측위 알고리즘 개발)

  • Lee, Sung-Ho;Kim, Dong-Hyouk;Roh, Gi-Hong;Park, Kyung-Soon;Sung, Tae-Kyung
    • Journal of Institute of Control, Robotics and Systems
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    • v.13 no.7
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    • pp.683-687
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    • 2007
  • In last decades, several linearization methods for the AOA measurements have been proposed, for example, Gauss-Newton method and Closed-Form solution. Gauss-Newton method can achieve high accuracy, but the convergence of the iterative process is not always ensured if the initial guess is not accurate enough. Closed-Form solution provides a non-iterative solution and it is less computational. It does not suffer from convergence problem, but estimation error is somewhat larger. This paper proposes a Self-Tuning Weighted Least Square AOA algorithm that is a modified version of the conventional Closed-Form solution. In order to estimate the error covariance matrix as a weight, a two-step estimation technique is used. Simulation results show that the proposed method has smaller positioning error compared to the existing methods.

Quasi Steady Stall Modelling of Aircraft Using Least-Square Method

  • Verma, Hari Om;Peyada, N.K.
    • International Journal of Aerospace System Engineering
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    • v.7 no.1
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    • pp.21-27
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    • 2020
  • Quasi steady stall is a phenomenon to characterize the aerodynamic behavior of aircraft at high angle of attack region. Generally, it is exercised from a steady state level flight to stall and its recovery to the initial flight in a calm weather. For a theoretical study, such maneuver is demonstrated in the form of aerodynamic model which consists of aircraft's stability and control derivatives. The current research paper is focused on the appropriate selection of aerodynamic model for the maneuver and estimation of the unknown model coefficients using least-square method. The statistical accuracy of the estimated parameters is presented in terms of standard deviations. Finally, the validation has been presented by comparing the measured data to the simulated data from different models.

Model of Least Square Support Vector Machine (LSSVM) for Prediction of Fracture Parameters of Concrete

  • Kulkrni, Kallyan S.;Kim, Doo-Kie;Sekar, S.K.;Samui, Pijush
    • International Journal of Concrete Structures and Materials
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    • v.5 no.1
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    • pp.29-33
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    • 2011
  • This article employs Least Square Support Vector Machine (LSSVM) for determination of fracture parameters of concrete: critical stress intensity factor ($K_{Ic}^s$) and the critical crack tip opening displacement ($CTOD_c$). LSSVM that is firmly based on the theory of statistical learning theory uses regression technique. The results are compared with a widely used Artificial Neural Network (ANN) Models of LSSVM have been developed for prediction of $K_{Ic}^s$ and $CTOD_c$, and then a sensitivity analysis has been performed to investigate the importance of the input parameters. Equations have been also developed for determination of $K_{Ic}^s$ and $CTOD_c$. The developed LSSVM also gives error bar. The results show that the developed model of LSSVM is very predictable in order to determine fracture parameters of concrete.

A Comparison of Estimation Methods for Weibull Distribution and Type I Censoring (와이블 분포와 정시중단 하에서의 MLE와 LSE의 정확도 비교)

  • Kim, Seong-Il;Park, Min-Yong;Park, Jung-Won
    • Journal of Korean Society for Quality Management
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    • v.38 no.4
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    • pp.480-490
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    • 2010
  • In this paper, two estimation methods(least square estimation and maximum likelihood estimation) were compared for Weibull distribution and Type I censoring. Data obtained by Monte Carlo simulation were analyzed using two estimation methods and analysis results were compared by MSE(Mean Squared Error). Comparison results show that maximum likelihood estimator is better for censored data and complete data with more than 30 samples and least square estimator is better for small size complete data(less than and equal to 20 samples).

Hyperbolic Location Estimation of Aircraft with Motion in a Plane (평면 비행중인 항공기의 쌍곡선 위치 추정 연구)

  • Jo, Sanghoon;Kang, Ja-Young
    • Journal of the Korean Society for Aviation and Aeronautics
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    • v.21 no.2
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    • pp.33-39
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    • 2013
  • Multilateration(MLAT) may complement secondary surveillance radar and also act as a real-time backup for the ADS-B system. This System is using time difference of arrival (TDOA) and based on triangulation principle. Each TDOA measurement defines a hyperbola describing possible aircraft locations. The accuracy in MLAT system depends on the positional relationship of the receiver and aircraft. There are various algorithms to localize aircraft based on TOA estimation. In this paper, we use least square method and extended Kalman filter and compare their results. Study results show that the extend Kalman filter provides a better performance than the least square method.

Detection of Ellipses using Least Square Method (최소자승법을 이용한 타원의 검출)

  • 이주용;서요한;이웅기
    • Journal of the Korea Society of Computer and Information
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    • v.1 no.1
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    • pp.95-104
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    • 1996
  • The Hough transform Is a robust technique Which Is useful in defecting straight lines in an picture. However, the extension of the conventional Hough transform to recover circles and ellipses has been limited by slow speed and excessive memory .This paper presents a method of detecting ellipses from the Image by using Least Square Method. This method Is reduced calculation cost and memory requirement .When detecting ellipse. Instead of obtaining accumulation of Hough transform for determination of ellipse parameters. particular points containing geometric properties of ellipse are selected. Parameters of the ellipse are calculated by Least Square Method using those particular points.

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Economic Assessment of a Wind Farm Project Using Least Square Monte-Carlo (LSMC) Simulation (최소자승몬테카를로 시뮬레이션을 이용한 풍력발전설비 투자계획)

  • Kim, Jin-A;Lee, Jong-Uk;Lee, Jae-Hee;Joo, Sung-Kwan
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.60 no.1
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    • pp.32-35
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    • 2011
  • The economic value of a wind farm project is influenced by various risk factors such as wind power output and electricity market price. In particular, there is uncertainty in the economic evaluation of a wind farm project due to uncertain wind power outputs, which are fluctuated by weather factors such as wind speed, and volatile electricity market prices. This paper presents a systematic method to assess the economic value and payback period of a wind farm project using Least Square Monte-Carlo (LSMC) simulation. Numerical example is presented to validate the effectiveness of the proposed economic assessment method for a wind farm project.

Modelling Voltage Variation at DC Railway Traction Substation using Recursive Least Square Estimation (순환최소자승법을 이용한 직류도시철도 변전소의 가선전압변동 모델링)

  • Bae, Chang-Han
    • The Transactions of the Korean Institute of Power Electronics
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    • v.20 no.6
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    • pp.534-539
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    • 2015
  • The DC overhead line voltage of an electric railway substation swings depending on the accelerating and regenerative-braking energy of trains, and it deteriorates the energy quality of the electric facility in the DC railway substation and restricts the powering and braking performance of subway trains. Recently, an energy storage system or a regenerative inverter has been introduced into railway traction substations to diminish both the variance of the overhead line voltage and the peak power consumption. In this study, the variance of the overhead line voltage in a DC railway substation is modelled by RC parallel circuits in each feeder, and the RC parameters are estimated using the recursive least mean square (RLMS) scheme. The forgetting factor values for the RLMS are selected using simulated annealing optimization, and the modelling scheme of the overhead line voltage variation is evaluated through raw data measured in a downtown railway substation.