• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.12A
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• pp.1781-1786
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• 2000

본 논문에서는, 적응 간섭 제거기(AIC : adaptive interference canceller)에 사용되는 적응 알고리즘 중 계산량이 적고, 하드웨어적 복잡성이 낮은 최소 평균 자승(LMS)알고리즘의 적응화 상수(constant step size)를 여러 개 사용하여 빠른 수렴 속도와 낮은 평균 자승 에러를 가지는 방법을 제안한다. 최소 평균 자승 알고리즘에서 적응화 상수는 수렴속도와 평균 자승 에러를 제거하는데, 적응화 상수가 증가할수록 수렴속도가 빨라지는 반면, 평균 자승 에러는 증가하게 된다. 이 논문에서는 수렴속도를 증가하는 동시에 평균 자승 에러를 줄이기 위해, 최소 평균 자승 알고리즘에서 세 개의 적응화 상수를 가지는 새로운 검출기를 제안한다. 이 구조에서, 매 반복횟수에 따른 각 그룹 출력 값들을 가지고, 선택(selection)부분에서 평균 자승 에러들을 비교하며, 가장 작은 평균 자승 에러를 나타내는 그룹의 에러 값과 필터 계수 값들이 선택되어져 여러 적응화 상수 최소 평균 자승 알고리즘(several step size LMS algorithm)부분에서 각 그룹의 필터 계수를 갱신하는데 필요한 정보로 이용된다.

• The Journal of the Acoustical Society of Korea
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• v.14 no.1E
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• pp.56-64
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• 1995

The least mean fourth (LMF) adaptive algorithm is a stochastic gradient method that minimizes the error in the mean fourth sense. Despite its potential advantages, the algorithm is much less popular than the conventional least mean square (LMS) algorithm in practice. This seems partly because the analysis of the LMF algorithm is much more difficult than that of the LMS algorithm, and thus not much still has been known about the algorithm. In this paper, we explore the statistical convergence behavior of the LMF algorithm when the input to the adaptive filter is zero-mean, wide-sense stationary, and Gaussian. Under a system idenrification mode, a set of nonlinear evolution equations that characterizes the mean and mean-squared behavior of the algorithm is derived. A condition for the conbergence is then found, and it turns out that the conbergence of the LMF algorithm strongly depends on the choice of initial conditions. Performances of the LMF algorithm are compared with those of the LMS algorithm. It is observed that the mean convergence of the LMF algorithm is much faster than that of the LMS algorithm when the two algorithms are designed to achieve the same steady-state mean-squared estimation error.

• Journal of Satellite, Information and Communications
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• v.7 no.3
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• pp.94-98
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• 2012

In order to serve the exact torque clamping force, the torque measurement system use the curve fitting algorithm by the least square. The corrected least square curve fitting algorithm which suggested in this paper can surpport more exact clamping force for fastner in variable industry field using the torque. At first, This paper introduces mathematical modeling for curve fitting algorithm, and simulate it. As a result, the corrected least square algorithm have shown lower standard error value than that of the used algoritm with torque, and so this corrected least square algorithm prove high accuracy than nomal least square algorithm. The suggested algorithm will contribute to improvement of cost and safety on industry field with bolt clamping force for precision industry parts, electronics parts, aircraft, aerospace, etc.

• Proceedings of the KIEE Conference
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• 2003.07a
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• pp.345-347
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• 2003

본 논문에서는 디지털 보호 계전기에 쓰이는 필터 중에서 최소 자승 알고리즘을 이용한 고성능 FIR 필터를 설계하였다. 기존의 DFT필터와 MATLAB 시뮬레이션을 이용하여 비교하였으며 FIR 필터의 VHDL모델 및 합성에 중점을 두었다. FIR 필터는 기본적으로 유한개의 임펄스 응답이 이루어지기 때문에 기타 다른 필터에 비하여 안정도가 높으며 선형적인 위상을 가지기 때문에 차단 주파수 대역의 왜곡현상을 없앨 수 있는 장점을 가지고 있다. 여러 가지 알고리즘으로 구현한 FIR 필터를 시뮬레이션 한 결과 최소 자승 알고리즘이 가장 우수한 결과를 나타내었다. 기본적으로 디지털 보호 계전기에서 디지털 필터의 기능은 사고 전압, 전류로부터 60Hz의 기본파 추출 CT, PT 왜곡 및 DC offset을 제거하는데 있다. 본 논문에서는 이러한 기능을 가지면서 샘플링 주파수와 차수를 같게 하여 FIR 필터와 DFT 필터의 주파수 응답과 연 산 속도를 비교 하였다. 본 논문에서 설계된 최소 자승 알고리즘을 이용한 FIR 필터는 같은 조건의 DFT필터에 비해 1고조파와 2고조파의 차이가 10db 이상 더 우수 하였으며 연산 속도 또한 2배 이상 좋은 결과를 보였다.

• The Journal of the Acoustical Society of Korea
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• v.40 no.3
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• pp.213-218
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• 2021

It is known that total least-squares method shows better estimation performance than least-squares method when noise is present at the input and output at the same time. When total least squares method is applied to data with time series characteristics, Recursive Total Least Squares (RTS) algorithm has been proposed to improve the real-time performance. However, RTLS has numerical instability in calculating the inverse matrix. In this paper, we propose an algorithm for reducing numerical instability as well as having similar convergence to RTLS. For this algorithm, we propose a new RTLS using Iterative Wiener Filter (IWF). Through the simulation, it is shown that the convergence of the proposed algorithm is similar to that of the RTLS, and the numerical robustness is superior to the RTLS.

• Journal of the Korean Institute of Telematics and Electronics
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• v.20 no.1
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• pp.37-44
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• 1983

A class of adaptive control algorithms applied to discrete-time single-input single-output deterministic linear systems is analyzed by using direct model reference adaptive control. Controller parameters are identified with weighted least square Method. And computer simulations reveal that proposed weighted least square method in which the value of depends on the identification error can be used regardless of the sufficient condition of reference input signal.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.4B
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• pp.309-316
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• 2012

The compensation algorithm for localization using the least-squires method in NLOS(Non Line of Sight) environment is suggested and the performance of the algorithm is analyzed in this paper. In order to improve the localization correction rate of the moving node, 1) the distance value of the moving node that is moving as an constant speed is measured by SDS-TWR(Symmetric Double-Sided Two-Way Ranging); 2) the location of the moving node is measured using the triangulation scheme; 3) the location of the moving node measured in 2) is compensated using the least-squares method. By the experiments in NLOS environment, it is confirmed that the average localization error rates are measured to ${\pm}1m$, ${\pm}0.2m$ and ${\pm}0.1m$ by the triangulation scheme, the Kalman filter and the least-squires method respectively. As a result, we can see that the localization error rate of the suggested algorithm is higher than that of the triangulation as average 86.0% and the Kalman filter as average 16.0% respectively.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.9C
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• pp.1330-1336
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• 2004

This paper addresses an adaptive motion vector prediction algorithm to improve the performance of video encoder. The block-based motion vector is characterized by non-stationary local statistics so that the coefficients of LS (Least Squares) based linear motion can be optimized. However, it requires very expensive computational cost. The proposed algorithm using LS approach with spatially varying motion-directed property adaptively controls the coefficients of the motion predictor and reduces the computational cost as well as the motion prediction error. Experimental results show the capability of the proposed algorithm.

• Journal of the Korea Convergence Society
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• v.8 no.9
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• pp.285-292
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• 2017

This paper describes a recursive least-squares based convergent algorithm for driving characteristic classification for personalized autonomous driving. Recently, various researches on autonomous driving technology have been conducted for level 4 fully autonomous driving. In order for commercialization of the autonomous vehicle, personalized autonomous driving is required to minimize passenger's insecureness to the autonomous vehicle. To address this problem. this study proposes mathematical model that represents driving characteristics and recursive least-squares based algorithm that can estimate the defined characteristics. The actual data of two drivers has been used to derive driving characteristics and the hypothesis testing method has been used to classify two drivers. It is shown that the proposed algorithms can derive driving characteristics and classify two drivers reasonably.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.5
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• pp.549-554
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• 2014

In the paper, we propose a new Signed LMS(Least Mean Square) algorithm for ICS(Interference Cancellation System). The proposed Signed LMS algorithm improved performances by adjusting step size values. At the convergence of 1000 iteration state, the MSE(Mean Square Error) performance of the proposed Signed LMS algorithm with step size of 0.067 is about 3 ~ 18 dB better than the conventional LMS, CMA algorithm. And the proposed Signed LMS algorithm requires 500 ~ 4000 less iterations than the and LMS and CMA algorithms at MSE of -25dB.