• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.10
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• pp.1326-1337
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• 1995

In this paper, we transformed the bilinear filter into an equivalent linear multichannel filter and derived QR decomposition based recursive least squares algorithms for bilinear lattice filters. We also defined order update relation of the forward and the backward input vectors by using the channel decomposition. The forward and the backward data matrices were defined by using the forward and the backward input vectors and orthogonalized with the QR decomposition. we can obtain the lattice equations of the bilinear filters by using the channel decomposition. we can be derived the lattice equations of the bilinear filters using this decomposition process which are the same as the lattice equations derived by Baik, we can use the coefficient transformation algorithm proposed by Baik. We derived the equation error and the output error algorithm of the QRD based RLS bilinear lattice algorithm. Also, we evaluated the performance of the proposed algorithms through the system identification of the bilinear system.

• Proceedings of the KIEE Conference
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• 1999.11c
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• pp.488-490
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• 1999

This paper describes the design of self-tuning speed controller for DC motor drive system using RLS(Recursive Least Squares) algorithm and Pole-Placement method. The model parameters, related to inertia and damping coefficient of motor, are estimated on-line by using RLS estimation algorithm. And a control signal is calculated by using pole placement method. Simulation and experimental results show that the proposed controller possesses excellent adaptation capability than a conventional PI/IP controller under parameter change.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.51-55
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• 1995

An adaptive unified predictive control (UPC) algorithm is applied to a batch polymerization reactor for poly(methyl methancrylate) (PMMA) and the effects of controller parameters are investigated. Computational studies are performed for a batch polymerization system model developed in this study. A transfer function in parametric form is estimated by recursive least squares (RLS) method, and the UPC algorithm is implemented to control the reactor temperature on the basis of this transfer function. The adaptive unified predictive controller shows a better performance than the PID controller for tracking set point changes, especially in the latter part of reaction course when gel effect becomes significant. Various performance can be acquired by selecting adequate values for parameters of the adaptive unified predictive controller; in other words, the optimal set of parameters exists for a given set of reaction conditions and control objective.

• Journal of Astronomy and Space Sciences
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• v.26 no.1
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• pp.75-88
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• 2009

The main purpose of the current research is to introduce the alternative algorithm of the non-recursive batch filter based on the unscented transformation in which the linearization process is unnecessary. The presented algorithm is applied to the orbit determination of a low earth orbiting satellite and compared its results with those of the well-known Bayesian batch least squares estimation and the iterative UKF smoother (IUKS). The system dynamic equations consist of the Earth's geo-potential, the atmospheric drag, solar radiation pressure and the lunar/solar gravitational perturbations. The range, azimuth and elevation angles of the satellite measured from ground stations are used for orbit determination. The characteristics of the non recursive unscented batch filter are analyzed for various aspects, including accuracy of the determined orbit, sensitivity to the initial uncertainty, measurement noise and stability performance in a realistic dynamic system and measurement model. As a result, under large non-linear conditions, the presented non-recursive batch filter yields more accurate results than the other batch filters about 5% for initial uncertainty test and 12% for measurement noise test. Moreover, the presented filter exhibits better convergence reliability than the Bayesian least squares. Hence, it is concluded that the non-recursive batch filter based on the unscented transformation is effectively applicable for highly nonlinear batch estimation problems.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2088-2090
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• 2003

A practical recursive weighted least square(WLS) solution is proposed to solve the passive ranging problem. Apart from the previous works based on the extended Kalman filter(EKF), to ensure the convergency at long-range, the proposed scheme makes use of line-of-sight(LOS) rate instead of bearing information. The influence of LOS rate measurement errors is investigated and it is asserted that the WLS estimates contain bias and scale factor errors. Together with simple compensation algorithm, the estimation errors of proposed filter can be reduced dramatically.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1249-1253
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• 2003

License plate recognition is very important in an automobile society. This is because, since plate detection accuracy has large influence on subsequent number recognition, it is very important. However, it is very difficult to do it, because a background and a body color of cars are similar to that of the license plate. In this paper, we propose a new thresholds determination method in the various background by using the real-coded genetic algorithm (RGA). By using RGA, the most likely plate colors are decided under various lighting conditions. First, the average brightness Y values of images are calculated. Next, relationship between the Y value and the most likely plate color thresholds (upper and lower bounds)are obtained by RGA. The relationship between thresholds decided from RGA and brightness average is aproximate by using the recursive least squares (RLS) algorithm. In the case of plate detection, thresholds are decided from these functions.

• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.173-176
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• 2000

시변 신호 추적 특성을 향상시키기 위하여, Gauss-Newton Variable Forgetting Factor RLS (GN-VFF-RLS) Algorithm을 제안한다. 최적화된 망각인자를 가정한 기존의 RLS 알고리듬과 비교하여, 제안된 방법은 특히 신호의 변화가 급격히 일어날 경우 주목할만한 추정 성능의 향상을 보여준다. 제안된 알고리듬의 시변 추정 특성을 신호 대 잡음비와 시변 정도에 대하여 모의 실험하고 기존의 추정 알고리듬들과 비교한다.

• Journal of the Korea Convergence Society
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• v.4 no.1
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• pp.33-41
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• 2013

This paper presents an adaptive recursive least squares algorithm (ARLS) for detecting voltage sag and voltage swell events in power systems. Different methods have been developed to detect voltage sag and voltage swell. Some of them use window techniques, which are too slow when voltage sag or swell mitigation is required. Others depend on the extraction of a single non-stationary sinusoidal signal out of a given multi-components input signal, and therefore they don't consider the harmonic components in calculating the voltage root mean square value (rms). The method, proposed in this paper, is capable of estimating the voltage rms taking into account all harmonic components. The method is tested by applying it to different, simulated signals using ATP program, and compared with voltage sag detection algorithms.

• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.406-409
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• 2000

This paper proposes a method that enables us to implement the recursive least squares (RLS) algorithm at, high throughput rate using fewer processing elements (PEs). It is known that the pipeline processing can provide a high throughput rate. But, pipelining is effective only when enough number of PEs are available. The proposed method achieves high throughput rate using a few PEs. The effectiveness of the proposed method is verified through simulations on programmable digital signal processors (in the following, DSP processors).

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.5B
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• pp.830-841
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• 2000

We extend the sue of the method of least square to develop a recursive algorithm for the design of adaptive transversal filters such that, given the least-square estimate of this vector of the filter at iteration n-1, we may compute the updated estimate of this vector at iteration a upon the arrival of new data. We begin the development of the RLS algorithm by reviewing some basic relations that pertain to the method of least squares. Then, by exploiting a relation in matrix algebra known as the matrix inversion lemma, we develop the RLS algorithm. An important feature of the RLS algorithm is that it utilizes information contained in the input data, extending back to the instant of time when the algorithm is initiated. In this paper, we propose new tap weight updated RLS algorithm in adaptive transversal filter with data-recycling buffer structure. We prove that convergence speed of learning curve of RLS algorithm with data-recycling buffer is faster than it of exiting RL algorithm to mean square error versus iteration number. Also the resulting rate of convergence is typically an order of magnitude faster than the simple LMS algorithm. We show that the number of desired sample is portion to increase to converge the specified value from the three dimension simulation result of mean square error according to the degree of channel amplitude distortion and data-recycle buffer number. This improvement of convergence character in performance, is achieved at the (B+1)times of convergence speed of mean square error increase in data recycle buffer number with new proposed RLS algorithm.