• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.3
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• pp.191-193
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• 2005

This paper proposes a phase measurement algorithm which is based on the recursive implementation of sliding-DFT. The proposed algorithm is designed to have a robust behavior against the erroneous factors of frequency drift, additive noise, and twiddle factor approximation. Four channel power-line phase measurement system is also designed and implemented based on the time-multiplexed sharing architecture of the proposed algorithm. The proposed algorithm's features of phase measurement accuracy and its robustness against the finite wordlength effects can provide a significant impact especially for the ASIC or microprocessor based embedded system applications where the enhanced processing speed and implementation simplicity are crucial design considerations.

• Proceedings of the KOSOMBE Conference
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• v.1995 no.05
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• pp.265-269
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• 1995

In this study, we present a training method of radial basis function networks based on recursive modified Gram-Schmidt algorithm for single step prediction of chaotic time series. With single step predictions of Mackey-Glass time series and alpha-rhythm EEG which has chaotic characteristics, the radial basis function network trained by this method is compared with one trained by a classical non-recursive method and the radial basis function model proposed by X.D. He and A. Lapedes. The results show the effectiveness of the training method.

• Proceedings of the KOSOMBE Conference
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• v.1995 no.05
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• pp.117-119
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• 1995

서울대학교 의공학과에서는 부분적인 심부전 판자의 보조 및 치료장비로 전기유압식 좌심실 보조 장치를 개발하고 있다. 이 장비의 경우 인체의 좌심방 및 대동맥에 직접 연결되므로 실제적으로 좌심실 보조 장치의 박출량을 센서를 통하여 알아내는 데에는 여러가지 어려움이 따른다. 이러한 필요성에 비추어 전기유압식 좌심실 보조 장치의 박출량을 시스템을 ARX모델로 모델링하여 RLS(Recursive Least Square) 알고리듬을 이용하여 추정하였다. 그 결과 비교적 높은 정착도로 박출량이 추정됨을 볼 수 있었다. 하지만, ARX모델의 특성상 원래 본 연구의 시작과정에서 분석한 시스템의 동적 특성을 완전하게 반영할 수 없었다. 앞으로 시스템의 파라미터 추정 과정에서 이미 주어진 동적 특성은 고정시키고 나머지 파라미터들만을 추정하는 알고리듬을 개발하는 것도 흥미로운 과제라 할 수 있다.

• Journal of Auto-vehicle Safety Association
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• v.12 no.2
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• pp.14-20
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• 2020

This paper presents a vehicle mass and road grade estimator for developing an automated bus. To consider the dynamic characteristics of a bus varying with the number of passengers, the longitudinal controller needs the estimation of the vehicle's mass and road grade in real-time and utilizes the information to adjust the control gains. Discrete Kalman filter is applied to estimate the time-varying road grade, and the recursive least squares algorithm is adopted to account for the constant mass estimation. After being implemented in MATLAB/Simulink, the estimators are evaluated with the dynamic model and experimental data of the target bus. The proposed estimators will be applied to complement the algorithm of the longitudinal controller and proceed with algorithm verification.

• KEPCO Journal on Electric Power and Energy
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• v.6 no.1
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• pp.49-52
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• 2020

Since power plant is an important system to provide electricity, it is necessary to monitor it in order to operate safely. Much information related with machine diagnosis exists in written form instead of digital data. So, it causes difficulties of analyzing and finding solutions. Rulebased expert system can provide flexible and effective solutions to users. In this paper, Recursive Bayesian Estimation is applied in order to increase accuracy of solutions.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2008.06a
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• pp.525-526
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• 2008

For measuring nonlinear Vehicle Modeling based on 7-Depth Sensor, the neural networks are proposed m adaptive and in realtime. The structure of it is similar to recurrent neural networks; a delayed output as the input and a delayed error between the output of plant and neural networks as a bias input. In addition, we compute the desired value of hidden layer by an optimal method instead of transfering desired values by backpropagation and each weights are updated by RLS(Recursive Least Square). Consequently, this neural networks are not sensitive to initial weights and a learning rate, and have a faster convergence rate than conventional neural networks. This new neural networks is Error Estimated Neural Networks. We can estimate nonlinear models in realtime by the proposed networks and control nonlinear models.

• The KIPS Transactions:PartA
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• v.8A no.1
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• pp.42-47
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• 2001

In this paper, we present a curve fitting algorithm using a ball curve. Our algorithm is recursive method for fitting, which is not a traditional ball function but a continuous ball function. This algorithm consists of two steps. The first step, it is classified the composite corner points to joint points until selected from the given data set. The second step is the curve fitting. The basis function for curve fitting is use to ball function. Also, the weighted least square method, to insert knot, is an efficient method for piecewise ball curve and ball curve segments will be smoothly connected at all composit points. The proposed algorithm will be applied to represent image representation, like fonts, digital image and GIS.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.5C
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• pp.521-526
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• 2009

In this paper, a complex-valued recursive least squares escalator filter algorithm with reduced computational complexity for complex-valued signal processing applications is presented. The local tap weight of RLS-ESC algorithm is updated by incrementing its old value by an amount equal to the local estimation error times the local gain scalar, and for the gain scalar, the local input autocorrelation is calculated at the previous time. By deriving a new gain scalar that can be calculated by using the current local input autocorrelation, reduced computational complexity is accomplished. Compared with the computational complexity of the complex-valued version of RLS-ESC algorithm, the computational complexity of the proposed method can be reduced by 50% without performance degradation. The reduced computational complexity of the proposed algorithm is even less than that of the LMS-ESC. Simulation results for complex channel equalization in 64QAM modulation schemes demonstrate that the proposed algorithm has superior convergence and constellation performance.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.52 no.8
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• pp.444-449
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• 2003

In this paper present Autonomous Adaptive Digital Over Current Relay for distribution networks which acts autonomous setting using the short circuit impedance measured by relay of power systems. Automation of relay setting is one of the basic requirements for distribution automation, although manual relay setting is used at present. The short circuit impedance from a power source in distribution networks essential for the Autonomous Relay Setting changes frequently in distribution networks. In this paper the short circuit impedance is calculated with voltage and current measured in real time operation of digital relay using the Recursive Least Squares. A new method of digital relay setting is introduced using the the short circuit impedance and load current.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.901-903
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• 1995

The ordinary differential equation (ODE) method has been widely used for the convergence analysis of stochastic recursive algorithms. The principal objective of this method is to associate to a given algorithm a differential equation with continuous righthand side. Usually some assumptions should be imposed to get such a differential equation. If any of assumptions fails, then the ODE method cannot be used. Recently a new method using differential inclusions (DIs) was introduced in [3], which is useful to deal with those cases. The DI method shares the same idea with the ODE method, but it is different in that a differential inclusion is identified instead of a differential equation with continuous righthand side. In this paper, we briefly review the DI method and then analyze a Robbins and Monro (RM)-type algorithm. Our focus is placed on the projected algorithm.