• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1999.11a
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• pp.248-251
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• 1999

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 i(oration n upon the arrival of new data. The RLS algorithm may be viewed as a special case of the Kalman filter. Indeed this special relationship between the RLS algorithm and the Kalman filter is considered. 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. The resulting rate of convergence is therefore typically an order of magnitude faster than the simple LMS algorithm. This improvement in performance, however, Is achieved at the expensive of a large increase in computational complexity.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.5 no.3
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• pp.169-173
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• 2012

This paper investigates the performance of Turbo equalization in wireless multipath channels. Turbo equalization mainly consists of a SISO(soft-in soft-out) equalizer and a SISO decoder. Iterative channel estimators can improve the accuracy of channel estimates by soft information fed back from the SISO decoder. Comparing iterative channel estimators with LMS(least mean square) and RLS(recursive least squares) algorithms, which are the most common algorithms to estimate and track a time-varying channel impulse response, the iterative channel estimator with RLS converges more faster than the one with LMS. However, the difference of BER(bit error rate) performances gradually decreases as the number of iterations for Turbo equalization increases.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.1 no.1
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• pp.123-132
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• 2002

In this study, the static and dynamic characteristics of endmilling process was modelled and the analytic realization of chatter mechanism was discussed. In this regard, We have discussed on the comparative assessment of recursive time series modeling algorithms that can represent the machining process and detect the abnormal machining behaviors in precision endmilling operation. In this study, simulation and experimental work were performed to show the malfunctional behaviors. For this purpose, new recursive least square method (RLSM) were adopted for the on-line system identification and monitoring of a machining process, we can apply these new algorithms in real process for detection of abnormal chatter. Also, The stability lobe of chatter was analysed by varying parameter of cutting dynamices in regenerative chatter mechanics.

• Speech Sciences
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• v.8 no.2
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• pp.73-81
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• 2001

In this paper, we focus on the real time implementation of the single channel adaptive noise canceller(ANC) by using TMS320C30 EVM board. The implemented single channel adaptive noise canceller is based on a reference paper [1] in which it is simulated by using the recursive average magnitude difference function(AMDF) to get a properly delayed input speech on a sample basis as a reference signal and normalized least mean square(NLMS) algorithm. To certify results of the real time implementation, we measured the processing time of the ANC and enhancement ratio according to various signalto-noise ratios(SNRs). Experimental results demonstrate that the processing time of the speech signal of 32ms length with delay estimation of every 10 samples is about 26.3 ms, and almost the same performance as given in [1] is obtained with the implemented system.

• 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.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1999.11a
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• pp.475-481
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• 1999

This paper presents a recursive least square estimation algorithm to estimate parameters of the vector controlled induction machine based on measurements of the stator voltage, curents and slip frequency. Due to its recursive structure, this algorithm has the potential to be used for on-line estimation and adaptive control. The algorithm is designed using regression model derived from the motor electrical equation. This model is valid when there is a tittle-scale separation between vector control system and adaptive system. Vector control performed at fast stage and slow stage is in charge of parameters estimation. The performance of tile algorithm is illustrated by means of simulation results and experiment.

• Journal of Auto-vehicle Safety Association
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• v.12 no.4
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• pp.13-22
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• 2020

This paper presents an adaptive feedback based actuator fault detection and tolerant control algorithms for longitudinal functional safety of autonomous driving. In order to ensure the functional safety of autonomous vehicles, fault detection and tolerant control algorithms are needed for sensors and actuators used for autonomous driving. In this study, adaptive feedback control algorithm to compute the longitudinal acceleration for autonomous driving has been developed based on relationship function using states. The relationship function has been designed using feedback gains and error states for adaptation rule design. The coefficients in the relationship function have been estimated using recursive least square with multiple forgetting factors. The MIT rule has been adopted to design the adaptation rule for feedback gains online. The stability analysis has been conducted based on Lyapunov direct method. The longitudinal acceleration computed by adaptive control algorithm has been compared to the actual acceleration for fault detection of actuators used for longitudinal autonomous driving.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.6
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• pp.491-499
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• 2001

In this paper, the scheme of an efficient fuzzy rule generation and fuzzy system construction using GA(genetic algorithm) and FCM(fuzzy c-means) clustering algorithm is proposed for TSK(Takagi-Sugeno-Kang) type fuzzy system. In the structure identification, input data is transformed by PCA(Principal Component Analysis) to reduce the correlation among input data components. And then, a set fuzzy rules are generated for a given criterion by FCM clustering algorithm . In the parameter identification premise parameters are optimally searched by GA. On the other hand, the consequent parameters are estimated by RLSE(Recursive Least Square Estimate) to reduce the search space. From this one can systematically obtain the valid number of fuzzy rules which shows satisfying performance for the given problem. Finally, we applied the proposed method to the Box-Jenkins data and rice taste data modeling problems and obtained a better performance than previous works.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.3
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• pp.428-451
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• 2010

Traffic matrix-based anomaly detection and DDoS attacks detection in networks are research focus in the network security and traffic measurement community. In this paper, firstly, a new type of unidirectional flow called IF flow is proposed. Merits and features of IF flows are analyzed in detail and then two efficient methods are introduced in our DDoS attacks detection and evaluation scheme. The first method uses residual variance ratio to detect DDoS attacks after Recursive Least Square (RLS) filter is applied to predict IF flows. The second method uses generalized likelihood ratio (GLR) statistical test to detect DDoS attacks after a Kalman filter is applied to estimate IF flows. Based on the two complementary methods, an evaluation formula is proposed to assess the seriousness of current DDoS attacks on router ports. Furthermore, the sensitivity of three types of traffic (IF flow, input link and output link) to DDoS attacks is analyzed and compared. Experiments show that IF flow has more power to expose anomaly than the other two types of traffic. Finally, two proposed methods are compared in terms of detection rate, processing speed, etc., and also compared in detail with Principal Component Analysis (PCA) and Cumulative Sum (CUSUM) methods. The results demonstrate that adaptive filter methods have higher detection rate, lower false alarm rate and smaller detection lag time.