• Proceedings of the KIPE Conference
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• 2014.07a
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• pp.219-220
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• 2014

기존의 SRF-PLL방법은 구현이 간단하고 정상전원에서 위상각 추정 성능이 우수하지만 불평형 전원하에서 위상각 추정 성능이 저하된다. 본논문에서는 상간전압의 위상변화, 상전압의 크기변동 및 오프셋이 발생된 불평형 전원하에서 변동된 값들을 실시간으로 보상하여 위상각을 검출하는 재귀형 최소 자승법을 이용한 SRF-PLL방법을 제안한다.

• Proceedings of the KIPE Conference
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• 2014.07a
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• pp.134-135
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• 2014

본 논문에서는 동적 전압 보상기를 위한 속응성을 향상시킨 입력 전압의 크기를 검출하는 기법을 제안한다. 동적 전압 보상기가 계통 전압에서 발생한 순시전압강하를 보상하기 위해서는 강하된 전압을 검출해야 한다. 외란에 강인하고 빠른 응답 특성을 가지는 재귀형 최소 자승법을 사용하여 입력 전압으로 부터 강하된 전압의 크기를 구하고 보상전압을 생성한다. 생성된 보상전압은 입력 전압에 더해져 안정된 부하전압을 공급한다. 시뮬레이션 결과를 통해 제안하는 방법의 타당성을 검증한다.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.1343-1344
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• 2015

본 연구에서는 증분형 FCM(Incremental Fuzzy C-Means: Incremental FCM) 클러스터링 알고리즘을 기반으로 방사형 기저함수 신경회로망(Radial Basis Function Neural Networks: RBFNN) 패턴 분류기를 설계한다. 방사형 기저함수 신경회로망은 조건부에서 가우시안 함수 또는 FCM을 사용하여 적합도를 구하였지만, 제안된 분류기에서는 빅 데이터간의 적합도를 구하기 위해 증분형 FCM을 사용한다. 또한, 빅 데이터를 학습하기 위해 결론부에서 재귀최소자승법(Recursive Least Square Estimation: RLSE)을 사용하여 다항식 계수를 추정한다. 마지막으로 추론부에서는 증분형 FCM에서 구한 적합도와 재귀최소자승법으로 구한 다항식을 이용하여 최종 출력을 구한다.

• 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 Society for Precision Engineering
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• v.12 no.11
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• pp.110-117
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• 1995

This study presents and algorithm and related techniques which could satisfy the important properties of check weighers and conveyor scales. The algorithm of Recursive Least Squares Regression is applied for the weighing system simulated as a dynamic model of the second order. Using the model and the algorithm, model parameters and then the mass being weighed can be determined from the step input. The performance of the algorithm was tested on a check weigher. Discussions were extended to the development of noise reduction techniques and to the lagged introduction of objects on the moving plate. It turns out that the algorithm shows several desirable features suitable for real-time signal processing with a microcomputer, which are high precision and stability in noisy environment.

• Journal of the Korean Society for Precision Engineering
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• v.11 no.6
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• pp.136-142
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• 1994

This study presents an algorithm and related techniques which could satisfy the important properties of check weighers and conveyor scales. The algorithm of Recursive Least Squares Regression is described for te weighing system simulated as a dynamic model of the second order. Using the model and the algorithm, model parameters and then the mass being weighed can be determined from the step input. The performance of the algorithm is illustrated in digital simulation. Discussions are extended to the development of fast converging algorithm. It turns out that the algorithm shows several desirable features suitable for microcomputer assisted real-time signal processing, which are high precision and stability in noisy environment.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.802-803
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• 2011

매입형 영구자석 동기전동기는 강건한 회전자 구조와 릴럭턴스 토크를 사용할 수 있다는 점, 악계자 제어를 하기 쉽다는 점에서 넓은 속도와 토크를 필요로하는 하이브리드 차량이나 전기 차량에 적합한 전동기이다. 영구자석의 감자곡선은 온도에도 의존하기 때문에 온도에 따라 전동기의 자속과 토크가 변하게 된다. 이 논문은 전기 스쿠터에 사용되는 매입형 영구자석 동기전동기의 온도에 따른 지령 전류 보정에 의한 제어법에 대해 연구하였다. 토크 리플을 줄이기 위해서 속도-토크 곡선과 감자곡선을 이용하여 구하였으며, 보정된 지령 전류를 구하기 위해 Lagrange 보간법과 재귀 최소자승(Recursive Least Square : RLS) 법을사용하여 전류맵을 만들었다.이 지령 전류는 토크와 인버터 출력 전류값을 계산하는데 사용된다. 전류맵을 만들기 위해서 측정한 온도는 20, 80, 100도이다. 본 논문에서 제안한 제어법을 이용하여 토크 리플이 줄어듬을 시뮬레이션과 실험을 통해서 확인할 수 있었다.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.12
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• pp.2246-2253
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• 2011

This paper presents the error correction method of magnetic position sensor using recursive least square method (RLSM) with forgetting factor. Magnetic position sensor is proposed for linear position detection of the linear motor which has tooth shape stator, consists of permanent magnet, iron core and linear hall sensor, and generates sine and cosine waveforms according to the movement of the mover of the linear motor. From the output of magnetic position sensor, the position of the linear motor can be detected using arc-tan function. But the variation of the air gap between magnetic position sensor and the stator and the error in manufacturing process can cause the variation in offset, phase and amplitude of the generated waveforms when the linear motor moves. These variations in sine and cosine waveforms are changed according to the current linear motor position, and it is very difficult to compensate the errors using constant value. In this paper, the generated sine and cosine waveforms from the magnetic position sensor are compensated on-line using the RLSM with forgetting factor. And the speed observer is introduced to reduce the effect of uncompensated harmonic component. The approaches are verified by some simulations and experiments.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.2
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• pp.204-210
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• 2005

This paper presents online identification algorithm for TSK fuzzy model. The proposed algorithm identify structure of premise part by using distance, and obtain the parameters of the piecewise linear function consisting consequent part by using recursive least square. Only input space was considered in Most researches on structure identification, but input and output space is considered in the proposed algorithm. By doing so, outliers are excluded in clustering effectively. The existing other algorithm has disadvantage that it is sensitive to noise by using data itself as cluster centers. The proposed algorithm is non-sensitive to noise not by using data itself as cluster centers. Model can be obtained through one pass and it is not needed to memorize many data in the proposed algorithm.