• Structural Engineering and Mechanics
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• 제34권1호
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• pp.1-14
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• 2010

The innovative intelligent fuzzy weighted input estimation method which efficiently and robustly estimates the unknown time-varying input force in on-line is presented in this paper. The algorithm includes the Kalman Filter (KF) and the recursive least square estimator (RLSE), which is weighted by the fuzzy weighting factor proposed based on the fuzzy logic inference system. To directly synthesize the Kalman filter with the estimator, this work presents an efficient robust forgetting zone, which is capable of providing a reasonable compromise between the tracking capability and the flexibility against noises. The capability of this inverse method are demonstrated in the input force estimation cases of the plate structure system. The proposed algorithm is further compared by alternating between the constant and adaptive weighting factors. The results show that this method has the properties of faster convergence in the initial response, better target tracking capability, and more effective noise and measurement bias reduction.

• 전자공학회논문지B
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• 제31B권7호
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• pp.33-41
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• 1994

A single channel adaptive noise canceling (ANC) technique with a recursive time delay estimator (RTDE) is presented for removing effects of additive noise on the speech signal. While the conventional method makes a reference signal for the adaptive filter using the pitch estimated on a frame basis from the input speech, the proposed method makes the reference signal using the delay estimated recursively on a sample-by-sample basis. As the RTDEs, the recursion formulae of autocorrelation function (ACF) and average magnitude difference function (AMDF) are derived. The normalized least mean square (NLMS) and recursive least square (RLS) algorithms are applied for adaptation of filter coefficients. Experimental results with noisy speech demonstrate that the proposed method improves the perceived speech quality as well as the signal-to-noise ratio and cepstral distance when compared with the conventional method.

• 한국항공운항학회지
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• 제20권4호
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• pp.1-6
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• 2012

The accurate information of mass properties is required for the precise control of the spacecraft. The mass properties, mass and inertia, are changeable by some reasons such as consumption of propellant, deployment of solar panel, sloshing, environmental effect, etc. The gyro-based attitude data including noise and bias reduces the control accuracy so it needs to be compensated for improvement. This paper introduces a real-time inertia estimation method for the attitude determination of STSAT-3, Korea Science Technology Satellite. In this method we first filter the gyro noise with the Extended Kalman Filter(EKF), and then estimate the moment of inertia by using the filtered data from the EKF based on the Recursive Least Square(RLS).

• 전기학회논문지
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• 제68권1호
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• pp.83-89
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• 2019

In this paper, beam forming using moving least squares method (MLSM) is studied. In the previous research, the least squares method (LSM), one of the data interpolation methods, was used to determine the desired beam pattern and obtain a beam pattern that minimizes the square of the error with the desired beam pattern. However, LSM has a disadvantage in that the beam pattern can not be formed to satisfy the exact steering angle of the desired beam pattern and the peak sidelobe level (PSLL) condition. To overcome this drawback, MLSM is used for beam forming. In order to verify, the proposed method is applied in beam forming of Bezier platform array antenna which is one of conformal array antenna platform.

• 한국정밀공학회지
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• 제15권11호
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• pp.172-179
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• 1998

This paper proposes an identification method for modes of a thin plate where multiple actuators and sensors are bonded. When a natural frequency of a mode is decoupled from all other natural frequencies, the mode can be identified separatedly with a bandpass filter. Since a thin plate has resonant peaks at natural frequencies, the bandpass filter can be designed to extract the signal of the mode to be identified. Parameters of the second order linear differential equation of the mode can be obtained to apply the Least square method to the extract the modal signal. The proposed identification method is applied to an all-clamped plate with two pairs of piezoelectric actuators and sensors. The outputs of the identified model match with the experimental data well.

• 동력기계공학회지
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• 제2권2호
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• pp.73-80
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• 1998

The identification of drilling joint dynamics which consists of drilling and structural dynamics and the on-line time series detection of malfunction process is substantial not only for the investigation of the static and dynamic characteristics but also for the analytic realization of diagnostic and control systems in drilling. Therefore, We have discussed on the comparative assessment of two recursive time series modeling algorithms that can represent the drilling operation and detect the abnormal geometric behaviors in precision roundshape machining such as turning, drilling and boring in precision diemaking. For this purpose, simulation and experimental work were performed to show the malfunctional behaviors for drilling operation. For this purpose, a new two recursive approach (Recursive Extended Instrument Variable Method : REIVM, Recursive Least Square Method : RLSM) may be adopted for the on-line system identification and monitoring of a malfunction behavior of drilling process, such as chipping, wear, chatter and hole lobe waviness.

• 한국위성정보통신학회논문지
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• 제3권2호
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• pp.32-37
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• 2008

Global Navigation Satellite System (GNSS) is been developing in many countries. The satellite navigation system has the importance in economic and military fields. For utilizing satellite navigation system properly, the technology of GNSS Ground Station is needed. GNSS Ground Station monitors the signal of navigation satellite and analyzes navigation solution. This study deals with the navigation software for GNSS Ground Station. This paper will introduce the navigation solution algorithm for GNSS Ground Station. The navigation solution can be calculated by the code-carrier smoothing method, the Kalman-filter method, the least-square method, and the weight least square method. The performance of each navigation algorithm in this paper is presented.

• 대한전기학회논문지
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• 제37권10호
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• pp.727-732
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• 1988

In this paper, a parameter estimation method of linear systems with bounded output disturbances is studied. The bound of the disturbances is assumed to known Weighting factors are proposed to modify LS(Least Square) algorithm in the parameter estimation method. The conditions of weighting factors are given so that the estimation method has good convergence properties. This condition is more relaxed form than other known conditions. The compensation term in the estimation equations is represented by a function of the output prediction error and this function should lie in a specified region on x-y plane to satisfy these conditions of weighting factors. A set of weighting factor is selected and an algorithm is proposed using this set of weighting factor. The proposed algorithm is compared with another existing algorithm by simulation and its performance in parameter estimation id discussed.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 하계학술대회 논문집 D
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• pp.2301-2303
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• 2004

This paper proposes a detection method for mechanically stuck control rod drive mechanisms (CRDMs) in the nuclear reactor. From an electrical viewpoint, a coil of an electro-mechanical CRDM is modelled as an R-L circuit with time-varying inductance. The inductance of the coil is estimated from the measured voltage and current by using the least square method. Numerical experiments are carried out to illustrate the proposed method.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2006년도 International Symposium on GPS/GNSS Vol.1
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• pp.485-489
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• 2006

In last decades, several linearization methods for the AOA measurements have been proposed, for example, Gauss-Newton method and closed-form solution. Gauss-Newton method can achieve high accuracy, but the convergence of the iterative process is not always ensured if the initial guess is not accurate enough. Closed-form solution provides a non-iterative solution and it is less computational. It does not suffer from convergence problem, but estimation error is somewhat larger. This paper proposes a self-tuning weighted least square AOA algorithm that is a modified version of the conventional closed-form solution. In order to estimate the error covariance matrix as a weight, two-step estimation technique is used. Simulation results show that the proposed method has smaller positioning error compared to the existing methods.