• Journal of Drive and Control
• /
• v.13 no.4
• /
• pp.59-67
• /
• 2016

This paper proposes a model-prediction-based collision-avoidance algorithm for excavators for which the recursive-least-squares (RLS) estimation of the excavator's rotational inertia is used. To estimate the rotational inertia of the excavator, the RLS estimation with multiple forgetting and two updating rules for the nominal parameter and the forgetting factors was conducted based on the excavator-swing dynamics. The average value of the estimated rotational inertia that is for the minimizing effects of the estimation error was computed using the recursive-average method with forgetting. Based on the swing dynamics, the computed average of the rotational inertia, the damping coefficient for braking, and the excavator's braking angle were predicted, and the predicted braking angle was compared with the detected-object angle for a safety evaluation. The safety level defined in this study consists of the three levels safe, warning, and emergency braking. The analytical rotational-inertia-based performance evaluation of the designed estimation algorithm was conducted using a typical working scenario. The results of the safety evaluation show that the predictive safety-evaluation algorithm of the proposed model can evaluate the safety level of the excavator during its operation.

• Journal of the Korean Statistical Society
• /
• v.30 no.1
• /
• pp.31-39
• /
• 2001

For estimating parameters of possibly nonlinear and/or non-stationary seasonal autoregressive(AR) processes, we introduce a new instrumental variable method which use the direction vector of the regressors in the same period as an instrument. On the basis of the new estimator, we propose new seasonal random walk tests whose limiting null distributions are standard normal regardless of the period of seasonality and types of mean adjustments. Monte-Carlo simulation shows that he powers of he proposed tests are better than those of the tests based on ordinary least squares estimator(OLSE).

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2003.05a
• /
• pp.333-337
• /
• 2003

열악한 환경에서 동작해야 하거나 물리적 접근이 어려운 곳에 장착되는 센서 시스템의 경우, 유선에 의한 정보전달이 어려울 뿐만 아니라 센서 내 전원설비가 제한적일 수도 있다. 따라서, 본 논문에서는 이러한 문제점에 대한 해결책으로서 밧데리 없이 유도결합에 의하여 원격 센서로부터 정보 취득이 가능한 한 방법을 제안하였다. 이 방법은 전원공급에 의한 유도 결합식의 원격센서 시스템과는 달리, 원격 센서의 정전용량을 변ㆍ복조 과정 없이 재귀적 최소 자승 추정법에 의해 센서의 정전용량을 고정도로 추정하는 것이다. 이를 위하여 시스템의 유도결합 모델을 사용하여 정확도가 높은 원격 센서 시스템을 구현할 수 있었다.

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

• Wind and Structures
• /
• v.17 no.4
• /
• pp.451-464
• /
• 2013

This study develops an extended inverse input estimation algorithm with intelligent adaptive fuzzy weighting to effectively estimate the unknown input wind load of nonlinear structural systems. This algorithm combines the extended Kalman filter and recursive least squares estimator with intelligent adaptive fuzzy weighting. This study investigated the unknown input wind load applied on a tower structural system. Nonlinear characteristics will exist in various structural systems. The nonlinear characteristics are particularly more obvious when applying larger input wind load. Numerical simulation cases involving different input wind load types are studied in this paper. The simulation results verify the nonlinear characteristics of the structural system. This algorithm is effective in estimating unknown input wind loads.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.8
• /
• pp.3295-3311
• /
• 2020

With the wide application of hyperspectral images, it becomes more and more important to compress hyperspectral images. Conventional recursive least squares (CRLS) algorithm has great potentiality in lossless compression for hyperspectral images. The prediction accuracy of CRLS is closely related to the correlations between the reference bands and the current band, and the similarity between pixels in prediction context. According to this characteristic, we present an improved CRLS with adaptive band selection and adaptive predictor selection (CRLS-ABS-APS). Firstly, a spectral vector correlation coefficient-based k-means clustering algorithm is employed to generate clustering map. Afterwards, an adaptive band selection strategy based on inter-spectral correlation coefficient is adopted to select the reference bands for each band. Then, an adaptive predictor selection strategy based on clustering map is adopted to select the optimal CRLS predictor for each pixel. In addition, a double snake scan mode is used to further improve the similarity of prediction context, and a recursive average estimation method is used to accelerate the local average calculation. Finally, the prediction residuals are entropy encoded by arithmetic encoder. Experiments on the Airborne Visible Infrared Imaging Spectrometer (AVIRIS) 2006 data set show that the CRLS-ABS-APS achieves average bit rates of 3.28 bpp, 5.55 bpp and 2.39 bpp on the three subsets, respectively. The results indicate that the CRLS-ABS-APS effectively improves the compression effect with lower computation complexity, and outperforms to the current state-of-the-art methods.

• Water for future
• /
• v.27 no.1
• /
• pp.89-99
• /
• 1994

Flood forecasting in Korea has been based on the off-line parameter estimation method. But recent flood forecasting studies explore on-line recursive parameter estimation algorithms. In this study, a simultaneous adaptive estimation of system states and parameters for rainfall-runoff model is investigated for on-line real-time flood forecasting and parameter estimation. The proposed flood routing system is composed of Flood forecasting in Korea has been based on the off-line parameter estimation method. But recent flood forecasting studies explore on-line recursive parameter estimation algorithms. In this study, a simultaneous adaptive estimation of system states and parameters for rainfall-runoff model is investigated for on-line real-time flood forecasting and parameter estimation. The proposed flood routing system is composed of ø-index in the assessment of effective rainfall and the cascade of nonlinear reservoirs accounting for translation effect in flood routing. To combine the flood routing model with a parameter estimation model, system states and parameters are treated with the extended state-space formulation. Generalized least squares and maximum a posterior estimation algorithms are comparatively examined as estimation techniques for the state-space model. The sensitivity analysis is to investigate the identifiability of the parameters. The index of sensitivity used in this study is the covariance matrix of the estimated parameters.-index in the assessment of effective rainfall and the cascade of nonlinear reservoirs accounting for translation effect in flood routing. To combine the flood routing model with a parameter estimation model, system states and parameters are treated with the extended state-space formulation. Generalized least squares and maximum a posterior estimation algorithms are comparatively examined as estimation techniques for the state-space model. The sensitivity analysis is to investigate the identifiability of the parameters. The index of sensitivity used in this study is the covariance matrix of the estimated parameters.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.30B no.5
• /
• pp.52-61
• /
• 1993

This paper deals with adaptive control method of a robot manipulator with one-flexible link. ARMA model is used as a prediction and estimation model, and adaptive control scheme consists of parameter estimation part and adaptive controller. Parameter estimation part estimates ARMA model's coefficients by using recursive least-squares(RLS) algorithm and generates the predicted output. Variable forgetting factor (VFF) is introduced to achieve an efficient estimation, and adaptive controller consists of reference model, error dynamics model and minimum prediction error controller. An optimal input is obtained by minimizing input torque, it's successive input change and the error between the predicted output and the reference output.

• Journal of Power Electronics
• /
• v.10 no.3
• /
• pp.270-276
• /
• 2010

In this paper, a capacitance estimation scheme for DC-link capacitors for single-phase AC/DC PWM converters is proposed. Under the no-load condition, a controlled AC current (30[Hz]) is injected into the input side, which then causes AC voltage ripples at the DC output side. Or, a controlled AC voltage can be directly injected into the DC output side. By extracting the AC voltage/current and power components on the DC output side using digital filters, the capacitance value can be calculated, where the recursive least squares (RLS) algorithm is used. The proposed methods can be simply implemented with software only and additional hardware is not required. From the experiment results, a high accuracy estimation of capacitances less than 0.85% has been obtained.

• Proceedings of the KIPE Conference
• /
• 2006.06a
• /
• pp.381-383
• /
• 2006

A novel online capacitance estimation method for a DC-link capacitor in a three-phase AC/DC/AC PWM converter is proposed. A controlled AC voltage with a lower frequency than the line frequency is Injected into the DC-link voltage, which then causes AC power ripples at the DC output side. By extracting the AC voltage and power components on the DC output side using digital filters, the capacitance can then be calculated using the recursive least squares method. The proposed method can be simply implemented with only software and no additional hardware. Experimental results confirm that the estimation error is less than 0.2%.