• International Journal of Automotive Technology
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• v.6 no.5
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• pp.475-481
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• 2005

This paper deals with the algorithm of estimating the longitudinal speed of a braking vehicle using measurements from an accelerometer and a standard wheel speed sensor. We evolve speed estimation algorithms of increasing complexity and accuracy on the basis of experimental tests. A final speed estimation algorithm based on a Kalman filtering is developed to reduce measurement noise of the wheel speed sensor, error of the tire radius, and accelerometer bias. This developed algorithm can give peak errors of less than 3 percent even when the accelerometer signal is significantly biased.

• Journal of Communications and Networks
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• v.4 no.3
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• pp.189-198
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• 2002

Precoder has been shown to be able to provide source diversity and design flexibility. In this paper we employ precoding techniques for block transmission based on a multirate filterbank structure. To accommodate multiuser communication with variable data rates, different precoders with corresponding coefficients and up/down sampling rates are used. However, due to unknown multipath distortion, different interferences may exist in the received data, such as multiuser interference, intersymbol interference and interblock interference. To estimate channel parameters for a desired user, we employ all structured signature waveforms associated with different symbols of that user and apply subspace techniques. Therefore better performance of channel estimator can be achieved than the conventional subspace method based only on the signature of the current symbol. The delay for that user can also be jointly estimated. Channel identifiability conditions and asymptotic channel estimation error are investigated in detail. Numerical examples are provided to justify the proposed method. gest either multicode (MC) or multiple processing gain (MPG) mechanism [2], while requiring data rates to be integral multiples of some basic low-rate. In order to support variable rate transmission however, a comprehensive scheme needs to be investigated.

• Proceedings of the KWS Conference
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• 1992.10a
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• pp.91-95
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• 1992

The resistance spot welding process has been extensively used for joining of sheet metals, which are subject to variation of many process variables. Many qualitive analyses of sampled process variables have been attempted to predict nugget size. In this paper, dynamic resistance and electrode movement signal which is a good indicative of the nugget size was examined by introducing an artificial neural network estimator. An artificial neural feedforward network with back-propagation of error was applied for the estimation of the nugget size. The prediction by the neural network is in good agreement with the actual nugget size for resistance spot welding of galvanized steel. The results are quite promising in that the quantitative estimation of the invisible nugget size can be achieved without conventional destructive testing of welds.

• Journal of Hydrospace Technology
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• v.1 no.1
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• pp.66-74
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• 1995

Unknown parameters can be determined by system identification techniques. Extended Kalman filter method was introduced as a real time estimator of hydrodynamic derivatives but it has the problem named the coefficient drift. In this study, 2nd order filter estimates hydrodynamic derivatives in Abkowitz model In order to reduce the coefficient drift, parallel processing is used. The measured state and ship trajectory are compared with the estimated values. Parallel processing of 2nd order filter gives very similar results to parallel processing of extended Kalman filter. Parallel processing cannot not remove the coefficient drift perfectly, but it reduces the estimation error.

• Communications for Statistical Applications and Methods
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• v.26 no.3
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• pp.273-293
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• 2019

We consider the problem of model selection in multiple linear regression with outliers and non-normal error distributions. In this article, the robust model selection criterion is proposed based on the robust estimation method with the least absolute deviation (LAD). The proposed criterion is shown to be consistent. We suggest proposed criterion based algorithms that are suitable for a large number of predictors in the model. These algorithms select only relevant predictor variables with probability one for large sample sizes. An exhaustive simulation study shows that the criterion performs well. However, the proposed criterion is applied to a real data set to examine its applicability. The simulation results show the proficiency of algorithms in the presence of outliers, non-normal distribution, and multicollinearity.

• Communications for Statistical Applications and Methods
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• v.26 no.1
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• pp.11-25
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• 2019

The present paper utilizes auxiliary information to neutralize the effect of non-response for estimating the population mean. Improved ratio type estimators for population mean have been proposed and their properties are studied. These estimators are suggested for both single phase sampling and two phase sampling in presence of non-response. Empirical studies are conducted to validate the theoretical results and demonstrate the performance of the proposed estimators. The proposed estimators are shown to perform better than those used by Cochran (Sampling Techniques (3rd ed), John Wiley & Sons, 1977), Khare and Srivastava (In Proceedings-National Academy Science, India, Section A, 65, 195-203, 1995), Rao (Randomization Approach in Incomplete Data in Sample Surveys, Academic Press, 1983; Survey Methodology 12, 217-230, 1986), and Singh and Kumar (Australian & New Zealand Journal of Statistics, 50, 395-408, 2008; Statistical Papers, 51, 559-582, 2010) under the derived optimality condition. Suitable recommendations are put forward for survey practitioners.

• Communications for Statistical Applications and Methods
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• v.29 no.5
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• pp.533-545
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• 2022

Collection of data on several variables, especially in the field of medicine, results in the problem of measurement errors. The presence of such measurement errors may influence the outcomes or estimates of the parameter in the model. In classification scenario, the presence of measurement errors will affect the intrinsic cum summary measures of Receiver Operating Characteristic (ROC) curve. In the context of ROC curve, only a few researchers have attempted to study the problem of measurement errors in estimating the area under their respective ROC curves in the framework of univariate setup. In this paper, we work on the estimation of area under the multivariate ROC curve in the presence of measurement errors. The proposed work is supported with a real dataset and simulation studies. Results show that the proposed bias-corrected estimator helps in correcting the AUC with minimum bias and minimum mean square error.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.1387-1392
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• 1990

In this paper, an iterative learning control algorithm for unknown linear discrete systems is proposed by employing a parameter estimator together with an inverse system model. Regardless of initial error and inherent parameter uncertainty, a good tracking control performance is obtained using the proposed learning control algorithm characterized by recursive operations. A sufficient condition for convergency is provided to show the effectiveness of the proposed algorithm. To investigate the performance of the algorithm a series of simulations and experiments were performed for the tracking control of a servo motor.

• Communications for Statistical Applications and Methods
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• v.30 no.3
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• pp.311-330
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• 2023

The present work describes simulation studies to compare the performances in terms of averaged mean squared error of bayesian wavelet shrinkage methods in estimating component curves from aggregated functional data. Five bayesian methods available in the literature were considered to be compared in the studies: The shrinkage rule under logistic prior, shrinkage rule under beta prior, large posterior mode (LPM) method, amplitude-scale invariant Bayes estimator (ABE) and Bayesian adaptive multiresolution smoother (BAMS). The so called Donoho-Johnstone test functions, logit and SpaHet functions were considered as component functions and the scenarios were defined according to different values of sample size and signal to noise ratio in the datasets. It was observed that the signal to noise ratio of the data had impact on the performances of the methods. An application of the methodology and the results to the tecator dataset is also done.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.4
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• pp.469-475
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• 2015

This paper describes about the method for designing an extended Kalman filter to accurately measure the position of the spatial-phase system using a semiconductor sensor. Spatial position is expressed by the correlation of the rotated coordinate system attached to the body from the inertia coordinate system (a fixed coordinate system). To express the attitude, quaternion was adapted as a state variable, Then, the state changes were estimated from the input value which was measured in the gyro sensor. The observed data is the value obtained from the acceleration sensor. By matching between the measured value in the acceleration sensor and the predicted calculation value, the best variable was obtained. To increase the accuracy of estimation, designation of the extended Kalman filter was performed, which showed excellent ability to adjust the estimation period relative to the sensor property. As a result, when a three-axis gyro sensor and a three-axis acceleration sensor were adapted in the estimator, the RMS(Root Mean Square) estimation error in simulation was retained less than 1.7[$^{\circ}$], and the estimator displayed good property on the prediction of the state in 100 ms measurement period.