Woochul Kim

Surface-detached V-shaped yoke of obliquely bonded magnetostrictive strips for high transduction of ultrasonic torsional waves

When slender magnetostrictive strips that are obliquely bonded on the surface of a pipe are subjected to a time-varying magnetic field, the strips deform mechanically because of the magnetostrictive effect and in turn, ultrasonic torsional waves are generated in the pipe. If the magnetic flux density in the strips can be increased by simply modifying the magnetic path, the sensitivity of the generated wave signal with respect to the applied magnetic field strength can be considerably enhanced even without increasing the magnetic field strength. Sensitivity has been one of the most important factors in sensors. To achieve this objective, yokes can be used, but improper yoke configurations can generate unwanted additional wave modes with no sensitivity improvement. Consequently, the problem of an optimal yoke configuration to increase the sensitivity was formulated as a topology optimization problem, and an interesting V-shaped yoke was found to be the optimal configuration. When the designed yokes were emp...

Asymptotically Best Bandwidth Selectors in Kernel Density Estimation

This paper gives asymptotically best data based choices of the bandwidth of the kernel density estimator. These bandwith selectors attain the fastest possible rate of convergence to the desired theoretical optimum and the best possible constant coefficient in the spirit of the usual Fisher Information, with the use of only nonnegative kernel estimators at all stages of the selection process.

A lower confidence bound on the probability of a correct selection

Abstract In the problem of selecting the best of k populations, a natural rule is to select the population corresponding to the largest sample value of an appropriate statistic. As a retrospective analysis, a conservative lower confidence bound on the probability of a correct selection is derived when the probability density function has the monotone likelihood ratio property under the location parameter setting. The result is applied to the normal populations with both known and unknown common variance. Tables to implement the confidence bound are provided.

Wavelet density estimation by approximation of log-densities

Probability density estimation is considered when log-density function belongs to the Besov function class Bspq. It is shown that n-2s/(2s+1) is a lower rate of convergence in Kullback-Leibler distance. Density functions are estimated by the maximum likelihood method in sequences of regular exponential families based on wavelet basis functions.

Design of a bias magnetic system of a magnetostrictive sensor for flexural wave measurement

We report on an investigation of the voltage output from a magnetostrictive sensor for the measurement of elastic flexural waves in a cylindrical steel waveguide. Since the sensor performance is strongly influenced by the bias magnetic field, the bias field optimization is one of the most critical issues in the design of magnetostrictive sensors. For a magnetic system consisting of a yoke and an electromagnet, we formulate a method for optimizing yoke topology in order to maximize the sensor output. Both linear and nonlinear magnetization relations are considered in our analysis. For the verification of the performance of the proposed sensors, we conducted several experiments involving flexural waves to assess the performance of the optimized sensors, and we analyze their results here.

Smoothing techniques via the bezier curve

Although the Bezier curve is very popular in the area of computational graphics it has rarely been used by statisticians. In this paper we develop methods and techniques for use of the Bezier curve in estimation of density and regression function. Also, asymptotic mean integrated square error for both estimators are derived. Comparisons with kernel estimator are conducted using simulation.

Bezier curve smoothing of the Kaplan-Meier estimator

Estimation of a survival function from randomly censored data is very important in survival analysis. The Kaplan-Meier estimator is a very popular choice, and kernel smoothing is a simple way of obtaining a smooth estimator. In this paper, we propose a new smooth version of the Kaplan-Meier estimator using a Bezier curve. We show that the proposed estimator is strongly consistent. Numerical results reveal the that proposed estimator outperforms the Kaplan-Meier estimator and its kernel weighted smooth version in the sense of mean integrated square error.

Bayesian test for asymmetry and nonstationarity in MTAR model with possibly incomplete data

We propose an easy and efficient Bayesian test procedure for asymmetry and nonstationarity in momentum threshold autoregressive model with possibly incomplete data. Estimation of parameters and missing observations is done by using a Markov chain Monte Carlo (MCMC) method. Testing for asymmetry and nonstationarity is done via test of multiple hypotheses representing various types of symmetry/asymmetry and stationarity/nonstationarity. This allows simultaneous consideration of parameters relevant to asymmetry and nonstationarity of the model, and also enables us to find the sources of asymmetry and nonstationarity when they exist. Posterior probabilities of the hypotheses are easily computed by using MCMC outputs under the full model, with almost no extra cost. We apply the proposed method to a set of Korea unemployment rate data.

Skewing and Generalized Jackknifing in Kernel Density Estimation

Abstract Kernel methods are very popular in nonparametric density estimation. In this article we suggest a simple estimator which reduces the bias to the fourth power of the bandwidth, while the variance of the estimator increases only by at most a moderate constant factor. Our proposal turns out to be a fourth order kernel estimator and may be regarded as a new version of the generalized jackknifing approach (Schucany W. R., Sommers, J. P. (1977). Improvement of Kernal type estimators. Journal of the American Statistical Association 72:420–423.) applied to kernel density estimation.

Some diagnostic resultsin nonparametric density estimation

Kernel method is most widely used in nonparametric density estimation, and data-driven bandwidth selection such as least squares cross-validation is frequently used to estimate the corresponding bandwidth parameter.In this paper, we define a version of Cooks distance (Cook 1977) to investigate the influence of few observations on the overall shape of the curve, and suggest useful formula to detect influential observations on the estimator of bandwidth.An example based on a real data set is given.