• Proceedings of the Korean Reliability Society Conference
• /
• 2005.06a
• /
• pp.265-269
• /
• 2005

Cox's proportional hazards model (PHM) has been widely applied in the analysis of lifetime data, and it can be characterized by the baseline hazard function and covariates influencing systems' lifetime, where the covariates describe operating environments (e.g. temperature, pressure, humidity). In this article, we consider the constant baseline hazard function and a discrete random variable of a covariate. The estimation procedure is developed in a parametric framework when there are not only complete data but also incomplete one. The Expectation-Maximization (EM) algorithm is employed to handle the incomplete data problem. Simulation results are presented to illustrate the accuracy and some properties of the estimation results.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.25 no.9
• /
• pp.952-958
• /
• 2014

In this paper, we propose a parametric sparse recovery algorithm(SRA) applied to a radar signal model, based on the compressive sensing(CS), for the ISAR(Inverse Synthetic Aperture Radar) image reconstruction from an incomplete radar-cross-section(RCS) data and for the estimation of rotation rate of a target. As the SRA, the iteratively-reweighted-least-square(IRLS) is combined with the radar signal model including chirp components with unknown chirp rate in the cross-range direction. In addition, the particle swarm optimization(PSO) technique is considered for searching correct parameters related to the rotation rate. Therefore, the parametric SRA based on the IRLS can reconstruct ISAR image and estimate the rotation rate of a target efficiently, although there exists missing data in observed RCS data samples. The performance of the proposed method in terms of image entropy is also compared with that of the traditional interpolation methods for the incomplete RCS data.

• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.150-155
• /
• 2003

We discussed a quantitative structure-activity relationships (QSAR) technique on incomplete data set. We proposed a new solver that used 2 kinds of multi-layer neural networks. One is to compensate the defect data, and another is to evaluate the QSAR. The solver can predict the defects in model QSAR data. By using them, we get very high precision QSAR. It is 5-10 times higher than that of a traditional method. However, in case of anti-cancer Carboquone, the prediction is not so complete. It was about O(3) wrong than the model calculation. The predicted values would have rather large error. It is caused by noisy observations of Carboquone. However, if we used the uncertain predictions, new data are included in QSAR. If not, they were omitted. The effect would not be little. Therefore, we evaluated the QSAR. The results are contrary to the expectation, are not so wrong. We believe that the wrong effect is suppressed by including information of new data.

• Structural Engineering and Mechanics
• /
• v.62 no.3
• /
• pp.303-310
• /
• 2017

The model updating problems, which are to find the optimal approximation to the discrete quadratic model obtained by the finite element method, are critically important to the vibration analysis. In this paper, the structured model updating problem is considered, where the coefficient matrices are required to be symmetric and positive semidefinite, represent the interconnectivity of elements in the physical configuration and minimize the dynamics equations, and furthermore, due to the physical feasibility, the physical parameters should be positive. To the best of our knowledge, the model updating problem involving all these constraints has not been proposed in the existed literature. In this paper, based on the semidefinite programming technique, we design a general-purpose numerical algorithm for solving the structured model updating problems with incomplete measured data and present some numerical results to demonstrate the effectiveness of our method.

• The Korean Journal of Applied Statistics
• /
• v.7 no.2
• /
• pp.145-157
• /
• 1994

For multivariate distributions satisfying stochastic ordering, we suggest maximum likelihood estimation with incomplete data via an EM algorithm. In this paper we restrict our attention to the contingency tables with partially cross-classified observations. We may use the existing isotonic regression program to implement EM algorithm, and we illustrate the estimation process through an example.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 1999.11a
• /
• pp.494-497
• /
• 1999

Neural networks are shown to be effective in being able to distinguish incomplete penetration-like weld defects by directly analyzing the plasma which is generated on each impingement of the laser on the materials. The performance is similar to that of existing methods based on extracted feature parameters. In each case around 93% of the defects in a database derived from 100 artificially produced defects of known types can be placed into one of two classes: incomplete penetration and bubbling. Especially we present simulator for weld defects classification and data analysis. The present method based on classification using plasma is faster, and the speed is sufficient to allow on-line classification during data collection.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 2007.11a
• /
• pp.357-361
• /
• 2007

This paper presents a multi-robot localization based on Bayesian Multidimensional Scaling (BMDS). We propose a robust MDS to handle both the incomplete and noisy data, which is applied to solve the multi-robot localization problem. To deal with the incomplete data, we use the Nystr${\ddot{o}}$m approximation which approximates the full distance matrix. To deal with the uncertainty, we formulate a Bayesian framework for MDS which finds the posterior of coordinates of objects by means of statistical inference. We not only verify the performance of MDS-based multi-robot localization by computer simulations, but also implement a real world localization of multi-robot team. Using extensive empirical results, we show that the accuracy of the proposed method is almost similar to that of Monte Carlo Localization(MCL).

• The Korean Journal of Applied Statistics
• /
• v.32 no.1
• /
• pp.29-40
• /
• 2019

Categorical data with non-responses are frequently observed in election poll surveys, and can be represented by incomplete contingency tables. To estimate supporting rates of candidates, the identification of the missing mechanism should be pre-determined because the estimates of non-responses can be changed depending on the assumed missing mechanism. However, it has been shown that it is not possible to identify the missing mechanism when using observed data. To overcome this problem, sensitivity analysis has been suggested. The previously proposed sensitivity analysis can be applicable only to two-way incomplete contingency tables with binary variables. The previous sensitivity analysis is inappropriate to use since more than two of the factors such as region, gender, and age are usually considered in election poll surveys. In this paper, sensitivity analysis suitable to an multi-dimensional incomplete contingency table is devised, and also applied to the 19th Korean presidential election poll survey data. As a result, the intervals of estimates from the sensitivity analysis include actual results as well as estimates from various missing mechanisms. In addition, the properties of the missing mechanism that produce estimates nearest to actual election results are investigated.

• The Korean Journal of Applied Statistics
• /
• v.12 no.2
• /
• pp.415-432
• /
• 1999

짝자료(paired data)에서 결측값이 발생했을 때에 이 자료를 처리할 수 있는 여러통계검정 방법들을 고찰해보았다. 결측값들을 어떻게 처리하는 지에 따라서 다섯 가지 방법으로 분류해 보았고, 이 방법들이 짝 t-검정에 미치는 효과를 모의실험을 통해 비교해 보았다. 결측값들에 대한 세 종류의 메카니즘을 고려하여 검정크기와 검정력을 구하였다.

• Journal of the Korean Data and Information Science Society
• /
• v.14 no.3
• /
• pp.579-590
• /
• 2003

When we analyze incomplete data, i.e., data with missing values, we need treatment for the missing values. A common way to deal with this problem is to delete the cases with missing values. Various other methods have been developed. Among them are EM algorithm and regression algorithm which can estimate missing values and impute the missing elements with the estimated values. In this paper, we introduce multiple imputation software SOLAS which generates multiple data sets and imputes with them.