• Journal of KSNVE
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• v.7 no.1
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• pp.61-69
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• 1997

Despite of the development in the finite element method, it is difficult to get the finite element model describing the dynamic characteristics of the complex structure exactly. Therefore a number of different methods have been developed in order to update the finite element model of a structure using vibration test data. This paper outlines the basic formulation for the frequency response function based updating method. One important advantage of this method is that the intermediate step of performing an eigensolution extraction is unnecessary. Using simulated experimental data, studies are conducted in the case of 10 DOF discrete system. The solution of noisy and incomplete experimental data is discussed. True measured frequency response function data are used for updating the finite element model of a beam and a plate. Its applicability to the joint identification is also considered.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2010-2014
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• 2005

Object detection and parameter estimation in point cloud data is a relevant subject to robotics, reverse engineering, computer vision, and sport mechanics. In this paper a software is presented for fully-automatic object detection and parameter estimation in unordered, incomplete and error-contaminated point cloud with a large number of data points. The software consists of three algorithmic modules each for object identification, point segmentation, and model fitting. The newly developed algorithms for orthogonal distance fitting (ODF) play a fundamental role in each of the three modules. The ODF algorithms estimate the model parameters by minimizing the square sum of the shortest distances between the model feature and the measurement points. Curvature analysis of the local quadric surfaces fitted to small patches of point cloud provides the necessary seed information for automatic model selection, point segmentation, and model fitting. The performance of the software on a variety of point cloud data will be demonstrated live.

• Proceedings of the Korean Society of Medical Physics Conference
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• 2002.09a
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• pp.382-384
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• 2002

Filtered-Back-Projection technique is used in X-ray CT image reconstruction. This requires X-ray transmission data from all directions. As the transverse cross-section of the body is approximately 50 cm, transmitted X-rays in this direction are strongly attenuated. If X-ray transmission data in this direction is avoided, exposure to the patients seems to be reduced one 20th of usual value. Some alternative method has to be found for clinically sufficient image quality. New methods are under development and tentative results are reported that utilizes the principle of superposition.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2002.09a
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• pp.28-34
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• 2002

The b-value in the magnitude-frequency relationship logN(m) = $\alpha$ - bmwhere N(m) is the number of earthquakes exceeding magnitude m, is important seismicity parameter In hazard analysis. Estimation of the b-value for earthquake data observed on KSRS array network is done employing the maximum likelihood technique. Assuming the whole Korea Peninsula as a single seismic source area, the b-value is computed at 0.9. The estimation for KMA earthquake data is also similar to that. Since estimate is a function of minimum magnitude, we can inspect the completeness of earthquake catalog in the fitting process of b-value. KSRS and KMA data lists are probably incomplete for magnitudes less than 2.0 and 3.0, respectively. Examples from probabilistic seismic hazard assessment calculated for a range of b-value show that the small change of b-value has seriously effect on the prediction of ground motion.

• Journal of the Korean Data and Information Science Society
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• v.15 no.4
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• pp.993-1002
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• 2004

Imputation procedures such as fully efficient fractional imputation(FEFI) or multiple imputation(MI) can be used to construct complete contingency tables from samples with partially classified responses. Variances of FEFI estimators of population proportions are derived. Simulation results, when data are missing completely at random, reveal that FEFI provides more efficient estimates of population than either multiple imputation(MI) based on data augmentation or complete case analysis, but neither FEFI nor MI provides an improvement over complete-case(CC) analysis with respect to accuracy of estimation of some parameters for association between two variables like $\theta_{i+}\theta_{+i}-\theta_{ij}$ and log odds-ratio.

• The Korean Journal of Applied Statistics
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• v.25 no.1
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• pp.115-123
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• 2012

In animal tumorigenicity data, tumor onsets occur at several sites and onset times cannot be exactly observed. Instead, the existence of tumors is examined only at death time or sacrifice time of the animal. Such an incomplete data structure makes it difficult to investigate the effect of treatment on tumor onset times; in addition, such dependence should be considered when censoring due to death is related with tumor onset. A bivariate frailty effect is incorporated to model bivariate tumor onsets and to connect death with tumor. For the inference of parameters, EM algorithm is applied and a real NTP(National Toxicology Program) dataset is analyzed as an illustrative example.

• The Korean Journal of Applied Statistics
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• v.21 no.6
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• pp.1109-1116
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• 2008

Biplots are the multivariate analogue of scatter plots. They are useful for giving a graphical description of the data matrix, for detecting patterns and for displaying results found by more formal methods of analysis. Nevertheless, when some values are missing in data matrix, most biplots are not directly applicable. In particular, we are interested in the shape variability of principal component biplot which is the most popular in biplots with missing values. For this, we estimate the missing data using the EM algorithm and mean imputation according to missing rates. Even though we estimate missing values of biplot of incomplete data, we have different shapes of biplots according to the imputation methods and missing rates. Therefore we propose a RMS(root mean square) for measuring and comparing the shape variability between the original biplots and the estimated biplots.

• Journal of the military operations research society of Korea
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• v.22 no.1
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• pp.114-128
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• 1996

In this paper, a life distribution fitting method based on generalized phase-type distributions(GPH) is presented. By fitting the life distribution to a GPH, we can utilize various useful properties of the GPH. Two different approaches are used according to the properties of the given failure time data. One is an approximation to a GPH through the piecewise Weibull failure rate(PWF) model and the other is a direct approximation to a GPH using the empirical distribution function. Two numerical examples are also presented. In the first example, both of the two approaches are utilized and compared for an incomplete data set. And in the second example, the direct approximation method from an empirical distribution is utilized for the analysis of a complete data set. In both cases, we could confirm the validity of the proposed method.

• Journal of the Korean Data and Information Science Society
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• v.16 no.1
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• pp.95-106
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• 2005

Complete-case analysis is easy to carry out and it may be fine with small amount of missing data. However, this method is not recommended in general because the estimates are usually biased and not efficient. There are numerous alternatives to complete-case analysis. A natural alternative procedure is available-case analysis. Available-case analysis uses all cases that contain the variables required for a specific task. The EM algorithm is a general approach for computing maximum likelihood estimates of parameters from incomplete data. These methods and multiple imputation(MI) are reviewed and the performances are compared by simulation studies in monotone missing pattern.

• Journal of the Korean Data and Information Science Society
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• v.9 no.1
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• pp.37-45
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• 1998

We consider the problem of obtaining the confidence bands for the survival function with incomplete data. It is a rather simple procedure for constructing confidence bands of survival function. This method uses the weak convergence of normalized cumulative hazard estimator to a mean zero Gaussian process whose distribution can be easily approximated through simulation. Finally, we compare the performance of the proposed confidence bands through Monte Carlo simulation and we applied to construct the proposed bands with the Leukemia patient data.