• Computers and Concrete
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• v.20 no.1
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• pp.23-29
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• 2017

In this study engineered steel fibres used as reinforcement for concrete were characterized by number of key mechanical and spatial parameters, which are easy to measure and quantify. Such commonly used parameters as length, diameter, fibre intrinsic efficiency ratio (FIER), hook geometry, tensile strength and ductility were considered. Effective classification of various fibres was demonstrated using simple multivariate computations involving principal component analysis (PCA). Contrary to univariate data mining approach, the proposed analysis can be efficiently adapted for fast, robust and direct classification of engineered steel fibres. The results have revealed that in case of particular spatial/geometrical conditions of steel fibres investigated the FIER parameter can be efficiently replaced by a simple aspect ratio. There is also a need of finding new parameters describing properties of steel fibre more precisely.

• Proceedings of the Korean Statistical Society Conference
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• 2000.11a
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• pp.187-191
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• 2000

In the analysis of repeated measurements, multivariate regression models that account for the correlations among the observations from the same subject are widely used. Like the usual univariate regression models, these multivariate regression models also need some model diagnostic procedures. In this paper, we propose a simple graphical method to detect outliers and to investigate the goodness of model fit in repeated measures data. The graphical method is based on the quantile-quantile(Q-Q) plots of the $X^2$ distribution and the standard normal distribution. We also propose diagnostic measures to detect influential observations. The proposed method is illustrated using two examples.

• IE interfaces
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• v.1 no.1
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• pp.9-15
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• 1988

A statistical analysis for detecting deviations from normal magnetic field intensities, caused by the introduction of magnetite materials into man-made fissures and cracks at subsurface levels is presented. For detecting such deviations it turns out the comparison of two different field measurements measured at two different sites far from each other is more efficient than the study of the only measurement by the univariate and bivariate time series analysis.

• Asian-Australasian Journal of Animal Sciences
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• v.29 no.2
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• pp.170-175
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• 2016

The objective of this study was to assess variance components and genetic parameters for fat and protein content in Tsigai sheep using multivariate animal models in which fat and protein content in individual months of lactation were treated as different traits, and univariate models in which fat and protein content were treated as repeated measures of the same traits. Test day measurements were taken between the second and the seventh month of lactation. The fixed effects were lactation number, litter size and days in milk. The random effects were animal genetic effect and permanent environmental effect of ewe. The effect of flock-year-month of test day measurement was fitted either as a fixed (FYM) or random (fym) effect. Heritabilities for fat content were estimated between 0.06 and 0.17 (FYM fitted) and between 0.06 and 0.11 (fym fitted). Heritabilities for protein content were estimated between 0.15 and 0.23 (FYM fitted) and between 0.10 and 0.18 (fym fitted). For fat content, variance ratios of permanent environmental effect of ewe were estimated between 0.04 and 0.11 (FYM fitted) and between 0.02 and 0.06 (fym fitted). For protein content, variance ratios of permanent environmental effect of ewe were estimated between 0.13 and 0.20 (FYM fitted) and between 0.08 and 0.12 (fym fitted). The proportion of phenotypic variance explained by fym effect ranged from 0.39 to 0.43 for fat content and from 0.25 to 0.36 for protein content. Genetic correlations between individual months of lactation ranged from 0.74 to 0.99 (fat content) and from 0.64 to 0.99 (protein content). Fat content heritabilities estimated with univariate animal models roughly corresponded with heritability estimates from multivariate models: 0.13 (FYM fitted) and 0.07 (fym fitted). Protein content heritabilities estimated with univariate animal models also corresponded with heritability estimates from multivariate models: 0.18 (FYM fitted) and 0.13 (fym fitted).

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.863-868
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• 2004

Reheating furnace is an essential facility of a rod mill plant where a billet is heated to the required rolling temperature so that it can be milled to produce wire. Sometimes, it is also necessary to control a transient billet temperature pattern according to the material characteristics to prevent a wire from breaking. Though it is very important objective to obtain a correct information of a billet temperature during furnace operation. Consequently, a billet temperature profile must be estimated. In this paper, a billet heat transfer model based on FEM (Finite Element Method) with spatially distributed emission factors is proposed and a measurement is also carried out for two different furnace operation conditions. Finally, the difference between the model outputs and the measurements is minimized by using the new optimization algorithm named uDEAS(Univariate Dynamic Encoding Algorithm for Searches) with multi-step tuning strategy. Hence, the information of billet temperatures can be obtained by using proposed model on various furnace operation conditions.

• International Journal of Control, Automation, and Systems
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• v.5 no.1
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• pp.43-50
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• 2007

Reheating furnace is an essential facility of a rod mill plant where a billet is heated to the required rolling temperature so that it can be milled to produce wire. Although it is very important to obtain information on billet temperatures, it is not feasible during furnace operation. Consequently, a billet temperature profile should be estimated. Moreover, this estimation should be done within an appropriate time interval for an on-line application. In this paper, a billet heat transfer model based on 2D FEM(Finite Element Method) with spatially distributed emission factors is proposed for an on-line billet temperature estimation and also a measurement is carried out for two extremely different furnace operation patterns. Finally, the difference between the model outputs and the measurements is minimized by using a new optimization algorithm named uDEAS(Univariate Dynamic Encoding Algorithm for Searches) with multi-step tuning strategy. The obtained emission factors are applied to a simulation for the data which are not used in the model tuning for validation.

• Imaging Science in Dentistry
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• v.51 no.3
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• pp.279-290
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• 2021

Purpose: Using images in the facial image comparison process poses a challenge for forensic experts due to limitations such as the presence of facial expressions. The aims of this study were to analyze how morphometric changes in the face during a spontaneous smile influence the facial image comparison process and to evaluate the reproducibility of measurements obtained by digital stereophotogrammetry in these situations. Materials and Methods: Three examiners used digital stereophotogrammetry to obtain 3-dimensional images of the faces of 10 female participants(aged between 23 and 45 years). Photographs of the participants' faces were captured with their faces at rest (group 1) and with a spontaneous smile (group 2), resulting in a total of 60 3-dimensional images. The digital stereophotogrammetry device obtained the images with a 3.5-ms capture time, which prevented undesirable movements of the participants. Linear measurements between facial landmarks were made, in units of millimeters, and the data were subjected to multivariate and univariate statistical analyses using Pirouette^® version 4.5 (InfoMetrix Inc., Woodinville, WA, USA) and Microsoft Excel^® (Microsoft Corp., Redmond, WA, USA), respectively. Results: The measurements that most strongly influenced the separation of the groups were related to the labial/buccal region. In general, the data showed low standard deviations, which differed by less than 10% from the measured mean values, demonstrating that the digital stereophotogrammetry technique was reproducible. Conclusion: The impact of spontaneous smiles on the facial image comparison process should be considered, and digital stereophotogrammetry provided good reproducibility.

• Korean Journal of Environmental Biology
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• v.34 no.4
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• pp.365-373
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• 2016

Much of the data used in the analysis of environmental ecological data is being obtained over time. If the number of time points is small, the data will not be given enough information, so repeated measurements or multiple survey points data should be used to perform a comprehensive analysis. The method used for that case is longitudinal data analysis or mixed model analysis. However, if the amount of information is sufficient due to the large number of time points, repetitive data are not needed and these data are analyzed using time series analysis technique. In particular, with a large number of data points in the current situation, when we want to predict how each variable affects each other, or what trends will be expected in the future, we should analyze the data using time series analysis techniques. In this study, we introduce univariate time series analysis, intervention time series model, transfer function model, and multivariate time series model and review research papers studied in Korea. We also introduce an error correction model, which can be used to analyze environmental ecological data.

• The Korean Journal of Applied Statistics
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• v.19 no.3
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• pp.481-504
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• 2006

VaR, a tail-related risk measure is now widely used as a tool for a measurement and a management of financial risks. For more accurate measurement of VaR, recently we are particularly concerned about the approach based on extreme value theory rather than the traditional method based on the assumption of normal distribution. However, many studies about the approaches using extreme value theory was done only for the univariate case. In this paper, we discuss portfolio risk measurements with modelling multivariate extreme value distributions by combining copulas and extreme value theory. We also discuss the estimation of ES together with VaR as portfolio risk measures. Finally, we investigate the relative superiority of EVT-copula approach than variance-covariance method through the back-testing of an empirical data.

• Journal of Korean Neurosurgical Society
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• v.40 no.3
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• pp.148-153
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• 2006

Objective : The authors conducted this study to present the long-term treatment outcomes [minimum 2 years] of Gamma knife radiosurgery[GKS] for trigeminal neuralgia[TN] and to demonstrate the correlation of treatment outcomes and the anatomical characteristics of TN. Methods : From 1997 to 2003, 44 consecutive patients suffering from medically intractable pain underwent GKS for TN. A single 4mm collimator was used with a median maximum dose of 80Gy [range $75{\sim}80Gy$] prescribed to the root entry zone of the trigeminal nerve. Median follow up duration was 30 months [range $24{\sim}78\;months$]. Anatomical measurements of trigeminal nerve in magnetic resonance images during GKS planning were correlated with clinical outcome. Results : Twenty-two patients [50%] achieved an excellent outcome [BNI grade I & II], 20 patients [45.5%] a good outcome [grade IIIa & IIIb], and only 2 patients [4.5%] a poor outcome [grade IV & V]. Eleven patients [25.0%] experienced pain recurrence after initial pain relief. Smaller volume of trigeminal nerve area irradiated more than 40Gy was significantly correlated with excellent outcome in both univariate and multivariate analyses respectively [P=0.033 and 0.040]. Conclusion : Anatomical considerations during the planning of GKS would be helpful for predicting clinical outcome in TN.