• Journal of the Computational Structural Engineering Institute of Korea
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• v.28 no.3
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• pp.309-316
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• 2015

A state-based peridynamic model is able to describe a general constitutive model from the standard continuum theory. The response of a material at a point is dependent on the deformation of all bonds connected to the point within the nonlocal horizon region. Therefore, the state-based peridynamic model permits both the volume and shear changes of the material which is promising to reproduce the complicated dynamic brittle fracture phenomena, such as crack branching, secondary cracks, cascade cracks, crack coalescence, etc. In this paper, the two-dimensional state-based peridynamic model for a linear elastic plane stress solid is employed. The damage model incorporates the energy release rate and the peridynamic energy potential. For brittle glass materials, the impact of the crack-parallel compressive stress waves on the crack branching pattern is investigated. The peridynamic solution for this problem captures the main features, observed experimentally, of dynamic crack propagation and branching. Cascade cracks under strong tensile loading and secondary cracks are also well reproduced with the state-based peridynamic simulations.

• Industrial Engineering and Management Systems
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• v.11 no.3
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• pp.233-240
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• 2012

The implementation of business process management (BPM) systems in large number of business organizations transforms BPM system into such a level of maturity and tends to collect large repositories of business process (BP) models. This issue encourages BP flexibility that leads to a large number of process variants derived from the same model, but differing in structure, to be stored in the large repositories of BP models. Therefore, the repositories may include thousands of activities and related business objects with variation of requirements and quality of service. It is a common practice to customize processes from reference processes or templates in order to reduce the time and effort required to design and deploy processes on all levels. In order to address redundancy and underutilization problems, a generic process model, called as reference BP, is absolutely necessary to cover the best of process variants. This study aims to develop multiple-objective business process genetic algorithm (MOBPGA) to find a set of non-dominated (Pareto) solutions of business reference model to enhance conventional approach which considered only a single objective on creating BP reference model by using proximity score measurement. A mixed-integer linear program is constructed to evaluate performance of the proposed MOBPGA on small-scale problems by using standard measures for multiple-objective techniques. The results will show the viability of applying MOBPGA in terms of simultaneously maximizing proximity score measurement, minimizing total duration, and total costs of the selected reference model.

• Korean Journal of Exercise Nutrition
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• v.25 no.1
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• pp.35-41
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• 2021

[Purpose] This pilot study aimed to develop a regression model to estimate the excess post-exercise oxygen consumption (EPOC) of Korean adults using various easy-to-measure dependent variables. [Methods] The EPOC and dependent variables for its estimation (e.g., sex, age, height, weight, body mass index, fat-free mass [FFM], fat mass, % body fat, and heart rate_sum [HR_sum]) were measured in 75 healthy adults (31 males, 44 females). Statistical analysis was performed to develop an EPOC estimation regression model using the stepwise regression method. [Results] We confirmed that FFM and HR_sum were important variables in the EPOC regression models of various exercise types. The explanatory power and standard errors of estimates (SEE) for EPOC of each exercise type were as follows: the continuous exercise (CEx) regression model was 86.3% (R²) and 85.9% (adjusted R²), and the mean SEE was 11.73 kcal, interval exercise (IEx) regression model was 83.1% (R²) and 82.6% (adjusted R²), while the mean SEE was 13.68 kcal, and the accumulation of short-duration exercise (AEx) regression models was 91.3% (R²) and 91.0% (adjusted R²), while the mean SEE was 27.71 kcal. There was no significant difference between the measured EPOC using a metabolic gas analyzer and the predicted EPOC for each exercise type. [Conclusion] This pilot study developed a regression model to estimate EPOC in healthy Korean adults. The regression model was as follows: CEx = -37.128 + 1.003 × (FFM) + 0.016 × (HR_sum), IEx = -49.265 + 1.442 × (FFM) + 0.013 × (HR_sum), and AEx = -100.942 + 2.209 × (FFM) + 0.020 × (HR_sum).

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2008.04a
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• pp.288-293
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• 2008

The standard prestressed concrete I-girder bridge (PSC I-girder bridge) is one of the most prevalent types for small and medium bridges in Korea. When determining the member forces in a section to assess the safety of girder in this type of bridge, the general practice is to use the simplified practical equations or the live load distribution factors proposed in design standards rather than the precise analysis through the finite element method or so. Meanwhile, the live load distribution factors currently used in Korean design practice are just a reflection of overseas research results or design standards without alterations. Therefore, it is necessary to develop an equation of the live load distribution factors fit for the design conditions of Korea, considering the standardized section of standard PSC I-girder bridges and the design strength of concrete. In this study, to develop an equation of the live load distribution factors, a parametric analysis and sensitivity analysis were carried out on the parameters such as width of bridge, span length, girder spacing, width of traffic lane, etc. Then, an equation of live load distribution factors was developed through the multiple linear regression analysis on the results of parametric analysis. When the actual practice engineers design a bridge with the equation of live load distribution factors developed here, they will determine the design of member forces ensuring the appropriate safety rate more easily. Moreover, in the preliminary design, this model is expected to save much time for the repetitive design to improve the structural efficiency of PSC I-girder bridges.

• The Journal of the Korea Contents Association
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• v.11 no.4
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• pp.74-82
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• 2011

To do fuzzy modelling of a nonlinear process needs to analyze the characteristics of input-output of fuzzy inference systems according to the division of entire input spaces and the fuzzy reasoning methods. For this, fuzzy model is expressed by identifying the structure and parameters of the system by means of input variables, fuzzy partition of input spaces, and consequence polynomial functions. In the premise part of the fuzzy rules Min-Max method using the minimum and maximum values of input data set and C-Means clustering algorithm forming input data into the clusters are used for identification of fuzzy model and membership functions are used as a series of triangular, gaussian-like, trapezoid-type membership functions. In the consequence part of the fuzzy rules fuzzy reasoning is conducted by two types of inferences such as simplified and linear inference. The identification of the consequence parameters, namely polynomial coefficients, of each rule are carried out by the standard least square method. And lastly, using gas furnace process which is widely used in nonlinear process we evaluate the performance and the system characteristics.

• The Korean Journal of Applied Statistics
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• v.21 no.6
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• pp.923-932
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• 2008

We are likely to face complex multivariate data which can be characterized by having a non-trivial correlation structure. For instance, omitted covariates may simultaneously affect more than one count in clustered data; hence, the modeling of the correlation structure is important for the efficiency of the estimator and the computation of correct standard errors, i.e., valid inference. A standard way to insert dependence among counts is to assume that they share some common unobservable variables. For this assumption, we fitted correlated random effect models considering multilevel model. Estimation was carried out by adopting the semiparametric approach through a finite mixture EM algorithm without parametric assumptions upon the random coefficients distribution.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.54 no.1
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• pp.29-35
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• 2009

The aim of the present study was to update the calibration that is used for the measurement of the total nitrogen content in the rice plant samples by using the visible and near infrared spectrum. Before the equation merge, correlation coefficient of calibration equation for nitrogen content on each rice parts was 0.945 (Leaf), 0.928 (Stem), and 0.864 (Whole plant), respectively. In the calibration models created by each part in the rice plant under the various regression method, the calibration model for the leaf was recorded with relatively high accuracy. Among of those, the calibration equation developed by Partial least squares (PLS) method was more accurate than the Multiple linear regression (MLR) method. The calibration equation was sensitive based on variety and location variations. However, we have merged and enlarged various of the samples that made not only to measure the nitrogen content more accurately, but also later sampling populations became more diversified. After merging, $R^2$ value becomes more accurate and significantly to 0.950 (L.), 0.974 (S.), 0.940 (W.). Also, after removal of outlier, R2 values increased into 0.998, 0.995, and 0.997. In view of the results so far achieved, Standard error of prediction (SEP) and SEP (C) were reduced in the stem and whole plant. Biases were reduced in the leaf, stem as well as whole plant. Slopes were high in the stem. Standard deviation reduced in the stem but $R^2$ was high in the stem and whole plant. Result was indicated that calibration equation make update, and updating robust calibration equation from merge function and multi-variate calibration.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.6 s.312
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• pp.36-45
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• 2006

Three-dimensional (3-D) texture coordinates mean the position information of torture segments that are mapped into polygons in a 3-D mesh model. In order to compress texture coordinates, previous works reused the same linear predictor that had already been employed to code geometry data. However, the previous approaches could not carry out linear prediction efficiently since texture coordinates were discontinuous along a coding order. Especially, discontinuities of texture coordinates became more serious in the 3-D mesh model including a non-atlas texture. In this paper, we propose a new scheme to code 3-D texture coordinates using as a texture image rearrangement. The proposed coding scheme first extracts texture segments from a texture. Then, we rearrange the texture segments consecutively along the coding order, and apply a linear prediction to compress texture coordinates. Since the proposed scheme minimizes discontinuities of texture coordinates, we can improve coding efficiency of texture coordinates. Experiment results show that the proposed scheme outperforms the MPEG-4 3DMC standard in terms of coding efficiency.

• Communications for Statistical Applications and Methods
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• v.27 no.1
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• pp.141-148
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• 2020

We consider estimating the proportion of true null hypotheses in multiple testing problems. A traditional multiple testing rate, family-wise error rate is too conservative and old to control type I error in multiple testing setups; however, false discovery rate (FDR) has received significant attention in many research areas such as GWAS data, FMRI data, and signal processing. Identify differentially expressed genes in microarray studies involves estimating the proportion of true null hypotheses in FDR procedures. However, we need to account for unknown dependence structures among genes in microarray data in order to estimate the proportion of true null hypothesis since the genuine dependence structure of microarray data is unknown. We compare various procedures in simulation data and real microarray data. We consider a hidden Markov model for simulated data with dependency. Cai procedure (2007) and a sliding linear model procedure (2011) have a relatively smaller bias and standard errors, being more proper for estimating the proportion of true null hypotheses in simulated data under various setups. Real data analysis shows that 5 estimation procedures among 9 procedures have almost similar values of the estimated proportion of true null hypotheses in microarray data.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.350-355
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• 1997

The problem of mathematical model for an unknown system by measureing its input-output data pairs is generally referred to as state estimates. The state estimation problem is often of importance in its own right since we may want to know the value of the states. For instance, in navigation, we may take noisy positional fixes using satelite or radar navigation, and the estimator can use these measurements to provide accurate estimates of current position, hedaing, and velocity. And the state estimates can also be used for control purposes. Then it is very important to know the state of plant. In this paper, the theory of the minimization of a loss function was used to design the fuzzy system. Here, the used teory is Least Square Esimation method. This parametrization has the Linear in the parameters charcteristic that allows standard parameter estimation technique to be used to estimate the parameters of the fuzzy system. The combination of the fuzzy system and the estimation m thod then performs as a nonlinear estimator. If several fuzzy label are defined for the input variables at the antecedent part, the fuzzy system then behaves as a collection of nonlinear estimators where different regions of rules have different parameters. In simulation results, the fuzzy model controlled a difference in the structure between the actual plant and the fuzzy estimator. It is also proved that the fuzzy system is equivalent to its transformed system. therefore we was able to get the state space equation of system with the estimated paramater.