• Nuclear Engineering and Technology
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• v.53 no.2
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• pp.414-422
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• 2021

The aim of this work considers a second order point reactor kinetics model based on the P1 approximation of transport theory, called in this work as P1 point reactor model. The P1 point reactor model implicitly considers the time derivative of the neutron source which has not been thus considered previously. The inverse method to calculate the reactivity in nuclear reactors -chosen because its high accuracy- Matrix Formulation. The numerical results shown that the Matrix Formulation for the reactivity estimation constitutes a method with insignificant calculation errors.

• Journal of the Korean Society for Precision Engineering
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• v.29 no.10
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• pp.1056-1061
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• 2012

FEM analysis is essential to shorten the development time and reduce the cost for developing high-performance machine tools. Mount joint parts play important role to ensure static and dynamic stability of machine tools. This paper suggests a computational modeling of mount joint part of machine tools. MATRIX27 element of ANSYS is adopted to model mount joint parts. MATRIX27 allows the definition of stiffness and damping matrices in matrix form. The matrix is assumed to relate two nodes, each with six degrees of freedom per node. Stiffness and damping values of commercial mount products are measured to build a database for FEM analysis. Jack mounts with rubber pad are exemplified in this paper. The database extracted from the experiments is also used to estimate of stiffness and damping of untested mounts. FEM analysis of machine tools system with the suggested mount computational model is performed. Static and dynamic results prove the feasibility of the suggested mount model.

• Smart Structures and Systems
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• v.17 no.6
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• pp.1017-1030
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• 2016

This study focuses on the system identification of reinforced concrete bridges using vector autoregressive model (VAR). First, the time series output response from a bridge establishes the autoregressive (AR) models. AR models are one of the most accurate methods for stationary time series. Burg's algorithm estimates the autoregressive coefficients (ARCs) at p-lag by reducing the sum of the forward and the backward errors. The computed ARCs are assembled in the state system matrix and the eigen-system realization algorithm (ERA) computes: the eigenvector matrix that contains the vectors of the mode shapes, and the eigenvalue matrix that contains the associated natural frequencies. By taking advantage of the characteristic of the AR model with ERA (ARMERA), civil engineering can address problems related to damage detection. Operational modal analysis using ARMERA is applied to three experiments. One experiment is coupled with an artificial neural network algorithm and it can detect damage locations and extension. The neural network uses a specific number of ARCs as input and multiple submatrix scaling factors of the structural stiffness matrix as output to represent the damage.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.22 no.6
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• pp.109-115
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• 2008

This paper proposes an adaptive Dynamic Matrix Control (DMC) and its application to boiler-turbine system In a conventional DMC, object system is described as a Step Response Model (SRM). However, a nonlinear system is not effectively described as a single SRM. In this paper, nine SRMs at various operating points are prepared. On-line interpolation is performed at every sampling step to find the suitable SRM. Therefore, the proposed adaptive DMC can consider the nonlinearity of boiler-turbine system. The simulation results show satisfactory results with a wide range operation of the boiler-turbine system.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.3
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• pp.91-102
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• 1994

For the analysis of textured image, it requires large storage space and computation time to calculate the matrix features such as SGLDM(Spatial Gray Level Dependence Matrix). NGLDM(Neighboring Gray Level Dependence Matrix). NSGLDM(Neighboring Spatial Gray Level Dependence Matrix) and GLRLM(Gray Level Run Length Matrix). In spite of a large amount of information that each matrix contains, a set of several correlated scalar features calculated from the matrix is not sufficient to approximate it. In this paper, we propose a new classifier for textured images based on these matrices in which the projected vectors of each matrix on the meaningful directions are used as features. In the proposed method, an unknown image is classified to the class of a known image that gives the maximum similarity between the projected model vector from the known image and the vector from the unknown image. In the experiment to classify images of agricultural products, the proposed method shows good performance as much as 85-95% of correct classification ratio.

• Journal of Pharmaceutical Investigation
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• v.29 no.1
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• pp.7-12
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• 1999

The captopril matrix tablets composed of polyethylene oxide(PEO) was prepared and administered to beagle dogs. Captopril matrix tablets were prepared using direct compressed method and wet granulation compressed method with various ratios of drug to PEO. The diffusion rate of captopril matrix tablets followed on the Higuchi's diffusion model. With increasing hardness of captopril matrix tablets, release rate was decreased. Each formulation was evaluated by the area under the curve (AUC) and time course of plasma captopril concentration after oral administration to beagle dogs. The $AUC_{0-12}$ were $9.126\;{\mu}g\;h/ml$ and $6.417\;{\mu}g\;h/ml$ for the matrix tablets and conventional tablets, respectively. Therefore, the bioavailability of captopril matrix tablets was greater than that of commercial product. It is suggested that captopril matrix tablets using PEO is a useful sustained release formulation.

• Communications for Statistical Applications and Methods
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• v.24 no.1
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• pp.81-96
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• 2017

Cumulative logit random effects models are typically used to analyze longitudinal ordinal data. The random effects covariance matrix is used in the models to demonstrate both subject-specific and time variations. The covariance matrix may also be homogeneous; however, the structure of the covariance matrix is assumed to be homoscedastic and restricted because the matrix is high-dimensional and should be positive definite. To satisfy these restrictions two Cholesky decomposition methods were proposed in linear (mixed) models for the random effects precision matrix and the random effects covariance matrix, respectively: modified Cholesky and moving average Cholesky decompositions. In this paper, we use these two methods to model the random effects precision matrix and the random effects covariance matrix in cumulative logit random effects models for longitudinal ordinal data. The methods are illustrated by a lung cancer data set.

• Korean Journal of Mathematics
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• v.16 no.4
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• pp.565-571
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• 2008

In this paper we present some trace inequalities for positive definite matrices in statistical mechanics. In order to prove the method of the uniform bound on the generating functional for the semi-classical model, we use some trace inequalities and matrix norms and properties of trace for positive definite matrices.

• Journal of Ocean Engineering and Technology
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• v.26 no.4
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• pp.86-91
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• 2012

A new identification method for a nonproportional damping matrix using the finite element (FE) model updating technique is proposed. Mass and stiffness matrices of the undamped system are identified by FE model updating method. Sensitivity analysis is used to update the FE model, and zero frequencies are considered as design parameters to supplement the information of vibration characteristics. The nonproportional damping matrix is identified through the proposed method. A numerical example is considered to verify the performance of the proposed method. As a result, the damping matrix of the nonproportional system is estimated accurately.

• Coupled systems mechanics
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• v.7 no.2
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• pp.197-209
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• 2018

Fiber-reinforced concrete (FRC) is a material with increasing application in civil engineering. Here it is assumed that the material consists of a great number of rather small fibers embedded into the concrete matrix. It would be advantageous to predict the mechanical properties of FRC using nondestructive testing; unfortunately, many testing methods for concrete are not applicable to FRC. In addition, design methods for FRC are either inaccurate or complicated. In three-point bending tests of FRC prisms, it has been observed that fiber reinforcement does not break but simply pulls out during specimen failure. Following that observation, this work is based on an assumption that the main components of a simple and rather accurate FRC model are mechanical properties of the concrete matrix and fiber pullout force. Properties of the concrete matrix could be determined from measurements on samples taken during concrete production, and fiber pullout force could be measured on samples with individual fibers embedded into concrete. However, there is no clear relationship between measurements on individual samples of concrete matrix with a single fiber and properties of the produced FRC. This work presents an inverse model for FRC that establishes a relation between parameters measured on individual material samples and properties of a structure made of the composite material. However, a deterministic relationship is clearly not possible since only a single beam specimen of 60 cm could easily contain over 100000 fibers. Our inverse model assumes that the probability density function of individual fiber properties is known, and that the global sample load-displacement curve is obtained from the experiment. Thus, each fiber is stochastically characterized and accordingly parameterized. A relationship between fiber parameters and global load-displacement response, the so-called forward model, is established. From the forward model, based on Levenberg-Marquardt procedure, the inverse model is formulated and successfully applied.