R. Matsumoto, M. Nakagaki

CMES-Computer Modeling in Engineering & Sciences, Vol.10, No.3, pp. 187-198, 2005, DOI:10.3970/cmes.2005.010.187

Abstract Large-scale molecular dynamics simulations of tensile deformation of amorphous metals containing a nano-crystalline particle were performed in order to clarify the effects of particle size and crystal volume fraction on the deformation mechanism and strength. It became clear that particle size has very little effect, while crystal volume fraction has a substantial influence. Elastic modulus and flow stress intensify as crystal volume fraction increases. Furthermore, the stress in the crystal phase continues to increase, even after yielding in the amorphous phase. Consequently, work-hardening effects appear, preventing localization of plastic deformation. Thus, the dispersed nano-crystalline particles improve the amount of tensile… More >

B. Pichler¹, H.A. Mang¹

CMES-Computer Modeling in Engineering & Sciences, Vol.1, No.3, pp. 43-55, 2000, DOI:10.3970/cmes.2000.001.345

Abstract In order to avoid a fully nonlinear analysis to obtain stability limits on nonlinear load-displacement paths, linear eigenvalue problems may be used to compute estimates of such limits. In this paper an asymptotic approach for assessment of the errors resulting from such estimates is presented. Based on the consistent linearization of the geometrically nonlinear static stability criterion – the so-called consistently linearized eigenvalue problem – higher-order estimation functions can be calculated. They are obtained from a scalar post-calculation performed after the solution of the eigenproblem. Different extensions of these higher-order estimation functions are presented. An ab initio criterion for the… More >

S.A. Yıldızel^1,2, A.U. Öztürk¹

CMC-Computers, Materials & Continua, Vol.52, No.1, pp. 41-52, 2016, DOI:10.3970/cmc.2016.052.041

Abstract In this study, artificial neural networks trained with swarm based artificial bee colony optimization algorithm was implemented for prediction of the modulus of rapture values of the fabricated glass fiber reinforced concrete panels. For the application of the ANN models, 143 different four-point bending test results of glass fiber reinforced concrete mixes with the varied parameters of temperature, fiber content and slump values were introduced the artificial bee colony optimization and conventional back propagation algorithms. Training and the testing results of the corresponding models showed that artificial neural networks trained with the artificial bee colony optimization algorithm have remarkable potential… More >

Praveen Ch.¹, Shishir Sinha^1,2

CMC-Computers, Materials & Continua, Vol.9, No.1, pp. 41-50, 2009, DOI:10.3970/cmc.2009.009.041

Abstract Fluid catalytic cracking (FCC) unit plays most important role in the economy of a modern refinery that it is use for value addition to the refinery products. Because of the importance of FCC unit in refining, considerable effort has been done by scientists till now on the modelling of this unit for better understanding and improved productivity. To model a FCC unit we have to know the unknown kinetic parameters of the governing equations.
The basic aim of this paper is to prove that MATLAB^TM can be used as a tool to find unknown kinetic parameters of governing equations for… More >

Chih-Wen Chang¹, Jiang-Ren Chang¹, Chein-Shan Liu²

CMC-Computers, Materials & Continua, Vol.7, No.3, pp. 139-154, 2008, DOI:10.3970/cmc.2008.007.139

Abstract In this paper, we propose a Lie-group shooting method to deal with the classical Blasius flat-plate problem and to find unknown initial conditions. The pivotal point is based on the erection of a one-step Lie group element$\mathbf G(T) and the formation of a generalized mid-point Lie group element$\mathbf G(r). Then, by imposing G(T) = G(r) we can derive some algebraic equations to recover the missing initial conditions. It is the first time that we can apply the Lie-group shooting method to solve the classical Blasius flat-plate problem. Numerical examples are worked out to persuade that the novel approach has better… More >

L.M. Xu¹, H. Fan^2,3, X M Xie³, C. Li³

CMC-Computers, Materials & Continua, Vol.7, No.3, pp. 119-128, 2008, DOI:10.3970/cmc.2008.007.119

Abstract In the present study we performed finite element simulation for bi-continuous heterogeneous solids via a random distribution of materials to predict effective elastic properties. With a random distributing scheme, a statistical analysis via finite element becomes feasible for the multi-phase heterogeneous solids. Using a two-phase bi-continuous material as example, the numerical prediction of the effective properties is obtained in terms of a mean value and standard deviation with a sample size of 30 for each of given volume fraction. The finite element simulation results fall within the analytical bounds proposed by Hashin and Shtrikman (1963) based on the principle of… More >

Chein-Shan Liu¹, Jiang-Ren Chang², Kai-Huey Chang², Yung-Wei Chen²

CMC-Computers, Materials & Continua, Vol.7, No.2, pp. 97-108, 2008, DOI:10.3970/cmc.2008.007.097

Abstract For the inverse vibration problem a mathematical method is required to determine unknown parameters from the measurement of vibration data. When both damping and stiffness functions are identified, it is a rather difficult problem. In this paper we will propose a feasible method to simultaneously estimate both the time-dependent damping and stiffness coefficients through three mathematical transformations. First, the second-order equation of motion is transformed into a self-adjoint first-order system by using the concept of integrating factor. Then, we transform these two ODEs into two hyperbolic type PDEs. Finally, we apply a one-step group preserving scheme for the semi-discretizations of… More >

Chein-Shan Liu^1,2

CMC-Computers, Materials & Continua, Vol.7, No.2, pp. 81-96, 2008, DOI:10.3970/cmc.2008.007.081

Abstract We consider an inverse problem for estimating an unknown heat conductivity parameter α(t) in a heat conduction equation T_t(x,t) = α(t)T_xx(x,t) with the aid of an extra measurement of temperature gradient on boundary. Basing on an establishment of the one-step Lie-group elements G(r) and G(l) for the semi-discretization of heat conduction equation in time domain, we can derive algebraic equations from G(r) = G(l). The new method, namely the Lie-group estimation method (LGEM), is examined through numerical examples to convince that it is highly accurate and efficient; the maximum estimation error is smaller than 10^-5 for smooth parameter and for… More >

Zhong Huang¹, Jian Su², Guangjun Wen¹, Wenxian Zheng³, Chu Chu¹, Yijun Zhang^4,*, Yibo Zhang⁵

CMC-Computers, Materials & Continua, Vol.61, No.1, pp. 399-408, 2019, DOI:10.32604/cmc.2019.05876

Abstract A priori knowledge of the number of tags is crucial for anti-collision protocols in slotted UHF RFID systems. The number of tags is used to decide optimal frame length in dynamic frame slotted ALOHA (DFSA) and to adjust access probability in random access protocols. Conventional researches estimate the number of tags in MAC layer based on statistics of empty slots, collided slots and successful slots. Usually, a collision detection algorithm is employed to determine types of time slots. Only three types are distinguished because of lack of ability to detect the number of tags in single time slot. In this… More >

Yang Yu¹, Zeyu Xiong^1,*, Yueshan Xiong¹, Weizi Li²

CMC-Computers, Materials & Continua, Vol.61, No.1, pp. 103-118, 2019, DOI:10.32604/cmc.2019.05154

Abstract Logistic regression is often used to solve linear binary classification problems such as machine vision, speech recognition, and handwriting recognition. However, it usually fails to solve certain nonlinear multi-classification problem, such as problem with non-equilibrium samples. Many scholars have proposed some methods, such as neural network, least square support vector machine, AdaBoost meta-algorithm, etc. These methods essentially belong to machine learning categories. In this work, based on the probability theory and statistical principle, we propose an improved logistic regression algorithm based on kernel density estimation for solving nonlinear multi-classification. We have compared our approach with other methods using non-equilibrium samples,… More >