The Experts below are selected from a list of 321 Experts worldwide ranked by ideXlab platform
Olga L Malkina - One of the best experts on this subject based on the ideXlab platform.
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visualization of the four component g tensor density as a three Dimensional Function
Chemical Physics Letters, 2015Co-Authors: James R Asher, Vladimir G Malkin, Olga L MalkinaAbstract:Abstract At the 4-component level of theory, the EPR g-tensor is a first-order property, i.e. each component of the g-tensor is an expectation value. This makes visualization of the g-tensor in terms of 3-Dimensional real-space Functions straightforward. These Functions can be considered as ‘g-value densities’, and be plotted to give a 3-Dimensional visualization of the g-tensor. This can be decomposed further into terms corresponding to different operators in the g-tensor calculation. Initial results of the visualization of the g-value density are presented, including of changes caused by H-bonding. This new approach expands existing techniques for analysis of factors affecting the g-tensor.
James R Asher - One of the best experts on this subject based on the ideXlab platform.
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visualization of the four component g tensor density as a three Dimensional Function
Chemical Physics Letters, 2015Co-Authors: James R Asher, Vladimir G Malkin, Olga L MalkinaAbstract:Abstract At the 4-component level of theory, the EPR g-tensor is a first-order property, i.e. each component of the g-tensor is an expectation value. This makes visualization of the g-tensor in terms of 3-Dimensional real-space Functions straightforward. These Functions can be considered as ‘g-value densities’, and be plotted to give a 3-Dimensional visualization of the g-tensor. This can be decomposed further into terms corresponding to different operators in the g-tensor calculation. Initial results of the visualization of the g-value density are presented, including of changes caused by H-bonding. This new approach expands existing techniques for analysis of factors affecting the g-tensor.
Zhe Yang - One of the best experts on this subject based on the ideXlab platform.
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modified dolphin swarm algorithm based on chaotic maps for solving high Dimensional Function optimization problems
IEEE Access, 2019Co-Authors: Weibiao Qiao, Zhe YangAbstract:In 2016, dolphin swarm algorithm (DSA) that has received sustained research interest due to its simplicity and effectiveness was proposed. However, when solving high-Dimensional Function optimization problems, DSA is prone to fall into local optimization problems, which leads to low optimization accuracy or even failure. In this paper, to solve this problem, chaotic mapping is introduced into DSA, and chaotic dolphin swarm algorithm (CDSA) is successfully proposed. Based on high-Dimensional Rastrigin Function, the optimal chaotic map is determined among eight chaotic maps (e.g., Logistic). Then, in view of high-Dimensional Levy Function, Rotated Hyper-Ellipsoid Function and Sum Squares Function respectively, the performance of CDSA and that of the state-of-the-art algorithms (e.g. (whale optimization algorithm) WOA) are compared. The results show that the performance of CDSA based on Kent map is best and the performance of CDSA outperform that of the state-of-the-art algorithms considered to be compared. Finally, it is concluded that such a new meta-heuristic algorithm could help to improve the shortcomings of DSA and increase the applied range of DSA.
Mustafa Servet Kiran - One of the best experts on this subject based on the ideXlab platform.
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Integration search strategies in tree seed algorithm for high Dimensional Function optimization
International Journal of Machine Learning and Cybernetics, 2019Co-Authors: Imral Gungor, Bulent Gursel Emiroglu, Ahmet Cevahir Cinar, Mustafa Servet KiranAbstract:The tree-seed algorithm, TSA for short, is a new population-based intelligent optimization algorithm developed for solving continuous optimization problems by inspiring the relationship between trees and their seeds. The locations of trees and seeds correspond to the possible solutions of the optimization problem on the search space. By using this model, the continuous optimization problems with lower dimensions are solved effectively, but its performance dramatically decreases on solving higher Dimensional optimization problems. In order to address this issue in the basic TSA, an integration of different solution update rules are proposed in this study for solving high Dimensional continuous optimization problems. Based on the search tendency parameter, which is a peculiar control parameter of TSA, five update rules and a withering process are utilized for obtaining seeds for the trees. The performance of the proposed method is investigated on basic 30-Dimensional twelve numerical benchmark Functions and CEC (congress on evolutionary computation) 2015 test suite. The performance of the proposed approach is also compared with the artificial bee colony algorithm, particle swarm optimization algorithm, genetic algorithm, pure random search algorithm and differential evolution variants. Experimental comparisons show that the proposed method is better than the basic method in terms of solution quality, robustness and convergence characteristics.
Shinill Kang - One of the best experts on this subject based on the ideXlab platform.
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On the solution of an ill‐posed design solidification problem using minimization techniques in finite‐ and infinite‐Dimensional Function spaces
International Journal for Numerical Methods in Engineering, 1993Co-Authors: Nicholas Zabaras, Shinill KangAbstract:This paper provides a comparative study of two alternative methodologies for the solution of an inverse design solidification problem. It is the one-Dimensional solidification problem of calculating the boundary heat flux history that achieves a desired freezing front velocity and desired heat fluxes at the freezing front. The front velocity h(t) and flux history qmS(t) on the solid side of the front control the obtained cast structure. As such, the potential applications of the proposed methods to the control of casting processes are enormous. The first technique utilizes a finite-Dimensional approximation of the unknown boundary heat flux Function q0(t). The second technique uses the adjoint method to calculate in L2 the derivative of the cost Functional, ‖Tm – T(h(t), t;q0)‖, that expresses the square error between the calculated T(h(t), t; q0) and the given freezing front temperature Tm. Both steepest descent (SDM) and conjugate gradient methods (CGM) are examined. A front tracking FEM technique is used for the discretization of the state space. A detailed numerical analysis of the space and time discretization of the ‘parameter’ and state spaces, of the effect of the end condition of the adjoint problem and of other parameters in the solution are examined.
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on the solution of an ill posed design solidification problem using minimization techniques in finite and infinite Dimensional Function spaces
International Journal for Numerical Methods in Engineering, 1993Co-Authors: Nicholas Zabaras, Shinill KangAbstract:This paper provides a comparative study of two alternative methodologies for the solution of an inverse design solidification problem. It is the one-Dimensional solidification problem of calculating the boundary heat flux history that achieves a desired freezing front velocity and desired heat fluxes at the freezing front. The front velocity h(t) and flux history qmS(t) on the solid side of the front control the obtained cast structure. As such, the potential applications of the proposed methods to the control of casting processes are enormous. The first technique utilizes a finite-Dimensional approximation of the unknown boundary heat flux Function q0(t). The second technique uses the adjoint method to calculate in L2 the derivative of the cost Functional, ‖Tm – T(h(t), t;q0)‖, that expresses the square error between the calculated T(h(t), t; q0) and the given freezing front temperature Tm. Both steepest descent (SDM) and conjugate gradient methods (CGM) are examined. A front tracking FEM technique is used for the discretization of the state space. A detailed numerical analysis of the space and time discretization of the ‘parameter’ and state spaces, of the effect of the end condition of the adjoint problem and of other parameters in the solution are examined.