arXiv subjects

Zhilong Xiu

Publications and source records attributed to Zhilong Xiu.

6 recordsLinked to original sources

arXiv2014

Application of Artificial Neural Networks in Predicting Abrasion Resistance of Solution Polymerized Styrene-Butadiene Rubber Based Composites

Abrasion resistance of solution polymerized styrene-butadiene rubber (SSBR) based composites is a typical and crucial property in practical applications. Previous studies show that the abrasion resistance can be calculated by the multiple linear regression model. In our study, considering this relationship can also be described into the non-linear conditions, a Multilayer Feed-forward Neural Networks model with 3 nodes (MLFN-3) was successfully established to describe the relationship between the abrasion resistance and other properties, using 23 groups of data, with the RMS error 0.07. Our studies have proved that Artificial Neural Networks (ANN) model can be used to predict the SSBR-based composites, which is an accurate and robust process.

cs.CE↗

arXiv2007

A Modified Iterative IOM Approach for Optimization of Biochemical Systems

The presented previously indirect optimization method (IOM) developed within biochemical systems theory (BST) provides a versatile and mathematically tractable optimization strategy for biochemical systems. However, due to the local approximations nature of the BST formalism, the iterative version of this technique possibly does not yield the true optimum solution. In this work, an algorithm is proposed to obtain the correct and consistent optimum steady-state operating point of biochemical systems. The existing linear optimization problem of the direct IOM approach is modified by adding an equality constraint of describing the consistency of solutions between the S-system and the original model. Lagrangian analysis is employed to derive the first order necessary optimality conditions for the above modified optimization problem. This leads to a procedure that may be regarded as a modified iterative IOM approach in which the optimization objective function includes an extra linear term. The extra term contains a comparison of metabolite concentration derivatives with respect to the enzyme activities between the S-system and the original model and ensures that the new algorithm is still carried out within linear programming techniques. The presented framework is applied to several biochemical systems and shown to the tractability and effectiveness of the method. The simulation is also studied to investigate the convergence properties of the algorithm and to give a performance comparison of standard and modified iterative IOM approach.

q-bio.QM↗

arXiv2007

Optimizing synchronizability of networks

In this paper, we investigate the factors that affect the synchronization of coupled oscillators on networks. By using the edge-intercrossing method, we keep the degree distribution unchanged to see other statistical properties' effects on network's synchronizability. By optimizing the eigenratio $R$ of the coupling matrix with \textit{Memory Tabu Search} (MTS), we observe that a network with lower degree of clustering, without modular structure and displaying disassortative connecting pattern may be easy to synchronize. Moreover, the optimal network contains fewer small-size loops. The optimization process on scale-free network strongly suggests that the heterogeneity plays the main role in determining the synchronizability.

cond-mat.dis-nn↗

arXiv2005

Optimization of network structure to random failures

Network's resilience to the malfunction of its components has been of great concern. The goal of this work is to determine the network design guidelines, which maximizes the network efficiency while keeping the cost of the network (that is the average connectivity) constant. With a global optimization method, memory tabu search (MTS), we get the optimal network structure with the approximately best efficiency. We analyze the statistical characters of the network and find that a network with a small quantity of hub nodes, high degree of clustering may be much more resilient to perturbations than a random network and the optimal network is one kind of highly heterogeneous networks. The results strongly suggest that networks with higher efficiency are more robust to random failures. In addition, we propose a simple model to describe the statistical properties of the optimal network and investigate the synchronizability of this model.

cond-mat.dis-nn↗

arXiv2005

Evolving Scale-Free Network Model with Tunable Clustering

The Barabási-Albert (BA) model is extended to include the concept of local world and the microscopic event of adding edges. With probability $p$, we add a new node with $m$ edges which preferentially link to the nodes presented in the network; with probability $1-p$, we add $m$ edges among the present nodes. A node is preferentially selected by its degree to add an edge randomly among its neighbors. Using continuum theory and rate equation method we get the analytical expressions of the power-law degree distribution with exponent $γ=3$ and the clustering coefficient $c(k)\sim k^{-1}+c$. The analytical expressions are in good agreement with the numerical calculations.

cond-mat.dis-nn↗

arXiv2005

Entropy Optimization of Scale-Free Networks Robustness to Random Failures

Many networks are characterized by highly heterogeneous distributions of links, which are called scale-free networks and the degree distributions follow $p(k)\sim ck^{-α}$. We study the robustness of scale-free networks to random failures from the character of their heterogeneity. Entropy of the degree distribution can be an average measure of a network's heterogeneity. Optimization of scale-free network robustness to random failures with average connectivity constant is equivalent to maximize the entropy of the degree distribution. By examining the relationship of entropy of the degree distribution, scaling exponent and the minimal connectivity, we get the optimal design of scale-free network to random failures. We conclude that entropy of the degree distribution is an effective measure of network's resilience to random failures.

cond-mat.dis-nn↗