Optimization of synchronizability in complex spatial networks

Many real-world phenomena can be modelled as spatial networks where nodes have distinct geographical location. Examples include power grids, transportation networks and the Internet. This paper focuses on optimizing the synchronizability of spatial networks. We consider the eigenratio of the Laplacian Matrix of the connection graph as a metric measuring the synchronizability of the network and develop an efficient rewiring mechanism to optimize the topology of the network for synchronizability, i.e., minimizing the eigenratio. The Euclidean distance between two connected nodes is considered as their connection weights, and the sum of all connection weights is defined as the network cost. The proposed optimization algorithm constructs spatial networks with a certain number of nodes and a predefined network cost. We also study the topological properties of the optimized networks. This algorithm can be used to construct spatial networks with optimal synchronization properties

Bayesian regularization of neural network to predict leakage current in a salt fog environment

Leakage current (LC) has been monitored extensively to assess the surface conditions of both ceramic and non-ceramic insulators. It has been reported that LC is highly correlated with insulator surface damage and the occurrence of flashover. Hence, it is imperative to predict the LC future value. The objective of this paper is to use Bayesian regularized neural network to predict both the fundamental and third harmonic components of LC under salt fog condition. Three different models of neural network are proposed and each is trained to predict the time series of both the fundamental and third harmonic of LC. The results have shown that there is a high correlation between the fundamental and third harmonic of LC when the nonlinearity of the leakage current increases. Moreover the future value of the LC has been successfully predicted

Demand Response Planning Tool using Markov Decision Process

Demand response has been used as a technique to influence the behavior of energy consumers to reduce their energy consumption based on participation in incentive-based or event-driven programs. In this paper, a Markov Decision Process is proposed as a decision making framework to study the behavior of energy consumers under different energy pricing policies utilizing rewards and/or penalties. Numerical results show that a combination of both rewards and penalties in the energy pricing policy offer the ideal reduction in power demand averaged over 30 minutes during high peak period

Tribologische Wechselwirkungen zwischen Kunststoff und PVD-hartstoffbeschichteten metallischen Oberflaechen Abschlussbericht

SIGLEAvailable from TIB Hannover: F95B902 / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekDeutsche Forschungsgemeinschaft (DFG), Bonn (Germany)DEGerman