Dynamic scaling form in wavelet-discriminated Edwards-Wilkinson growth equation

We present an analysis of dynamic scaling of the Edwards-Wilkinson growth model from wavelets' perspective. Scaling function for the surface width is determined using wavelets' formalism, by computing the surface width for each wavelet scale, we show that an exact and simple form of the scaling function is obtained. These predictions are confirmed by computer simulation of a growth model described by the EW equation, and by numerical calculations

Multifractal properties of Pyrex and silicon surfaces blasted with sharp particles

The blasting of brittle materials with sharp particles is an important fabrication technology in many industrial processes. In particular, for microsystems, it allows the production of devices with feature sizes down to few tens of microns. An important parameter of this process is the surface roughness of post-blasted surfaces. In this work the scaling properties of Pyrex glass and silicon surfaces after bombardment with alumina particles are investigated. The targets were bombarded at normal incidence using alumina particles with two different average sizes, 29 µm and 9 µm. This investigation indicates that the resulting surfaces are multifractal. Applying multifractal detrended fluctuation analysis (MFDFA) allowed us to determine the singularity spectrum of the surfaces. This spectrum did not depend on the target material or on the size of the particles. Several parameters quantifying relevant quantities were determined. It was found that long range correlations are responsible for the observed multifractal behaviour

Development of an intelligent decision supporting home energy management system

One of the main goals of Smart grid is to achieve Demand Response by increasing the end users’ participation in decision-making and increasing the awareness that will help consumers to efficiently manage their energy consumption. However the existing demand response (DR) mechanism reduces power consumption based on predetermined policies of load priority (direct load control and pricing techniques) during the peak times without considering consumer comfort and environmental issues. Demand response has been achieved by forcefully shutdown the consumers’ loads during peak hours which violate users’ comfort life style. This is due to lacking of intelligent energy management system and smart automation tools at home level. The main objective of this thesis paper is to develop a model based intelligent decision supporting Energy Management system which will understand the customer consumption behaviours while simultaneously reduce the energy consumptions. To achieve these, a Fuzzy Multi Criteria Decision Making (MCDM) based load controller has been developed to prioritize the consumers’ preferences and to take decision on behalf of the consumers in order to best manage the use of their appliances. The Fuzzy Multi Criteria Decision Making (MCDM) methodology has been used because it can solve decision and planning problems involving multiple criteria. Furthermore a comparative analysis for the power consumption and cost saving performance is carried out to show the benefit of using renewable energy sources along with the proposed fuzzy MCDM based load controller. Simulation results show that the proposed load controller successfully limits the power consumption during the peak hours and concurrently maximizes the savings of energy consumption cost without violating consumer comfort level