Different methods for Blind Source Separation (BSS) have been recently proposed. Most of these methods are suitable for separating either a mixture of sub-Gaussian source or a mixture of super-Gaussian sources. In this paper, a unified statistical approach for separating the mixture of sub-Gaussian and super-Gaussian source is proposed. Source separation techniques use an objective function to be optimized. The optimization process requires probability density function to be expressed in the terms of the random variable. Two different density models have been used for representing sub-Gaussian and super-Gaussian sources. Optimization of the objective function yields different nonlinear functions. Kurtosis has been ušed as measure of Gaussianity of a source. Depending upon the sign of kurtosis one of the nonlinearities is ušed in the proposed algorithm. Simulations with artificiaily generated as well as audio signals demonstrate effectiveness of the proposed approach.
Web 2.0 has led to the expansion and evolution of web-based communities that enable people to share information and communicate on shared platforms. The inclination of individuals towards other individuals of similar choices, decisions and preferences to get related in a social network prompts the development of groups or communities. The identification of community structure is one of the most challenging task that has received a lot of attention from the researchers. Network community structure detection can be expressed as an optimisation problem. The objective function selected captures the instinct of a community as a group of nodes in which intra-group connections are much denser than inter-group connections. However, this problem often cannot be well solved by traditional optimisation methods due to the inherent complexity of network structure. Therefore, evolutionary algorithms have been embraced to deal with community detection problem. Many objective functions have been proposed to capture the notion of quality of a network community. In this paper, we assessed the performance of four important objective functions namely Modularity, Modularity Density, Community Score and Community Fitness on real-world benchmark networks, using Genetic Algorithm (GA). The performance measure taken to assess the quality of partitions is NMI (Normalized mutual information). From the experimental results, we found that the communities' identified by these objectives have different characteristics and modularity density outperformed the other three objective functions by uncovering the true community structure of the networks. The experimental results provide a direction to researchers on choosing an objective function to measure the quality of community structure in various domains like social networks, biological networks, information and technological networks.
Nowadays we can commonly encounter with revitalizations of an original HPPs which were earlier fitted with Francis turbines. They were often placed to the locations with low head and higher discharge, which means high specific speed (ns > 400). Generally it is quite complex to design Francis turbines for such high specific speed. These very old turbines usually have lower efficiency due to the earlier limited possibilities of hydraulic design. An exchange of a water turbine with another type can be quite expensive and therefore it can be more suitable to change only an old runner for a new one. In this article the design process of high specific speed turbine ns = 430 is described. Optimization was done as the full-automatic cycle and was based on a simplex optimization method as well as on a genetic algorithm. For the parameterization of the runner blade, the BladeGen software was used and CFD (Computational Fluid Dynamics) analysis was run in Ansys CFX v.14 software. The final shape runner blade was reached after computing about 1000 variants, which lasted about 250 computational hours. and Obsahuje seznam literatury