A new concept of a mixed strategy is given for m-dimensional lexicographic noncooperative Γ(Γ^0,Γ^1,...,Γ^(m-1)) game when on a set of pure strategies m-dimensional probability distributions are given. In this case each Γ^k(k=0,1,...,m-1) criteria of Γ game corresponds to its probability distributions on sets of pure strategies. Besides, a lexicographic m-dimensional order relation is given on set of -dimensional probability distribution. The given construction is made by the methodology of nonstandard analysis Therefore, the given mixed strategy is called a nonstandard mixed strategy, and a lexicographic game in such strategies is called a nonstandard mixed extension. An equilibrium situation in mixed strategies is defined in Γ game. A nonstandard mixed extension of lexicographic matrix games is studied thoroughly. In such games, necessary and sufficient conditions of the existence of a saddle point are proved. The analyzed examples show that if in a lexicographic matrix game doesn’t exist a saddle point in standard mixed strategies then a saddle point maybe doesn’t exist in nonstandard mixed strategies. If in a lexicographic matrix game doesn’t exist a saddle point in standard mixed strategies then there can be existed a saddle point in nonstandard mixed strategies. Thus, lexicographic games’ nonstandard mixed distribution is a generalization of a standard mixed extension.

We report on the development of an automated acoustic bird recognizer with improved noise robustness, which is part of a long-term project, aiming at the establishment of an automated biodiversity monitoring system at the Hymettus Mountain near Athens, Greece. In particular, a typical audio processing strategy, which has been proved quite successful in various audio recognition applications, was amended with a simple and effective mechanism for integration of temporal contextual information in the decision-making process. In the present implementation, we consider integration of temporal contextual information by joint post-processing of the recognition results for a number of preceding and subsequent audio frames. In order to evaluate the usefulness of the proposed scheme on the task of acoustic bird recognition, we experimented with six widely used classifiers and a set of real-field audio recordings for two bird species which are present at the Hymettus Mountain. The highest achieved recognition accuracy obtained on the real-field data was approximately 93%, while experiments with additive noise showed significant robustness in low signal-to-noise ratio setups. In all cases, the integration of temporal contextual information was found to improve the overall accuracy of the recognizer.

In this paper, we first propose a new generalized derivative for non-smooth functions and then we utilize this generalized derivative to convert a class of non-smooth optimal control problem to the corresponding smooth form. In the next step, we apply the discretization method to approximate the obtained smooth problem to the nonlinear programming problem. Finally, by solving the last problem, we obtain an approximate optimal solution for main problem.

This paper proposes an approach for the protection of transmission lines with FACTS based on Artificial Neural Networks (ANN) using Wavelet Transform (WT). The required features for the proposed algorithm are extracted from the measured transient current and voltage waveforms using discrete wavelet transform (DWT). Those features are employed for fault detection and faulted phase selection using ANN. The type of FACTS compensated transmission lines is the Thyristor-Controlled Series Capacitor (TCSC). System simulation and test results indicate the feasibility of using neural networks using wavelet transforms in the fault detection, classification and faulted phase selection of FACTS compensated transmission lines.

This paper presents a new approach for solution of the economic load dispatch (ELD) problem with valve-point effect using a modified particle swarm optimization (MPSO) technique. The practical ELD problems have non-smooth cost function with equality and inequality constraints, which make the problem of finding the global optimum difficult when using any mathematical approaches. In this paper, a modified particle swarm optimization (MPSO) mechanism is proposed to deal with the equality and inequality constraints in the ELD problems through the application of Gaussian and Cauchy probability distributions. The MPSO approach introduces new diversification and intensification strategy into the particles thus preventing PSO algorithm from premature convergence. To demonstrate the effectiveness of the proposed approach, the numerical studies have been performed for three different test systems, i.e. six, thirteen and forty generating unit systems, respectively. The results shows that performance of the proposed approach reveal the efficiently and robustness when compared results of other optimization algorithms reported in literature.

Presence of distributed generation (DG) in distribution systems has significant impacts on the operational characteristics of these systems, also using capacitor for reactive compensation and loss reduction is so common. Injected harmonic currents from non-linear loads into distribution system distort all of voltages and currents and must be considered when placing the capacitor banks so that the resonance will not occur. In this paper discrete particle swarm optimization (DPSO) approach is used for the optimal placement and sizing of distributed generations and capacitors in distribution systems for simultaneous voltage profile improvement, loss and total harmonic distortion (THD) reduction. There is a term in the objective function which prevents harmonic resonance between capacitor reactance and system reactance. Constraints include voltage limit, voltage THD, number/ size of capacitors and generators. For evaluating the proposed algorithm, the IEEE 33-bus test system is modified and employed.

One of the best nonlinear robust controllers which can be used in uncertain nonlinear systems is sliding mode controller (SMC), but pure SMC results in chattering in a noisy environment. This effect can be eliminated by optimizing the sliding surface slope. This paper investigates a novel methodology in designing a SMC by a new heuristic search, so called "colonial competitive algorithm "in order to tune the sliding surface slope and the switching gain of the discontinuous part in SMC structure. This process decreases the integral of absolute errors which results in tracking the desired inputs by the outputs in designing a controller for robot manipulator. Simulation results prove that the optimized performance obtained through CCA significantly reduces the chattering phenomena and results in better trajectory tracking compared to typical trial and error methods.

The main purpose of this article is to highlight the fact that there are some drawbacks in the existing definition of complementation of fuzzy sets and hence the geometrical representation of fuzzy sets on the basis of such definition which itself is defective would have no meaning. As a result the theorems or formulas which were rooted in the geometrical representation would become unacceptable and it is realized that in most cases of practical significance it is desirable to consider an additional requirement in defining fuzzy complement. It is important to mention here the fact that all these existing properties are being seen through the application of complementation of fuzzy sets which is rooted in the reference function. The current definition of complementation would infact remove those drawbacks and cosequently produce the results which seems to be logical.

Robust clustering techniques are real life clustering techniques for noisy data. They work efficiently in the presence of noise. Fuzzy C-means (FCM) is the first clustering algorithm, based upon fuzzy sets, proposed by J C Bezdek but it does not give accurate results in the presence of noise. In this paper, FCM and various robust clustering algorithms namely: Possibilistic C-Means (PCM), Possibilistic Fuzzy C-means (PFCM), Credibilistic Fuzzy C-means (CFCM), Noise Clustering (NC) and Density Oriented Fuzzy C-Means (DOFCM) are studied and compared based upon robust characteristics of a clustering algorithm. For the performance analysis of these algorithms in noisy environment, they are applied on various noisy synthetic data sets, standard data sets like DUNN data-set, Bensaid data set. In comparison to FCM, PCM, PFCM, CFCM, and NC, DOFCM clustering method identified outliers very well and selected more desirable cluster centroids.

As per the present scenario lot of power shortages are there in all over the world especially country like India the grid transferring problem is also high. Almost the power from the fossil fuels are becoming so less some of the examples of the fossil fuels are (coal, lignite, oil, and gases).So most of them looking in forward for the power from green or renewable based energies like solar, wind, biomass, tidal etc. Which does not cause any pollution to the environment. In this paper the simulation and analysis of the PV panel and also high efficient boost converter design and simulation is also performed. Even though the solar based systems are renewable based energies when compared to other renewable energies like wind, biomass it does not connect to more number of grid connections. Lot of necessary steps want to be taken one of the main important factor that high efficient boost converter is needed, here in this paper the input voltage to the boost converter is given as 15V and receives the output voltage of 55.64V