Abstract
Several approaches exist to automatically derive test cases that check the conformance of the implementation of abstract data types (ADTs) with respect to their specification. However, they lack support for the testing of implementations of ADTs defined by generic classes. In this paper, we present a novel technique to automatically derive, from specifications, unit test cases for Java generic classes that, in addition to the usual testing data, encompass implementations for the type parameters. The proposed technique relies on the use of Alloy Analyzer to find model instances for each test goal. JUnit test cases and Java implementations of the parameters are extracted from these model instances. © 2012 Springer-Verlag.

Abstract
This paper presents the classification of power quality (PQ) disturbances using modular probabilistic neural network (MPNN), support vector machines (SVMs) and least square support vector machines (LS-SVMs) in grid-connected wind energy systems. Different types of sag and swell disturbances due to the change in load and wind speed are created using MATLAB/Simulink. Classification scheme encompasses suitable features extracted by S-transform (ST) and is subsequently trained with MPNN, SVM and LS-SVM to effectively classify the PQ disturbances. The accuracy and reliability of the proposed classifier are also validated on signals with noise content. A comparative study is also carried out to determine the efficacy of the proposed techniques. © 2012 IEEE.

Abstract
Many real world data mining applications involve analyzing geo-referenced data. Frequently, this type of data sets are incomplete in the sense that not all geographical coordinates have measured values of the variable(s) of interest. This incompleteness may be caused by poor data collection, measurement errors, costs management and many other factors. These missing values may cause several difficulties in many applications. Spatial imputation/interpolation methods try to fill in these unknown values in geo-referenced data sets. In this paper we propose a new spatial imputation method based on machine learning algorithms and a series of data preprocessing steps. The key distinguishing factor of this method is allowing the use of data from faraway regions, contrary to the state of the art on spatial data mining. Images (e. g. from a satellite or video surveillance cameras) may also suffer from this incompleteness where some pixels are missing, which again may be caused by many factors. An image can be seen as a spatial data set in a Cartesian coordinates system, where each pixel (location) registers some value (e. g. degree of gray on a black and white image). Being able to recover the original image from a partial or incomplete version of the reality is a key application in many domains (e. g. surveillance, security, etc.). In this paper we evaluate our general methodology for spatial interpolation on this type of problems. Namely, we check the ability of our method to fill in unknown pixels on several images. We compare it to state of the art methods and provide strong experimental evidence of the advantages of our proposal.

Abstract

Abstract
MicroGrids comprise low voltage distribution systems with distributed energy sources, storage devices and controllable loads, operated connected to the main power network or autonomously, in a controlled coordinated way. In case of MicroGrids autonomous operation, management of instantaneous active power balance imposes unique challenges. Traditionally, power grids are supplied by sources having rotating masses and these are regarded as essential for the inherent stability of the system. In contrast, MicroGrids are dominated by inverter interfaced sources that are inertia-less, but do offer the possibility of a more flexible operation. When a forced or scheduled islanding takes place in a MicroGrid, it must have the ability to operate stably and autonomously, requiring the use of suitable control strategies. The MicroGrid power sources can also be exploited in order to locally promote a service restoration strategy following a general blackout. A sequence of actions for the black start procedure is also presented and it is expected to be an advantage in terms of reliability as a result from the presence of very large amounts of dispersed generation in distribution grids. © Springer-Verlag Berlin Heidelberg 2012.

Abstract
Serious games have been used with success for training field operatives in tasks where there is a danger of injury or life threatening situations. This paper presents the development of a serious game aimed at the areas of security and safety supporting the training of specialists through supervised situational scenarios. The training plans involve security against third parties, focusing on social level security, and also safety actions on events such as floods and fires. The game provides a 3D virtual environment of the real location/facility to be secured and a multiplayer platform to allow collaborative training and supervising. (C) 2012 The Authors. Published by Elsevier B. V. Selection and/or peer-review under responsibility of the scientific programme committee of VS-Games 2012