Abstract
A better environment can be achieved through the reduced emission of pollutants, the optimization of green energy production and the optimization of maintenance interventions, which is an important contribution in getting equipment functioning as efficiently and effectively as possible and, of no less importance, to minimize the downtime caused by faults. These are the key points presented in this paper, which also emphasizes the very recent contribution of 3D models in aiding fault diagnosis and terology in general. The way to achieve the above-mentioned objectives is through on-condition maintenance in two main fields, wind farms and Diesel engines. In wind farms, maintenance is done through the control of variables, such as vibration signals and the balance of electrical currents. As for Diesel engines, on-condition variables are the emissions of PM10, NO(x), CO, HC and CO(2). However, there are problems in both situations, namely, in the first case, the distance and accessibility of the generators and, in the second case, the problems associated with the fact that the equipment is not static. Another common problem in both situations is the measuring and transmission of the values of the on-condition variables, because, in the case of wind farms, the machine is placed on top of the tower and, in the case of Diesel engines, the vehicles are in operation most of the time and most of the measurements need be made while the vehicles are running. Also, although the two situations seem different, they have many issues in common, such as those above-mentioned, for which we will propose convergent solutions that have an Integrated Modular System for Terology (SMIT - Sistema Modular Integrado de Terologia) as a base platform. In addition, to collect, transmit and manage data, we also propose low-cost hardware devices and open-source software, with time series, Hidden Markov Models and genetic algorithms incorporated into them, to enable the prediction of new maintenance interventions. Another important development that is mentioned, with the objective of achieving a more effective terology system, is the implementation of 3D models to aid fault diagnosis and maintenance interventions in general. All these subjects are treated in this paper in a cohesive and synergistic way in order to achieve more effective terology management with an environmental perspective.

Abstract

Abstract
The use of open-source software in many institutions and organizations is increasing. However, a balance should be considered between the software cost and the cost of its technical support and reliability. In this article, a maintenance system for wind farms will be presented. It is connected to an information system for maintenance, called SNIFF (Terology Integrated Modular System) as a general base to manage the assets and as a support strategic line to the evolution of this system, which incorporates on-condition maintenance modules, and the support to the research and development done around this theme. The SMIT system is based on a TCP/IP network, using a Linux server running a PostgreSQL database and Apache web server with PHP, and Octave and R software for numerical analysis. Maintenance technicians, chiefs, economic and production management personnel can access SMIT database through SMIT clients for Windows. In addition, this maintenance system for wind systems uses also special low cost hardware for data acquisition on floor level. The hardware uses a distributed TCP/IP network to synchronize SMIT server master clock through Precision Time Protocol. Usually, the manufactures construct, deploy and give the means for the suppliers to perform the wind system's maintenance. This is a very competitive area, where companies tend to hide the development details and implementations. Within this scenario, the development of maintenance management models for multiple wind equipments is important, and will allow countries to be more competitive in a growing market. For on-condition monitoring, the algorithms are based on Support Vector Machines and time series analysis running under Octave and R open-source software's.

Abstract
The game of chess has been a major testbed for research in artificial intelligence, since it requires focus on intelligent reasoning. Particularly, several challenges arise to machine learning systems when inducing a model describing legal moves of the chess, including the collection of the examples, the learning of a model correctly representing the official rules of the game, covering all the branches and restrictions of the correct moves, and the comprehensibility of such a model. Besides, the game of chess has inspired the creation of numerous variants, ranging from faster to more challenging or to regional versions of the game. The question arises if it is possible to take advantage of an initial classifier of chess as a starting point to obtain classifiers for the different variants. We approach this problem as an instance of theory revision from examples. The initial classifier of chess is inspired by a FOL theory approved by a chess expert and the examples are defined as sequences of moves within a game. Starting from a standard revision system, we argue that abduction and negation are also required to best address this problem. Experimental results show the effectiveness of our approach.

Abstract
In the introduction it was argued that ubiquitous knowledge discovery systems have to be able to sense their environment and receive data from other devices, to adapt continuously to changing environmental conditions (including their own condition) and evolving user habits and need be capable of predictive self-diagnosis. In the last chapter, resource constraints arising from ubiquitous environments have been discussed in some detail. It has been argued that algorithms have to be resource-aware because of real-time constraints and of limited computing and battery power as well as communication resources. © 2010 Springer-Verlag.

Abstract
The goal of this paper is to coordinate directional overcurrent relays using the Evolutionary Particle Swarm Optimization (EPSO) Algorithm. EPSO Algorithm has gained a lot of interest for its simplicity, robustness and easy implementation. Coordinate directional overcurrent relays on a meshed network deals with a large volume of data, with many calculations and constraints. So that, this work shows the viability of how EPSO algorithm can solve a non-linear coordination problem.