Catálogo de publicaciones - libros

Modern Power Systems often operate close to their stability limits in order to meet the continuously growing demand, due to the difficulties in expanding the generation and transmission system. An effective way to face power system contingencies that can lead to instability is load shedding. In this paper we propose a method to assess the dynamic performance of the Greek mainland Power System and to propose a load shedding scheme in order to maintain voltage stability under various loading conditions and operating states in the presence of critical contingencies including outages of one or more generating units in the south part of the system. A Self Organizing Map is utilized in order to classify the Load profiles of the Power System. With a decision tree the dynamic performance of each class is assessed. The classification of Load Profiles by the SOM, provide the load shedding scheme.

This paper reports on the results of an ongoing project for the development of a platform for e-Learning, which automatically constructs curricula based on available educational resources and the learners needs and abilities. The system under development, called PASER (Planner for the Automatic Synthesis of Educational Resources), uses an automated planner, which given the initial state of the problem (learner’s profile, preferences, needs and abilities), the available actions (study an educational resource, take an exam, join an e-learning course, etc.) and the goals (obtain a certificate, learn a subject, acquire a skill, etc.) constructs a complete educational curriculum that achieves the goals. PASER is compliant with the evolving educational metadata standards that describe learning resources (LOM), content packaging (CP), educational objectives (RDCEO) and learner related information (LIP).

This paper introduces quantitative measurements/metrics of qualitative entertainment features within computer game environments and proposes artificial intelligence (AI) techniques for optimizing entertainment in such interactive systems. A human-verified metric of interest (i.e. player entertainment in real-time) for predator/prey games and a neuro-evolution on-line learning (i.e. during play) approach have already been reported in the literature to serve this purpose. In this paper, an alternative quantitative approach to entertainment modeling based on psychological studies in the field of computer games is introduced and a comparative study of the two approaches is presented. Artificial neural networks (ANNs) and fuzzy ANNs are used to model player satisfaction (interest) in real-time and investigate quantitatively how the qualitative factors of and contribute to human entertainment. We demonstrate that appropriate non-extreme levels of challenge and curiosity generate high values of entertainment and we discuss the extensibility of the approach to other genres of digital entertainment and edutainment.

In this paper we are introducing the employment of features extracted from Fujisaki’s parameterization of pitch contour for the task of emotion recognition from speech. In evaluating the proposed features we have trained a decision tree inducer as well as the instance based learning algorithm. The datasets utilized for training the classification models, were extracted from two emotional speech databases. Fujisaki’s parameters benefited all prediction models with an average raise of 9,52% in the total accuracy.

In this paper, a two-class pattern recognition problem is studied, namely the automatic detection of speech disorders such as vocal fold paralysis and edema by processing the speech signal recorded from patients affected by the aforementioned pathologies as well as speakers unaffected by these pathologies. The data used were extracted from the Massachusetts Eye and Ear Infirmary database of disordered speech. The linear prediction coefficients are used as input to the pattern recognition problem. Two techniques are developed. The first technique is an optimal linear classifier design, while the second one is based on the dual-space linear discriminant analysis. Two experiments were conducted in order to assess the performance of the techniques developed namely the detection of vocal fold paralysis for male speakers and the detection of vocal fold edema for female speakers. Receiver operating characteristic curves are presented. Long-term mean feature vectors are proven very efficient in detecting the voice disorders yielding a probability of detection that may approach 100% for a probability of false alarm equal to 9.52%.

The selection of the winding material in power transformers is an important task, since it has significant impact on the transformer manufacturing cost. This winding material selection has to be checked in every transformer design, which means that for each design, there is a need to optimize the transformer twice and afterwards to select the most economical design. In this paper, an Artificial Neural Network (ANN) is proposed for the selection of the winding material in power transformers, which significantly contributes in the reduction of the effort needed in the transformer design. The proposed ANN architecture provides 94.7% classification success rate on the test set. Consequently, this method is very suitable for industrial use because of its accuracy and implementation speed.

A multi-layered biomedical literature mining approach is presented aiming to the discovery of gene-gene correlations and the construction of respective . Utilization of the -memory data structure enables efficient manipulation of different gene nomenclatures. The whole approach is coupled with a texts (biomedical abstracts) method. Experimental validation and evaluation results show the rationality, efficiency and reliability of the approach.

Autonomy is a crucial property of an artificial agent. The type of representational structures and the role they play in the preservation of an agent’s autonomy are pointed out. A framework of self-organised Peircean semiotic processes is introduced and it is then used to demonstrate the emergence of grounded representational structures in agents interacting with their environment.

There exist many approaches to agent-based conflict resolution which adopts argumentation as their underlying conflict resolution machinery. In most argumentation systems, the credibility of argument sources plays a minimal role. This paper focuses on combining credibility of sources in a source sensitive argumentation.

This paper presents the main characteristics of Logic Models Creator (LMC). LMC is a new educational environment for young students to build and explore logic models in graphical form. LMC allows visual representation of logic models using IF/THEN/ELSE constructs. In this paper we provide an overview of LMC architecture and discuss briefly an example of use of LMC. As discussed, LMC users in the reported case study managed to achieve effective communication and task evaluation during exploration of problems involving decision making.