Catálogo de publicaciones - libros

In this paper, the problem of the optimization of energetic flows in hybrid electric vehicles is faced. We consider a hybrid electric vehicle equipped with batteries, a thermal engine (or fuel cells), ultracapacitors and an electric engine. The energetic flows are optimized by using a control strategy based on the prediction of short-term and medium-term vehicle states (energy consumption, vehicle load, current route, traffic flow, etc.). The prediction will be performed by a neuro-fuzzy control unit, where the predictive model exploits the robustness of fuzzy logic in managing the said uncertainties and the neural approach as a data driven tool for non-linear control modeling.

We present a method to recognize the presence of lung cancer in individuals by classifying the olfactory signal acquired through an electronic nose based on an array of MOS sensors. We analyzed the breath of 101 persons, of which 58 as control and 43 suffering from different types of lung cancer (primary and not) at different stages. In order to find the components able to discriminate between the two classes ‘healthy’ and ‘sick’ as best as possible and to reduce the dimensionality of the problem, we extracted the most significative features and projected them into a lower dimensional space, using Nonparametric Linear Discriminant Analysis. Finally, we used these features as input to a pattern classification algorithm, based on Fuzzy k -Nearest Neighbors (Fuzzy k -NN). The observed results, all validated using cross-validation, have been satisfactory achieving an accuracy of 92.6%, a sensitivity of 95.3% and a specificity of 90.5%. These results put the electronic nose as a valid implementation of lung cancer diagnostic technique, being able to obtain excellent results with a non invasive, small, low cost and very fast instrument.

The implementation of training algorithms for SVMs on embedded architectures differs significantly from the electronic support of trained SVM systems. This mostly depends on the complexity and the computational intricacies brought about by the optimization process, which implies a Quadratic-Programming problem and usually involves large data sets. This work presents a general approach to the efficient implementation of SVM training on Digital Signal Processor (DSP) devices. The methodology optimizes efficiency by suitably adjusting the established, effective Keerthi’s optimization algorithm for large data sets. Besides, the algorithm is reformulated to best exploit the computational features of DSP devices and boost efficiency accordingly. Experimental results tackle the training problem of SVMs by involving real-world benchmarks, and confirm both the computational efficiency of the approach.

The paper shows methods that could be suitably applied to manage uncertainties in space systems design and modeling. Attention is focused on the Interval Analysis, as one of the most promising methods. In this research two issues typical faced in space-related researchs have been treated: optimization and ordinary differential equations (ODEs) solving taking into account possible uncertainties included in the models; the first is generally related to space system design phases while the second deals with systems dynamics analysis. Because of lack of space this paper presents results from the first area only: more specifically, the optimization of a control system design to accomplish robust reconfiguration manoeuvre is achieved taking into account uncertainties on the technological parameters of the propulsion unit to obtain a robust design from the beginning of the study. Results turned out that the Interval Analysis is effective and efficient to handle these classes of uncertainties.

In the fuzzy setting we define a collector of entropies, which allows us to consider the reliability of observers. This leads to a system of functional equations. We are able to find the general solution of the system for collectors, which are compatible with a law of the kind ”Inf” in [2]. Finally we give a class of solutions for a collector for which we dont’n take into account a law of compositivity for entropies.

This paper is a continuation of our previous papers on entropy of the Atanassov intuitionistic fuzzy sets (A-IFSs, for short). We discuss the necessity of taking into account all three functions (membership, non-membership and hesitation margin) describing A-IFSs while considering the entropy.

In this work we present several possible extensions of fuzzy Datalog. At first the concept of fuzzy Datalog will be summarized, then its extension for intuitionistic- and interval-valued fuzzy logic is given and the concept of bipolar fuzzy Datalog is introduced.

In this paper, we describe the optimization of membership functions in an Intuitionistic Fuzzy Logic application developed in the Matlab software environment. A unique formulation, known as Combs method, is used to control the problem of ‘exponential rule expansion’ in the rule base. The optimization is performed using a Particle Swarm Optimization (PSO) algorithm to adjust the geometry of trapezoidal membership functions. The technique is tested against the Wisconsin Breast Cancer Database. The use of Combs method shows great promise in significantly expanding the range and complexity of problems that can be addressed using Intuitionistic Fuzzy Logic.

The paper aimed to establish a framework for handling the relationships between different agents in mobile environments. In such environments, objects are vague due to incompleteness and local nature of sensed data. The lack of relevant data stimulated us to use intuitionistic fuzzy (IF) logic for modeling spatial relationships between them. In this paper uncertainty modeling of spatial relationships are analyzed from the view point of intuitionistic fuzzy (IF) logic and in order to provide a paradigm that treats with uncertain topological relationships in mobile GIS environments, a logical framework is presented in which the concept of spatial influenceability is combined with the IF logic.

This paper presents a generalized approach to the intuitionistic fuzzification of gray-scale images. Parametric membership and non-membership functions are applied in order to derive the optimal representation of the image in the intuitionistic fuzzy domain. A two-dimensional intuitionistic fuzzy entropy measure is employed as the optimization criterion for driving the aforementioned procedure. Finally, experimental results demonstrate the ability of the proposed method to efficiently enhance low-contrasted gray-scale images.