Catálogo de publicaciones - libros

We give an axiomatic system of a logic characterized by the class of Boolean algebras with conjugate, which has a close connection with the theory of rough sets, and prove that the logic is decidable.

This paper presents an intelligent systematic methodology for enhancing diagnosability of discrete event systems by adding sensors. The methodology consists of the following iteractive steps. First, Petri nets are used to model the target system. Then, an algorithm of polynomial complexity is adopted to analyze a sufficient condition of diagnosability of the modeled system. Here, diagnosability is defined in the context of the discrete event systems theory, which was first introduced by Sampath [3]. If the system is found to be possibly non-diagnosable, T-components of the Petri net model are computed to find a location in the system for adding a sensor. The objective is to distinguish multiple T-components with the same observable event sequences. The diagnosability-checking algorithm is used again to see if the system with the newly added sensor is diagnosable. The process is repeated until either the system is diagnosable or diagnosability of the system cannot be enhanced.

Next-generation wireless networks will provide information transmission in Heterogeneous Wireless Networks. It not only offers a variety of wireless access services for integrating different wireless networks, but also takes into account the high bit rate, the QoS management, and the friendly mobility. In this article, the scheme of choosing the suitable network with the best service for a mobile terminal in a heterogeneous wireless network is studied. A new scheme based on fuzzy logic is proposed to employ the important traffic criteria, including bandwidth, dropping rate, blocking rate, signal, and velocity. Finally, the simulation is used to investigate the performance of proposed schemes. The simulation results show that the proposed schemes have better performance than conventional schemes.

In this paper, we use maximal itemsets to represent itemsets in a database. We show that the set of supreme covers, which are the maximal itemsets whose proper subsets are not maximal itemsets, induces an equivalence relation on the set itemsets. Based on maximal itemsets, we propose a large itemset generation algorithm with dynamic support, which runs in time (′2+′), where is the maximum number of items in a maximal itemset, ′ is the number of the maximal itemsets with minimum support greater than the required support, and is the number of the maximal itemsets.

This paper presents a fuzzy multiple criteria decision making model to the evaluation of airline competitiveness over a period. The evaluation problem is formulated as a fuzzy multiple criteria decision making problem and solved by our strength-weakness based approach. After the strength and weakness matrices for airlines are derived, the weights of criteria, strength matrix and weakness matrix can be aggregated into strength indices and weakness indices for airlines, by which each airline can identify his own strength and weakness. The strength and weakness indices can be further integrated into an overall performance indices, by which airlines can identify their competitiveness ranking.

Decision makers may present their preferences over alternatives as fuzzy preference relations. Usually, there exist inconsistencies in the preference relation given by decision makers. In this paper, we propose methods based on goal programming to obtain fuzzy preference relations that satisfy additive consistency from the subjective preference relations given by decision makers.

In multiple criteria decision making (MCDA) problems, a decision maker often needs to select or rank alternatives that are associated with non-commensurate and conflicting criteria. This paper formulates a multiple criteria decision making problem as a fuzzy multiple objective data envelopment analysis model where inputs correspond to cost criteria and outputs correspond to benefit criteria. The fuzzy multiple objective data envelopment analysis model is different from the traditional DEA model in that a common set of weights is determined so that the efficiencies of all the DMUs are maximized simultaneously by maximizing the fuzzy degree of all efficiencies.

Traditionally, the HDI is calculated under the assumption that all component indices are given the same weights. Although this assumption has been supported in the Human Development Reports, it has met also considerable criticism in the literature. In this paper, we present a new model to determine the weights of component indices in the light of data envelopment analysis (DEA). We develop a fuzzy multiple objective DEA model to assess the relative performance of the countries in terms of human development by using optimal weights for the component indices of the HDI.

In this paper, we propose a visualization architecture that constructs a map suggesting clusters in sequence that involve classification utilizing the class label information for the display method of the map. This architecture is based on Self-Organizing Maps (SOM) that are to create clusters and to arrange the similar clusters near within the low dimensional map. This proposed method consists of three steps, firstly the winner neuron trajectories are obtained by SOM, secondly, connectivity weights are obtained by a single layer perceptron based on the winner neuron trajectories, finally, the map is visualized by reversing the obtained weights into the map. In the experiments using time series of real-world medical data, we evaluate the visualization and classification performance by comparing the display method by the number of sample ratio for classes belonging to each cluster.

We address the problem of maximizing the spread of information in a large-scale social network based on the . When we solve the , that is, the optimization problem of selecting the most influential nodes, we need to compute the expected number of nodes influenced by a given set of nodes. However, an exact calculation or a good estimate of this quantity needs a large amount of computation. Thus, very large computational quantities are needed to approximately solve the influence maximization problem based on a natural greedy algorithm. In this paper, we propose methods to efficiently obtain good approximate solutions for the influence maximization problem in the case where the propagation probabilities through links are small. Using real data on a large-scale blog network, we experimentally demonstrate the effectiveness of the proposed methods.