Catálogo de publicaciones - libros

When sensor networks are deployed in hostile environments, an adversary may compromise some sensor nodes and use them to inject false sensing reports. False reports can lead to not only false alarms but also the depletion of limited energy resource in battery powered networks. The interleaved hop-by-hop authentication scheme detects such false reports through interleaved authentication. In this scheme, the choice of a security threshold value is important since it trades off security and overhead. In this paper, we propose a fuzzy logic-based adaptive threshold determining method for the interleaved authentication scheme. The fuzzy rule-based system is exploited to determine a security threshold value by considering the number of cluster nodes, the number of compromised nodes, and the energy level of nodes. The proposed method can conserve energy, while it provides sufficient resilience.

No single software development estimation technique is best for all situations. A careful comparison of the results of several approaches is most likely to produce realistic estimates. On the other hand, unless engineers have the capabilities provided by personal training, they cannot properly support their teams or consistently and reliably produce quality products. In this paper, an investigation aimed to compare a personal Fuzzy Logic System (FLS) with linear regression is presented. The evaluation criteria are based upon ANOVA of MRE and MER, as well as MMRE, MMER and pred(25). One hundred five programs were developed by thirty programmers. From these programs, a FLS is generated for estimating the effort of twenty programs developed by seven programmers. The adequacy checking as well as a validation of the FLS are made. Results show that a FLS can be used as an alternative for estimating the development effort at personal level.

Nowadays the dynamic behavior of a computer network system can be modeled from the perspective of a control system. One strategy to be follow is the real-time modeling of magnetic levitation system. After this representation, next stage is how a control approach can be affected and modified. In that respect, this paper proposes a control reconfiguration strategy from the definition of an Intelligent Fuzzy System computer network reconfiguration. Several stages are including, how computer network takes place, as well as how control techniques are modified using Takagi-Sugeno Fuzzy Control.

The application of fuzzy control to large-scale complex systems is not a trivial task. For such systems the number of the fuzzy IF-THEN rules exponentially explodes. If we have possible linguistic properties for each of variables, with which we will have possible combinations of input values. Large-scale systems require special approaches for modeling and control. In our work the sensory fusion method is studied in an attempt to reduce the size of the inference engine for large-scale systems. This method reduces the number of rules considerably. But, in order to do so, the adequate parameters should be estimated, which, in the traditional way, depends on the experience and knowledge of a skilled operator. In this work, we are proposing a method to automatically estimate the corresponding parameters for the sensory fusion rule base reduction method to be applied to fuzzy control complex systems. In our approach, the parameters of the sensory fusion method are found through the use of genetic algorithms. The implementation process, the simulation experiments, as well as some results are described in the paper.

This paper deals with a fault detection system design for uncertain nonlinear systems modeled as T-S fuzzy systems with the integral quadratic constraints. In order to generate a residual signal, we used a left coprime factorization of the T-S fuzzy system. Using a multi-objective filter, the fault occurrence can be detected effectively. A simulation study with nuclear steam generator level control system shows that the suggested method can be applied to detect the fault in actual applications.

Expert knowledge in most of application domains is uncertain, incomplete and perception-based. For processing such expert knowledge an expert system should be able to represent and manipulate perception-based evaluations of uncertainties of facts and rules, to support multiple-valuedness of variables, and to make conclusions with unknown values of variables. This paper describes an uncertainty model based on two algebras of conjunctive and disjunctive multi-sets used by the inference engine for processing perception-based evaluations of uncertainties. The discussion is illustrated by examples of the expert system, called SMART-Agua, which is aimed to diagnose and give solution to water production problems in petroleum wells.

This paper presents a new version of an existing hybrid model for the development of knowledge-based systems, where case-based reasoning is used as a problem solver. Numeric predictive attributes are modeled in terms of fuzzy sets to define neurons in an associative Artificial Neural Network (ANN). After the Fuzzy-ANN is trained, its weights and the membership degrees in the training examples are used to automatically generate a local distance function and an attribute weighting scheme. Using this distance function and following the Nearest Neighbor rule, a new hybrid Connectionist Fuzzy Case-Based Reasoning model is defined. Experimental results show that the model proposed allows to develop knowledge-based systems with a higher accuracy than when using the original model. The model takes the advantages of the approaches used, providing a more natural framework to include expert knowledge by using linguistic terms.

Belief propagation (BP) algorithm has been becoming increasingly a popular method for probabilistic inference on general graphical models. When networks have loops, it may not converge and, even if converges, beliefs, i.e., the result of the algorithm, may not be equal to exact marginal probabilities. When networks have loops, the algorithm is called Loopy BP (LBP). Tatikonda and Jordan applied Gibbs measures theory to LBP algorithm and derived a sufficient convergence condition. In this paper, we utilize Gibbs measure theory to investigate the discrepancy between a marginal probability and the corresponding belief. Consequently, in particular, we obtain an error bound if the algorithm converges under a certain condition. It is a general result for the accuracy of the algorithm. We also perform numerical experiments to see the effectiveness of the result.

In this paper, we pursue application of Gibbs measure theory to LBP in two ways. First, we show this theory can be applied directly to LBP for factor graphs, where one can use higher-order potentials. Consequently, we show beliefs are just marginal probabilities for a certain Gibbs measure on a computation tree. We also give a convergence criterion using this tree. Second, to see the usefulness of this approach, we apply a well-known general condition and a special one, which are developed in Gibbs measure theory, to LBP. We compare these two criteria and another criterion derived by the best present result. Consequently, we show that the special condition is better than the others and also show the general condition is better than the best present result when the influence of one-body potentials is sufficiently large. These results surely encourage the use of Gibbs measure theory in this area.

We analyse how a learner modelling engine that uses belief functions for evidence and belief representation, called , reacts to different input information about the learner in terms of changes in the state of its beliefs and the decisions that it derives from them. The paper covers induction of evidence with different strengths from the qualitative and quantitative properties of the input, the amount of indirect evidence derived from direct evidence, and differences in beliefs and decisions that result from interpreting different sequences of events simulating learners evolving in different directions. The results here presented substantiate our vision of is a proof of existence for a generic and potentially comprehensive learner modelling subsystem that explicitly represents uncertainty, conflict and ignorance in beliefs. These are key properties of learner modelling engines in the bizarre world of open Web-based learning environments that rely on the content+metadata paradigm.