Transient stability analysis of electrical power systems using a neural network based on fuzzy ARTMAP

This work presents a methodology to analyze transient stability for electric energy systems using artificial neural networks based on fuzzy ARTMAP architecture. This architecture seeks exploring similarity with computational concepts on fuzzy set theory and ART (Adaptive Resonance Theory) neural network. The ART architectures show plasticity and stability characteristics, which are essential qualities to provide the training and to execute the analysis. Therefore, it is used a very fast training, when compared to the conventional backpropagation algorithm formulation. Consequently, the analysis becomes more competitive, compared to the principal methods found in the specialized literature. Results considering a system composed of 45 buses, 72 transmission lines and 10 synchronous machines are presented. © 2003 IEEE.

Characterization of the temperature, load and damage effects using piezoelectric transducer patches based on fuzzy clustering

Structural Health Monitoring (SHM) denotes a system with the ability to detect and interpret adverse changes in a structure. One of the critical challenges for practical implementation of SHM system is the ability to detect damage under changing environmental conditions. This paper aims to characterize the temperature, load and damage effects in the sensor measurements obtained with piezoelectric transducer (PZT) patches. Data sets are collected on thin aluminum specimens under different environmental conditions and artificially induced damage states. The fuzzy clustering algorithm is used to organize the sensor measurements into a set of clusters, which can attribute the variation in sensor data due to temperature, load or any induced damage.

Application of an iterative method and an evolutionary algorithm in fuzzy optimization

This work develops two approaches based on the fuzzy set theory to solve a class of fuzzy mathematical optimization problems with uncertainties in the objective function and in the set of constraints. The first approach is an adaptation of an iterative method that obtains cut levels and later maximizes the membership function of fuzzy decision making using the bound search method. The second one is a metaheuristic approach that adapts a standard genetic algorithm to use fuzzy numbers. Both approaches use a decision criterion called satisfaction level that reaches the best solution in the uncertain environment. Selected examples from the literature are presented to compare and to validate the efficiency of the methods addressed, emphasizing the fuzzy optimization problem in some import-export companies in the south of Spain. © 2012 Brazilian Operations Research Society.

Estimação da densidade de solos utilizando sistemas de inferência fuzzy

Pós-graduação em Agronomia (Energia na Agricultura) - FCA

Classificador Fisherface Fuzzy para o reconhecimento de faces

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Estimating outcomes in newborn infants using fuzzy logic

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Corrosion prevention for Al2O3/SiC/C/MgAl2O4 based ceramics by means of impregnation with cerium-carrying solution

A cerium-carrying solution was developed so as to aprtially fill the open porosity of Al2O3/SiC/C/MgAl2O4 based refractory lining microstructure used in torpedo ladles, thereby enhancing wear resistance. The protection mchanism was cleared up and introduced from the impregnation technique using a cerium-carrying solution.

Estimation of croplands using indicator kriging and fuzzy classification

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

A Concept of Semantics Extraction from Web Data by Induction of Fuzzy Ontologies

Traditionally, ontologies describe knowledge representation in a denotational, formalized, and deductive way. In addition, in this paper, we propose a semiotic, inductive, and approximate approach to ontology creation. We define a conceptual framework, a semantics extraction algorithm, and a first proof of concept applying the algorithm to a small set of Wikipedia documents. Intended as an extension to the prevailing top-down ontologies, we introduce an inductive fuzzy grassroots ontology, which organizes itself organically from existing natural language Web content. Using inductive and approximate reasoning to reflect the natural way in which knowledge is processed, the ontology’s bottom-up build process creates emergent semantics learned from the Web. By this means, the ontology acts as a hub for computing with words described in natural language. For Web users, the structural semantics are visualized as inductive fuzzy cognitive maps, allowing an initial form of intelligence amplification. Eventually, we present an implementation of our inductive fuzzy grassroots ontology Thus,this paper contributes an algorithm for the extraction of fuzzy grassroots ontologies from Web data by inductive fuzzy classification.

FORA - A Fuzzy Set Based Framework for Online Reputation Management

The Social Web offers increasingly simple ways to publish and disseminate personal or opinionated information, which can rapidly exhibit a disastrous influence on the online reputation of organizations. Based on social Web data, this study describes the building of an ontology based on fuzzy sets. At the end of a recurring harvesting of folksonomies by Web agents, the aggregated tags are purified, linked, and transformed to a so-called fuzzy grassroots ontology by means of a fuzzy clustering algorithm. This self-updating ontology is used for online reputation analysis, a crucial task of reputation management, with the goal to follow the online conversation going on around an organization to discover and monitor its reputation. In addition, an application of the Fuzzy Online Reputation Analysis (FORA) framework, lesson learned, and potential extensions are discussed in this article.

A study of the time complexity of an Elitist Genetic Algorithm used for associating optical observations of MEO and GEO Space Debris

Currently several thousands of objects are being tracked in the MEO and GEO regions through optical means. The problem faced in this framework is that of Multiple Target Tracking (MTT). In this context both the correct associations among the observations, and the orbits of the objects have to be determined. The complexity of the MTT problem is defined by its dimension S. Where S stands for the number of ’fences’ used in the problem, each fence consists of a set of observations that all originate from dierent targets. For a dimension of S ˃ the MTT problem becomes NP-hard. As of now no algorithm exists that can solve an NP-hard problem in an optimal manner within a reasonable (polynomial) computation time. However, there are algorithms that can approximate the solution with a realistic computational e ort. To this end an Elitist Genetic Algorithm is implemented to approximately solve the S ˃ MTT problem in an e cient manner. Its complexity is studied and it is found that an approximate solution can be obtained in a polynomial time. With the advent of improved sensors and a heightened interest in the problem of space debris, it is expected that the number of tracked objects will grow by an order of magnitude in the near future. This research aims to provide a method that can treat the correlation and orbit determination problems simultaneously, and is able to e ciently process large data sets with minimal manual intervention.

Hybrid incremental modeling based on least squares and fuzzy K-NN for monitoring tool wear in turning processes

There is now an emerging need for an efficient modeling strategy to develop a new generation of monitoring systems. One method of approaching the modeling of complex processes is to obtain a global model. It should be able to capture the basic or general behavior of the system, by means of a linear or quadratic regression, and then superimpose a local model on it that can capture the localized nonlinearities of the system. In this paper, a novel method based on a hybrid incremental modeling approach is designed and applied for tool wear detection in turning processes. It involves a two-step iterative process that combines a global model with a local model to take advantage of their underlying, complementary capacities. Thus, the first step constructs a global model using a least squares regression. A local model using the fuzzy k-nearest-neighbors smoothing algorithm is obtained in the second step. A comparative study then demonstrates that the hybrid incremental model provides better error-based performance indices for detecting tool wear than a transductive neurofuzzy model and an inductive neurofuzzy model.

Phospholipase C-linked receptors regulate the ATP-sensitive potassium channel by means of phosphatidylinositol 4,5-bisphosphate metabolism

In the COS7 cells transfected with cDNAs of the Kir6.2, SUR2A, and M1 muscarinic receptors, we activated the ATP-sensitive potassium (KATP) channel with a K+ channel opener and recorded the whole-cell KATP current. The KATP current was reversibly inhibited by the stimulation of the M1 receptor, which is linked to phospholipase C (PLC) by the Gq protein. The receptor-mediated inhibition was observed even when protein kinase C (PKC) was inhibited by H-7 or by chelating intracellular Ca2+ with 10 mM 1,2-bis(2-aminophenoxy)ethane-N,N,N′,N′-tetraacetate (BAPTA) included in the pipette solution. However, the receptor-mediated inhibition was blocked by U-73122, a PLC inhibitor. M1-receptor stimulation failed to inhibit the KATP current activated by the injection of exogenous phosphatidylinositol 4,5-bisphosphate (PIP2) through the whole-cell patch pipette. The receptor-mediated inhibition became irreversible when the replenishment of PIP2 was blocked by wortmannin (an inhibitor of phosphatidylinositol kinases), or by including adenosine 5′-[β,γ–imido]triphosphate (AMPPNP, a nonhydrolyzable ATP analogue) in the pipette solution. In inside-out patch experiments, the ATP sensitivity of the KATP channel was significantly higher when the M1 receptor in the patch membrane was stimulated by acetylcholine. The stimulatory effect of pinacidil was also attenuated under this condition. We postulate that stimulation of PLC-linked receptors inhibited the KATP channel by increasing the ATP sensitivity, not through PKC activation, but most probably through changing PIP2 levels.

Retrieval of phenological stages of onion fields during the first year of growth by means of C-band polarimetric SAR measurements

The phenological stages of onion fields in the first year of growth are estimated using polarimetric observables and single-polarization intensity channels. Experiments are undertaken on a time series of RADARSAT-2 C-band full-polarimetric synthetic aperture radar (SAR) images collected in 2009 over the Barrax region, Spain, where ground truth information about onion growth stages is provided by the European Space Agency (ESA)-funded agricultural bio/geophysical retrieval from frequent repeat pass SAR and optical imaging (AgriSAR) field campaign conducted in that area. The experimental results demonstrate that polarimetric entropy or copolar coherence when used jointly with the cross-polarized intensity allows unambiguously distinguishing three phenological intervals.