Expert system for the assessment of power transformer insulation condition based on type-2 fuzzy logic systems

An efficient expert system for the power transformer condition assessment is presented in this paper. Through the application of Duval`s triangle and the method of the gas ratios a first assessment of the transformer condition is obtained in the form of a dissolved gas analysis (DGA) diagnosis according IEC 60599. As a second step, a knowledge mining procedure is performed, by conducting surveys whose results are fed into a first Type-2 Fuzzy Logic System (T2-FLS), in order to initially evaluate the condition of the equipment taking only the results of dissolved gas analysis into account. The output of this first T2-FLS is used as the input of a second T2-FLS, which additionally weighs up the condition of the paper-oil system. The output of this last T2-FLS is given in terms of words easily understandable by the maintenance personnel. The proposed assessing methodology has been validated for several cases of transformers in service. (C) 2010 Elsevier Ltd. All rights reserved.

A calibration approach based on Takagi-Sugeno fuzzy inference system for digital electronic compasses

Recently, the development of industrial processes brought on the outbreak of technologically complex systems. This development generated the necessity of research relative to the mathematical techniques that have the capacity to deal with project complexities and validation. Fuzzy models have been receiving particular attention in the area of nonlinear systems identification and analysis due to it is capacity to approximate nonlinear behavior and deal with uncertainty. A fuzzy rule-based model suitable for the approximation of many systems and functions is the Takagi-Sugeno (TS) fuzzy model. IS fuzzy models are nonlinear systems described by a set of if then rules which gives local linear representations of an underlying system. Such models can approximate a wide class of nonlinear systems. In this paper a performance analysis of a system based on IS fuzzy inference system for the calibration of electronic compass devices is considered. The contribution of the evaluated IS fuzzy inference system is to reduce the error obtained in data acquisition from a digital electronic compass. For the reliable operation of the TS fuzzy inference system, adequate error measurements must be taken. The error noise must be filtered before the application of the IS fuzzy inference system. The proposed method demonstrated an effectiveness of 57% at reducing the total error based on considered tests. (C) 2011 Elsevier Ltd. All rights reserved.

Evolutionary fuzzy clustering of relational data

This paper is concerned with the computational efficiency of fuzzy clustering algorithms when the data set to be clustered is described by a proximity matrix only (relational data) and the number of clusters must be automatically estimated from such data. A fuzzy variant of an evolutionary algorithm for relational clustering is derived and compared against two systematic (pseudo-exhaustive) approaches that can also be used to automatically estimate the number of fuzzy clusters in relational data. An extensive collection of experiments involving 18 artificial and two real data sets is reported and analyzed. (C) 2011 Elsevier B.V. All rights reserved.

A fuzzy reed-frost model for epidemic spreading

In this paper, we present a fuzzy approach to the Reed-Frost model for epidemic spreading taking into account uncertainties in the diagnostic of the infection. The heterogeneities in the infected group is based on the clinical signals of the individuals (symptoms, laboratorial exams, medical findings, etc.), which are incorporated into the dynamic of the epidemic. The infectivity level is time-varying and the classification of the individuals is performed through fuzzy relations. Simulations considering a real problem with data of the viral epidemic in a children daycare are performed and the results are compared with a stochastic Reed-Frost generalization

Real-Time Voltage Regulation in Power Distribution System Using Fuzzy Control

Two different fuzzy approaches to voltage control in electric power distribution systems are introduced in this paper. The real-time controller in each case would act on power transformers equipped with under-load tap changers. Learning systems are employed to turn the voltage-control relays into adaptive devices. The scope of this study has been limited to the power distribution substation, and the voltage measurements and control actions are carried out on the secondary bus. The capacity of fuzzy systems to handle approximate data, together with their unique ability to interpret qualitative information, make it possible to design voltage-control strategies that satisfy the requirements of the Brazilian regulatory bodies and the real concerns of the electric power distribution companies. Fuzzy control systems based on these two strategies have been implemented and the test results were highly satisfactory.

Fuzzy Control System for Voltage Regulation In Power Transformers

A fuzzy control strategy for voltage regulation in electric power distribution systems is introduced in this article. This real-time controller would act on power transformers equipped with under-load tap changers. The fuzzy system was employed to turn the voltage-control relays into adaptive devices. The scope of the present study has been limited to the power distribution substation, and both the voltage measurements and control actions are carried out on the secondary bus. The capacity of fuzzy systems to handle approximate data, together with their unique ability to interpret qualitative information, make it possible to design voltage control strategies that satisfy both the requirements of the Brazilian regulatory bodies and the real concerns of the electric power distribution companies. A prototype based on the fuzzy control strategy proposed in this paper has also been implemented for validation purposes and its experimental results were highly satisfactory.

A classification methodology for the risk of weed infestation using fuzzy logic

Despite modern weed control practices, weeds continue to be a threat to agricultural production. Considering the variability of weeds, a classification methodology for the risk of infestation in agricultural zones using fuzzy logic is proposed. The inputs for the classification are attributes extracted from estimated maps for weed seed production and weed coverage using kriging and map analysis and from the percentage of surface infested by grass weeds, in order to account for the presence of weed species with a high rate of development and proliferation. The output for the classification predicts the risk of infestation of regions of the field for the next crop. The risk classification methodology described in this paper integrates analysis techniques which may help to reduce costs and improve weed control practices. Results for the risk classification of the infestation in a maize crop field are presented. To illustrate the effectiveness of the proposed system, the risk of infestation over the entire field is checked against the yield loss map estimated by kriging and also with the average yield loss estimated from a hyperbolic model.

Model for Differential Nursing Diagnosis of Alterations in Urinary Elimination Based on Fuzzy Logic

Nursing diagnoses associated with alterations of urinary elimination require different interventions, Nurses, who are not specialists, require support to diagnose and manage patients with disturbances of urine elimination. The aim of this study was to present a model based on fuzzy logic for differential diagnosis of alterations in urinary elimination, considering nursing diagnosis approved by the North American Nursing Diagnosis Association, 2001-2002. Fuzzy relations and the maximum-minimum composition approach were used to develop the system. The model performance was evaluated with 195 cases from the database of a previous study, resulting in 79.0% of total concordance and 19.5% of partial concordance, when compared with the panel of experts. Total discordance was observed in only three cases (1.5%). The agreement between model and experts was excellent (kappa = 0.98, P < .0001) or substantial (kappa = 0.69, P < .0001) when considering the overestimative accordance (accordance was considered when at least one diagnosis was equal) and the underestimative discordance (discordance was considered when at least one diagnosis was different), respectively. The model herein presented showed good performance and a simple theoretical structure, therefore demanding few computational resources.

A Bayesian approach to fuzzy hypotheses testing for the estimation of optimal age for vaccination against measles

Fuzzy Bayesian tests were performed to evaluate whether the mother`s seroprevalence and children`s seroconversion to measles vaccine could be considered as ""high"" or ""low"". The results of the tests were aggregated into a fuzzy rule-based model structure, which would allow an expert to influence the model results. The linguistic model was developed considering four input variables. As the model output, we obtain the recommended age-specific vaccine coverage. The inputs of the fuzzy rules are fuzzy sets and the outputs are constant functions, performing the simplest Takagi-Sugeno-Kang model. This fuzzy approach is compared to a classical one, where the classical Bayes test was performed. Although the fuzzy and classical performances were similar, the fuzzy approach was more detailed and revealed important differences. In addition to taking into account subjective information in the form of fuzzy hypotheses it can be intuitively grasped by the decision maker. Finally, we show that the Bayesian test of fuzzy hypotheses is an interesting approach from the theoretical point of view, in the sense that it combines two complementary areas of investigation, normally seen as competitive. (C) 2007 IMACS. Published by Elsevier B.V. All rights reserved.

A fuzzy Reed-Frost model for epidemic spreading

In this paper, we present a fuzzy approach to the Reed-Frost model for epidemic spreading taking into account uncertainties in the diagnostic of the infection. The heterogeneities in the infected group is based on the clinical signals of the individuals (symptoms, laboratorial exams, medical findings, etc.), which are incorporated into the dynamic of the epidemic. The infectivity level is time-varying and the classification of the individuals is performed through fuzzy relations. Simulations considering a real problem with data of the viral epidemic in a children daycare are performed and the results are compared with a stochastic Reed-Frost generalization.

Generalized external indexes for comparing data partitions with overlapping categories

There is a family of well-known external clustering validity indexes to measure the degree of compatibility or similarity between two hard partitions of a given data set, including partitions with different numbers of categories. A unified, fully equivalent set-theoretic formulation for an important class of such indexes was derived and extended to the fuzzy domain in a previous work by the author [Campello, R.J.G.B., 2007. A fuzzy extension of the Rand index and other related indexes for clustering and classification assessment. Pattern Recognition Lett., 28, 833-841]. However, the proposed fuzzy set-theoretic formulation is not valid as a general approach for comparing two fuzzy partitions of data. Instead, it is an approach for comparing a fuzzy partition against a hard referential partition of the data into mutually disjoint categories. In this paper, generalized external indexes for comparing two data partitions with overlapping categories are introduced. These indexes can be used as general measures for comparing two partitions of the same data set into overlapping categories. An important issue that is seldom touched in the literature is also addressed in the paper, namely, how to compare two partitions of different subsamples of data. A number of pedagogical examples and three simulation experiments are presented and analyzed in details. A review of recent related work compiled from the literature is also provided. (c) 2010 Elsevier B.V. All rights reserved.

On the efficiency of evolutionary fuzzy clustering

This paper tackles the problem of showing that evolutionary algorithms for fuzzy clustering can be more efficient than systematic (i.e. repetitive) approaches when the number of clusters in a data set is unknown. To do so, a fuzzy version of an Evolutionary Algorithm for Clustering (EAC) is introduced. A fuzzy cluster validity criterion and a fuzzy local search algorithm are used instead of their hard counterparts employed by EAC. Theoretical complexity analyses for both the systematic and evolutionary algorithms under interest are provided. Examples with computational experiments and statistical analyses are also presented.

Inflammation markers in healthy and periodontitis patients: a preliminary data screening

Advances in diagnostic research are moving towards methods whereby the periodontal risk can be identified and quantified by objective measures using biomarkers. Patients with periodontitis may have elevated circulating levels of specific inflammatory markers that can be correlated to the severity of the disease. The purpose of this study was to evaluate whether differences in the serum levels of inflammatory biomarkers are differentially expressed in healthy and periodontitis patients. Twenty-five patients (8 healthy patients and 17 chronic periodontitis patients) were enrolled in the study. A 15 mL blood sample was used for identification of the inflammatory markers, with a human inflammatory flow cytometry multiplex assay. Among 24 assessed cytokines, only 3 (RANTES, MIG and Eotaxin) were statistically different between groups (p<0.05). In conclusion, some of the selected markers of inflammation are differentially expressed in healthy and periodontitis patients. Cytokine profile analysis may be further explored to distinguish the periodontitis patients from the ones free of disease and also to be used as a measure of risk. The present data, however, are limited and larger sample size studies are required to validate the findings of the specific biomarkers.

Uso da Classificação Internacional de Doenças na análise do absenteísmo odontológico

OBJETIVO: Comparar o uso das codificações da classificação de doenças e agravos em solicitações de afastamento do trabalho por motivo odontológico. MÉTODOS: Foram analisadas 240 solicitações emitidas em um serviço público federal entre janeiro de 2008 e dezembro de 2009. O uso da Classificação Estatística Internacional de Doenças e Problemas Relacionados à Saúde - Décima Revisão (CID-10) foi comparado ao sistema de Classificação Internacional de Doenças em Odontologia e Estomatologia (CID-OE). Foi determinada a especificidade da codificação nas solicitações de afastamento, bem como da codificação atribuída por peritos oficiais em inspeções indiretas, perícias e juntas odontológicas. RESULTADOS: Do total de atestados, 22,9% não apresentaram a CID, 7,1% apresentaram a CID-9, 3,3% a CID-OE e 66,7% a CID-10. A maioria das codificações foi concordante (55,1%), com maior especificidade nas codificações atribuídas após avaliação dos cirurgiões-dentistas peritos oficiais. CONCLUSÕES: É necessário aperfeiçoar a utilização da CID-10 entre os profissionais de Odontologia e perícia odontológica no trabalho. Sugere-se a incorporação do uso da CID-OE e da Classificação Internacional de Funcionamento, Incapacidade e Saúde para a análise dos afastamentos do trabalho, fornecendo dados relevantes para o monitoramento do absenteísmo por motivo odontológico.

Hydrometeor and latent heat profiles of tropical cyclones Conson, Ivan and Catarina using PR/TRMM data

ABSTRACT Microphysical and thermodynamical features of two tropical systems, namely Hurricane Ivan and Typhoon Conson, and one sub-tropical, Catarina, have been analyzed based on space-born radar PR measurements available on the TRMM satellite. The procedure to classify the reflectivity profiles followed the Heymsfield et al (2000) and Steiner et al (1995) methodologies. The water and ice content have been calculated using a relationship obtained with data of the surface SPOL radar and PR in Rondonia State in Brazil. The diabatic heating rate due to latent heat release has been estimated using the methodology developed by Tao et al (1990). A more detailed analysis has been performed for Hurricane Catarina, the first of its kind in South Atlantic. High water content mean value has been found in Conson and Ivan at low levels and close to their centers. Results indicate that hurricane Catarina was shallower than the other two systems, with less water and the water was concentrated closer to its center. The mean ice content in Catarina was about 0.05 g kg-1 while in Conson it was 0.06 g kg-1 and in Ivan 0.08 g kg-1. Conson and Ivan had water content up to 0.3 g kg-1 above the 0ºC layer, while Catarina had less than 0.15 g kg-1. The latent heat released by Catarina showed to be very similar to the other two systems, except in the regions closer to the center.