Disturbance detection for optimal database storage in electrical distribution systems using artificial immune systems with negative selection

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

Artificial Immune Systems with Negative Selection Applied to Health Monitoring of Aeronautical Structures

In this paper we present a system for aircraft structural health monitoring based on artificial immune systems with negative selection. Inspired by a biological process, the principle of discrimination proper/non-proper, identifies and characterizes the signs of structural failure. The main application of this method is to assist in the inspection of aircraft structures, to detect and characterize flaws and decision making in order to avoid disasters. We proposed a model of an aluminum beam to perform the tests of the method. The results obtained by this method are excellent, showing robustness and accuracy.

Análise de distúrbios de tensão em sistemas de distribuição de energia elétrica baseada em sistemas imunológicos artificiais

Pós-graduação em Engenharia Elétrica - FEIS

Artificial Immune Systems Applied to the Analysis of Structural Integrity of a Building

This paper presents the application of artificial immune systems for analysis of the structural integrity of a building. Inspired by a biological process, it uses the negative selection algorithm to perform the identification and characterization of structural failure. This paper presents the application of artificial immune systems for analysis of the structural integrity of a building. Inspired by a biological process, it uses the negative selection algorithm to perform the identification and characterization of structural failure. This methodology can assist professionals in the inspection of mechanical and civil structures, to identify and characterize flaws, in order to perform preventative maintenance to ensure the integrity of the structure and decision-making. In order to evaluate the methodology was made modeling a two-story building and several situations were simulated (base-line condition and improper conditions), yielding a database of signs, which were used as input data for the negative selection algorithm. The results obtained by the present method efficiency, robustness and accuracy.

Monitoramento e identificação de falhas em estruturas aeronáuticas e mecânicas utilizando técnicas de computação inteligente

Pós-graduação em Engenharia Mecânica - FEIS

Diagnóstico de distúrbios de tensão em sistemas de distribuição de energia elétrica usando um algoritmo imuno-neural

Pós-graduação em Engenharia Elétrica - FEIS

Aplicação de sistemas imunológicos artificiais para biometria facial: Reconhecimento de identidade baseado nas características de padrões binários

Pós-graduação em Engenharia Elétrica - FEIS

A metabolic pathway assembled by enzyme selection may support herbivory of leaf-cutter ants on plant starch

Mutualistic associations shape the evolution in different organism groups. The association between the leaf-cutter ant Atta sexdens and the basidiomycete fungus Leucoagaricus gongylophorus has enabled them to degrade starch from plant material generating glucose, which is a major food source for both mutualists. Starch degradation is promoted by enzymes contained in the fecal fluid that ants deposit on the fungus culture in cut leaves inside the nests. To understand the dynamics of starch degradation in ant nests, we purified and characterized starch degrading enzymes from the ant fecal fluid and from laboratory cultures of L. gongylophorus and found that the ants intestine positively selects fungal α-amylase and a maltase likely produced by the ants, as a negative selection is imposed to fungal maltase and ant α-amylases. Selected enzymes are more resistant to catabolic repression by glucose and proposed to structure a metabolic pathway in which the fungal α-amylase initiates starch catalysis to generate byproducts which are sequentially degraded by the maltase to produce glucose. The pathway is responsible for effective degradation of starch and proposed to represent a major evolutionary innovation enabling efficient starch assimilation from plant material by leaf-cutters. © 2013 Elsevier Ltd.

Algoritmo híbrido neural-imuno aplicado ao diagnóstico de distúrbios de tensão em sistemas de distribuição de energia elétrica

Pós-graduação em Engenharia Elétrica - FEIS

Chromosome polymorphism in the Brazilian dwarf brocket deer, Mazama nana (Mammalia, Cervidae)

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

Structure, genomic DNA typing, and kinetic characterization of the D allozyme of placental alkaline phosphatase (PLAP/ALPP)

The D allozyme of placental alkaline phosphatase (PLAP) displays enzymatic properties at variance with those of the common PLAP allozymes. We have deduced the amino acid sequence of the PLAP D allele by PCR cloning of its gene, ALPP Two coding substitutions were found in comparison With the cDNA of the common PLAP F allele, i.e., 692C>G and 1352A>G, which translate into a P209R and E429G substitution. A single nucleotide primer extension (SNuPE) assay was developed using PCR primers that enable the amplification of a 1.9 kb PLAP fragment. Extension primers were then used on this PCR fragment to detect the 692C>G and 1352A>G substitution. The SNuPE assay on these two nucleotide substitutions enabled us to distinguish the PLAP F and D alleles from the PLAP S/I alleles. Functional studies on the D allozyme were made possible by constructing and expressing a PLAP D cDNA, i.e., [Arg209, Gly429] PLAP, into wildtype Chinese hamster ovary cells. We determined the k(cat) and K-m, of the PLAP S, F. and D allozymes using the non,physiological substrate p-nitrophenylphosphate at an optimal pH (9.8) as well as two physiological substrates, i.e., pyridoxal-5'-phosphate and inorganic pyrophosphate at physiological pH (7.5). We found that the biochemical properties of the D allozyme of PLAP are significantly different from those of the common PLAP allozymes. These biochemical findings suggest that a suboptimal enzymatic function by the PLAP D allozyme may be the basis for the apparent negative selective pressure of the PLAP D allele. The development of the SNuPE assay will enable us to test the hypothesis that the PLAP D allele is subjected to intrauterine selection by examining genomic DNA from statistically informative population samples. Hum Mutat 19:258-267, 2002. (C) 2002 Wiley-Liss, Inc.

What is the importance of selecting features for non-technical losses identification?

Although non-technical losses automatic identification has been massively studied, the problem of selecting the most representative features in order to boost the identification accuracy has not attracted much attention in this context. In this paper, we focus on this problem applying a novel feature selection algorithm based on Particle Swarm Optimization and Optimum-Path Forest. The results demonstrated that this method can improve the classification accuracy of possible frauds up to 49% in some datasets composed by industrial and commercial profiles. © 2011 IEEE.

Caracterização molecular e bioquímica de uma esterase macho-específica em Zaprionus indianus

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

A novel algorithm for feature selection using Harmony Search and its application for non-technical losses detection

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Feature selection through gravitational search algorithm

In this paper we deal with the problem of feature selection by introducing a new approach based on Gravitational Search Algorithm (GSA). The proposed algorithm combines the optimization behavior of GSA together with the speed of Optimum-Path Forest (OPF) classifier in order to provide a fast and accurate framework for feature selection. Experiments on datasets obtained from a wide range of applications, such as vowel recognition, image classification and fraud detection in power distribution systems are conducted in order to asses the robustness of the proposed technique against Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and a Particle Swarm Optimization (PSO)-based algorithm for feature selection.