Artificial immune systems for classification of petroleum well drilling operations

This paper presents two approaches of Artificial Immune System for Pattern Recognition (CLONALG and Parallel AIRS2) to classify automatically the well drilling operation stages. The classification is carried out through the analysis of some mud-logging parameters. In order to validate the performance of AIS techniques, the results were compared with others classification methods: neural network, support vector machine and lazy learning.

Artificial immune systems applied to optimal capacitor placement in radial distribution networks

The capacitor placement problem for radial distribution networks aims to determine capacitor types, sizes, locations and control scheme. This is a combinatorial problem that can be formulated as a mixed integer nonlinear program. The paper presents an algorithm inspired in artificial immune systems and developed for this specific problem. A good performance was obtained through experimental tests applied to known systems. © 2006 IEEE.

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 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.

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.

Artificial immune networks Copt-aiNet and Opt-aiNet applied to the reconfiguration problem of radial electrical distribution systems

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Fuzzy distributed artificial intelligence systems

In this paper, we introduce a DAI approach called hereinafter Fuzzy Distributed Artificial Intelligence (FDAI). Through the use of fuzzy logic, we have been able to develop mechanisms that we feel may effectively improve current DAI systems, giving much more flexibility and providing the subsidies which a formal theory can bring. The appropriateness of the FDAI approach is explored in an important application, a fuzzy distributed traffic-light control system, where we have been able to aggregate and study several issues concerned with fuzzy and distributed artificial intelligence. We also present a number of current research directions necessary to develop the FDAI approach more fully.

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

Pós-graduação em Engenharia Mecânica - 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 malware detection system inspired on the human immune system

Malicious programs (malware) can cause severe damage on computer systems and data. The mechanism that the human immune system uses to detect and protect from organisms that threaten the human body is efficient and can be adapted to detect malware attacks. In this paper we propose a system to perform malware distributed collection, analysis and detection, this last inspired by the human immune system. After collecting malware samples from Internet, they are dynamically analyzed so as to provide execution traces at the operating system level and network flows that are used to create a behavioral model and to generate a detection signature. Those signatures serve as input to a malware detector, acting as the antibodies in the antigen detection process. This allows us to understand the malware attack and aids in the infection removal procedures. © 2012 Springer-Verlag.

Estimação do diâmetro de furos em processo de furação utilizando sistemas de inteligência artificial: uma análise comparativa entre diferentes técnicas

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Sistema de coleta, análise e detecção de código malicioso baseado no sistema imunológico humano

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Perda de matéria seca em grãos de milho submetidos a sistemas de secagem natural e artificial

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

Perda de matéria seca em grãos de milho submetidos a sistemas de secagem natural e artificial.

The permanence of the corn grain in the field, after physiological maturity, is an important cause of crop losses, both in quantitative and qualitative aspect. By ceasing the supply of assimilated substances to grains, due to physiological maturity, the synthesis reactions are overcome by breathing, responsible for the maintenance of the living tissues of the grains, which occur at the expense of reserves accumulated during grain formation. In addition, there are losses from fungus and insects attack due to adverse weather conditions. Technological advances in recent decades, the develop of grain dryers with different capacities and efficiencies, has led to the early withdrawal of the product from the field, still damp, reducing spoilage. Moreover, the use of artificial drying systems can represent a significant cost to the producer. Thus, the present work aimed to study the effect of natural and artificial methods of drying on maize dry matter losses, for Botucatu, city of Sao Paulo state, Brazil. The cornfield production was conducted at the Experimental Farm “Lageado” and the experimental treatments were conducted in the Laboratory of Agricultural Products Processing, in the Department of Rural Engineering, where the drying systems were tested. The treatments were: shade (control), artificial with heated air, artificial unheated air and, drying attached to the plant. The following analyzes for quality monitoring were performed: weight test, thousand-grain weight test and, grain dry weight. The results showed significant loss in quality of drying beans attached to the plant, by assessing the dry matter loss and by the variation of the grain weight. The weight test showed that the worst performance was the artificial with heated air treatment. We used mathematical modeling techniques to describe the dry matter loss and adjusted the mathematical model to the experimental data analyzed. From the experimental data obtained during drying the grain attached to the plant, it was still possible to fit a regression model that estimates the loss of grain dry matter under the climate from Botucatu during the 2011/2012 harvest period.

HISTORICAL PERSPECTIVE and HUMAN CONSEQUENCES of AFRICANIZED BEE STINGS IN THE AMERICAS

In 1956, Africanized bees began to spread in the American continent from southern Brazil, where original African bees mated with European bees. A few years later, in 1990, these Africanized bees reached the United States and were found in Texas. Currently, these hybrid bees are found in several North American states and will probably reach the Canadian border in the future. Although the presence of Africanized bees had produced positive effects on Brazilian economy, including improvement in crop pollination and in honey production, turning Brazil into a major exporter, the negative impacts-such as swarming, aggressive behavior, and the ability to mass attack-resulted in serious and fatal envenomation with humans and animals. Victims of bee attacks usually develop a severe envenomation syndrome characterized by the release of a large amount of cytokines [interleukins (IL) IL-1, IL-6, IL-8], and tumor necrosis factor (TNF). Subsequently, such cytokines produce an acute inflammatory response that triggers adverse effects on skeletal muscles; bone marrow; hepatic and renal functions; and cardiovascular, central nervous, and immune systems. Finally, the aim of the present review is to study historical characteristics and current status of Africanized bees' spread, the composition of their venom, the impact of the bees on the Brazilian economy and ecology, and clinical aspects of their stings including immune response, and to suggest a protocol for bee sting management since there is no safe and effective antivenom available.