Application of Volterra series in nonlinear mechanical system identification and in structural health monitoring problems

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

System identification and active vibration control of a flexible structure

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

Non-destructive evaluation tool for monitoring and detection of structural damage by using neural network.

This work studies the capability of generalization of Neural Network using vibration based measurement data aiming at operating condition and health monitoring of mechanical systems. The procedure uses the backpropagation algorithm to classify the input patters of a system with different stiffness ratios. It has been investigated a large set of input data, containing various stiffness ratios as well as a reduced set containing only the extreme ones in order to study generalizing capability of the network. This allows to definition of Neural Networks capable to use a reduced set of data during the training phase. Once it is successfully trained, it could identify intermediate failure condition. Several conditions and intensities of damages have been studied by using numerical data. The Neural Network demonstrated a good capacity of generalization for all case. Finally, the proposal was tested with experimental data.

Structural damage identification and location using grammian matrices

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

Occurrence of coagulase-positive Staphylococcus in various food products commercialized in Botucatu, SP, Brazil and detection of toxins from food and isolated strains

The aim of this work was to assess the microbiological quality of commercialized desserts, sandwiches and finger food in Botucatu, SP, for human consumption. A total of 172 food samples were analyzed for fecal coliforms and coagulase-positive Staphylococcus and 69 (40.1%) were in disagreement with the standards established by Decree No. 12 (Brazilian Food Sanitation Standard, 2001). Coagulase-positive Staplylococcus was isolated from 26 (15.1%) samples. Toxins were not isolated directly from foods but 27 (54%) coagulase-positive Staphylococcus strains were enterotoxigenic, and toxin type C was the most frequently detected. These results suggest that these products may act as an important vehicle of transmission for well-established pathogens. (c) 2006 Elsevier Ltd. All rights reserved.

Spot-test identification and rapid quantitative sequential analysis of dipyrone

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

Study of the effect of the peptide loading and solvent system in SPPS by HRMAS-NMR

The SPPS methodology has continuously been investigated as a valuable model to monitor the solvation properties of polymeric materials. In this connection, the present work applied HRMAS-NMR spectroscopy to examine the dynamics of an aggregating peptide sequence attached to a resin core with varying peptide loading (up to 80%) and solvent system. Low and high substituted BHAR were used for assembling the VQAAIDYING sequence and some of its minor fragments. The HRMAS-NMR results were in agreement with the swelling of each resin, i.e. there was an improved resolution of resonance peaks in the better solvated conditions. Moreover, the peptide loading and the attached peptide sequence also affected the spectra. Strong peptide chain aggregation was observed mainly in highly peptide loaded resins when solvated in CDCl3. Conversely, due to the better swelling of these highly loaded resins in DMSO, improved NMR spectra were acquired in this polar aprotic solvent, thus enabling the detection of relevant sequence-dependent conformational alterations. The more prominent aggregation was displayed by the VQAAIDYING segment and not by any of its intermediary fragments and these findings were also corroborated by EPR studies of these peptide-resins labelled properly with an amino acid-type spin probe. Copyright (c) 2005 European Peptide Society and John Wiley & Sons, Ltd.

Determination of Oxalate in Grass by FIA with a Spectrophotometric Detection System Using a Two Enzyme Reactor

A new methodology for soluble oxalic acid determination in grass samples was developed using a two enzyme reactor in an FIA system. The reactor consisted of 3 U of oxalate oxidase and 100 U of peroxidase immobilized on Sorghum vulgare seeds activated with glutaraldehyde. The carbon dioxide was monitored spectrophotometrically, after reacting with an acid-base indicator (Bromocresol Purple) after it permeated through a PTFE membrane. A linear response range was observed between 0.25 and 1.00mmol l-1 of oxalic acid; the data was fit by the equation A=-0.8(±1.5)+ 57.2(±2.5)[oxalate], with a correlation coefficient of 0.9971 and a relative standard deviation of 2% for n=5. The variance for a 0.25 mmol l-1 oxalic acid standard solution was lower than 4% for 11 measurements. The FIA system allows analysis of 20 samples per hour without prior treatment. The proposed method showed a good correlation with that of the Sigma Kit.

Power losses analysis and cooling system design of three topologies of multilevel inverters

This paper presents the comparison of three topologies of multilevel inverters applied to drive an induction motor of 500 kVA/4.16 kV. The multilevel inverters analyzed are: a neutral point clamped inverter, a symmetrical cascaded multilevel inverter and a hybrid asymmetrical cascaded multilevel inverter. The performance indexes used in the comparison are total harmonic distortion, first order distortion factor, semiconductors power losses distribution and heat-sink volume. The comparison is developed with the purpose of finding the efficiency and the heat-sink volume where the three systems present the same output filter. ©2008 IEEE.

Intrusion detection system using optimum-path forest

Intrusion detection systems that make use of artificial intelligence techniques in order to improve effectiveness have been actively pursued in the last decade. Neural networks and Support Vector Machines have been also extensively applied to this task. However, their complexity to learn new attacks has become very expensive, making them inviable for a real time retraining. In this research, we introduce a new pattern classifier named Optimum-Path Forest (OPF) to this task, which has demonstrated to be similar to the state-of-the-art pattern recognition techniques, but extremely more efficient for training patterns. Experiments on public datasets showed that OPF classifier may be a suitable tool to detect intrusions on computer networks, as well as allow the algorithm to learn new attacks faster than the other techniques. © 2011 IEEE.

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.

Surface-enhanced Raman scattering (SERS) applied to cancer diagnosis and detection of pesticides, explosives, and drugs

The inelastic scattering of light, Raman scattering, presents a very low cross section. However, the signal can be amplified by several orders of magnitude, leading to the so-called surface-enhanced Raman scattering (SERS) phenomenon. Basically, the SERS effect is achieved when the target molecule (analyte) is adsorbed onto metallic nanoparticles, usually noble metals. This article presents an overview of the applications of SERS to cancer diagnosis and the detection of pesticides, explosives, and drugs (illicit and pharmacological). SERS is routinely applied nowadays to detect and identify analytes at very low concentrations, including for single-molecule detection. However, the application of SERS as an analytical tool requires reliable and reproducible SERS substrates, in terms of enhancement factors, which depends on the size, shape, and aggregation of the metallic nanoparticles. Therefore, the production of reliable and reproducible SERS substrates is a challenge in the field. Besides, the metallic nanoparticles can also induce changes in the system by possible interactions with the analyte under investigation, which must be taken into account. This review will present work in which, under certain specific experimental conditions, SERS has been analytically applied.

Design and implementation of wireless sensor networks for impedance-based structural health monitoring using ZigBee and Global System for Mobile Communications

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Thermal study and characterization of nicotinates of some alkaline earth metals using TG-DSC-FTIR and DSC-system photovisual

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

Identification and antimicrobial susceptibility patterns of Pasteurella multocida isolated from chickens and japanese quails in Brazil

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