Identification of structural damage in flexible structures using system norm and neural networks

Nowadays there is great interest in damage identification using non destructive tests. Predictive maintenance is one of the most important techniques that are based on analysis of vibrations and it consists basically of monitoring the condition of structures or machines. A complete procedure should be able to detect the damage, to foresee the probable time of occurrence and to diagnosis the type of fault in order to plan the maintenance operation in a convenient form and occasion. In practical problems, it is frequent the necessity of getting the solution of non linear equations. These processes have been studied for a long time due to its great utility. Among the methods, there are different approaches, as for instance numerical methods (classic), intelligent methods (artificial neural networks), evolutions methods (genetic algorithms), and others. The characterization of damages, for better agreement, can be classified by levels. A new one uses seven levels of classification: detect the existence of the damage; detect and locate the damage; detect, locate and quantify the damages; predict the equipment's working life; auto-diagnoses; control for auto structural repair; and system of simultaneous control and monitoring. The neural networks are computational models or systems for information processing that, in a general way, can be thought as a device black box that accepts an input and produces an output. Artificial neural nets (ANN) are based on the biological neural nets and possess habilities for identification of functions and classification of standards. In this paper a methodology for structural damages location is presented. This procedure can be divided on two phases. The first one uses norms of systems to localize the damage positions. The second one uses ANN to quantify the severity of the damage. The paper concludes with a numerical application in a beam like structure with five cases of structural damages with different levels of severities. The results show the applicability of the presented methodology. A great advantage is the possibility of to apply this approach for identification of simultaneous damages.

Pitch detection algorithms based on zero-cross rate and autocorrelation function for musical notes

This paper discusses two pitch detection algorithms (PDA) for simple audio signals which are based on zero-cross rate (ZCR) and autocorrelation function (ACF). As it is well known, pitch detection methods based on ZCR and ACF are widely used in signal processing. This work shows some features and problems in using these methods, as well as some improvements developed to increase their performance. © 2008 IEEE.

Análise de séries temporais de coordenadas estimadas com gps: Uma proposta metodológica para detecção, remoção e recuperação de efeitos sazonais

GPS active networks are more and more used in geodetic surveying and scientific experiments, as water vapor monitoring in the atmosphere and lithosphere plate movement. Among the methods of GPS positioning, Precise Point Positioning (PPP) has provided very good results. A characteristic of PPP is related to the modeling and / or estimation of the errors involved in this method. The accuracy obtained for the coordinates can reach few millimeters. Seasonal effects can affect such accuracy if they are not consistent treated during the data processing. Coordinates time series analyses have been realized using Fourier or Harmonics spectral analyses, wavelets, least squares estimation among others. An approach is presented in this paper aiming to investigate the seasonal effects included in the stations coordinates time series. Experiments were carried out using data from stations Manaus (NAUS) and Fortaleza (BRFT) which belong to the Brazilian Continuous GPS Network (RBMC). The coordinates of these stations were estimated daily using PPP and were analyzed through wavelets for identification of the periods of the seasonal effects (annual and semi-annual) in each time series. These effects were removed by means of a filtering process applied in the series via the least squares adjustment (LSQ) of a periodic function. The results showed that the combination of these two mathematical tools, wavelets and LSQ, is an interesting and efficient technique for removal of seasonal effects in time series.

The Brazilian Seismographic Integrated Systems (BRASIS): Infrastructure and data management

In geophysics and seismology, raw data need to be processed to generate useful information that can be turned into knowledge by researchers. The number of sensors that are acquiring raw data is increasing rapidly. Without good data management systems, more time can be spent in querying and preparing datasets for analyses than in acquiring raw data. Also, a lot of good quality data acquired at great effort can be lost forever if they are not correctly stored. Local and international cooperation will probably be reduced, and a lot of data will never become scientific knowledge. For this reason, the Seismological Laboratory of the Institute of Astronomy, Geophysics and Atmospheric Sciences at the University of São Paulo (IAG-USP) has concentrated fully on its data management system. This report describes the efforts of the IAG-USP to set up a seismology data management system to facilitate local and international cooperation. © 2011 by the Istituto Nazionale di Geofisica e Vulcanologia. All rights reserved.

A multi-agent architecture for intelligent data analysis

The present study introduces a multi-agent architecture designed for doing automation process of data integration and intelligent data analysis. Different from other approaches the multi-agent architecture was designed using a multi-agent based methodology. Tropos, an agent based methodology was used for design. Based on the proposed architecture, we describe a Web based application where the agents are responsible to analyse petroleum well drilling data to identify possible abnormalities occurrence. The intelligent data analysis methods used was the Neural Network.

Electrical consumers data clustering through optimum-path forest

Non-technical losses identification has been paramount in the last decade. Since we have datasets with hundreds of legal and illegal profiles, one may have a method to group data into subprofiles in order to minimize the search for consumers that cause great frauds. In this context, a electric power company may be interested in to go deeper a specific profile of illegal consumer. In this paper, we introduce the Optimum-Path Forest (OPF) clustering technique to this task, and we evaluate the behavior of a dataset provided by a brazilian electric power company with different values of an OPF parameter. © 2011 IEEE.

Influência da combinação de dados GPS e GLONASS no georreferenciamento de imóveis rurais

The land question has been a widely discussed topic in Brazil, regarding land tenure. Law No. 10.267/01 was a major breakthrough for the agrarian issue. Since then on all rural properties must be georeferenced to the Brazilian Geodetic System (BGS). Therefore, satellite positioning and conventional methods are extensively used. Changes have been occurring in satellite positioning systems due to the addition of new signals in GPS (Global System Positioning), restructuring of GLONASS (Global Orbiting Navigation Satellite System), and the new systems like Galileo and Compass as well. To evaluate the effects of combining GPS and GLONASS data, several batches of processings were performed on different configurations. The data processing was performed to determine the coordinates of points of basic support and those materializing the neighborhood of the rural properties. As a result, it was found that the use of accurate ephemeris in transporting coordinates to support points has no significant influence, since transportation with broadcast ephemeris also meets the accuracy requirements for the Standard Technique for Georreferencing Rural Properties. On the other hand, when GPS and GLONASS data were used, such combination provides the best results. In the case of neighboring points, the use of GPS and GLONASS data is also recommended because such data meet the precision requirement and showed better results than those from where data were processed separately.

Solução rápida das ambigüidades GPS para aplicações no posicionamento relativo de linhas de base curtas

Pós-graduação em Ciências Cartográficas - FCT

Estudo comparativo de métodos geoestatísticos aplicados em agricultura de precisão

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Desenvolvimento de um sistema informatizado para coleta, armazenamento e processamento dos dados referentes à cesta básica do Município de Botucatu -SP

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Geoestística e sensoriamento remoto na classificação de imagens em áreas cultivadas com citros

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Análise de métodos de produção de interfaces visuais para recuperação da informação

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Evaluation report of the regional workshop on the measurement of poverty in the Caribbean

1. The member and associate member countries of the Economic Commission for Latin America and the Caribbean/Caribbean Development and Cooperation Committee (ECLAC/CDCC) have committed to pursuing and achieving the Millennium Development Goals, a common set of goals and targets to bring all people up to minimum acceptable standards of human development by 2015. 2. However, in spite of various capacity-building initiatives, Caribbean countries continued to experience difficulties in addressing additional demands of monitoring and measuring progress created by the Millennium Development Goals and other Internationally Agreed Development Goals. Therefore, it was necessary to implement activities to ensure the further building/strengthening of institutional capabilities for generating reliable social, economic and environmental statistics among Caribbean States. 3. The ECLAC project entitled “Strengthening the Capacity of National Statistical Offices in the Caribbean Small Island Developing States to fulfil the Millennium Development Goals and other Internationally Agreed Development Goals” sought to build and strengthen institutional capabilities for generating and compiling reliable social, economic and environmental statistics in the Caribbean subregion, through the provision of technical support, as well as the conduct of training workshops for statisticians and policymakers. 4. Within the objectives of that project, ECLAC Subregional Headquarters for the Caribbean convened a regional training workshop on the measurement of poverty in the Caribbean in Port of Spain, to build the capacity of government officials and other relevant stakeholders. 5. The overall objective of the workshop was to develop and strengthen the national technical capacity of public officials in data processing, systematization and dissemination of poverty indicators and measurement in the Caribbean subregion. The workshop further sought to review and discuss the current approaches to poverty measurement and monitoring in an effort to identify methods to ensure that monitoring and reporting of the Millennium Development Goals were conducted according to internationally agreed upon methodologies. Furthermore, the workshop also intended to review different methods of poverty measurements, including the multidimensional methodology for the measurement of poverty. 6. Participants were introduced to different methods of poverty measurements and other aggregation proposals which would enable countries to better measure progress towards Goal 1 on poverty, report on it and apply evidence-based approaches to national policymaking and planning.

Implementação e análise do desempenho de técnicas de monitoramento através de medição de vibração e correlação com corrente estatórica

A crescente demanda por energia elétrica aliada à grande importância deste setor para o sistema econômico nacional tem levado o governo e empresas particulares a investirem em estudos que possibilitem melhorar o desempenho dos sistemas envolvidos nesse processo, em virtude dos relevantes ganhos que esta iniciativa pode produzir. Neste contexto, esse trabalho é uma contribuição ao estudo do desenvolvimento de uma metodologia de diagnóstico de defeitos para a máquina hidrogeradora número 3 da Usina Hidrelétrica de Coaracy Nunes, localizada no Estado do Amapá. Em muitas situações os métodos de análise de vibrações são utilizados para detectar a presença de falhas nesse tipo de máquina, neste trabalho também será utilizada a análise dos sinais de corrente para fornecer indicações similares. Este trabalho tem por objetivo apresentar uma metodologia de diagnóstico de defeito em máquinas elétricas através dos sinais de vibração e correlação com a análise da corrente do estator. No decorrer deste trabalho apresenta-se uma revisão bibliográfica das técnicas de monitoramento e diagnóstico das condições das máquinas elétricas, através dos ensaios de vibração correlacionados com as características da corrente estatórica. O resultado da correlação da medição de vibração com a medição de corrente se baseia em uma metodologia implementada por um sistema de aquisição e de processamento de dados desenvolvido na plataforma LabView. Os resultados experimentais foram obtidos a partir de defeitos mecânicos (desbalanceamento mecânico e defeitos nas pistas, externa e interna dos rolamentos) induzidos em uma bancada experimental concebida com intuito de representar um sistema de geração. Finalizando, os sinais de vibração e corrente foram analisados e comparados para verificar se os defeitos que foram evidenciados pelo método convencional de vibração alteravam o comportamento dos sinais de corrente. Os bons resultados desse trabalho mostram a viabilidade em estudos futuros nesta área.

Migração pré-empilhamento Kirchhoff feixes gaussianos 2,5D nos domínios afastamento comum e ângulo-comum

O método de migração do tipo Kirchhoff se apresenta na literatura como uma das ferramentas mais importantes de todo o processamento sísmico, servindo de base para a resolução de outros problemas de imageamento, devido ao um menor custo computacional em relação aos métodos que tem por base a solução numérica da equação da onda. No caso da aplicação em três dimensões (3D), mesmo a migração do tipo Kirchhoff torna-se dispendiosa, no que se refere aos requisitos computacionais e até mesmo numéricos para sua efetiva aplicação. Desta maneira, no presente trabalho, objetivando produzir resultados com uma razão sinal/ruído maior e um menor esforço computacional, foi utilizado uma simplificação do meio denominado 2.5D, baseado nos fundamentos teóricos da propagação de feixes gaussianos. Assim, tendo como base o operador integral com feixes gaussianos desenvolvido por Ferreira e Cruz (2009), foi derivado um novo operador integral de superposição de campos paraxiais (feixes gaussianos), o mesmo foi inserido no núcleo do operador integral de migração Kirchhoff convencional em verdadeira amplitude, para a situação 2,5D, definindo desta maneira um novo operador de migração do tipo Kirchhoff para a classe pré-empilhamento em verdadeira amplitude 2.5D (KGB,do inglês Kirchhoff-Gausian-Beam). Posteriormente, tal operador foi particularizado para as configurações de medida afastamento comum (CO, do inglês common offset) e ângulo de reflexão comum (CA, do inglês common angle), ressaltando ainda, que na presente Tese foi também idealizada uma espécie de flexibilização do operador integral de superposição de feixes gaussianos, no que concerne a sua aplicação em mais de um domínio, quais sejam, afastamento comum e fonte comum. Nesta Tese são feitas aplicações de dados sintéticos originados a partir de um modelo anticlinal.