A Pre-processing Moving Mesh Method for Discontinuous Galerkin Approximations of Advection-Diffusion-Reaction Problems

We propose a pre-processing mesh re-distribution algorithm based upon harmonic maps employed in conjunction with discontinuous Galerkin approximations of advection-diffusion-reaction problems. Extensive two-dimensional numerical experiments with different choices of monitor functions, including monitor functions derived from goal-oriented a posteriori error indicators are presented. The examples presented clearly demonstrate the capabilities and the benefits of combining our pre-processing mesh movement algorithm with both uniform, as well as, adaptive isotropic and anisotropic mesh refinement.

Non-specific conducting polymer-based array capable of monitoring odour emissions from a biofiltration system in a piggery building

A commercial non-specific gas sensor array system was evaluated in terms of its capability to monitor the odour abatement performance of a biofiltration system developed for treating emissions from a commercial piggery building. The biofiltration system was a modular system comprising an inlet ducting system, humidifier and closed-bed biofilter. It also included a gravimetric moisture monitoring and water application system for precise control of moisture content of an organic woodchip medium. Principal component analysis (PCA) of the sensor array measurements indicated that the biofilter outlet air was significantly different to both inlet air of the system and post-humidifier air. Data pre-processing techniques including normalising and outlier handling were applied to improve the odour discrimination performance of the non-specific gas sensor array. To develop an odour quantification model using the sensor array responses of the non-specific sensor array, PCA regression, artificial neural network (ANN) and partial least squares (PLS) modelling techniques were applied. The correlation coefficient (r(2)) values of the PCA, ANN, and PLS models were 0.44, 0.62 and 0.79, respectively.

利用宽频带台阵资料研究华北地区地壳及上地幔间断面结构

Intense tectonic renovation has occurred in the eastern continent of china since Mesozoic, as evidenced by the high heat flow, widespread magma extrusion and volcanic activities, and development of large sedimentary basins. To explain the cause and mechanism for the tectonic process in this period, some researchers have put forward various models, such as mantle plume, subduction of the Pacific slab, Yangtze Block-North China Block collision, etc. Their seismological evidence, however, is still scarce..During the period from 2000 to 2003, large temporary seismic arrays were established in North China by the Institute of the Geology and Geophysics, Chinese Academy of Sciences. Total 129 portable seismic stations were linearly emplaced across the western and eastern boundaries of the Bohai Bay Basin, and accumulated a large amount of high-quality data. Moreover, abundant data were also collected at the capital digital seismic network established in the ninth five-year period of national economic and social development. These provide an unprecedented opportunity for us to study the deep structure and associated geodynamic mechanism of lithospheric processes in North China using seismological techniques.Seismology is a kind of observation-based science. The development of seismic observations greatly promotes the improvement of seismologic theory and methodology. At the beginning of this thesis, I review the history of seismic observation progress, and present some routine processing techniques used in the array seismology. I also introduce two popular seismic imaging methods (receiver function method and seismic tomography).Receiver function method has been widely used to study the crustal and upper mantle structures, and many relevant research results have been published. In this thesis I elaborate the theory of this method, including the basic concept of receiver functions and the methodology for data pre-processing, stacking and migration. I also address some problems often encountered in practical applications of receiver function imaging.By using the teleseismic data collected at the temporary seismic arrays in North China, in particular, the traveltime information of P-to-S conversion and multiple reverberations of the Moho discontinuity, I obtain the distributions of the crustal thickness and the poisson ratio at the northwest boundary area of the Bohai Bay Basin and discuss the geological implications of the results.Through detailed intestigations on the crustal structural feature around the middle part of the Tanlu fault, considerable disparity in poisson ratios is found in the western and eastern sides of the Tanlu fault. Moreover, an obvious Moho offset is coincidently observed at the same surface location. A reasonable density model for the Tanlu fault area is also derived by simulating the observed gravity variations. Both receiver function study and gravity anomaly modeling suggest that the crustal difference between the western and eastern sides of the Tanlu fault is mainly resulted from their different compositions.With common conversion point imaging of receiver functions, I estimate the depths of the upper and lower boundaries of the mantle transition zone, i.e., the 410 and 660 km discontinuities, beneath most part of the North China continent The thickness of the transition zone (TTZ) in the study area is calculated by subtracting the depth of .410 km discontinuity from that of the 660km discontinuity. The resultant TTZ is 10-15 km larger in the east than in the west of the study area. Phase transitions at the 410 km and the 660 km discontinuities are known to have different Clapeyron slopes. Therefore, the TTZ is sensitive to the temperature changes in the transition zone. Previous studies have shown that the TTZ would be smaller in the mantle plume areas and become larger when the remnants of subducted slabs are present The hypothesis of mantle plume cannot give a reasonable interpretation to the observed TTZ beneath North China, Instead, the receiver function imaging results favor a dynamic model that correlates the thermal structure of the mantle transition zone and associated upper mantle dynamics of North China to the Pacific plate subduction process.

The application of Near-Infrared Reflectance Spectroscopy (NIRS) to detect melamine adulteration of soya bean meal

Soya bean products are used widely in the animal feed industry as a protein based feed ingredient and
have been found to be adulterated with melamine. This was highlighted in the Chinese scandal of
2008. Dehulled soya (GM and non-GM), soya hulls and toasted soya were contaminated with melamine
and spectra were generated using Near Infrared Reflectance Spectroscopy (NIRS). By applying chemometrics
to the spectral data, excellent calibration models and prediction statistics were obtained. The coefficients
of determination (R2) were found to be 0.89–0.99 depending on the mathematical algorithm used,
the data pre-processing applied and the sample type used. The corresponding values for the root mean
square error of calibration and prediction were found to be 0.081–0.276% and 0.134–0.368%, respectively,
again depending on the chemometric treatment applied to the data and sample type. In addition, adopting
a qualitative approach with the spectral data and applying PCA, it was possible to discriminate
between the four samples types and also, by generation of Cooman’s plots, possible to distinguish
between adulterated and non-adulterated samples.

The feasibility of using near infrared and Raman spectroscopic techniques to detect fraudulent adulteration of chili powders with Sudan dye

Chili powder is a globally traded commodity which has been found to be adulterated with Sudan dyes from 2003 onwards. In this study, chili powders were adulterated with varying quantities of Sudan I dye (0.1-5%) and spectra were generated using near infrared reflectance spectroscopy (NIRS) and Raman
spectroscopy (on a spectrometer with a sample compartment modified as part of the study). Chemometrics were applied to the spectral data to produce quantitative and qualitative calibration models and prediction statistics. For the quantitative models coefficients of determination (R2) were found to be
0.891-0.994 depending on which spectral data (NIRS/Raman) was processed, the mathematical algorithm used and the data pre-processing applied. The corresponding values for the root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) were found to be 0.208-0.851%
and 0.141-0.831% respectively, once again depending on the spectral data and the chemometric treatment applied to the data. Indications are that the NIR spectroscopy based models are superior to the models produced from Raman spectral data based on a comparison of the values of the chemometric
parameters. The limit of detection (LOD) based on analysis of 20 blank chili powders against each calibration model gave 0.25% and 0.88% for the NIR and Raman data, respectively. In addition, adopting a qualitative approach with the spectral data and applying PCA or PLS-DA, it was possible to discriminate
between adulterated chili powders from non-adulterated chili powders.

Statistical Machine Learning Techniques for the Prediction of Learning Disabilities in School-Age Children

Learning Disability (LD) is a general term that describes specific kinds of learning problems. It is a neurological condition that affects a child's brain and impairs his ability to carry out one or many specific tasks. The learning disabled children are neither slow nor mentally retarded. This disorder can make it problematic for a child to learn as quickly or in the same way as some child who isn't affected by a learning disability. An affected child can have normal or above average intelligence. They may have difficulty paying attention, with reading or letter recognition, or with mathematics. It does not mean that children who have learning disabilities are less intelligent. In fact, many children who have learning disabilities are more intelligent than an average child. Learning disabilities vary from child to child. One child with LD may not have the same kind of learning problems as another child with LD. There is no cure for learning disabilities and they are life-long. However, children with LD can be high achievers and can be taught ways to get around the learning disability. In this research work, data mining using machine learning techniques are used to analyze the symptoms of LD, establish interrelationships between them and evaluate the relative importance of these symptoms. To increase the diagnostic accuracy of learning disability prediction, a knowledge based tool based on statistical machine learning or data mining techniques, with high accuracy,according to the knowledge obtained from the clinical information, is proposed. The basic idea of the developed knowledge based tool is to increase the accuracy of the learning disability assessment and reduce the time used for the same. Different statistical machine learning techniques in data mining are used in the study. Identifying the important parameters of LD prediction using the data mining techniques, identifying the hidden relationship between the symptoms of LD and estimating the relative significance of each symptoms of LD are also the parts of the objectives of this research work. The developed tool has many advantages compared to the traditional methods of using check lists in determination of learning disabilities. For improving the performance of various classifiers, we developed some preprocessing methods for the LD prediction system. A new system based on fuzzy and rough set models are also developed for LD prediction. Here also the importance of pre-processing is studied. A Graphical User Interface (GUI) is designed for developing an integrated knowledge based tool for prediction of LD as well as its degree. The designed tool stores the details of the children in the student database and retrieves their LD report as and when required. The present study undoubtedly proves the effectiveness of the tool developed based on various machine learning techniques. It also identifies the important parameters of LD and accurately predicts the learning disability in school age children. This thesis makes several major contributions in technical, general and social areas. The results are found very beneficial to the parents, teachers and the institutions. They are able to diagnose the child’s problem at an early stage and can go for the proper treatments/counseling at the correct time so as to avoid the academic and social losses.

Running Climate Models On The NERC Cluster Grid Using G-Rex

Compute grids are used widely in many areas of environmental science, but there has been limited uptake of grid computing by the climate modelling community, partly because the characteristics of many climate models make them difficult to use with popular grid middleware systems. In particular, climate models usually produce large volumes of output data, and running them usually involves complicated workflows implemented as shell scripts. For example, NEMO (Smith et al. 2008) is a state-of-the-art ocean model that is used currently for operational ocean forecasting in France, and will soon be used in the UK for both ocean forecasting and climate modelling. On a typical modern cluster, a particular one year global ocean simulation at 1-degree resolution takes about three hours when running on 40 processors, and produces roughly 20 GB of output as 50000 separate files. 50-year simulations are common, during which the model is resubmitted as a new job after each year. Running NEMO relies on a set of complicated shell scripts and command utilities for data pre-processing and post-processing prior to job resubmission. Grid Remote Execution (G-Rex) is a pure Java grid middleware system that allows scientific applications to be deployed as Web services on remote computer systems, and then launched and controlled as if they are running on the user's own computer. Although G-Rex is general purpose middleware it has two key features that make it particularly suitable for remote execution of climate models: (1) Output from the model is transferred back to the user while the run is in progress to prevent it from accumulating on the remote system and to allow the user to monitor the model; (2) The client component is a command-line program that can easily be incorporated into existing model work-flow scripts. G-Rex has a REST (Fielding, 2000) architectural style, which allows client programs to be very simple and lightweight and allows users to interact with model runs using only a basic HTTP client (such as a Web browser or the curl utility) if they wish. This design also allows for new client interfaces to be developed in other programming languages with relatively little effort. The G-Rex server is a standard Web application that runs inside a servlet container such as Apache Tomcat and is therefore easy to install and maintain by system administrators. G-Rex is employed as the middleware for the NERC1 Cluster Grid, a small grid of HPC2 clusters belonging to collaborating NERC research institutes. Currently the NEMO (Smith et al. 2008) and POLCOMS (Holt et al, 2008) ocean models are installed, and there are plans to install the Hadley Centre’s HadCM3 model for use in the decadal climate prediction project GCEP (Haines et al., 2008). The science projects involving NEMO on the Grid have a particular focus on data assimilation (Smith et al. 2008), a technique that involves constraining model simulations with observations. The POLCOMS model will play an important part in the GCOMS project (Holt et al, 2008), which aims to simulate the world’s coastal oceans. A typical use of G-Rex by a scientist to run a climate model on the NERC Cluster Grid proceeds as follows :(1) The scientist prepares input files on his or her local machine. (2) Using information provided by the Grid’s Ganglia3 monitoring system, the scientist selects an appropriate compute resource. (3) The scientist runs the relevant workflow script on his or her local machine. This is unmodified except that calls to run the model (e.g. with “mpirun”) are simply replaced with calls to "GRexRun" (4) The G-Rex middleware automatically handles the uploading of input files to the remote resource, and the downloading of output files back to the user, including their deletion from the remote system, during the run. (5) The scientist monitors the output files, using familiar analysis and visualization tools on his or her own local machine. G-Rex is well suited to climate modelling because it addresses many of the middleware usability issues that have led to limited uptake of grid computing by climate scientists. It is a lightweight, low-impact and easy-to-install solution that is currently designed for use in relatively small grids such as the NERC Cluster Grid. A current topic of research is the use of G-Rex as an easy-to-use front-end to larger-scale Grid resources such as the UK National Grid service.

Simulação numérica de escoamentos hipersônicos sobre corpos rombudos pelo método de elementos finitos

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Sistema embarcado para monitoramento de harpia (Harpia harpyja)

Esta dissertação consiste no projeto e implementação parcial de um sistema integrado de monitoramento da ave Harpia (Harpia harpyja), espécie encontrada na Amazônia, Cerrado e Mata Atlântica. O sistema de monitoramento é estruturado em três etapas: coleta, armazenamento e transmissão de dados. A primeira etapa consiste na coleta de dados a partir de sensores, podendo detectar a presença de pássaros no ninho, também o sistema conta com o auxílio de uma câmera responsável pela captura de vídeo e áudio. A segunda etapa destina-se ao pré-processamento e armazenamento de todas as informações coletadas. A terceira etapa é responsável pela transmissão dos dados através de satélite, utilizando o Sistema Brasileiro de Coleta de Dados Ambientais (SBCDA). Além disso, foi realizado o desenvolvimento de um protótipo utilizado para o monitoramento. Técnicas de sistemas embarcados são expostas para o leitor e o processo de detecção desta espécie é avaliado.

Segurança em redes sem fio: estudo sobre o desenvolvimento de conjuntos de dados para comparação de IDS

Pós-graduação em Engenharia Elétrica - FEIS

NMR, Metabonomics and Molecular Profiles: Applications to the Quality Assessment of Foodstuffs

Nuclear Magnetic Resonance (NMR) is a branch of spectroscopy that is based on the fact that many atomic nuclei may be oriented by a strong magnetic field and will absorb radiofrequency radiation at characteristic frequencies. The parameters that can be measured on the resulting spectral lines (line positions, intensities, line widths, multiplicities and transients in time-dependent experi-ments) can be interpreted in terms of molecular structure, conformation, molecular motion and other rate processes. In this way, high resolution (HR) NMR allows performing qualitative and quantitative analysis of samples in solution, in order to determine the structure of molecules in solution and not only. In the past, high-field NMR spectroscopy has mainly concerned with the elucidation of chemical structure in solution, but today is emerging as a powerful exploratory tool for probing biochemical and physical processes. It represents a versatile tool for the analysis of foods. In literature many NMR studies have been reported on different type of food such as wine, olive oil, coffee, fruit juices, milk, meat, egg, starch granules, flour, etc using different NMR techniques. Traditionally, univariate analytical methods have been used to ex-plore spectroscopic data. This method is useful to measure or to se-lect a single descriptive variable from the whole spectrum and , at the end, only this variable is analyzed. This univariate methods ap-proach, applied to HR-NMR data, lead to different problems due especially to the complexity of an NMR spectrum. In fact, the lat-ter is composed of different signals belonging to different mole-cules, but it is also true that the same molecules can be represented by different signals, generally strongly correlated. The univariate methods, in this case, takes in account only one or a few variables, causing a loss of information. Thus, when dealing with complex samples like foodstuff, univariate analysis of spectra data results not enough powerful. Spectra need to be considered in their wholeness and, for analysing them, it must be taken in consideration the whole data matrix: chemometric methods are designed to treat such multivariate data. Multivariate data analysis is used for a number of distinct, differ-ent purposes and the aims can be divided into three main groups: • data description (explorative data structure modelling of any ge-neric n-dimensional data matrix, PCA for example); • regression and prediction (PLS); • classification and prediction of class belongings for new samples (LDA and PLS-DA and ECVA). The aim of this PhD thesis was to verify the possibility of identify-ing and classifying plants or foodstuffs, in different classes, based on the concerted variation in metabolite levels, detected by NMR spectra and using the multivariate data analysis as a tool to inter-pret NMR information. It is important to underline that the results obtained are useful to point out the metabolic consequences of a specific modification on foodstuffs, avoiding the use of a targeted analysis for the different metabolites. The data analysis is performed by applying chemomet-ric multivariate techniques to the NMR dataset of spectra acquired. The research work presented in this thesis is the result of a three years PhD study. This thesis reports the main results obtained from these two main activities: A1) Evaluation of a data pre-processing system in order to mini-mize unwanted sources of variations, due to different instrumental set up, manual spectra processing and to sample preparations arte-facts; A2) Application of multivariate chemiometric models in data analy-sis.

Abflusslose Senken – Instrumente in der Landschaftsanalyse und Indikatoren rezenter Krustenbewegungen

Innerhalb des Untersuchungsgebiets Schleswig-Holstein wurden 39.712 topographische Hohlformen detektiert. Genutzt wurden dazu ESRI ArcMap 9.3 und 10.0. Der Datenaufbereitung folgten weitere Kalkulationen in MATLAB R2010b. Jedes Objekt wurde räumlich mit seinen individuellen Eigenschaften verschnitten. Dazu gehörten Fläche, Umfang, Koordinaten (Zentroide), Tiefe und maximale Tiefe der Hohlform und Formfaktoren wie Rundheit, Konvexität und Elongation. Ziel der vorgestellten Methoden war die Beantwortung von drei Fragestellungen: Sind negative Landformen dazu geeignet Landschaftseinheiten und Eisvorstöße zu unterscheiden und zu bestimmen? Existiert eine Kopplung von Depressionen an der rezenten Topographie zu geologischen Tiefenstrukturen? Können Senken unterschiedlicher Entstehung anhand ihrer Formcharakteristik unterteilt werden? Die vorgenommene Klassifikation der großen Landschaftseinheiten basiert auf der Annahme, dass sowohl Jungmoränengebiete, ihre Vorflächen als auch Altmoränengebiete durch charakteristische, abflusslose Hohlformen, wie Toteislöcher, Seen, etc. abgegrenzt werden können. Normalerweise sind solche Depressionen in der Natur eher selten, werden jedoch für ehemalige Glaziallandschaften als typisch erachtet. Ziel war es, die geologischen Haupteinheiten, Eisvorstöße und Moränengebiete der letzten Vereisungen zu differenzieren. Zur Bearbeitung wurde ein Detektionsnetz verwendet, das auf quadratischen Zellen beruht. Die Ergebnisse zeigen, dass durch die alleinige Nutzung von Depressionen zur Klassifizierung von Landschaftseinheiten Gesamtgenauigkeiten von bis zu 71,4% erreicht werden können. Das bedeutet, dass drei von vier Detektionszellen korrekt zugeordnet werden können. Jungmoränen, Altmoränen, periglazialeVorflächen und holozäne Bereiche können mit Hilfe der Hohlformen mit großer Sicherheit voneinander unterschieden und korrekt zugeordnet werden. Dies zeigt, dass für die jeweiligen Einheiten tatsächlich bestimmte Senkenformen typisch sind. Die im ersten Schritt detektierten Senken wurden räumlich mit weiterreichenden geologischen Informationen verschnitten, um zu untersuchen, inwieweit natürliche Depressionen nur glazial entstanden sind oder ob ihre Ausprägung auch mit tiefengeologischen Strukturen in Zusammenhang steht. 25.349 (63,88%) aller Senken sind kleiner als 10.000 m² und liegen in Jungmoränengebieten und können vermutlich auf glaziale und periglaziale Einflüsse zurückgeführt werden. 2.424 Depressionen liegen innerhalb der Gebiete subglazialer Rinnen. 1.529 detektierte Hohlformen liegen innerhalb von Subsidenzgebieten, von denen 1.033 innerhalb der Marschländer im Westen verortet sind. 919 große Strukturen über 1 km Größe entlang der Nordsee sind unter anderem besonders gut mit Kompaktionsbereichen elsterzeitlicher Rinnen zu homologisieren.344 dieser Hohlformen sind zudem mit Tunneltälern im Untergrund assoziiert. Diese Parallelität von Depressionen und den teils über 100 m tiefen Tunneltälern kann auf Sedimentkompaktion zurückgeführt werden. Ein Zusammenhang mit der Zersetzung postglazialen, organischen Materials ist ebenfalls denkbar. Darüber hinaus wurden in einer Distanz von 10 km um die miozän aktiven Flanken des Glückstadt-Grabens negative Landformen detektiert, die Verbindungen zu oberflächennahen Störungsstrukturen zeigen. Dies ist ein Anzeichen für Grabenaktivität während und gegen Ende der Vereisung und während des Holozäns. Viele dieser störungsbezogenen Senken sind auch mit Tunneltälern assoziiert. Entsprechend werden drei zusammenspielende Prozesse identifiziert, die mit der Entstehung der Hohlformen in Verbindung gebracht werden können. Eine mögliche Interpretation ist, dass die östliche Flanke des Glückstadt-Grabens auf die Auflast des elsterzeitlichen Eisschilds reagierte, während sich subglazial zeitgleich Entwässerungsrinnen entlang der Schwächezonen ausbildeten. Diese wurden in den Warmzeiten größtenteils durch Torf und unverfestigte Sedimente verfüllt. Die Gletschervorstöße der späten Weichselzeit aktivierten erneut die Flanken und zusätzlich wurde das Lockermaterial exariert, wodurch große Seen, wie z. B. der Große Plöner See entstanden sind. Insgesamt konnten 29 große Depressionen größer oder gleich 5 km in Schleswig-Holstein identifiziert werden, die zumindest teilweise mit Beckensubsidenz und Aktivität der Grabenflanken verbunden sind, bzw. sogar auf diese zurückgehen.Die letzte Teilstudie befasste sich mit der Differenzierung von Senken nach deren potentieller Genese sowie der Unterscheidung natürlicher von künstlichen Hohlformen. Dazu wurde ein DEM für einen Bereich im Norden Niedersachsens verwendet, das eine Gesamtgröße von 252 km² abdeckt. Die Ergebnisse zeigen, dass glazial entstandene Depressionen gute Rundheitswerte aufweisen und auch Elongation und Exzentrizität eher kompakte Formen anzeigen. Lineare negative Strukturen sind oft Flüsse oder Altarme. Sie können als holozäne Strukturen identifiziert werden. Im Gegensatz zu den potentiell natürlichen Senkenformen sind künstlich geschaffene Depressionen eher eckig oder ungleichmäßig und tendieren meist nicht zu kompakten Formen. Drei Hauptklassen topographischer Depressionen konnten identifiziert und voneinander abgegrenzt werden: Potentiell glaziale Senken (Toteisformen), Flüsse, Seiten- und Altarme sowie künstliche Senken. Die Methode der Senkenklassifikation nach Formparametern ist ein sinnvolles Instrument, um verschiedene Typen unterscheiden zu können und um bei geologischen Fragestellungen künstliche Senken bereits vor der Verarbeitung auszuschließen. Jedoch zeigte sich, dass die Ergebnisse im Wesentlichen von der Auflösung des entsprechenden Höhenmodells abhängen.

Detecting false testimonies in reputation systems using self-organizing maps

It has been demonstrated that rating trust and reputation of individual nodes is an effective approach in distributed environments in order to improve security, support decision-making and promote node collaboration. Nevertheless, these systems are vulnerable to deliberate false or unfair testimonies. In one scenario, the attackers collude to give negative feedback on the victim in order to lower or destroy its reputation. This attack is known as bad mouthing attack. In another scenario, a number of entities agree to give positive feedback on an entity (often with adversarial intentions). This attack is known as ballot stuffing. Both attack types can significantly deteriorate the performances of the network. The existing solutions for coping with these attacks are mainly concentrated on prevention techniques. In this work, we propose a solution that detects and isolates the abovementioned attackers, impeding them in this way to further spread their malicious activity. The approach is based on detecting outliers using clustering, in this case self-organizing maps. An important advantage of this approach is that we have no restrictions on training data, and thus there is no need for any data pre-processing. Testing results demonstrate the capability of the approach in detecting both bad mouthing and ballot stuffing attack in various scenarios.

Sistema especialista para análise pós-evento da proteção em subestações distribuidoras.

É importante que as redes elétricas tenham altos índices de confiabilidade, de forma a se manter a agilidade e a manutenção ideais para um melhor funcionamento. Por outro lado, o crescimento inesperado da carga, falhas em equipamentos e uma parametrização inadequada das funções de proteção tornam a análise de eventos de proteção mais complexas e demoradas. Além disso, a quantidade de informações que pode ser obtida de relés digitais modernos tem crescido constantemente. Para que seja possível uma rápida tomada de decisão e manutenção, esse projeto de pesquisa teve como objetivo a implementação de um sistema completo de diagnóstico que é ativado automaticamente quando um evento de proteção ocorrer. As informações a serem analisadas são obtidas de uma base de dados e de relés de proteção, via protocolo de comunicação IEC 61850 e arquivos de oscilografia. O trabalho aborda o sistema Smart Grid completo incluindo: a aquisição de dados nos relés, detalhando o sistema de comunicação desenvolvido através de um software com um cliente IEC61850 e um servidor OPC e um software com um cliente OPC, que é ativado por eventos configurados para dispará-lo (por exemplo, atuação da proteção); o sistema de pré-tratamento de dados, onde os dados provenientes dos relés e equipamentos de proteção são filtrados, pré-processados e formatados; e o sistema de diagnóstico. Um banco de dados central mantém atualizados os dados de todas essas etapas. O sistema de diagnóstico utiliza algoritmos convencionais e técnicas de inteligência artificial, em particular, um sistema especialista. O sistema especialista foi desenvolvido para lidar com diferentes conjuntos de dados de entrada e com uma possível falta de dados, sempre garantindo a entrega de diagnósticos. Foram realizados testes e simulações para curtos-circuitos (trifásico, dupla-fase, dupla-fase-terra e fase-terra) em alimentadores, transformadores e barras de uma subestação. Esses testes incluíram diferentes estados do sistema de proteção (funcionamento correto e impróprio). O sistema se mostrou totalmente eficaz tanto no caso de disponibilidade completa quanto parcial de informações, sempre fornecendo um diagnóstico do curto-circuito e analisando o funcionamento das funções de proteção da subestação. Dessa forma, possibilita-se uma manutenção muito mais eficiente pelas concessionárias de energia, principalmente no que diz respeito à prevenção de defeitos em equipamentos, rápida resposta a problemas, e necessidade de reparametrização das funções de proteção. O sistema foi instalado com sucesso em uma subestação de distribuição da Companhia Paulista de Força e Luz.