Previsão da Classificação da qualidade dos efluentes da indústria petrolífera com recurso a sistemas inteligentes

O presente trabalho realizou-se na Refinaria de Sines e teve como principal objectivo a utilização de ferramentas oriundas da Área Científica da Inteligência Artificial no desenvolvimento de modelos de previsão da classificação da Água Residual Industrial de acordo com a Legislação em vigor, com vista à minimização dos impactes ambientais e das tarifas aplicadas pela Concessionária (Águas de Santo André) à Refinaria. Actualmente a avaliação da qualidade do efluente é realizada através de métodos analíticos após colheita de uma amostra do efluente final. Esta abordagem é muito restritiva já que não permite actuar sobre o efluente em questão pois apenas pode evitar que, no futuro, uma mistura semelhante volte a ser refinada. Devido a estas limitações, o desenvolvimento de modelos de previsão baseados em Data Mining mostrou ser uma alternativa para uma questão pró-activa da qualidade dos efluentes que pode contribuir decisivamente para o cumprimento das metas definidas pela Empresa. No decurso do trabalho, foram desenvolvidos dois modelos de previsão da qualidade do efluente industrial com desempenhos muito semelhantes. Um deles utiliza a composição das misturas processadas e o outro, utiliza informações relativas ao crude predominante na mistura. ABSTRACT; This study has taken place at the Sines Refinery and its main objective is the use of Artificial Intelligence tools for the development of predictive models to classify industrial residual waters according with the Portuguese Law, based on the characteristics of the mixtures of crude oil that arrive into the Refinery to be processed, to minimize the Environmental impacts and the application of taxes. Currently, the evaluation of the quality of effluent is performed by analytical methods after harvesting a sample of the final effluent. This approach is very restrictive since it does not act on the intended effluent; it can only avoid that in the future a similar mixture is refined. Duet these limitations, the development of forecasting models based on Data Mining has proved to be an alternative on the important issue which is the quality of effluent, which may contribute to the achievement of targets set by the Company. During this study, two models were developed to predict the quality of industrial effluents with very similar performances. One uses the composition of processed mixtures and the other uses information regarding the predominant oil in the mixture.

Caracterização dos fatores contribuintes em acidentes de pequenas aeronaves da aviação geral brasileira utilizando inteligência artificial

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Civil e Ambiental, 2016.

Fault detection with Conditional Gaussian Network

International audience

Uso de agentes inteligentes no controle simultâneo da largura e do reforço dos cordões de solda no processo GMAW-S

Tese (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Mecânica, 2016.

MINING AND VERIFICATION OF TEMPORAL EVENTS WITH APPLICATIONS IN COMPUTER MICRO-ARCHITECTURE RESEARCH

Computer simulation programs are essential tools for scientists and engineers to understand a particular system of interest. As expected, the complexity of the software increases with the depth of the model used. In addition to the exigent demands of software engineering, verification of simulation programs is especially challenging because the models represented are complex and ridden with unknowns that will be discovered by developers in an iterative process. To manage such complexity, advanced verification techniques for continually matching the intended model to the implemented model are necessary. Therefore, the main goal of this research work is to design a useful verification and validation framework that is able to identify model representation errors and is applicable to generic simulators. The framework that was developed and implemented consists of two parts. The first part is First-Order Logic Constraint Specification Language (FOLCSL) that enables users to specify the invariants of a model under consideration. From the first-order logic specification, the FOLCSL translator automatically synthesizes a verification program that reads the event trace generated by a simulator and signals whether all invariants are respected. The second part consists of mining the temporal flow of events using a newly developed representation called State Flow Temporal Analysis Graph (SFTAG). While the first part seeks an assurance of implementation correctness by checking that the model invariants hold, the second part derives an extended model of the implementation and hence enables a deeper understanding of what was implemented. The main application studied in this work is the validation of the timing behavior of micro-architecture simulators. The study includes SFTAGs generated for a wide set of benchmark programs and their analysis using several artificial intelligence algorithms. This work improves the computer architecture research and verification processes as shown by the case studies and experiments that have been conducted.

MobiMed: Framework for Rapid Application Development of Medical Mobile Apps

In the medical field images obtained from high definition cameras and other medical imaging systems are an integral part of medical diagnosis. The analysis of these images are usually performed by the physicians who sometimes need to spend long hours reviewing the images before they are able to come up with a diagnosis and then decide on the course of action. In this dissertation we present a framework for a computer-aided analysis of medical imagery via the use of an expert system. While this problem has been discussed before, we will consider a system based on mobile devices. Since the release of the iPhone on April 2003, the popularity of mobile devices has increased rapidly and our lives have become more reliant on them. This popularity and the ease of development of mobile applications has now made it possible to perform on these devices many of the image analyses that previously required a personal computer. All of this has opened the door to a whole new set of possibilities and freed the physicians from their reliance on their desktop machines. The approach proposed in this dissertation aims to capitalize on these new found opportunities by providing a framework for analysis of medical images that physicians can utilize from their mobile devices thus remove their reliance on desktop computers. We also provide an expert system to aid in the analysis and advice on the selection of medical procedure. Finally, we also allow for other mobile applications to be developed by providing a generic mobile application development framework that allows for access of other applications into the mobile domain. In this dissertation we outline our work leading towards development of the proposed methodology and the remaining work needed to find a solution to the problem. In order to make this difficult problem tractable, we divide the problem into three parts: the development user interface modeling language and tooling, the creation of a game development modeling language and tooling, and the development of a generic mobile application framework. In order to make this problem more manageable, we will narrow down the initial scope to the hair transplant, and glaucoma domains.

A machine learning approach for recognising woody plants on railway trackbeds

The purpose of this work in progress study was to test the concept of recognising plants using images acquired by image sensors in a controlled noise-free environment. The presence of vegetation on railway trackbeds and embankments presents potential problems. Woody plants (e.g. Scots pine, Norway spruce and birch) often establish themselves on railway trackbeds. This may cause problems because legal herbicides are not effective in controlling them; this is particularly the case for conifers. Thus, if maintenance administrators knew the spatial position of plants along the railway system, it may be feasible to mechanically harvest them. Primary data were collected outdoors comprising around 700 leaves and conifer seedlings from 11 species. These were then photographed in a laboratory environment. In order to classify the species in the acquired image set, a machine learning approach known as Bag-of-Features (BoF) was chosen. Irrespective of the chosen type of feature extraction and classifier, the ability to classify a previously unseen plant correctly was greater than 85%. The maintenance planning of vegetation control could be improved if plants were recognised and localised. It may be feasible to mechanically harvest them (in particular, woody plants). In addition, listed endangered species growing on the trackbeds can be avoided. Both cases are likely to reduce the amount of herbicides, which often is in the interest of public opinion. Bearing in mind that natural objects like plants are often more heterogeneous within their own class rather than outside it, the results do indeed present a stable classification performance, which is a sound prerequisite in order to later take the next step to include a natural background. Where relevant, species can also be listed under the Endangered Species Act.

Identificación de las relaciones reales y posibles entre la administración basada en modelos, la ciencia computacional de la organización y la ingeniería emergente en el contexto de la gestión organizacional

Las teorías administrativas se han basado, casi sin excepción, en los fundamentos y los modelos de la ciencia clásica (particularmente, en los modelos de la física newtoniana). Sin embargo, las organizaciones actualmente se enfrentan a un mundo globalizado, plagado de información (y no necesariamente conocimiento), hiperconectado, dinámico y cargado de incertidumbre, por lo que muchas de las teorías pueden mostrar limitaciones para las organizaciones. Y quizá no por la estructura, la lógica o el alcance de las mismas, sino por la falta de criterios que justifiquen su aplicación. En muchos casos, las organizaciones siguen utilizando la intuición, las suposiciones y las verdades a medias en la toma de decisiones. Este panorama pone de manifiesto dos hechos: de un lado, la necesidad de buscar un método que permita comprender las situaciones de cada organización para apoyar la toma de decisiones. De otro lado, la necesidad de potenciar la intuición con modelos y técnicas no tradicionales (usualmente provenientes o inspiradas por la ingeniería). Este trabajo busca anticipar los pilares de un posible método que permita apoyar la toma de decisiones por medio de la simulación de modelos computacionales, utilizando las posibles interacciones entre: la administración basada en modelos, la ciencia computacional de la organización y la ingeniería emergente.

Cálculo de constantes ópticas de películas delgadas de Cu3BiS3 a través del método de Wolfe

Se calculó la obtención de las constantes ópticas usando el método de Wolfe. Dichas contantes: coeficiente de absorción (α), índice de refracción (n) y espesor de una película delgada (d ), son de importancia en el proceso de caracterización óptica del material. Se realizó una comparación del método del Wolfe con el método empleado por R. Swanepoel. Se desarrolló un modelo de programación no lineal con restricciones, de manera que fue posible estimar las constantes ópticas de películas delgadas semiconductoras, a partir únicamente, de datos de transmisión conocidos. Se presentó una solución al modelo de programación no lineal para programación cuadrática. Se demostró la confiabilidad del método propuesto, obteniendo valores de α = 10378.34 cm−1, n = 2.4595, d =989.71 nm y Eg = 1.39 Ev, a través de experimentos numéricos con datos de medidas de transmitancia espectral en películas delgadas de Cu3BiS3.