A Polar Representation for Complex Interval Numbers

The present work defines the basic elements for the introduction to the Study of Complex variables under the mathematical interval context with the goal of using it as a foundation for the understanding of pure mathematical problems, associating the mathematical interval to support the results. The present article contributes to the complex interval theory taking into consideration Euler’s Identity and redefining the polar representation of interval complex numbers. In engineering, the present article could be used as a subsidy for many applications where complex variable theory is applicable and requires accurate results

New developmental stages for common marmosets (Callithrix jacchus) using mass and age variables obtained by K-means algorithm and self-organizing maps (SOM)

LEÃO, Adriano de Castro; DÓRIA NETO, Adrião Duarte; SOUSA, Maria Bernardete Cordeiro de. New developmental stages for common marmosets (Callithrix jacchus) using mass and age variables obtained by K-means algorithm and self-organizing maps (SOM). Computers in Biology and Medicine, v. 39, p. 853-859, 2009This study proposes new developmental stages for Callithrix jacchus, usingK-Means algorithm and an artificial neural network–self-organising maps (SOM) as computational tools, based on weight and age. Eight developmental stages are proposed: infantI ,IIandIII ,Juvenile I and II ,Subadult ,Young adult and Older adult. This classification is consistent with the first appearance of several behavioural and physiological characteristics and thus may have generality indefining critical developmental periods. It also reveals differences in male and female development and establishes stage for the onset of the final adult life cycle. This classification is also important to understanding the biology of the ontogenetic development of common marmosets, providing new insights for the management and care of captive animals and improving age estimate indicators when specimens are captured in long term monitoring of free ranging group

The national inventory of geological heritage: methodological approach and results

A existência de um inventário nacional de património geológico é fundamental para se poderem implementar estratégias de geoconservação. Este trabalho apresenta a metodologia usada no desenvolvimento do mais completo inventário de geossítios, realizado até ao momento em Portugal, assim como os principais resultados obtidos. O inventário vai integrar o Sistema de Informação do Património Natural e o Cadastro Nacional dos Valores Naturais Classificados, ambos geridos pelo Instituto de Conservação da Natureza e da Biodiversidade.The existence of a national inventory of the geological heritage is of paramount importance for the implementation of a geoconservation strategy. This paper presents the methodological approach used to produce the most complete geosites inventory in Portugal, so far, and the obtained results. This inventory will be uploaded into the National Database of Natural Heritage managed by the Portuguese authority for nature conservation.Este trabalho é apoiado pela Fundação para a Ciência e a Tecnologia, através do financiamento plurianual do CGUP e do projecto de investigação “Identificação, caracterização e conservação do património geológico: uma estratégia de geoconservação para Portugal” (PTDC/CTE-GEX/64966/2006).info:eu-repo/semantics/publishedVersio

Acoplamento por abertura entre microfita/guia e microfita/microfita

Orientadores: Attilio Jose Giarola, Adaildo Gomes D'AssunçãoDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia de CampinasResumo: Neste trabalho é apresentado um estudo do acoplamento por abertura entre o guia de onda retangular e a linha de microfita. É analisado também o acoplamento por abertura entre duas microfitas. É utilizada, na análise do acoplamento por abertura, a teoria dos momentos de dipolos equivalentes, que consiste em substituir a abertura por momentos de dipolos, os quais irradiam campos eletromagnéticos equivalentes aos irradiados pela abertura. Considera-se também, neste trabalho, a distribuição de campos eletromagnéticos existentes na microfita, para o modelo quase estático utilizado. Esta forma de distribuição de campos permite que sejam obtidas expressões de acoplamento nas quais se considera o campo na abertura uniforme ou não uniforme. A partir do estudo do acop,lamento por uma única abertura, foi desenvolvida uma análise do acoplamento por múltiplas aberturas entre microfitas, e entre guia de onda retangular e microfita. Utilizando-se a análise desenvolvida para o acoplamento por múltiplas aberturas entre duas microfitas, foram projetados e construídos dois acopladores direcionais com quatro aberturas. A comparação dos resultados experimentais obtidos com os previstos teoricamente revela uma boa concordância.Abstract: Not informedMestradoMestre em Engenharia Elétric

Pipeline Inspection Gauge’s Velocity Simulation Based on Pressure Differential Using Artificial Neural Networks

Industrial pipelines must be inspected to detect typical failures, such as obstructions and deformations, during their lifetime. In the petroleum industry, the most used non-destructive technique to inspect buried pipelines is pigging. This technique consists of launching a Pipeline Inspection Gauge (PIG) inside the pipeline, which is driven by the pressure differential produced by fluid flow. The purpose of this work is to study the application of artificial neural networks to calculate the PIG’s velocity based on the pressure differential. We launch a prototype PIG inside a testing pipeline, where this PIG gathers velocity data from an odometer-based system, while a supervisory system gathers pressure data from the testing pipeline. Then we train a Multilayer Perceptron (MLP) and a Nonlinear Autoregressive Network with eXogenous Inputs (NARX) network with the gathered data to predict velocity. The results suggest it is possible to use a neural network to model the PIG’s velocity from pressure differential measurements. Our method is a new approach to the typical speed measurements based only on odometer, since the odometer is prone to fail and present poor results under some circumstances. Moreover, it can be used to provide redundancy, improving reliability of data obtained during the test

Velocity Prediction of a Pipeline Inspection Gauge (PIG) with Machine Learning

A device known as a pipeline inspection gauge (PIG) runs through oil and gas pipelines which performs various maintenance operations in the oil and gas industry. The PIG velocity, which plays a role in the efficiency of these operations, is usually determined indirectly from odometers installed in it. Although this is a relatively simple technique, the loss of contact between the odometer wheel and the pipeline results in measurement errors. To help reduce these errors, this investigation employed neural networks to estimate the speed of a prototype PIG, using the pressure difference that acts on the device inside the pipeline and its acceleration instead of using odometers. Static networks (e.g., multilayer perceptron) and recurrent networks (e.g., long short-term memory) were built, and in addition, a prototype PIG was developed with an embedded system based on Raspberry Pi 3 to collect speed, acceleration and pressure data for the model training. The implementation of the supervised neural networks used the Python library TensorFlow package. To train and evaluate the models, we used the PIG testing pipeline facilities available at the Petroleum Evaluation and Measurement Laboratory of the Federal University of Rio Grande do Norte (LAMP/UFRN). The results showed that the models were able to learn the relationship among the differential pressure, acceleration and speed of the PIG. The proposed approach can complement odometer-based systems, increasing the reliability of speed measurements