A contribution to semantic description of images and videos: an application of soft biometrics

Humans have a high ability to extract visual data information acquired by sight. Trought a learning process, which starts at birth and continues throughout life, image interpretation becomes almost instinctively. At a glance, one can easily describe a scene with reasonable precision, naming its main components. Usually, this is done by extracting low-level features such as edges, shapes and textures, and associanting them to high level meanings. In this way, a semantic description of the scene is done. An example of this, is the human capacity to recognize and describe other people physical and behavioral characteristics, or biometrics. Soft-biometrics also represents inherent characteristics of human body and behaviour, but do not allow unique person identification. Computer vision area aims to develop methods capable of performing visual interpretation with performance similar to humans. This thesis aims to propose computer vison methods which allows high level information extraction from images in the form of soft biometrics. This problem is approached in two ways, unsupervised and supervised learning methods. The first seeks to group images via an automatic feature extraction learning , using both convolution techniques, evolutionary computing and clustering. In this approach employed images contains faces and people. Second approach employs convolutional neural networks, which have the ability to operate on raw images, learning both feature extraction and classification processes. Here, images are classified according to gender and clothes, divided into upper and lower parts of human body. First approach, when tested with different image datasets obtained an accuracy of approximately 80% for faces and non-faces and 70% for people and non-person. The second tested using images and videos, obtained an accuracy of about 70% for gender, 80% to the upper clothes and 90% to lower clothes. The results of these case studies, show that proposed methods are promising, allowing the realization of automatic high level information image annotation. This opens possibilities for development of applications in diverse areas such as content-based image and video search and automatica video survaillance, reducing human effort in the task of manual annotation and monitoring.

Padrões temporais e espaciais na detecção de resíduos e contaminantes na produção de aves e suínos no Brasil

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Agronomia e Medicina Veterinária, 2016.

Desenvolvimento de um sistema Inteligente para a análise de cartas dinamométricas no método de elevação por bombeio mecânico

The artificial lifting of oil is needed when the pressure of the reservoir is not high enough so that the fluid contained in it can reach the surface spontaneously. Thus the increase in energy supplies artificial or additional fluid integral to the well to come to the surface. The rod pump is the artificial lift method most used in the world and the dynamometer card (surface and down-hole) is the best tool for the analysis of a well equipped with such method. A computational method using Artificial Neural Networks MLP was and developed using pre-established patterns, based on its geometry, the downhole card are used for training the network and then the network provides the knowledge for classification of new cards, allows the fails diagnose in the system and operation conditions of the lifting system. These routines could be integrated to a supervisory system that collects the cards to be analyzed

Estudo e implementação de algoritmos inteligentes para detecção e classificação de falhas na medição de gás natural

This master dissertation presents the study and implementation of inteligent algorithms to monitor the measurement of sensors involved in natural gas custody transfer processes. To create these algoritmhs Artificial Neural Networks are investigated because they have some particular properties, such as: learning, adaptation, prediction. A neural predictor is developed to reproduce the sensor output dynamic behavior, in such a way that its output is compared to the real sensor output. A recurrent neural network is used for this purpose, because of its ability to deal with dynamic information. The real sensor output and the estimated predictor output work as the basis for the creation of possible sensor fault detection and diagnosis strategies. Two competitive neural network architectures are investigated and their capabilities are used to classify different kinds of faults. The prediction algorithm and the fault detection classification strategies, as well as the obtained results, are presented

Reconfiguração dinâmica de estratégias distribuídas em dispositivos foundation fieldbus para a otimização de processos na indústria do petróleo

The petrochemical industry has as objective obtain, from crude oil, some products with a higher commercial value and a bigger industrial utility for energy purposes. These industrial processes are complex, commonly operating with large production volume and in restricted operation conditions. The operation control in optimized and stable conditions is important to keep obtained products quality and the industrial plant safety. Currently, industrial network has been attained evidence when there is a need to make the process control in a distributed way. The Foundation Fieldbus protocol for industrial network, for its interoperability feature and its user interface organized in simple configuration blocks, has great notoriety among industrial automation network group. This present work puts together some benefits brought by industrial network technology to petrochemical industrial processes inherent complexity. For this, a dynamic reconfiguration system for intelligent strategies (artificial neural networks, for example) based on the protocol user application layer is proposed which might allow different applications use in a particular process, without operators intervention and with necessary guarantees for the proper plant functioning

Implementação de um Módulo de Supervisão para um Sistema de Detecção de Vazamentos em Dutos de Petróleo

The transport of fluids through pipes is used in the oil industry, being the pipelines an important link in the logistics flow of fluids. However, the pipelines suffer deterioration in their walls caused by several factors which may cause loss of fluids to the environment, justifying the investment in techniques and methods of leak detection to minimize fluid loss and environmental damage. This work presents the development of a supervisory module in order to inform to the operator the leakage in the pipeline monitored in the shortest time possible, in order that the operator log procedure that entails the end of the leak. This module is a component of a system designed to detect leaks in oil pipelines using sonic technology, wavelets and neural networks. The plant used in the development and testing of the module presented here was the system of tanks of LAMP, and its LAN, as monitoring network. The proposal consists of, basically, two stages. Initially, assess the performance of the communication infrastructure of the supervisory module. Later, simulate leaks so that the DSP sends information to the supervisory performs the calculation of the location of leaks and indicate to which sensor the leak is closer, and using the system of tanks of LAMP, capture the pressure in the pipeline monitored by piezoresistive sensors, this information being processed by the DSP and sent to the supervisory to be presented to the user in real time

Controle preditivo neural aplicado à processos petroquímicos

A pesquisa tem como objetivo desenvolver uma estrutura de controle preditivo neural, com o intuito de controlar um processo de pH, caracterizado por ser um sistema SISO (Single Input - Single Output). O controle de pH é um processo de grande importância na indústria petroquímica, onde se deseja manter constante o nível de acidez de um produto ou neutralizar o afluente de uma planta de tratamento de fluidos. O processo de controle de pH exige robustez do sistema de controle, pois este processo pode ter ganho estático e dinâmica nãolineares. O controlador preditivo neural envolve duas outras teorias para o seu desenvolvimento, a primeira referente ao controle preditivo e a outra a redes neurais artificiais (RNA s). Este controlador pode ser dividido em dois blocos, um responsável pela identificação e outro pelo o cálculo do sinal de controle. Para realizar a identificação neural é utilizada uma RNA com arquitetura feedforward multicamadas com aprendizagem baseada na metodologia da Propagação Retroativa do Erro (Error Back Propagation). A partir de dados de entrada e saída da planta é iniciado o treinamento offline da rede. Dessa forma, os pesos sinápticos são ajustados e a rede está apta para representar o sistema com a máxima precisão possível. O modelo neural gerado é usado para predizer as saídas futuras do sistema, com isso o otimizador calcula uma série de ações de controle, através da minimização de uma função objetivo quadrática, fazendo com que a saída do processo siga um sinal de referência desejado. Foram desenvolvidos dois aplicativos, ambos na plataforma Builder C++, o primeiro realiza a identificação, via redes neurais e o segundo é responsável pelo controle do processo. As ferramentas aqui implementadas e aplicadas são genéricas, ambas permitem a aplicação da estrutura de controle a qualquer novo processo

Processamento Inteligente de Sinais de Pressão e Temperatura Adquiridos Através de Sensores Permanentes em Poços de Petróleo

Originally aimed at operational objectives, the continuous measurement of well bottomhole pressure and temperature, recorded by permanent downhole gauges (PDG), finds vast applicability in reservoir management. It contributes for the monitoring of well performance and makes it possible to estimate reservoir parameters on the long term. However, notwithstanding its unquestionable value, data from PDG is characterized by a large noise content. Moreover, the presence of outliers within valid signal measurements seems to be a major problem as well. In this work, the initial treatment of PDG signals is addressed, based on curve smoothing, self-organizing maps and the discrete wavelet transform. Additionally, a system based on the coupling of fuzzy clustering with feed-forward neural networks is proposed for transient detection. The obtained results were considered quite satisfactory for offshore wells and matched real requisites for utilization

Um Modelo híbrido para previsão de curvas de produção de petróleo

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Redes de conocimiento y responsabilidad social empresaria

Objetivos de la Investigación: 1. Indagar acerca de las diferentes técnicas y herramientas que nos propone la teoría para establecer relaciones entre redes de conocimiento y RSE. 2. Indagar acerca del grado de aplicación de los fundamentos teóricos en la práctica. 3. Establecer las competencias necesarias para el ejercicio efectivo de la Responsabilidad Social Empresaria de las compañías establecidas en la localidad de Campana

¿Cómo usan las redes sociales los jóvenes de Latinoamérica?

Resumen: La difusión de las redes sociales en Internet ha pasado a formar parte de la vida cotidiana de los jóvenes. Las motivaciones que guían la elección de algunas de ellas, la frecuencia de uso y su impacto en el consumo de medios de comunicación tradicionales conforman el núcleo de este trabajo. Los jóvenes mostraron estar familiarizados con los fines y aplicaciones de las diferentes redes sociales, aunque en su gran mayoría eligen Facebook como su favorita. Manifiestan que, en términos generales, las redes sociales ofrecen una forma de comunicación completa, ya que permiten compartir texto, fotos, videos y enlaces, y estar en contacto permanente con familiares, amigos y conocidos

Redes neuronales artificiales aplicadas al análisis de datos en ingeniería ambiental e impacto ambiental

Resumen: Se realiza una revisión sobre los antecedentes de las Redes Neuronales Artificiales (RNA) como método de análisis de bases de datos medioambientales, aplicado en las diversas áreas de la Ingeniería Ambiental en general y de Impacto Ambiental en particular. Se describe como ejemplo, la aplicación de RNA en los algoritmos de inversión de datos obtenidos por sensado remoto satelital, para la medición de variables geofísicas

Redes inalámbricas de uso comunitario : un análisis comparativo de protocolos

Resumen: Las redes malladas inalámbricas (Wireless Mesh Networks) son en particular un dominio rápidamente creciente y esto trae muchos desafíos. La principal función de los protocolos de encaminamiento es seleccionar el camino entre el nodo fuente y destino de una manera rápida y fiable. Estas redes pueden utilizar los protocolos de encaminamiento de otras redes ya existentes, pero modificándolos para que funcionen correctamente con ellas. En este trabajo se analizan distintos protocolos de encaminamiento y se presentan sus descripciones para luego compararlos de acuerdo al Tipo de Protocolo, Alcance de trasmisiones y Métrica de ruteo.

Identificación de variables en modelización de líneas de producción industrial y su análisis mediante redes neuronales artificiales

Resumen: En el marco de la integración de etapas en procesos de producción industrial en serie, se identifican, clasifican y caracterizan las variables que permiten modelizar dicho proceso, como estrategia con vistas a su optimización. El estudio se aplica en el ámbito de la industria metalúrgica, a partir de los datos de un conjunto de empresas de la región centro y sur de Santa Fe. La estructura secuencial de un proceso industrial hace que cualquier falla en una etapa cause demoras o reducción de calidad en el producto final obtenido. Como herramienta integradora de análisis, se plantea la implementación de un sistema de redes neuronales artificiales.