An Intelligent System for Petroleum Well Drilling Cutting Analysis

Cutting analysis is a important and crucial task task to detect and prevent problems during the petroleum well drilling process. Several studies have been developed for drilling inspection, but none of them takes care about analysing the generated cutting at the vibrating shale shakers. Here we proposed a system to analyse the cutting's concentration at the vibrating shale shakers, which can indicate problems during the petroleum well drilling process, such that the collapse of the well borehole walls. Cutting's images are acquired and sent to the data analysis module, which has as the main goal to extract features and to classify frames according to one of three previously classes of cutting's volume. A collection of supervised classifiers were applied in order to allow comparisons about their accuracy and efficiency. We used the Optimum-Path Forest (OPF), Artificial Neural Network using Multi layer Perceptrons (ANN-MLP), Support Vector Machines (SVM) and a Bayesian Classifier (BC) for this task. The first one outperformed all the remaining classifiers. Recall that we are also the first to introduce the OPF classifier in this field of knowledge. Very good results show the robustness of the proposed system, which can be also integrated with other commonly system (Mud-Logging) in order to improve the last one's efficiency.

A novel intelligent system to nitrogen content prediction in plants using indirect chlorophyll measurements

The accurate identification of the nitrogen content in plants is extremely important since it involves economic aspects and environmental impacts, Several experimental tests have been carried out to obtain characteristics and parameters associated with the health of plants and its growing. The nitrogen content identification in plants involves a lot of non-linear parameters and complexes mathematical models. This paper describes a novel approach for identification of nitrogen content thought SPAD index using artificial neural networks (ANN). The network acts as identifier of relationships among, crop varieties, fertilizer treatments, type of leaf and nitrogen content in the plants (target). So, nitrogen content can be generalized and estimated and from an input parameter set. This approach can form the basis for development of an accurate real time system to predict nitrogen content in plants.

An intelligent system for pavement management

This paper describes a method for the evaluation of pavement condition through artificial neural networks using the MLP backpropagation technique. Two of the most used procedures for detecting the pavement conditions were applied: the overall severity index and the irregularity index. Tests with the model demonstrated that the simulation with the neural network gives better results than the procedures recommended by the highway officials. This network may also be applied for the construction of a graphic computer environment.

An intelligent system to real time rainfall prediction using radar data

This work presents a new approach for rainfall measurements making use of weather radar data for real time application to the radar systems operated by institute of Meteorological Research (IPMET) - UNESP - Bauru - SP-Brazil. Several real time adjustment techniques has been presented being most of them based on surface rain-gauge network. However, some of these methods do not regard the effect of the integration area, time integration and distance rainfall-radar. In this paper, artificial neural networks have been applied for generate a radar reflectivity-rain relationships which regard all effects described above. To evaluate prediction procedure, cross validation was performed using data from IPMET weather Doppler radar and rain-gauge network under the radar umbrella. The preliminary results were acceptable for rainfalls prediction. The small errors observed result from the spatial density and the time resolution of the rain-gauges networks used to calibrate the radar.

An intelligent system to detect drilling problems through drilled cuttings return analysis

Cuttings return analysis is an important tool to detect and prevent problems during the petroleum well drilling process. Several measurements and tools have been developed for drilling problems detection, including mud logging, PWD and downhole torque information. Cuttings flow meters were developed in the past to provide information regarding cuttings return at the shale shakers. Their use, however, significantly impact the operation including rig space issues, interferences in geological analysis besides, additional personel required. This article proposes a non intrusive system to analyze the cuttings concentration at the shale shakers, which can indicate problems during drilling process, such as landslide, the collapse of the well borehole walls. Cuttings images are acquired by a high definition camera installed above the shakers and sent to a computer coupled with a data analysis system which aims the quantification and closure of a cuttings material balance in the well surface system domain. No additional people at the rigsite are required to operate the system. Modern Artificial intelligence techniques are used for pattern recognition and data analysis. Techniques include the Optimum-Path Forest (OPF), Artificial Neural Network using Multilayer Perceptrons (ANN-MLP), Support Vector Machines (SVM) and a Bayesian Classifier (BC). Field test results conducted on offshore floating vessels are presented. Results show the robustness of the proposed system, which can be also integrated with other data to improve the efficiency of drilling problems detection. Copyright 2010, IADC/SPE Drilling Conference and Exhibition.

Hybrid visual-driven decision support system in video monitor manufacturing

An intelligent system that emulates human decision behaviour based on visual data acquisition is proposed. The approach is useful in applications where images are used to supply information to specialists who will choose suitable actions. An artificial neural classifier aids a fuzzy decision support system to deal with uncertainty and imprecision present in available information. Advantages of both techniques are exploited complementarily. As an example, this method was applied in automatic focus checking and adjustment in video monitor manufacturing. Copyright © 2005 IFAC.

Formulação do controle preventivo em sistemas de distribuição de energia elétrica baseada na lógica fuzzy e redes neurais

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Sistema inteligente para a predição de grupo de risco de evasão discente

Pós-graduação em Engenharia Elétrica - FEIS

Predição do sequestro do carbono em área reflorestada utilizando redes neurais artificiais

Pós-graduação em Engenharia Mecânica - FEIS

Desenvolvimento de um sistema inteligente para o diagnóstico e prognóstico de falhas em sistemas de distribuição de energia elétrica

Pós-graduação em Engenharia Elétrica - FEIS

GRIOT - sistema CAD inteligente para design de calçados

O presente trabalho é o estudo dos aspectos da metodologia projetual, face às novas tecnologias da informática, Inteligência Artificial (Sistemas Especialistas) e CAD, consideradas as reais possibilidades de automatização no processo de concepção em Design. Esse artigo propõe uma metodologia para a construção de sistema inteligente capaz de auxiliar o designer nas tarefas projetuais. A indústria de calçados foi utilizada como estudo de caso para a aplicação da metodologia, onde as reais possibilidades de automação são verificadas.

Automatic detection of thermal damage in grinding process by artificial neural network

Esse trabalho tem por objetivo o desenvolvimento de um sistema inteligente para detecção da queima no processo de retificação tangencial plana através da utilização de uma rede neural perceptron multi camadas, treinada para generalizar o processo e, conseqüentemente, obter o limiar de queima. em geral, a ocorrência da queima no processo de retificação pode ser detectada pelos parâmetros DPO e FKS. Porém esses parâmetros não são eficientes nas condições de usinagem usadas nesse trabalho. Os sinais de emissão acústica e potência elétrica do motor de acionamento do rebolo são variáveis de entrada e a variável de saída é a ocorrência da queima. No trabalho experimental, foram empregados um tipo de aço (ABNT 1045 temperado) e um tipo de rebolo denominado TARGA, modelo ART 3TG80.3 NVHB.

Paraconsistent Autonomous Mobile Robot Emmy III

This work presents some improvements regarding to the autonomous mobile robot Emmy based on Paraconsistent Annotated Evidential Logic ET. A discussion on navigation system is presented.

Sistema inteligente para la selección de brocas de perforación de pozos

The present work begins with a review of the literature on bit selection methods for oil well drilling. A proposal for the structure and organization of a drilling database and a knowledge base, is described. Previous studies formed the principal elements in the process of selection of drills for proposed drilling. The procedure was implemented as a computer system for the selection of tricone bits. A drilling bit database for three different Brazilian sedimentary basins was obtained for several wells drilled, and knowledge was collected from drilling engineers from different fields both electronically and also by means of interviews. It can be concluded that the selection process showed good results based on tests, which were carried out.

Interleukin-15: Its role in microbial infections

Interleukin-15 (IL-15) is a pleiotropic cytokine which regulates the proliferation, survival and the secretory activities of many distinct cell types in the body. This cytokine is produced by macrophages and many other cell types in response to infectious agents; it controls growth and differentiation of T and B lymphocytes, activation of Natural Killer (NK) and phagocytic cells, and contributes to the homeostasis of the immune system. The present review focuses on the biological and modulatory effects of IL-15 in microbial infections and shows that this cytokine may play a role in the host defense against infections by inducing activation of effector cells from both innate and adaptive immune system.