Cultivo consorciado de milho com amendoim forrageiro e calopogônio e os efeitos na cultura da soja em rotação

This work aimed to study the intercropping of corn with Arachis pintoi cv. MG-100 and Calopogonium muconoides, regarding to vegetative growth and corn yield, capacity of production of forage dry matter, infestation of weeds and the effect of this system of production in the soybean crop in rotation. This research was carried out under field conditions form December 2008 to April 2010 in the UNESP, Campus of Jaboticabal, São Paulo State, Brazil. The experiment was arranged in a randomized block in split-plot design with four replications. Two forages species (A. pintoi and C. muconoides), four amounts of seeds (400, 800, 1200 e 1600 points of cultural value ha(-1)) and one treatment additional with single corn were studied. The intercropping did not affect the vegetative growth and corn yield when compared to the single corn. The C. muconoides had greater density and dry matter of plants than A. pintoi. The number of plants and dry matter of C. muconoides increased with increasing the amount of seeds sowed in the area. These variables were not affected by the sowing density of A. pintoi. The straw production by the forage species before of the soybean crop in rotation was inexpressive (less than 1100 kg ha(-1)). The intercropping did not affect the weed occurrence and the development of the soybean crop in rotation.

Atributos bioquímicos e químicos do solo rizosférico e não rizosférico de culturas em rotação no sistema de semeadura direta

Sistemas autossustentáveis favorecem as populações microbianas devido à conservação e ao aumento da matéria orgânica no solo. Além disso, as plantas que fazem parte desses sistemas promovem o efeito rizosférico, por meio da zona de influência das raízes, que resulta no aumento da atividade e na modificação da população microbiana. O objetivo deste trabalho foi avaliar o efeito da rotação de culturas de inverno sobre sequências de verão, em sistema de semeadura direta, nos atributos bioquímicos (amilase, urease, celulase e protease) e químicos (carbono orgânico total - COT, carboidratos totais e proteínas totais) em solo rizosférico (SR) e não rizosférico (SNR). Este estudo foi constituído de três culturas de inverno: milho (Zea mays L.), girassol (Helianthus anuus L.) e guandu (Cajanus cajan (L.) Millsp), que estavam em rotação sobre três sequências de verão: soja/soja (Glycine max L.), milho/milho e soja/milho, e duas posições no solo: solo aderido às raízes das plantas (SR) e solo da entrelinha de plantio (SNR). As atividades da amilase, celulase, protease e urease no SR foram 16, 85, 62 e 100 % maiores do que no SNR; para COT e proteínas totais a diferença foi de 21 %. Das culturas de inverno, o milho foi a que mais estimulou as atividades das enzimas amilase, celulase, urease e protease no SR, bem como a atividade das enzimas amilase, urease e protease no SNR. de modo geral, os teores de proteínas totais não foram influenciados pelas culturas de inverno e pelas sequências de verão; os carboidratos totais foram influenciados pelas culturas de inverno milho e girassol. Para o COT houve influência apenas da sequência de verão milho/milho. Os atributos bioquímicos e químicos avaliados neste estudo podem ser utilizados como indicadores das alterações no solo promovidas pelas culturas de inverno e pelas sequências de verão.

Análise e otimização de superfícies seletivas de Frequência utilizando redes neurais artificiais e algoritmos de otimização natural

The bidimensional periodic structures called frequency selective surfaces have been well investigated because of their filtering properties. Similar to the filters that work at the traditional radiofrequency band, such structures can behave as band-stop or pass-band filters, depending on the elements of the array (patch or aperture, respectively) and can be used for a variety of applications, such as: radomes, dichroic reflectors, waveguide filters, artificial magnetic conductors, microwave absorbers etc. To provide high-performance filtering properties at microwave bands, electromagnetic engineers have investigated various types of periodic structures: reconfigurable frequency selective screens, multilayered selective filters, as well as periodic arrays printed on anisotropic dielectric substrates and composed by fractal elements. In general, there is no closed form solution directly from a given desired frequency response to a corresponding device; thus, the analysis of its scattering characteristics requires the application of rigorous full-wave techniques. Besides that, due to the computational complexity of using a full-wave simulator to evaluate the frequency selective surface scattering variables, many electromagnetic engineers still use trial-and-error process until to achieve a given design criterion. As this procedure is very laborious and human dependent, optimization techniques are required to design practical periodic structures with desired filter specifications. Some authors have been employed neural networks and natural optimization algorithms, such as the genetic algorithms and the particle swarm optimization for the frequency selective surface design and optimization. This work has as objective the accomplishment of a rigorous study about the electromagnetic behavior of the periodic structures, enabling the design of efficient devices applied to microwave band. For this, artificial neural networks are used together with natural optimization techniques, allowing the accurate and efficient investigation of various types of frequency selective surfaces, in a simple and fast manner, becoming a powerful tool for the design and optimization of such structures

Identificação remota de sistemas operacionais utilizando análise de processos aleatórios e redes neurais artificiais

A new method to perform TCP/IP fingerprinting is proposed. TCP/IP fingerprinting is the process of identify a remote machine through a TCP/IP based computer network. This method has many applications related to network security. Both intrusion and defence procedures may use this process to achieve their objectives. There are many known methods that perform this process in favorable conditions. However, nowadays there are many adversities that reduce the identification performance. This work aims the creation of a new OS fingerprinting tool that bypass these actual problems. The proposed method is based on the use of attractors reconstruction and neural networks to characterize and classify pseudo-random numbers generators

Sistema Híbrido de Inferência Baseado em Análise de Componentes Principais e Redes Neurais Artificiais Aplicado a Plantas de Processamento de Gás Natural

Nowadays, where the market competition requires products with better quality and a constant search for cost savings and a better use of raw materials, the research for more efficient control strategies becomes vital. In Natural Gas Processin Units (NGPUs), as in the most chemical processes, the quality control is accomplished through their products composition. However, the chemical composition analysis has a long measurement time, even when performed by instruments such as gas chromatographs. This fact hinders the development of control strategies to provide a better process yield. The natural gas processing is one of the most important activities in the petroleum industry. The main economic product of a NGPU is the liquefied petroleum gas (LPG). The LPG is ideally composed by propane and butane, however, in practice, its composition has some contaminants, such as ethane and pentane. In this work is proposed an inferential system using neural networks to estimate the ethane and pentane mole fractions in LPG and the propane mole fraction in residual gas. The goal is to provide the values of these estimated variables in every minute using a single multilayer neural network, making it possibly to apply inferential control techniques in order to monitor the LPG quality and to reduce the propane loss in the process. To develop this work a NGPU was simulated in HYSYS R software, composed by two distillation collumns: deethanizer and debutanizer. The inference is performed through the process variables of the PID controllers present in the instrumentation of these columns. To reduce the complexity of the inferential neural network is used the statistical technique of principal component analysis to decrease the number of network inputs, thus forming a hybrid inferential system. It is also proposed in this work a simple strategy to correct the inferential system in real-time, based on measurements of the chromatographs which may exist in process under study

Desenvolvimento de uma arquitetura em hardware prototipada em FPGA para aplicações genéricas utilizando redes neurais artificiais embarcadas

This work proposes hardware architecture, VHDL described, developed to embedded Artificial Neural Network (ANN), Multilayer Perceptron (MLP). The present work idealizes that, in this architecture, ANN applications could easily embed several different topologies of MLP network industrial field. The MLP topology in which the architecture can be configured is defined by a simple and specifically data input (instructions) that determines the layers and Perceptron quantity of the network. In order to set several MLP topologies, many components (datapath) and a controller were developed to execute these instructions. Thus, an user defines a group of previously known instructions which determine ANN characteristics. The system will guarantee the MLP execution through the neural processors (Perceptrons), the components of datapath and the controller that were developed. In other way, the biases and the weights must be static, the ANN that will be embedded must had been trained previously, in off-line way. The knowledge of system internal characteristics and the VHDL language by the user are not needed. The reconfigurable FPGA device was used to implement, simulate and test all the system, allowing application in several real daily problems

Um método para localização facial Invariante a rotação

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Utilização de redes neurais artificiais para detecção e diagnóstico de falhas

In a real process, all used resources, whether physical or developed in software, are subject to interruptions or operational commitments. However, in situations in which operate critical systems, any kind of problem may bring big consequences. Knowing this, this paper aims to develop a system capable to detect the presence and indicate the types of failures that may occur in a process. For implementing and testing the proposed methodology, a coupled tank system was used as a study model case. The system should be developed to generate a set of signals that notify the process operator and that may be post-processed, enabling changes in control strategy or control parameters. Due to the damage risks involved with sensors, actuators and amplifiers of the real plant, the data set of the faults will be computationally generated and the results collected from numerical simulations of the process model. The system will be composed by structures with Artificial Neural Networks, trained in offline mode using Matlab®

Separação cega de fontes lineares e não lineares usando algoritmo genético, redes neurais artificiais RBF e negentropia de Rényi como medida de independência

Conventional methods to solve the problem of blind source separation nonlinear, in general, using series of restrictions to obtain the solution, often leading to an imperfect separation of the original sources and high computational cost. In this paper, we propose an alternative measure of independence based on information theory and uses the tools of artificial intelligence to solve problems of blind source separation linear and nonlinear later. In the linear model applies genetic algorithms and Rényi of negentropy as a measure of independence to find a separation matrix from linear mixtures of signals using linear form of waves, audio and images. A comparison with two types of algorithms for Independent Component Analysis widespread in the literature. Subsequently, we use the same measure of independence, as the cost function in the genetic algorithm to recover source signals were mixed by nonlinear functions from an artificial neural network of radial base type. Genetic algorithms are powerful tools for global search, and therefore well suited for use in problems of blind source separation. Tests and analysis are through computer simulations

Modelos de dispositivos de microondas e ópticos através de redes neurais artificiais de alimentação direta

This dissertation contributes for the development of methodologies through feed forward artificial neural networks for microwave and optical devices modeling. A bibliographical revision on the applications of neuro-computational techniques in the areas of microwave/optical engineering was carried through. Characteristics of networks MLP, RBF and SFNN, as well as the strategies of supervised learning had been presented. Adjustment expressions of the networks free parameters above cited had been deduced from the gradient method. Conventional method EM-ANN was applied in the modeling of microwave passive devices and optical amplifiers. For this, they had been proposals modular configurations based in networks SFNN and RBF/MLP objectifying a bigger capacity of models generalization. As for the training of the used networks, the Rprop algorithm was applied. All the algorithms used in the attainment of the models of this dissertation had been implemented in Matlab

Redes neurais artificiais no ambiente de redes industriais foundation fieldbus usando blocos funcionais padrões

Industrial automation networks is in focus and is gradually replacing older architectures of systems used in automation world. Among existing automation networks, most prominent standard is the Foundation Fieldbus (FF). This particular standard was chosen for the development of this work thanks to its complete application layer specification and its user interface, organized as function blocks and that allows interoperability among different vendors' devices. Nowadays, one of most seeked solutions on industrial automation are the indirect measurements, that consist in infering a value from measures of other sensors. This can be made through implementation of the so-called software sensors. One of the most used tools in this project and in sensor implementation are artificial neural networks. The absence of a standard solution to implement neural networks in FF environment makes impossible the development of a field-indirect-measurement project, besides other projects involving neural networks, unless a closed proprietary solution is used, which dos not guarantee interoperability among network devices, specially if those are from different vendors. In order to keep the interoperability, this work's goal is develop a solution that implements artificial neural networks in Foundation Fieldbus industrial network environment, based on standard function blocks. Along the work, some results of the solution's implementation are also presented

Predição da incrustação em um trocador de calor baseada em redes neurais artificiais

A serious problem that affects an oil refinery s processing units is the deposition of solid particles or the fouling on the equipments. These residues are naturally present on the oil or are by-products of chemical reactions during its transport. A fouled heat exchanger loses its capacity to adequately heat the oil, needing to be shut down periodically for cleaning. Previous knowledge of the best period to shut down the exchanger may improve the energetic and production efficiency of the plant. In this work we develop a system to predict the fouling on a heat exchanger from the Potiguar Clara Camarão Refinery, based on data collected in a partnership with Petrobras. Recurrent Neural Networks are used to predict the heat exchanger s flow in future time. This variable is the main indicator of fouling, because its value decreases gradually as the deposits on the tubes reduce their diameter. The prediction could be used to tell when the flow will have decreased under an acceptable value, indicating when the exchanger shutdown for cleaning will be needed

Utilização de análise multivariada e redes neurais artificiais na determinação do comportamento de colonização de populações de macrófitas aquáticas no reservatório de Santana

As análises de agrupamento e de componentes principais e as redes neurais artificiais foram utilizadas na determinação de padrões de comportamento das populações de macrófitas aquáticas que colonizaram o reservatório de Santana, Piraí-RJ, durante o ano de 2004. As análises de agrupamento dividiram o comportamento das populações durante o ano em dois grupos distintos, apresentando um padrão no primeiro semestre que difere daquele observado no segundo semestre do ano. A análise de componentes principais demonstrou que esse comportamento da comunidade (grupo de populações) é influenciado principalmente pelas espécies S. montevidensis, Heteranthera reniformis, Ludwigia sp., Rhynchospora aurea, C. iria, C. ferax e Aeschynomene denticulata no primeiro grupo e por Echinochloa polystachya, Polygonum lapathifolium, Alternanthera phyloxeroides, Pistia stratiotes, Eichhornia azurea, Brachiaria arrecta e Oxyscarium cubense no segundo grupo. As redes neurais artificiais agruparam as populações de macrófitas aquáticas em nove grupos, conforme sua densidade nos diferentes meses do ano. A aplicação da análise de componentes principais (ACP) nos valores de frequência das populações presentes nos primeiros três grupos de Kohonen permitiu discriminar três grupos de meses, cujas populações apresentaram características diferentes de colonização. A aplicação das redes neurais artificiais permitiu melhor discriminação dos meses e das espécies que compõem as comunidades correspondentes, quando utilizada a análise de componentes principais.

Modelagem de Superfícies Seletivas de Freqüência e Antenas de Microfita utilizando Redes Neurais Artificiais

This work has as main objective the application of Artificial Neural Networks, ANN, in the resolution of problems of RF /microwaves devices, as for example the prediction of the frequency response of some structures in an interest region. Artificial Neural Networks, are presently a alternative to the current methods of analysis of microwaves structures. Therefore they are capable to learn, and the more important to generalize the acquired knowledge, from any type of available data, keeping the precision of the original technique and adding the low computational cost of the neural models. For this reason, artificial neural networks are being increasily used for modeling microwaves devices. Multilayer Perceptron and Radial Base Functions models are used in this work. The advantages/disadvantages of these models and the referring algorithms of training of each one are described. Microwave planar devices, as Frequency Selective Surfaces and microstrip antennas, are in evidence due the increasing necessities of filtering and separation of eletromagnetic waves and the miniaturization of RF devices. Therefore, it is of fundamental importance the study of the structural parameters of these devices in a fast and accurate way. The presented results, show to the capacities of the neural techniques for modeling both Frequency Selective Surfaces and antennas

Infiltração de água no solo sob escarificação e rotação de culturas

Nos solos com restrições físicas e, ou, físico-hídricas ao crescimento de raízes, aumentar o potencial de armazenagem de água por meio de melhorias na infiltração pode ser uma estratégia viável para aumento da produtividade das culturas. Nesse sentido, este trabalho teve como objetivo avaliar a infiltração de água em um Nitossolo Vermelho distrófico, com três sistemas de rotação de culturas sob semeadura direta com e sem escarificação inicial. O sistema de rotação de culturas constou de: (1) milheto/soja/sorgo/milho/sorgo (M/S/So/Mi/So), (2) milheto/soja/Brachiaria ruziziensis/milho/Brachiaria ruziziensis (M/S/B/Mi/B) e (3) milheto/soja/Brachiaria ruziziensis + mamona/milho/Brachiaria ruziziensis + mamona (M/S/B+Ma/Mi/B+Ma). A infiltração de água no solo foi avaliada em campo com anéis concêntricos instalados na superfície, a 0,10 e 0,20 m de profundidade, em 2006 e 2007. Após o primeiro ano, o manejo com escarificação inicial do solo apresentou a maior infiltração de água. A rotação Brachiaria ruziziensis + mamona proporcionou maior infiltração da água no solo. A atividade do sistema radicular das espécies nas parcelas sem escarificação inicial aumentou a velocidade de infiltração da água no solo.