967 resultados para Least-Squares estimation


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

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Dados de rebanhos bovinos comerciais foram analisados com o objetivo de estimar as interações dos efeitos genéticos com o ambiente que podem influenciar a avaliação de características de crescimento em rebanhos de animais puros e cruzados. O conjunto de dados analisado foi obtido a partir de animais das raças Hereford, Nelore e seus cruzamentos. As características em estudo foram os pesos à desmama e ao sobreano dos animais. As análises estatísticas foram realizadas pelo método dos quadrados mínimos e o modelo proposto incluiu os efeitos de região, grupo de contemporâneos dentro de região, mês de nascimento e sexo do bezerro, os efeitos lineares e quadráticos para a idade do bezerro e idade da vaca ao parto, ambas analisadas dentro de sexo, e os efeitos de grupo genético e da interação grupo genético × região. de modo geral, o desempenho de todos os grupos genéticos foi influenciado pelo efeito de região. Além disso, observou-se tendência de que o aumento da proporção de genes zebuínos promoveu diminuição na diferença de desempenho entre as regiões. Todos os genótipos foram beneficiados no ambiente menos restritivo, o que indica a existência de interação genótipo-ambiente e comprova a importância de que sistemas de cruzamento sejam realizados de forma a manter a adaptação das matrizes e de seus produtos.

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Objetivou-se investigar os efeitos das características do pelame sobre a idade ao primeiro parto (IPP) e o intervalo de partos (IEP) de vacas holandesas manejadas em sistema de estabulação livre com ventiladores e aspersores e estimar os parâmetros genéticos destas características. Os dados foram analisados pelo método dos quadrados mínimos, considerando os efeitos: ano; estação; número de inseminações; origem do pai; pai dentro da origem; idade (somente para IEP); porcentagem de malhas negras; espessura do pelame; comprimento dos pêlos; número de pêlos por unidade de área da epiderme; diâmetro dos pêlos; transmitância e refletância efetiva do pelame. O método da Máxima Verossimilhança Restrita foi utilizado para estimar os componentes de (co)variância sob um modelo animal. Os resultados incluíram as estimativas de herdabilidade para IPP (0,23±0,08), IEP (0,19±0,10), malhas negras (0,75±0,08), número (0,05±0,04), espessura (0,04±0,05), comprimento (0,36±0,09) e diâmetro (0,63±0,08) de pêlos. As estimativas de correlação genética entre IPP (-0,37±0,17), IEP (0,49±0,27) e diâmetro apresentaram valores significativos e favoráveis. Entretanto, as correlações genéticas entre espessura (-0,56±0,46), número (-0,66±0,43), porcentagem de malhas negras (0,04±0,16) e IPP foram desfavoráveis para seleção conjunta para melhor adaptação e precocidade sexual. O alto valor estimado para herdabilidade e as correlações genéticas favoráveis entre diâmetro e IPP e IEP indicaram ser possível selecionar para melhorar conjuntamente a adaptação e o desempenho reprodutivo.

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Several mobile robots show non-linear behavior, mainly due friction phenomena between the mechanical parts of the robot or between the robot and the ground. Linear models are efficient in some cases, but it is necessary take the robot non-linearity in consideration when precise displacement and positioning are desired. In this work a parametric model identification procedure for a mobile robot with differential drive that considers the dead-zone in the robot actuators is proposed. The method consists in dividing the system into Hammerstein systems and then uses the key-term separation principle to present the input-output relations which shows the parameters from both linear and non-linear blocks. The parameters are then simultaneously estimated through a recursive least squares algorithm. The results shows that is possible to identify the dead-zone thresholds together with the linear parameters

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There are two main approaches for using in adaptive controllers. One is the so-called model reference adaptive control (MRAC), and the other is the so-called adaptive pole placement control (APPC). In MRAC, a reference model is chosen to generate the desired trajectory that the plant output has to follow, and it can require cancellation of the plant zeros. Due to its flexibility in choosing the controller design methodology (state feedback, compensator design, linear quadratic, etc.) and the adaptive law (least squares, gradient, etc.), the APPC is the most general type of adaptive control. Traditionally, it has been developed in an indirect approach and, as an advantage, it may be applied to non-minimum phase plants, because do not involve plant zero-pole cancellations. The integration to variable structure systems allows to aggregate fast transient and robustness to parametric uncertainties and disturbances, as well. In this work, a variable structure adaptive pole placement control (VS-APPC) is proposed. Therefore, new switching laws are proposed, instead of using the traditional integral adaptive laws. Additionally, simulation results for an unstable first order system and simulation and practical results for a three-phase induction motor are shown

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The pattern classification is one of the machine learning subareas that has the most outstanding. Among the various approaches to solve pattern classification problems, the Support Vector Machines (SVM) receive great emphasis, due to its ease of use and good generalization performance. The Least Squares formulation of SVM (LS-SVM) finds the solution by solving a set of linear equations instead of quadratic programming implemented in SVM. The LS-SVMs provide some free parameters that have to be correctly chosen to achieve satisfactory results in a given task. Despite the LS-SVMs having high performance, lots of tools have been developed to improve them, mainly the development of new classifying methods and the employment of ensembles, in other words, a combination of several classifiers. In this work, our proposal is to use an ensemble and a Genetic Algorithm (GA), search algorithm based on the evolution of species, to enhance the LSSVM classification. In the construction of this ensemble, we use a random selection of attributes of the original problem, which it splits the original problem into smaller ones where each classifier will act. So, we apply a genetic algorithm to find effective values of the LS-SVM parameters and also to find a weight vector, measuring the importance of each machine in the final classification. Finally, the final classification is obtained by a linear combination of the decision values of the LS-SVMs with the weight vector. We used several classification problems, taken as benchmarks to evaluate the performance of the algorithm and compared the results with other classifiers

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Natural gas, although basically composed by light hydrocarbons, also presents contaminant gases in its composition, such as CO2 (carbon dioxide) and H2S (hydrogen sulfide). The H2S, which commonly occurs in oil and gas exploration and production activities, causes damages in oil and natural gas pipelines. Consequently, the removal of hydrogen sulfide gas will result in an important reduction in operating costs. Also, it is essential to consider the better quality of the oil to be processed in the refinery, thus resulting in benefits in economic, environmental and social areas. All this facts demonstrate the need for the development and improvement in hydrogen sulfide scavengers. Currently, the oil industry uses several processes for hydrogen sulfide removal from natural gas. However, these processes produce amine derivatives which can cause damage in distillation towers, can cause clogging of pipelines by formation of insoluble precipitates, and also produce residues with great environmental impact. Therefore, it is of great importance the obtaining of a stable system, in inorganic or organic reaction media, able to remove hydrogen sulfide without formation of by-products that can affect the quality and cost of natural gas processing, transport, and distribution steps. Seeking the study, evaluation and modeling of mass transfer and kinetics of hydrogen removal, in this study it was used an absorption column packed with Raschig rings, where the natural gas, with H2S as contaminant, passed through an aqueous solution of inorganic compounds as stagnant liquid, being this contaminant gas absorbed by the liquid phase. This absorption column was coupled with a H2S detection system, with interface with a computer. The data and the model equations were solved by the least squares method, modified by Levemberg-Marquardt. In this study, in addition to the water, it were used the following solutions: sodium hydroxide, potassium permanganate, ferric chloride, copper sulfate, zinc chloride, potassium chromate, and manganese sulfate, all at low concentrations (»10 ppm). These solutions were used looking for the evaluation of the interference between absorption physical and chemical parameters, or even to get a better mass transfer coefficient, as in mixing reactors and absorption columns operating in counterflow. In this context, the evaluation of H2S removal arises as a valuable procedure for the treatment of natural gas and destination of process by-products. The study of the obtained absorption curves makes possible to determine the mass transfer predominant stage in the involved processes, the mass transfer volumetric coefficients, and the equilibrium concentrations. It was also performed a kinetic study. The obtained results showed that the H2S removal kinetics is greater for NaOH. Considering that the study was performed at low concentrations of chemical reagents, it was possible to check the effect of secondary reactions in the other chemicals, especially in the case of KMnO4, which shows that your by-product, MnO2, acts in H2S absorption process. In addition, CuSO4 and FeCl3 also demonstrated to have good efficiency in H2S removal

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Waste stabilization ponds (WSP) have been widely used for sewage treatment in hot climate regions because they are economic and environmentally sustainable. In the present study a WSP complex comprising a primary facultative pond (PFP) followed by two maturation ponds (MP-1 and MP-2) was studied, in the city of Natal-RN. The main objective was to study the bio-degradability of organic matter through the determination of the kinetic constant k throughout the system. The work was carried out in two phases. In the first, the variability in BOD, COD and TOC concentrations and an analysis of the relations between these parameters, in the influent raw sewage, pond effluents and in specific areas inside the ponds was studied. In the second stage, the decay rate for organic matter (k) was determined throughout the system based on BOD tests on the influent sewage, pond effluents and water column samples taken from fixed locations within the ponds, using the mathematical methods of Least Squares and the Thomas equation. Subsequently k was estimated as a function of a hydrodynamic model determined from the dispersion number (d), using empirical methods and a Partial Hydrodynamic Evaluation (PHE), obtained from tracer studies in a section of the primary facultative pond corresponding to 10% of its total length. The concentrations of biodegradable organic matter, measured as BOD and COD, gradually reduced through the series of ponds, giving overall removal efficiencies of 71.95% for BOD and of 52.45% for COD. Determining the values for k, in the influent and effluent samples of the ponds using the mathematical method of Least Squares, gave the following values respectively: primary facultative pond (0,23 day-1 and 0,09 day-1), maturation 1 (0,04 day-1 and 0,03 day-1) and maturation 2 (0,03 day-1 and 0,08 day-1). When using the Thomas method, the values of k in the influents and effluents of the ponds were: primary facultative pond (0,17 day-1 and 0,07 day-1), maturation 1 (0,02 day-1 and 0,01 day-1) and maturation 2 (0,01 day-1 and 0,02 day-1). From the Partial Hydrodynamic Evaluation, in the first section of the facultative pond corresponding to 10% of its total length, it can be concluded from the dispersion number obtained of d = 0.04, that the hydraulic regime is one of dispersed flow with a kinetic constant value of 0.20 day-1

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Ionospheric scintillations are caused by time-varying electron density irregularities in the ionosphere, occurring more often at equatorial and high latitudes. This paper focuses exclusively on experiments undertaken in Europe, at geographic latitudes between similar to 50 degrees N and similar to 80 degrees N, where a network of GPS receivers capable of monitoring Total Electron Content and ionospheric scintillation parameters was deployed. The widely used ionospheric scintillation indices S4 and sigma(phi) represent a practical measure of the intensity of amplitude and phase scintillation affecting GNSS receivers. However, they do not provide sufficient information regarding the actual tracking errors that degrade GNSS receiver performance. Suitable receiver tracking models, sensitive to ionospheric scintillation, allow the computation of the variance of the output error of the receiver PLL (Phase Locked Loop) and DLL (Delay Locked Loop), which expresses the quality of the range measurements used by the receiver to calculate user position. The ability of such models of incorporating phase and amplitude scintillation effects into the variance of these tracking errors underpins our proposed method of applying relative weights to measurements from different satellites. That gives the least squares stochastic model used for position computation a more realistic representation, vis-a-vis the otherwise 'equal weights' model. For pseudorange processing, relative weights were computed, so that a 'scintillation-mitigated' solution could be performed and compared to the (non-mitigated) 'equal weights' solution. An improvement between 17 and 38% in height accuracy was achieved when an epoch by epoch differential solution was computed over baselines ranging from 1 to 750 km. The method was then compared with alternative approaches that can be used to improve the least squares stochastic model such as weighting according to satellite elevation angle and by the inverse of the square of the standard deviation of the code/carrier divergence (sigma CCDiv). The influence of multipath effects on the proposed mitigation approach is also discussed. With the use of high rate scintillation data in addition to the scintillation indices a carrier phase based mitigated solution was also implemented and compared with the conventional solution. During a period of occurrence of high phase scintillation it was observed that problems related to ambiguity resolution can be reduced by the use of the proposed mitigated solution.

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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In this work we used chemometric tools to classify and quantify the protein content in samples of milk powder. We applied the NIR diffuse reflectance spectroscopy combined with multivariate techniques. First, we carried out an exploratory method of samples by principal component analysis (PCA), then the classification of independent modeling of class analogy (SIMCA). Thus it became possible to classify the samples that were grouped by similarities in their composition. Finally, the techniques of partial least squares regression (PLS) and principal components regression (PCR) allowed the quantification of protein content in samples of milk powder, compared with the Kjeldahl reference method. A total of 53 samples of milk powder sold in the metropolitan areas of Natal, Salvador and Rio de Janeiro were acquired for analysis, in which after pre-treatment data, there were four models, which were employed for classification and quantification of samples. The methods employed after being assessed and validated showed good performance, good accuracy and reliability of the results, showing that the NIR technique can be a non invasive technique, since it produces no waste and saves time in analyzing the samples

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In this work calibration models were constructed to determine the content of total lipids and moisture in powdered milk samples. For this, used the near-infrared spectroscopy by diffuse reflectance, combined with multivariate calibration. Initially, the spectral data were submitted to correction of multiplicative light scattering (MSC) and Savitzsky-Golay smoothing. Then, the samples were divided into subgroups by application of hierarchical clustering analysis of the classes (HCA) and Ward Linkage criterion. Thus, it became possible to build regression models by partial least squares (PLS) that allowed the calibration and prediction of the content total lipid and moisture, based on the values obtained by the reference methods of Soxhlet and 105 ° C, respectively . Therefore, conclude that the NIR had a good performance for the quantification of samples of powdered milk, mainly by minimizing the analysis time, not destruction of the samples and not waste. Prediction models for determination of total lipids correlated (R) of 0.9955, RMSEP of 0.8952, therefore the average error between the Soxhlet and NIR was ± 0.70%, while the model prediction to content moisture correlated (R) of 0.9184, RMSEP, 0.3778 and error of ± 0.76%

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This work is combined with the potential of the technique of near infrared spectroscopy - NIR and chemometrics order to determine the content of diclofenac tablets, without destruction of the sample, to which was used as the reference method, ultraviolet spectroscopy, which is one of the official methods. In the construction of multivariate calibration models has been studied several types of pre-processing of NIR spectral data, such as scatter correction, first derivative. The regression method used in the construction of calibration models is the PLS (partial least squares) using NIR spectroscopic data of a set of 90 tablets were divided into two sets (calibration and prediction). 54 were used in the calibration samples and the prediction was used 36, since the calibration method used was crossvalidation method (full cross-validation) that eliminates the need for a validation set. The evaluation of the models was done by observing the values of correlation coefficient R 2 and RMSEC mean square error (calibration error) and RMSEP (forecast error). As the forecast values estimated for the remaining 36 samples, which the results were consistent with the values obtained by UV spectroscopy