18 resultados para Kernel Smoothing

em Universidade Federal do Rio Grande do Norte(UFRN)


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In the present time, public organizations are employing more and more solutions that uses information technology in order to ofer more transparency and better services for all citizens. Integrated Systems are IT which carry in their kernel features of integration and the use of a unique database. These systems bring several benefits and face some obstacles that make their adoption difficult. The conversion to a integrated system may take years and, thus, the study of the adoption of this IT in public sector organizations become very stimulant due to some peculiarities of this sector and the features of this technology. First of all, information about the particular integrated system in study and about its process of conversion are offered. Then, the researcher designs the configuration of the conversion process aim of this study the agents envolved and the moments and the tools used to support the process in order to elaborate the methodology of the conversion process understood as the set of procedures and tools used during all the conversion process. After this, the researcher points out, together with all the members of the conversion team, the negative and positive factors during the project. Finally, these factors were analysed through the Hospitality Theory lens which, in the researcher opinion, was very useful to understand the elements, events and moments that interfered in the project. The results consolidated empirically the Hospitality Theory presumptions, showing yet a limitation of this theory in the case in study

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The Brazil is the third largest producer of cashew nuts in the world. Despite the social and economic importance of the cashew nut, its production is still carried out artisanally. One of the main problems encountered in the cashew production chain are the conditions under which the roasting of the nut occurs to obtain the kernel from the shell. In the present study was conducted a biomonitoring of the genotoxic and cytotoxicity effects associated with the elements from the cashew nut roasting in João Câmara - RN, semi-arid region of Brazil. To assess the genotoxic was used the bioassay of micronucleus (MN) in Tradescantia pallida. In addition, it was performed a comparative between the Tradescantia pallida and KU-20 and other biomarkers of DNA damage, such as the nucleoplasmic bridges (NBP) and nuclear fragments (NF) were quantified. The levels of particulate matter (PM1.0, PM2.5, PM10) and black carbon (BC) were also measured and the inorganic chemical composition of the PM2.5 collected was determined using X-ray fluorescence spectrometry analysis and the assessment of the cytotoxicity by MTT assay and exclusion method by trypan blue. . For this purpose, were chosen: the Amarelão community where the roasting occurs and the Santa Luzia farm an area without influence of this process. The mean value of PM2.5 (Jan 2124.2 μg/m3; May 1022.2 μg/m3; Sep 1291.9 μg/m3) and BC (Jan 363.6 μg/m3; May 70.0 μg/m3; Sep 69.4 μg/m3) as well as the concentration of the elements Al, Si, P, S, Cl, K, Ca, Ti, Cr, Mn, Fe, Ni, Cu, Zn, Se, Br and Pb obtained at Amarelão was significantly higher than at Santa Luzia farm. The genotoxicity tests with T. pallida indicated a significant increase in the number of MN, NBP and NF and it was found a negative correlation between the frequency of these biomarkers and the rainfall. The concentrations of 200 μg/mL and 400 μg/mL of PM2.5 were cytotoxic to MRC-5 cells. All together, the results indicated genotoxicity and citotoxicity for the community of Amarelão, and the high rates of PM2.5 considered a potential contributor to this effect, mainly by the high presence of transition metals, especially Fe, Ni, Cu, Cr and Zn, these elements have the potential to cause DNA damage. Other nuclear alterations, such as the NPBs and NFs may be used as effective biomarkers of DNA damage in tetrads of Tradescantia pallida. The results of this study enabled the identification of a serious occupational problem. Accordingly, preventative measures and better practices should be adopted to improve both the activity and the quality of life of the population. These measures are of fundamental importance for the sustainable development of this activity.

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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

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One of the greatest challenges of demography, nowadays, is to obtain estimates of mortality, in a consistent manner, mainly in small areas. The lack of this information, hinders public health actions and leads to impairment of quality of classification of deaths, generating concern on the part of demographers and epidemiologists in obtaining reliable statistics of mortality in the country. In this context, the objective of this work is to obtain estimates of deaths adjustment factors for correction of adult mortality, by States, meso-regions and age groups in the northeastern region, in 2010. The proposal is based on two lines of observation: a demographic one and a statistical one, considering also two areas of coverage in the States of the Northeast region, the meso-regions, as larger areas and counties, as small areas. The methodological principle is to use the General Equation and Balancing demographic method or General Growth Balance to correct the observed deaths, in larger areas (meso-regions) of the states, since they are less prone to breakage of methodological assumptions. In the sequence, it will be applied the statistical empirical Bayesian estimator method, considering as sum of deaths in the meso-regions, the death value corrected by the demographic method, and as reference of observation of smaller area, the observed deaths in small areas (counties). As results of this combination, a smoothing effect on the degree of coverage of deaths is obtained, due to the association with the empirical Bayesian Estimator, and the possibility of evaluating the degree of coverage of deaths by age groups at counties, meso-regions and states levels, with the advantage of estimete adjustment factors, according to the desired level of aggregation. The results grouped by State, point to a significant improvement of the degree of coverage of deaths, according to the combination of the methods with values above 80%. Alagoas (0.88), Bahia (0.90), Ceará (0.90), Maranhão (0.84), Paraíba (0.88), Pernambuco (0.93), Piauí (0.85), Rio Grande do Norte (0.89) and Sergipe (0.92). Advances in the control of the registry information in the health system, linked to improvements in socioeconomic conditions and urbanization of the counties, in the last decade, provided a better quality of information registry of deaths in small areas

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The use of the maps obtained from remote sensing orbital images submitted to digital processing became fundamental to optimize conservation and monitoring actions of the coral reefs. However, the accuracy reached in the mapping of submerged areas is limited by variation of the water column that degrades the signal received by the orbital sensor and introduces errors in the final result of the classification. The limited capacity of the traditional methods based on conventional statistical techniques to solve the problems related to the inter-classes took the search of alternative strategies in the area of the Computational Intelligence. In this work an ensemble classifiers was built based on the combination of Support Vector Machines and Minimum Distance Classifier with the objective of classifying remotely sensed images of coral reefs ecosystem. The system is composed by three stages, through which the progressive refinement of the classification process happens. The patterns that received an ambiguous classification in a certain stage of the process were revalued in the subsequent stage. The prediction non ambiguous for all the data happened through the reduction or elimination of the false positive. The images were classified into five bottom-types: deep water; under-water corals; inter-tidal corals; algal and sandy bottom. The highest overall accuracy (89%) was obtained from SVM with polynomial kernel. The accuracy of the classified image was compared through the use of error matrix to the results obtained by the application of other classification methods based on a single classifier (neural network and the k-means algorithm). In the final, the comparison of results achieved demonstrated the potential of the ensemble classifiers as a tool of classification of images from submerged areas subject to the noise caused by atmospheric effects and the water column

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The Support Vector Machines (SVM) has attracted increasing attention in machine learning area, particularly on classification and patterns recognition. However, in some cases it is not easy to determinate accurately the class which given pattern belongs. This thesis involves the construction of a intervalar pattern classifier using SVM in association with intervalar theory, in order to model the separation of a pattern set between distinct classes with precision, aiming to obtain an optimized separation capable to treat imprecisions contained in the initial data and generated during the computational processing. The SVM is a linear machine. In order to allow it to solve real-world problems (usually nonlinear problems), it is necessary to treat the pattern set, know as input set, transforming from nonlinear nature to linear problem. The kernel machines are responsible to do this mapping. To create the intervalar extension of SVM, both for linear and nonlinear problems, it was necessary define intervalar kernel and the Mercer s theorem (which caracterize a kernel function) to intervalar function

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They are in this study the experimental results of the analysis of thermal performance of composite material made from a plant matrix of polyurethane derived from castor oil of kernel of mamona (COF) and loading of clay-mineral called vermiculite expanded. Bodies of evidence in the proportions in weight of 10%, 15% and 20% were made to determine the thermal properties: conductivity (k), diffusivity (ά) and heat capacity (C), for purposes of comparison, the measurements were also performed the properties of polyurethane of castor without charge and also the oil polyurethane (PU), both already used in thermal insulation. Plates of 0.25 meters of material analyzed were manufactured for use as insulation material in a chamber performance thermal coverage. Thermocouples were distributed on the surface of the cover, and inside the material inside the test chamber and this in turn was subjected to artificial heating, consisting of a bank of incandescent lamps of 3000 w. The results obtained with the composite materials were compared with data from similar tests conducted with the camera alone with: (a) of oil PU, (b) of COF (c) glass wool, (d ) of rock wool. The heat resistance tests were performed with these composites, obtaining temperature limits for use in the range of 100 º C to 130 º C. Based on the analysis of the results of performance and thermal properties, it was possible to conclude that the COF composites with load of expanded vermiculite present behavior very close to those exhibited by commercial insulation material

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Currently there is still a high demand for quality control in manufacturing processes of mechanical parts. This keeps alive the need for the inspection activity of final products ranging from dimensional analysis to chemical composition of products. Usually this task may be done through various nondestructive and destructive methods that ensure the integrity of the parts. The result generated by these modern inspection tools ends up not being able to geometrically define the real damage and, therefore, cannot be properly displayed on a computing environment screen. Virtual 3D visualization may help identify damage that would hardly be detected by any other methods. One may find some commercial softwares that seek to address the stages of a design and simulation of mechanical parts in order to predict possible damages trying to diminish potential undesirable events. However, the challenge of developing softwares capable of integrating the various design activities, product inspection, results of non-destructive testing as well as the simulation of damage still needs the attention of researchers. This was the motivation to conduct a methodological study for implementation of a versatile CAD/CAE computer kernel capable of helping programmers in developing softwares applied to the activities of design and simulation of mechanics parts under stress. In this research it is presented interesting results obtained from the use of the developed kernel showing that it was successfully applied to case studies of design including parts presenting specific geometries, namely: mechanical prostheses, heat exchangers and piping of oil and gas. Finally, the conclusions regarding the experience of merging CAD and CAE theories to develop the kernel, so as to result in a tool adaptable to various applications of the metalworking industry are presented

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The present work had as objective to apply an experimental planning aiming at to improve the efficiency of separation of a new type of mixer-settler applied to treat waste water contaminated with oil. An unity in scale of laboratory, was installed in the Post-graduation Program of Chemical Engineering of UFRN. It was constructed in partnership with Petrobras S.A. This called device Misturador-Decantador a Inversão de Fases (MDIF) , possess features of conventional mixer-settler and spray column type. The equipment is composed of three main parts: mixing chamber; chamber of decantation and chamber of separation. The efficiency of separation is evaluated analyzing the oil concentrations in water in the feed and the output of the device. For the analysis one used the gravimetric method of oil and greases analysis (TOG). The system in study is a water of formation emulsified with oil. The used extractant is a mixture of Turpentine spirit hydro-carbons, supplied for Petrobras. It was applied, for otimization of the efficiency of separation of the equipment, an experimental planning of the composite central type, having as factorial portion fractionary factorial planning 2 5-2, with the magnifying of the type star and five replications in the central point. In this work, the following independents variables were studied: contents of oil in the feed of the device; volumetric ratio (O/A); total flowrate ; agitation in the mixing chamber and height of the organic bed. Minimum and maximum limits for the studied variables had been fixed according previous works. The analysis of variance for the equation of the empirical model, revealed statistically significant and useful results for predictions ends. The variance analysis also presented the distribution of the error as a normal distribution and was observed that as the dispersions do not depend on the levels of the factors, the independence assumption can be verified. The variation around the average is explained by 98.98%, or either, equal to the maximum value, being the smoothing of the model in relation to the experimental points of 0,98981. The results present a strong interaction between the variable oil contents in the feed and agitation in the mixing chamber, having great and positive influence in the separation efficiency. Another variable that presented a great positive influence was the height of the organic bed. The best results of separation efficiency had been obtained for high flowrates when associates the high oil concentrations and high agitation. The results of the present work had shown excellent agreement with the results carried out through previous works with the mixer-settler of phase inversion

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A chemical process optimization and control is strongly correlated with the quantity of information can be obtained from the system. In biotechnological processes, where the transforming agent is a cell, many variables can interfere in the process, leading to changes in the microorganism metabolism and affecting the quantity and quality of final product. Therefore, the continuously monitoring of the variables that interfere in the bioprocess, is crucial to be able to act on certain variables of the system, keeping it under desirable operational conditions and control. In general, during a fermentation process, the analysis of important parameters such as substrate, product and cells concentration, is done off-line, requiring sampling, pretreatment and analytical procedures. Therefore, this steps require a significant run time and the use of high purity chemical reagents to be done. In order to implement a real time monitoring system for a benchtop bioreactor, these study was conducted in two steps: (i) The development of a software that presents a communication interface between bioreactor and computer based on data acquisition and process variables data recording, that are pH, temperature, dissolved oxygen, level, foam level, agitation frequency and the input setpoints of the operational parameters of the bioreactor control unit; (ii) The development of an analytical method using near-infrared spectroscopy (NIRS) in order to enable substrate, products and cells concentration monitoring during a fermentation process for ethanol production using the yeast Saccharomyces cerevisiae. Three fermentation runs were conducted (F1, F2 and F3) that were monitored by NIRS and subsequent sampling for analytical characterization. The data obtained were used for calibration and validation, where pre-treatments combined or not with smoothing filters were applied to spectrum data. The most satisfactory results were obtained when the calibration models were constructed from real samples of culture medium removed from the fermentation assays F1, F2 and F3, showing that the analytical method based on NIRS can be used as a fast and effective method to quantify cells, substrate and products concentration what enables the implementation of insitu real time monitoring of fermentation processes

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We investigate several diffusion equations which extend the usual one by considering the presence of nonlinear terms or a memory effect on the diffusive term. We also considered a spatial time dependent diffusion coefficient. For these equations we have obtained a new classes of solutions and studied the connection of them with the anomalous diffusion process. We start by considering a nonlinear diffusion equation with a spatial time dependent diffusion coefficient. The solutions obtained for this case generalize the usual one and can be expressed in terms of the q-exponential and q-logarithm functions present in the generalized thermostatistics context (Tsallis formalism). After, a nonlinear external force is considered. For this case the solutions can be also expressed in terms of the q-exponential and q-logarithm functions. However, by a suitable choice of the nonlinear external force, we may have an exponential behavior, suggesting a connection with standard thermostatistics. This fact reveals that these solutions may present an anomalous relaxation process and then, reach an equilibrium state of the kind Boltzmann- Gibbs. Next, we investigate a nonmarkovian linear diffusion equation that presents a kernel leading to the anomalous diffusive process. Particularly, our first choice leads to both a the usual behavior and anomalous behavior obtained through a fractionalderivative equation. The results obtained, within this context, correspond to a change in the waiting-time distribution for jumps in the formalism of random walks. These modifications had direct influence in the solutions, that turned out to be expressed in terms of the Mittag-Leffler or H of Fox functions. In this way, the second moment associated to these distributions led to an anomalous spread of the distribution, in contrast to the usual situation where one finds a linear increase with time

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In this work we studied the asymptotic unbiasedness, the strong and the uniform strong consistencies of a class of kernel estimators fn as an estimator of the density function f taking values on a k-dimensional sphere

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In this work, the paper of Campos and Dorea [3] was detailed. In that article a Kernel Estimator was applied to a sequence of random variables with general state space, which were independent and identicaly distributed. In chapter 2, the estimator´s properties such as asymptotic unbiasedness, consistency in quadratic mean, strong consistency and asymptotic normality were verified. In chapter 3, using R software, numerical experiments were developed in order to give a visual idea of the estimate process

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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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The increasingly request for processing power during last years has pushed integrated circuit industry to look for ways of providing even more processing power with less heat dissipation, power consumption, and chip area. This goal has been achieved increasing the circuit clock, but since there are physical limits of this approach a new solution emerged as the multiprocessor system on chip (MPSoC). This approach demands new tools and basic software infrastructure to take advantage of the inherent parallelism of these architectures. The oil exploration industry has one of its firsts activities the project decision on exploring oil fields, those decisions are aided by reservoir simulations demanding high processing power, the MPSoC may offer greater performance if its parallelism can be well used. This work presents a proposal of a micro-kernel operating system and auxiliary libraries aimed to the STORM MPSoC platform analyzing its influence on the problem of reservoir simulation