10 resultados para Predictive Mean Squared Efficiency

em Universidade Federal do Rio Grande do Norte(UFRN)


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The increase in ultraviolet radiation (UV) at surface, the high incidence of non-melanoma skin cancer (NMSC) in coast of Northeast of Brazil (NEB) and reduction of total ozone were the motivation for the present study. The overall objective was to identify and understand the variability of UV or Index Ultraviolet Radiation (UV Index) in the capitals of the east coast of the NEB and adjust stochastic models to time series of UV index aiming make predictions (interpolations) and forecasts / projections (extrapolations) followed by trend analysis. The methodology consisted of applying multivariate analysis (principal component analysis and cluster analysis), Predictive Mean Matching method for filling gaps in the data, autoregressive distributed lag (ADL) and Mann-Kendal. The modeling via the ADL consisted of parameter estimation, diagnostics, residuals analysis and evaluation of the quality of the predictions and forecasts via mean squared error and Pearson correlation coefficient. The research results indicated that the annual variability of UV in the capital of Rio Grande do Norte (Natal) has a feature in the months of September and October that consisting of a stabilization / reduction of UV index because of the greater annual concentration total ozone. The increased amount of aerosol during this period contributes in lesser intensity for this event. The increased amount of aerosol during this period contributes in lesser intensity for this event. The application of cluster analysis on the east coast of the NEB showed that this event also occurs in the capitals of Paraiba (João Pessoa) and Pernambuco (Recife). Extreme events of UV in NEB were analyzed from the city of Natal and were associated with absence of cloud cover and levels below the annual average of total ozone and did not occurring in the entire region because of the uneven spatial distribution of these variables. The ADL (4, 1) model, adjusted with data of the UV index and total ozone to period 2001-2012 made a the projection / extrapolation for the next 30 years (2013-2043) indicating in end of that period an increase to the UV index of one unit (approximately), case total ozone maintain the downward trend observed in study period

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In last decades, neural networks have been established as a major tool for the identification of nonlinear systems. Among the various types of networks used in identification, one that can be highlighted is the wavelet neural network (WNN). This network combines the characteristics of wavelet multiresolution theory with learning ability and generalization of neural networks usually, providing more accurate models than those ones obtained by traditional networks. An extension of WNN networks is to combine the neuro-fuzzy ANFIS (Adaptive Network Based Fuzzy Inference System) structure with wavelets, leading to generate the Fuzzy Wavelet Neural Network - FWNN structure. This network is very similar to ANFIS networks, with the difference that traditional polynomials present in consequent of this network are replaced by WNN networks. This paper proposes the identification of nonlinear dynamical systems from a network FWNN modified. In the proposed structure, functions only wavelets are used in the consequent. Thus, it is possible to obtain a simplification of the structure, reducing the number of adjustable parameters of the network. To evaluate the performance of network FWNN with this modification, an analysis of network performance is made, verifying advantages, disadvantages and cost effectiveness when compared to other existing FWNN structures in literature. The evaluations are carried out via the identification of two simulated systems traditionally found in the literature and a real nonlinear system, consisting of a nonlinear multi section tank. Finally, the network is used to infer values of temperature and humidity inside of a neonatal incubator. The execution of such analyzes is based on various criteria, like: mean squared error, number of training epochs, number of adjustable parameters, the variation of the mean square error, among others. The results found show the generalization ability of the modified structure, despite the simplification performed

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The work is to make a brief discussion of methods to estimate the parameters of the Generalized Pareto distribution (GPD). Being addressed the following techniques: Moments (moments), Maximum Likelihood (MLE), Biased Probability Weighted Moments (PWMB), Unbiased Probability Weighted Moments (PWMU), Mean Power Density Divergence (MDPD), Median (MED), Pickands (PICKANDS), Maximum Penalized Likelihood (MPLE), Maximum Goodness-of-fit (MGF) and the Maximum Entropy (POME) technique, the focus of this manuscript. By way of illustration adjustments were made for the Generalized Pareto distribution, for a sequence of earthquakes intraplacas which occurred in the city of João Câmara in the northeastern region of Brazil, which was monitored continuously for two years (1987 and 1988). It was found that the MLE and POME were the most efficient methods, giving them basically mean squared errors. Based on the threshold of 1.5 degrees was estimated the seismic risk for the city, and estimated the level of return to earthquakes of intensity 1.5°, 2.0°, 2.5°, 3.0° and the most intense earthquake never registered in the city, which occurred in November 1986 with magnitude of about 5.2º

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The detection and diagnosis of faults, ie., find out how , where and why failures occur is an important area of study since man came to be replaced by machines. However, no technique studied to date can solve definitively the problem. Differences in dynamic systems, whether linear, nonlinear, variant or invariant in time, with physical or analytical redundancy, hamper research in order to obtain a unique solution . In this paper, a technique for fault detection and diagnosis (FDD) will be presented in dynamic systems using state observers in conjunction with other tools in order to create a hybrid FDD. A modified state observer is used to create a residue that allows also the detection and diagnosis of faults. A bank of faults signatures will be created using statistical tools and finally an approach using mean squared error ( MSE ) will assist in the study of the behavior of fault diagnosis even in the presence of noise . This methodology is then applied to an educational plant with coupled tanks and other with industrial instrumentation to validate the system.

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The great interest in nonlinear system identification is mainly due to the fact that a large amount of real systems are complex and need to have their nonlinearities considered so that their models can be successfully used in applications of control, prediction, inference, among others. This work evaluates the application of Fuzzy Wavelet Neural Networks (FWNN) to identify nonlinear dynamical systems subjected to noise and outliers. Generally, these elements cause negative effects on the identification procedure, resulting in erroneous interpretations regarding the dynamical behavior of the system. The FWNN combines in a single structure the ability to deal with uncertainties of fuzzy logic, the multiresolution characteristics of wavelet theory and learning and generalization abilities of the artificial neural networks. Usually, the learning procedure of these neural networks is realized by a gradient based method, which uses the mean squared error as its cost function. This work proposes the replacement of this traditional function by an Information Theoretic Learning similarity measure, called correntropy. With the use of this similarity measure, higher order statistics can be considered during the FWNN training process. For this reason, this measure is more suitable for non-Gaussian error distributions and makes the training less sensitive to the presence of outliers. In order to evaluate this replacement, FWNN models are obtained in two identification case studies: a real nonlinear system, consisting of a multisection tank, and a simulated system based on a model of the human knee joint. The results demonstrate that the application of correntropy as the error backpropagation algorithm cost function makes the identification procedure using FWNN models more robust to outliers. However, this is only achieved if the gaussian kernel width of correntropy is properly adjusted.

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In the last decades the study of integer-valued time series has gained notoriety due to its broad applicability (modeling the number of car accidents in a given highway, or the number of people infected by a virus are two examples). One of the main interests of this area of study is to make forecasts, and for this reason it is very important to propose methods to make such forecasts, which consist of nonnegative integer values, due to the discrete nature of the data. In this work, we focus on the study and proposal of forecasts one, two and h steps ahead for integer-valued second-order autoregressive conditional heteroskedasticity processes [INARCH (2)], and in determining some theoretical properties of this model, such as the ordinary moments of its marginal distribution and the asymptotic distribution of its conditional least squares estimators. In addition, we study, via Monte Carlo simulation, the behavior of the estimators for the parameters of INARCH(2) processes obtained using three di erent methods (Yule- Walker, conditional least squares, and conditional maximum likelihood), in terms of mean squared error, mean absolute error and bias. We present some forecast proposals for INARCH(2) processes, which are compared again via Monte Carlo simulation. As an application of this proposed theory, we model a dataset related to the number of live male births of mothers living at Riachuelo city, in the state of Rio Grande do Norte, Brazil.

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In the last decades the study of integer-valued time series has gained notoriety due to its broad applicability (modeling the number of car accidents in a given highway, or the number of people infected by a virus are two examples). One of the main interests of this area of study is to make forecasts, and for this reason it is very important to propose methods to make such forecasts, which consist of nonnegative integer values, due to the discrete nature of the data. In this work, we focus on the study and proposal of forecasts one, two and h steps ahead for integer-valued second-order autoregressive conditional heteroskedasticity processes [INARCH (2)], and in determining some theoretical properties of this model, such as the ordinary moments of its marginal distribution and the asymptotic distribution of its conditional least squares estimators. In addition, we study, via Monte Carlo simulation, the behavior of the estimators for the parameters of INARCH(2) processes obtained using three di erent methods (Yule- Walker, conditional least squares, and conditional maximum likelihood), in terms of mean squared error, mean absolute error and bias. We present some forecast proposals for INARCH(2) processes, which are compared again via Monte Carlo simulation. As an application of this proposed theory, we model a dataset related to the number of live male births of mothers living at Riachuelo city, in the state of Rio Grande do Norte, Brazil.

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Objective: The aim of the present study is to assess the current situation of white enamel lesions on vestibular surfaces of permanent upper incisors, diagnosed 6 years ago, without clinical intervention. Methods: A prospective study reassessed 53 students of both sexes, aged between 13 and 18 years old, all attending the public school system in Natal, Brazil. Data collection was performed by duly calibrated examiners, and a clinical chart consisting of demographic data on dental caries, oral hygiene, and gingival condition was prepared. A tactile-visual examination was conducted using a clinical mirror and periodontal probe. Data compilation and analysis were carried out using a SPSS software. In this analysis the chi-squared test was used for qualitative independent variables. To identify the net effect of treatment, multiple logistic analysis with forward stepwise model selection was performed. Results: The final sample was composed of 106 lesions in the 53 individuals, with mean age of 15.02 years, visible plaque index (VPI) of 23.34%, and gingival blood indices (GBI) of 25.92%. A statistically significant relationship (p = 0.003) was found between initial DMFS and prognosis of white enamel lesion. Conclusions: We observed that past caries experience and dental plaque were the main predictive factors for negative lesion outcomes, demonstrating the need for oral hygiene control through continuing preventive measures

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Even nowadays there is in Brasil a large number of edentulous and a precarious financial condition of most of the population. In addition, World Health Organization aims for oral health, which consists on the maintenance of a natural dentition, functional and aesthetic composed of at least 20 teeth, without need of prosthetic intervention throughout life. From this and considering the lack of researches about the permanence of edentulous spaces in the oral cavity, and also avoiding overtreatment, this research has been proposed. Thus, the aim was to evaluate the effect of different lengths of the shortened lower dental arch in the presence or absence of a removable partial denture (RPD) on masticatory function, quality of life and occurrence of temporomandibular dysfunction. To achieve this goal, we compared the masticatory efficiency (colorimetric test), the oral comfort through the analysis of the impact of oral health in quality of life (OHIP-14), the presence of temporomandibular dysfunction (RDC/TMD) and the general quality of life (WHOQOL) of patients with shortened dental arches (SDA) (n=60), which is an arch with a reduction of teeth starting posteriorly, and patients with complete dental arch (Complete DA) (n = 34). The group of patients whit SDA was divided among PPR wears (PPD + SDA) (n = 17) and non-wears (n = 43). The population of this study consisted on patients who received or looked for treatment at the clinics of the Department of Dentistry of UFRN, from clinical analysis and records. The sample was chosen by convenience. For statistical analysis, it was a database in SPSS 17.0, followed by descriptive analysis with frequencies, absolute values, tests of central tendency and variability. The statistical tests used were chi-squared and analysis of variance as well as Tukey s post test, when applicable, all with a 95% confidence level. The results shown a prevalence of TMD of 47,1% among patients using PRP and 69,8% among those who didn t, but this result wasn t statistically significant. The mean of the results of masticatory efficiency, WHOQOL and OHIP didn t show association to the presence or absence of PPR and to the lower number of occlusal units of the patients (0, 1, 2 or more occlusal). The association only occurred among the different groups of SDA and the patients with complete dental arch. Taking into account the results, it could be observed that studied patients with low posterior support using lower PRP didn t have better masticatory efficiency, general quality of life, less impacts of their oral conditions in quality of life or not even less temporomandibular dysfunction or better masticatory efficiency when compared to those who didn t use the prosthesis

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Objectives To evaluate the change in masticatory efficiency and quality of life of patients treated with mandibular Kennedy class I removable partial dentures (RPDs) and maxillary complete dentures at the Department of Dentistry of the Federal University of Rio Grande do Norte. Materials and methods A total of 33 Kennedy class I patients were rehabilitated with maxillary complete dentures, and mandibular RPDs were selected for this non-randomized prospective intervention study. The patients had a mean age of 59.1 years. Masticatory efficiency was evaluated by colorimetric assay using fuchsin capsules. The measurements were conducted at baseline and 2 and 6 months after prosthesis insertion. Quality of life was evaluated using the Oral Health Impact Profile (OHIP-14) at baseline and 6 months after denture insertion. The Kolmogorov-Smirnov normality test was applied. Masticatory efficiency was evaluated by repeated measures ANOVA. Oral health-related quality of life was compared using the paired t test. Results There was no statistically significant difference in masticatory efficiency after denture insertion (p = 0.101). Significant differences were found (p = 0.010) for oral health-related quality of life. A significant improvement in psychological discomfort (p < 0.01) and psychological disability (p < 0.01) was observed. Mean difference value (95 % confidence interval) was 6.8 (3.8 to 9.7) points, reflecting a low impact of oral health on quality of life, considering the 0–56 range of variation of the OHIP-14 and a Cohen’s d of 1.13. Conclusion According to the results of the present study, rehabilitation with Kennedy class I RPDs and complete dentures did not influence masticatory efficiency but improved oral health-related quality of life. Clinical relevance The association between the patient’s quality of life and the masticatory efficiency is important for treatment predictability.