Máquina de vetores-suporte intervalar

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

Aplicação de sistemas multi-classificadores no diagnóstico de falhas em motores de indução trifásicos

Equipment maintenance is the major cost factor in industrial plants, it is very important the development of fault predict techniques. Three-phase induction motors are key electrical equipments used in industrial applications mainly because presents low cost and large robustness, however, it isn t protected from other fault types such as shorted winding and broken bars. Several acquisition ways, processing and signal analysis are applied to improve its diagnosis. More efficient techniques use current sensors and its signature analysis. In this dissertation, starting of these sensors, it is to make signal analysis through Park s vector that provides a good visualization capability. Faults data acquisition is an arduous task; in this way, it is developed a methodology for data base construction. Park s transformer is applied into stationary reference for machine modeling of the machine s differential equations solution. Faults detection needs a detailed analysis of variables and its influences that becomes the diagnosis more complex. The tasks of pattern recognition allow that systems are automatically generated, based in patterns and data concepts, in the majority cases undetectable for specialists, helping decision tasks. Classifiers algorithms with diverse learning paradigms: k-Neighborhood, Neural Networks, Decision Trees and Naïves Bayes are used to patterns recognition of machines faults. Multi-classifier systems are used to improve classification errors. It inspected the algorithms homogeneous: Bagging and Boosting and heterogeneous: Vote, Stacking and Stacking C. Results present the effectiveness of constructed model to faults modeling, such as the possibility of using multi-classifiers algorithm on faults classification

Modelagem de máquina pneumática recolhedora de frutos de café em terreiro usando análise por elementos finitos

Teve-se o objetivo de desenvolver um modelo matemático por meio de análise de elementos finitos, utilizando o programa computacional ANSYS®, versão 5.7, para otimizar o projeto de máquina recolhedora de frutos de café no terreiro. A modelagem da máquina foi realizada com base no levantamento das características aerodinâmicas dos frutos de café e da vazão de ar necessária para o transporte pneumático dos frutos. Foram obtidas, experimentalmente, as pressões estáticas nos dutos da máquina, sendo esses valores comparados com os resultados determinados pelo programa ANSYS, no intuito de validar o modelo. Com base nos resultados numéricos obtidos, concluiu-se que a modelagem desenvolvida apresentou resultados próximos aos determinados experimentalmente, obtendo erro relativo médio nos valores simulados de pressão de 9,2%. Por meio da modelagem, identificaram-se faixas de pressão que dificultariam o transporte pneumático dos frutos de café em alguns pontos da máquina. Esses problemas foram corrigidos e, com isso, o fluxo de ar proporcionado pelo ventilador foi suficiente para succionar os frutos de café no terreiro e transportá-los para dentro do reservatório da máquina. A modelagem desenvolvida atendeu às necessidades propostas no trabalho para o recolhimento dos frutos de café utilizando transporte pneumático eficientemente.

Construção,modelagem e controle de um pêndulo invertido com CLP e software scada

Pós-graduação em Engenharia Elétrica - FEIS

An investigation of computer literacy and attitudes amongst Greek post-graduate dental students

An accurate assessment of the computer skills of students is a pre-requisite for the success of any e-learning interventions. The aim of the present study was to assess objectively the computer literacy and attitudes in a group of Greek post-graduate students, using a task-oriented questionnaire developed and validated in the University of Malmö, Sweden. 50 post-graduate students in the Athens University School of Dentistry in April 2005 took part in the study. A total competence score of 0-49 was calculated. Socio-demographic characteristics were recorded. Attitudes towards computer use were assessed. Descriptive statistics and linear regression modeling were employed for data analysis. Total competence score was normally distributed (Shapiro-Wilk test: W = 0.99, V = 0.40, P = 0.97) and ranged from 5 to 42.5, with a mean of 22.6 (+/-8.4). Multivariate analysis revealed 'gender', 'e-mail ownership' and 'enrollment in non-clinical programs' as significant predictors of computer literacy. Conclusively, computer literacy of Greek post-graduate dental students was increased amongst males, students in non-clinical programs and those with more positive attitudes towards the implementation of computer assisted learning.

DEVELOPMENT OF A PREDICTIVE COMBUSTION MODEL OF A SPARK IGNITED ENGINE WITH GASOLINE DIRECT INJECTION, VARIABLE VALVE TIMING, DURATION AND LIFT TECHNOLOGIES

There is a need by engine manufactures for computationally efficient and accurate predictive combustion modeling tools for integration in engine simulation software for the assessment of combustion system hardware designs and early development of engine calibrations. This thesis discusses the process for the development and validation of a combustion modeling tool for Gasoline Direct Injected Spark Ignited Engine with variable valve timing, lift and duration valvetrain hardware from experimental data. Data was correlated and regressed from accepted methods for calculating the turbulent flow and flame propagation characteristics for an internal combustion engine. A non-linear regression modeling method was utilized to develop a combustion model to determine the fuel mass burn rate at multiple points during the combustion process. The computational fluid dynamic software Converge ©, was used to simulate and correlate the 3-D combustion system, port and piston geometry to the turbulent flow development within the cylinder to properly predict the experimental data turbulent flow parameters through the intake, compression and expansion processes. The engine simulation software GT-Power © is then used to determine the 1-D flow characteristics of the engine hardware being tested to correlate the regressed combustion modeling tool to experimental data to determine accuracy. The results of the combustion modeling tool show accurate trends capturing the combustion sensitivities to turbulent flow, thermodynamic and internal residual effects with changes in intake and exhaust valve timing, lift and duration.

The reporting quality of randomized controlled trials in orthodontics.

OBJECTIVES Accurate trial reporting facilitates evaluation and better use of study results. The objective of this article is to investigate the quality of reporting of randomized controlled trials (RCTs) in leading orthodontic journals, and to explore potential predictors of improved reporting. METHODS The 50 most recent issues of 4 leading orthodontic journals until November 2013 were electronically searched. Reporting quality assessment was conducted using the modified CONSORT statement checklist. The relationship between potential predictors and the modified CONSORT score was assessed using linear regression modeling. RESULTS 128 RCTs were identified with a mean modified CONSORT score of 68.97% (SD = 11.09). The Journal of Orthodontics (JO) ranked first in terms of completeness of reporting (modified CONSORT score 76.21%, SD = 10.1), followed by American Journal of Orthodontics and Dentofacial Orthopedics (AJODO) (73.05%, SD = 10.1). Journal of publication (AJODO: β = 10.08, 95% CI: 5.78, 14.38; JO: β = 16.82, 95% CI: 11.70, 21.94; EJO: β = 7.21, 95% CI: 2.69, 11.72 compared to Angle), year of publication (β = 0.98, 95% CI: 0.28, 1.67 for each additional year), region of authorship (Europe: β = 5.19, 95% CI: 1.30, 9.09 compared to Asia/other), statistical significance (significant: β = 3.10, 95% CI: 0.11, 6.10 compared to non-significant) and methodologist involvement (involvement: β = 5.60, 95% CI: 1.66, 9.54 compared to non-involvement) were all significant predictors of improved modified CONSORT scores in the multivariable model. Additionally, median overall Jadad score was 2 (IQR = 2) across journals, with JO (median = 3, IQR = 1) and AJODO (median = 3, IQR = 2) presenting the highest score values. CONCLUSION The reporting quality of RCTs published in leading orthodontic journals is considered suboptimal in various CONSORT areas. This may have a bearing in trial result interpretation and use in clinical decision making and evidence- based orthodontic treatment interventions.

Assessing the reporting quality in abstracts of randomized controlled trials in leading journals of oral implantology.

AIM Abstracts of randomized clinical trials are extremely important as trial appraisal is often based on the information included here. The objective of this study was to assess the quality of the reporting of RCT abstracts in journals of Oral Implantology. MATERIAL AND METHODS Six leading Implantology journals were screened for RCTs between years 2008 and 2012. A 21-item modified CONSORT for abstracts checklist was used to examine the completeness of abstract reporting. Descriptive statistics and linear regression modeling were employed for data analysis. RESULTS One hundred and sixty three RCT abstracts were included in this study. The majority of the RCTs were published in the Clinical Oral Implants Research (42.9%). The mean overall reporting quality score was 58.6% (95% CI: 57.6-59.7). The highest score was noted in the European Journal of Oral Implantology (63.8%; 95% CI: 61.8-65.8). Multivariate analysis demonstrated that abstract quality score was related to publication journal and number of research centers involved. Most abstracts adequately reported interventions (89.0%), objectives (77.9%) and conclusions (74.8%) while failed to report randomization procedures, allocation concealment, effect estimate, confidence intervals, and funding. Registration of RCTs was not reported in any of the abstracts. CONCLUSIONS The reporting quality in abstracts of RCTs published in Oral Implantology journals needs to be improved. Editors and authors should be encouraged to endorse the CONSORT for abstracts guidelines in order to achieve optimal quality in abstract reporting.

Dietary intake patterns and relationships to polybrominated diphenyl ether (PBDE) and phthalate body burden

Polybrominated diphenyl ethers (PBDEs) and phthalates are chemicals of concern because of high levels measured in people and the environment as well as the demonstrated toxicity in animal studies and limited epidemiological studies. Exposure to these chemicals has been associated with a range of toxicological outcomes, including developmental effects, behavioral changes, endocrine disruption, effects on sexual health, and cancer. Previous research has shown that both of these classes of chemicals contaminate food in the United States and worldwide. However, how large a role diet plays in exposure to these chemicals is currently unknown. To address this question, an exploratory analysis of data collected as part of the 2003-04 National Health and Nutrition Examination Survey (NHANES) was conducted. Associations between dietary intake (assessed by 24-hour dietary recalls) for a range of food types (meat, poultry, fish, and dairy) and levels PBDEs and phthalate metabolites were analyzed using multiple linear regression modeling. Levels of individual PBDE congeners 28, 47, 99, 100 as well as total PBDEs were found to be significantly associated with the consumption of poultry. Metabolites of di-(2-ethylhexyl) phthalate (DEHP) were found to be associated with the consumption of poultry, as well as with an increased consumption of fat of animal origin. These results, combined with results from previous studies, suggest that diet is an important route of intake for both PBDEs and phthalates. Further research needs to be conducted to determine the sources of food contamination with these toxic chemicals as well as to describe the levels of contamination of US food in a large, representative sample.^

Anal warts and their predictors among HIV-infected women

Human papillomavirus (HPV) is a necessary cause of cervical cancer and is also strongly associated with anal cancer. While different factors such as CD4+ cell count, HIV RNA viral load, smoking status, and cytological screening results have been identified as risk factors for the infection of HPV high-risk types and associated cancers, much less is known about the association between those risk factors and the infection of HPV low-risk types and anogential warts. In this dissertation, a public dataset (release P09) obtained from the Women's Interagency HIV Study (WIHS) was used to examine the effects of those risk factors on the size of the largest anal warts in HIV-infected women in the United States. Linear mixed modeling was used to address this research question. ^ The prevalence of anal warts at baseline for WIHS participants was higher than other populations. Incidence of anal warts in HIV-infected women was significantly higher than that of HIV-uninfected women [4.15 cases per 100 person-years (95% CI: 3.83–4.77) vs. 1.30 cases per 100 person-years (95% CI: 1.00–1.58), respectively]. There appeared to be an inverse association between the size of the largest anal wart and CD4+ cell count at baseline visit, however it was not statistically significant. There was no association between size of the largest anal wart and CD4+ cell count or HIV RNA viral load over time among HIV-infected women. There was also no association between the size of the largest anal wart and current smoking over time in HIV-infected women, even though smokers had larger warts at baseline than non-smokers. Finally, even though a woman with Pap smear results of ASCUS/LGSIL was found to have an anal wart larger than a woman with normal cervical Pap smear results the relationship between the size of the largest anal wart with cervical Pap smear results over time remains unclear. ^ Although the associations between these risk factors and the size of the largest anal wart over time in HIV-infected women could not be firmly established, this dissertation poses several questions concerning anal wart development for further exploration: (1) the role of immune function (i.e., CD4+ cell count), (2) the role of smoking status and the interaction between smoking status with other risk factors (e.g., CD4+ cell count or HIV RNA viral load), (3) the molecular mechanism of smoking on anal warts over time, (4) the potential for development of a screening program using anal Pap smear in HIV-infected women, and (5) how cost-effective and efficacious would an anal Pap smear screening program be in this high-risk population. ^

Artificial Neural Nets with Interaction of Afferents

The aim is to obtain computationally more powerful, neuro physiologically founded, artificial neurons and neural nets. Artificial Neural Nets (ANN) of the Perceptron type evolved from the original proposal by McCulloch an Pitts classical paper [1]. Essentially, they keep the computing structure of a linear machine followed by a non linear operation. The McCulloch-Pitts formal neuron (which was never considered by the author’s to be models of real neurons) consists of the simplest case of a linear computation of the inputs followed by a threshold. Networks of one layer cannot compute anylogical function of the inputs, but only those which are linearly separable. Thus, the simple exclusive OR (contrast detector) function of two inputs requires two layers of formal neurons

Short-term wind power forecast based on cluster analysis and artificial neural networks

[EN]In this paper an architecture for an estimator of short-term wind farm power is proposed. The estimator is made up of a Linear Machine classifier and a set of k Multilayer Perceptrons, training each one for a specific subspace of the input space. The splitting of the input dataset into the k clusters is done using a k-means technique, obtaining the equivalent Linear Machine classifier from the cluster centroids...

Evaluation of a Quality Improvement (QI) Intervention for Maternal, Newborn, and Child Health (MNCH) in Colombia: The Mil Días Intervention of SINERGIAS in the Department of Bolívar

Thesis (Master's)--University of Washington, 2016-06

Environmental data mining and modeling based on machine learning algorithms and geostatistics

The paper presents some contemporary approaches to spatial environmental data analysis. The main topics are concentrated on the decision-oriented problems of environmental spatial data mining and modeling: valorization and representativity of data with the help of exploratory data analysis, spatial predictions, probabilistic and risk mapping, development and application of conditional stochastic simulation models. The innovative part of the paper presents integrated/hybrid model-machine learning (ML) residuals sequential simulations-MLRSS. The models are based on multilayer perceptron and support vector regression ML algorithms used for modeling long-range spatial trends and sequential simulations of the residuals. NIL algorithms deliver non-linear solution for the spatial non-stationary problems, which are difficult for geostatistical approach. Geostatistical tools (variography) are used to characterize performance of ML algorithms, by analyzing quality and quantity of the spatially structured information extracted from data with ML algorithms. Sequential simulations provide efficient assessment of uncertainty and spatial variability. Case study from the Chernobyl fallouts illustrates the performance of the proposed model. It is shown that probability mapping, provided by the combination of ML data driven and geostatistical model based approaches, can be efficiently used in decision-making process. (C) 2003 Elsevier Ltd. All rights reserved.

Inverter-fed linear asynchronous machine

The behaviors of an arc-shaped stator induction machine (the sector-motor) and a disc-secondary linear induction motor are analyzed in this work for different values of the frequency. Variable frequency is produced by a voltage source controlled-current inverter which keeps constant the r.m.s. value of the phase current, also assuring a sinusoidal waveform. For the simulations of the machine developed thrust, an equivalent circuit is used. It is obtained through the application of the one-dimensional theory to the modeling. The circuit parameters take into account the end effects, always present is these kind of machines. The phase current waveforms are analyzed for their harmonic contents. Experimental measurements were carried out in laboratory and are presented with the simulations, for comparison.