Forecast comparison with nonlinear methods for brazilian industrial production

Este trabalho avalia as previsões de três métodos não lineares — Markov Switching Autoregressive Model, Logistic Smooth Transition Autoregressive Model e Autometrics com Dummy Saturation — para a produção industrial mensal brasileira e testa se elas são mais precisas que aquelas de preditores naive, como o modelo autorregressivo de ordem p e o mecanismo de double differencing. Os resultados mostram que a saturação com dummies de degrau e o Logistic Smooth Transition Autoregressive Model podem ser superiores ao mecanismo de double differencing, mas o modelo linear autoregressivo é mais preciso que todos os outros métodos analisados.

Forecast comparison with nonlinear methods for Brazilian industrial production

This work assesses the forecasts of three nonlinear methods | Markov Switching Autoregressive Model, Logistic Smooth Transition Auto-regressive Model, and Auto-metrics with Dummy Saturation | for the Brazilian monthly industrial production and tests if they are more accurate than those of naive predictors such as the autoregressive model of order p and the double di erencing device. The results show that the step dummy saturation and the logistic smooth transition autoregressive can be superior to the double di erencing device, but the linear autoregressive model is more accurate than all the other methods analyzed.

Nonlinear methods of heart rate variability analysis in diabetes

Background: The autonomic dysfunction stands out among the complications associated to diabetes mellitus (DM) and may be evaluated through the heart rate variability (HRV), a noninvasive tool to investigate the autonomic nervous system that provides information of health impairments and may be analyzed by using linear and nonlinear methods. Several studies have shown that HRV measured in a linear form is altered in DM. Nevertheless, a few studies investigate the nonlinear behavior of HRV. Therefore, this study aims at gathering information regarding the autonomic changes in subjects with DM identified by nonlinear analysis of HRV.Methods: For that, searches were performed on Medline, SciELO, Lilacs and Cochrane databases using the crossing between the key-words: diabetic autonomic neuropathy, autonomic nervous system, diabetes mellitus and heart rate variability. As inclusion criteria, articles published on a period from 2000 to 2010 with DM type land type II population which assessed the autonomic nervous system by nonlinear indices HRV were considered.Results: The electronic search resulted in a total of 1873 references with the exclusion of 1623 titles and abstracts and from the 250 abstracts remaining, 8 studies were selected to the final analysis that completed the inclusion criteria.Conclusions: In general, the analysis showed that the nonlinear techniques of HRV allowed detecting autonomic changes in DM. The methods of nonlinear analysis are indicated as a possible tool to be used for early diagnosis and prognosis of autonomic dysfunction in DM.

Impact of artifact correction methods on R-R interbeat signals to quantifying heart rate variability (HRV) according to linear and nonlinear methods.

In the analysis of heart rate variability (HRV) are used temporal series that contains the distances between successive heartbeats in order to assess autonomic regulation of the cardiovascular system. These series are obtained from the electrocardiogram (ECG) signal analysis, which can be affected by different types of artifacts leading to incorrect interpretations in the analysis of the HRV signals. Classic approach to deal with these artifacts implies the use of correction methods, some of them based on interpolation, substitution or statistical techniques. However, there are few studies that shows the accuracy and performance of these correction methods on real HRV signals. This study aims to determine the performance of some linear and non-linear correction methods on HRV signals with induced artefacts by quantification of its linear and nonlinear HRV parameters. As part of the methodology, ECG signals of rats measured using the technique of telemetry were used to generate real heart rate variability signals without any error. In these series were simulated missing points (beats) in different quantities in order to emulate a real experimental situation as accurately as possible. In order to compare recovering efficiency, deletion (DEL), linear interpolation (LI), cubic spline interpolation (CI), moving average window (MAW) and nonlinear predictive interpolation (NPI) were used as correction methods for the series with induced artifacts. The accuracy of each correction method was known through the results obtained after the measurement of the mean value of the series (AVNN), standard deviation (SDNN), root mean square error of the differences between successive heartbeats (RMSSD), Lomb\'s periodogram (LSP), Detrended Fluctuation Analysis (DFA), multiscale entropy (MSE) and symbolic dynamics (SD) on each HRV signal with and without artifacts. The results show that, at low levels of missing points the performance of all correction techniques are very similar with very close values for each HRV parameter. However, at higher levels of losses only the NPI method allows to obtain HRV parameters with low error values and low quantity of significant differences in comparison to the values calculated for the same signals without the presence of missing points.

Hyperspectral Image Classification with Nonlinear Methods

This thesis introduces two related lines of study on classification of hyperspectral images with nonlinear methods. First, it describes a quantitative and systematic evaluation, by the author, of each major component in a pipeline for classifying hyperspectral images (HSI) developed earlier in a joint collaboration [23]. The pipeline, with novel use of nonlinear classification methods, has reached beyond the state of the art in classification accuracy on commonly used benchmarking HSI data [6], [13]. More importantly, it provides a clutter map, with respect to a predetermined set of classes, toward the real application situations where the image pixels not necessarily fall into a predetermined set of classes to be identified, detected or classified with.

The particular components evaluated are a) band selection with band-wise entropy spread, b) feature transformation with spatial filters and spectral expansion with derivatives c) graph spectral transformation via locally linear embedding for dimension reduction, and d) statistical ensemble for clutter detection. The quantitative evaluation of the pipeline verifies that these components are indispensable to high-accuracy classification.

Secondly, the work extends the HSI classification pipeline with a single HSI data cube to multiple HSI data cubes. Each cube, with feature variation, is to be classified of multiple classes. The main challenge is deriving the cube-wise classification from pixel-wise classification. The thesis presents the initial attempt to circumvent it, and discuss the potential for further improvement.

Linear and nonlinear analysis of heart rate variability in healthy subjects and after acute myocardial infarction in patients

The objectives of this study were to evaluate and compare the use of linear and nonlinear methods for analysis of heart rate variability (HRV) in healthy subjects and in patients after acute myocardial infarction (AMI). Heart rate (HR) was recorded for 15 min in the supine position in 10 patients with AMI taking β-blockers (aged 57 ± 9 years) and in 11 healthy subjects (aged 53 ± 4 years). HRV was analyzed in the time domain (RMSSD and RMSM), the frequency domain using low- and high-frequency bands in normalized units (nu; LFnu and HFnu) and the LF/HF ratio and approximate entropy (ApEn) were determined. There was a correlation (P < 0.05) of RMSSD, RMSM, LFnu, HFnu, and the LF/HF ratio index with the ApEn of the AMI group on the 2nd (r = 0.87, 0.65, 0.72, 0.72, and 0.64) and 7th day (r = 0.88, 0.70, 0.69, 0.69, and 0.87) and of the healthy group (r = 0.63, 0.71, 0.63, 0.63, and 0.74), respectively. The median HRV indexes of the AMI group on the 2nd and 7th day differed from the healthy group (P < 0.05): RMSSD = 10.37, 19.95, 24.81; RMSM = 23.47, 31.96, 43.79; LFnu = 0.79, 0.79, 0.62; HFnu = 0.20, 0.20, 0.37; LF/HF ratio = 3.87, 3.94, 1.65; ApEn = 1.01, 1.24, 1.31, respectively. There was agreement between the methods, suggesting that these have the same power to evaluate autonomic modulation of HR in both AMI patients and healthy subjects. AMI contributed to a reduction in cardiac signal irregularity, higher sympathetic modulation and lower vagal modulation.

Calibração de câmeras para análises subaquáticas de movimentos = : Cameras calibration for underwater motion analysis

Universidade Estadual de Campinas . Faculdade de Educação Física

Neural correlations during brain activation in arithmetical tasks – an approach using electroencephalographic data

Dissertação apresentada na Faculdade de Ciências e Tecnologiea da Universidade Nova de Lisboa, para obtenção do Grau de Mestre em Engenharia Biomédica

Effects of different levels of positive airway pressure on breathing pattern and heart rate variability after coronary artery bypass grafting surgery

The application of continuous positive airway pressure (CPAP) produces important hemodynamic alterations, which can influence breathing pattern (BP) and heart rate variability (HRV). The aim of this study was to evaluate the effects of different levels of CPAP on postoperative BP and HRV after coronary artery bypass grafting (CABG) surgery and the impact of CABG surgery on these variables. Eighteen patients undergoing CABG were evaluated postoperatively during spontaneous breathing (SB) and application of four levels of CPAP applied in random order: sham (3 cmH2O), 5 cmH2O, 8 cmH2O, and 12 cmH2O. HRV was analyzed in time and frequency domains and by nonlinear methods and BP was analyzed in different variables (breathing frequency, inspiratory tidal volume, inspiratory and expiratory time, total breath time, fractional inspiratory time, percent rib cage inspiratory contribution to tidal volume, phase relation during inspiration, phase relation during expiration). There was significant postoperative impairment in HRV and BP after CABG surgery compared to the preoperative period and improvement of DFAα1, DFAα2 and SD2 indexes, and ventilatory variables during postoperative CPAP application, with a greater effect when 8 and 12 cmH2O were applied. A positive correlation (P < 0.05 and r = 0.64; Spearman) was found between DFAα1 and inspiratory time to the delta of 12 cmH2O and SB of HRV and respiratory values. Acute application of CPAP was able to alter cardiac autonomic nervous system control and BP of patients undergoing CABG surgery and 8 and 12 cmH2O of CPAP provided the best performance of pulmonary and cardiac autonomic functions.

Permutation entropy based real-time chatter detection using audio signal in turning process

Machine tool chatter is an unfavorable phenomenon during metal cutting, which results in heavy vibration of cutting tool. With increase in depth of cut, the cutting regime changes from chatter-free cutting to one with chatter. In this paper, we propose the use of permutation entropy (PE), a conceptually simple and computationally fast measurement to detect the onset of chatter from the time series using sound signal recorded with a unidirectional microphone. PE can efficiently distinguish the regular and complex nature of any signal and extract information about the dynamics of the process by indicating sudden change in its value. Under situations where the data sets are huge and there is no time for preprocessing and fine-tuning, PE can effectively detect dynamical changes of the system. This makes PE an ideal choice for online detection of chatter, which is not possible with other conventional nonlinear methods. In the present study, the variation of PE under two cutting conditions is analyzed. Abrupt variation in the value of PE with increase in depth of cut indicates the onset of chatter vibrations. The results are verified using frequency spectra of the signals and the nonlinear measure, normalized coarse-grained information rate (NCIR).

Zur Druck-Querzug-Festigkeit von Stahlbeton und stahlfaserverstärktem Stahlbeton in scheibenförmigen Bauteilen

Am Fachgebiet Massivbau (Institut für Konstruktiven Ingenieurbau – IKI) des Fachbereichs Bauingenieurwesen der Universität Kassel wurden Bauteilversuche an zweiaxial auf Druck-Zug belasteten, faserfreien und faserverstärkten Stahlbetonscheiben durchgeführt. Dabei wurden die Auswirkungen der Querzugbeanspruchung und der Rissbildung auf die Druckfestigkeit, auf die Stauchung bei Erreichen der Höchstlast sowie auf die Drucksteifigkeit des stabstahl- und faserbewehrten Betons an insgesamt 56 faserfreien und faserverstärkten Beton- und Stahlbetonscheiben untersucht. Auf der Grundlage der experimentell erhaltenen Ergebnisse wird ein Vorschlag zur Abminderung der Druckfestigkeit des gerissenen faserfreien und faserbewehrten Stahlbetons in Abhängigkeit der aufgebrachten Zugdehnung formuliert. Die Ergebnisse werden den in DIN 1045-1 [D4], Eurocode 2 [E3, E4], CEB-FIP Model Code 1990 [C1] und ACI Standard 318-05 [A1] angegebenen Bemessungsregeln für die Druckstrebenfestigkeit des gerissenen Stahlbetons gegenübergestellt und mit den Untersuchungen anderer Wissenschaftler verglichen. Die bekannten Widersprüche zwischen den Versuchsergebnissen, den vorgeschlagenen Modellen und den Regelwerken aus U.S.A., Kanada und Europa können dabei weitgehend aufgeklärt werden. Für nichtlineare Verfahren der Schnittgrößenermittlung und für Verformungsberechnungen wird ein Materialmodell des gerissenen faserfreien und faserbewehrten Stahlbetons abgeleitet. Hierzu wird die für einaxiale Beanspruchungszustände gültige Spannungs-Dehnungs-Linie nach Bild 22 der DIN 1045-1 auf den Fall der zweiaxialen Druck-Zug-Beanspruchung erweitert.

Efeitos da hidratação sobre a modulação autonômica e parâmetros cardiorrespiratórios durante e após exercício físico de longa duração

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Métodos lineares e não lineares de análise de séries temporais e sua aplicação no estudo da variabilidade da frequência cardíaca de jovens saudáveis

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Estudo e aplicação de diferentes métodos de resolução para problemas dinâmicos não-lineares em presença de atrito

The friction phenomena is present in mechanical systems with two surfaces that are in contact, which can cause serious damage to structures. Your understanding in many dynamic problems became the target of research due to its nonlinear behavior. It is necessary to know and thoroughly study each existing friction model found in the literature and nonlinear methods to define what will be the most appropriate to the problem in question. One of the most famous friction model is the Coulomb Friction, which is considered in the studied problems in the French research center Laboratoire de Mécanique des Structures et des Systèmes Couplés (LMSSC), where this search began. Regarding the resolution methods, the Harmonic Balance Method is generally used. To expand the knowledge about the friction models and the nonlinear methods, a study was carried out to identify and study potential methodologies that can be applied in the existing research lines in LMSSC and then obtain better final results. The identified friction models are divided into static and dynamic. Static models can be Classical Models, Karnopp Model and Armstrong Model. The dynamic models are Dahl Model, Bliman and Sorine Model and LuGre Model. Concerning about nonlinear methods, we study the Temporal Methods and Approximate Methods. The friction models analyzed with the help of Matlab software are verified from studies in the literature demonstrating the effectiveness of the developed programming