Predictive Models for the Characterization of Internal Defects in Additive Materials from Active Thermography Sequences Supported by Machine Learning Methods.

The present article addresses a generation of predictive models that assesses the thickness and length of internal defects in additive manufacturing materials. These modes use data from the application of active transient thermography numerical simulation. In this manner, the raised procedure is an ad-hoc hybrid method that integrates finite element simulation and machine learning models using different predictive feature sets and characteristics (i.e., regression, Gaussian regression, support vector machines, multilayer perceptron, and random forest). The performance results for each model were statistically analyzed, evaluated, and compared in terms of predictive performance, processing time, and outlier sensibility to facilitate the choice of a predictive method to obtain the thickness and length of an internal defect from thermographic monitoring. The best model to predictdefect thickness with six thermal features was interaction linear regression. To make predictive models for defect length and thickness, the best model was Gaussian process regression. However, models such as support vector machines also had significative advantages in terms of processing time and adequate performance for certain feature sets. In this way, the results showed that the predictive capability of some types of algorithms could allow for the detection and measurement of internal defects in materials produced by additive manufacturing using active thermography as a non-destructive test.

Measuring the Water Content in Wood Using Step-Heating Thermography and Speckle Patterns-Preliminary Results.

The relationship between wood and its degree of humidity is one of the most important aspects of its use in construction and restoration. The wood presents a behavior similar to a sponge, therefore, moisture is related to its expansion and contraction. The nondestructive evaluation (NDE) of the amount of moisture in wood materials allows to define, e.g., the restoration procedures of buildings or artworks. In this work, an integrated study of two non-contact techniques is presented. Infrared thermography (IRT) was able to retrieve thermal parameters of the wood related to the amount of water added to the samples, while the interference pattern generated by speckles was used to quantify the expansion and contraction of wood that can be related to the amount of water. In twenty-seven wooded samples, a known quantity of water was added in a controlled manner. By applying advanced image processing to thermograms and specklegrams, it was possible to determine fundamental values controlling both the absorption of water and the main thermophysical parameters that link the samples. On the one hand, results here shown should be considered preliminary because the experimental values obtained by IRT need to be optimized for low water contents introduced into the samples. On the other hand, speckle interferometry by applying an innovative procedure provided robust results for both high and low water contents.

Predictors of Enrollment in Cardiac Rehabilitation Programs in Spain.

PURPOSE: Cardiac rehabilitation (CR) is very effective for secondary prevention of cardiovascular disorders. The objective of this study was to analyze population factors associated with nonenrollment of cardiac patients in these programs. METHODS: Retrospective study of 756 patients referred to the cardiac rehabilitation program (CRP) of a tertiary referral hospital with a service area population of more than 640 000 from January 2009 to June 2012. We assessed the relationship between population characteristics of these patients and nonenrollment by logistic regression analysis. RESULTS: There were 2386 hospital admissions for an acute coronary syndrome during the study period. Out of the 2355 patients who were alive at discharge, 756 (632 men and 124 women) were referred for CR (32.1% vs 3% state average and vs 51% European average). Of these patients, 20.9% did not enroll. The referral rate was lower among women than among men (P < .001). The characteristics associated with a lower rate of enrollment in the program were age (OR: 1.05; 95% CI: 1.02-1.09), living alone (OR: 4.54; 95% CI: 2.53-8.16), living further than 50 km from the CR unit (OR: 2.90; 95% CI: 1.29-6.41) and, in women, having a history of cardiovascular disease (recurrent myocardial infarction) (OR: 6.35; 95% CI: 2.53-11.81). CONCLUSIONS: The rate of referral for CR in our setting is well above the national average but still could be improved. We identified older age, living alone, travel distance to the cardiac rehabilitation unit, and, in women, a history of a previous myocardial infarction as barriers to enrollment in CRPs.

Efficient dynamic events discrimination technique for fiber distributed Brillouin sensors.

A technique to detect real time variations of temperature or strain in Brillouin based distributed fiber sensors is proposed and is investigated in this paper. The technique is based on anomaly detection methods such as the RX-algorithm. Detection and isolation of dynamic events from the static ones are demonstrated by a proper processing of the Brillouin gain values obtained by using a standard BOTDA system. Results also suggest that better signal to noise ratio, dynamic range and spatial resolution can be obtained. For a pump pulse of 5 ns the spatial resolution is enhanced, (from 0.541 m obtained by direct gain measurement, to 0.418 m obtained with the technique here exposed) since the analysis is concentrated in the variation of the Brillouin gain and not only on the averaging of the signal along the time.

Brillouin frequency shift of standard optical fibers set in water vapor medium.

The dependence of the Brillouin frequency shift (BFS) on UV-cured acrylate coating and uncoated fibers for media that have different water vapor concentrations is experimentally investigated. The BFS is proportional to the temperature within the fiber, but it also depends on the water vapor contained in the surroundings of the fiber. A hypothesis based on the efficiency of the heat transfer due to the different humidity concentration in the media is proposed, and the temperature difference that depends on the heat transfer is quantified in standard fibers. A shift of approximately 0.22 MHz for relative humidity change between 60% and 98% at 20 degrees C is measured.