Deep Learning Architectures for time of arrival detection in Acoustic Emissions Monitoring

Acoustic Emission (AE) monitoring can be used to detect the presence of damage as well as determine its location in Structural Health Monitoring (SHM) applications. Information on the time difference of the signal generated by the damage event arriving at different sensors is essential in performing localization. This makes the time of arrival (ToA) an important piece of information to retrieve from the AE signal. Generally, this is determined using statistical methods such as the Akaike Information Criterion (AIC) which is particularly prone to errors in the presence of noise. And given that the structures of interest are surrounded with harsh environments, a way to accurately estimate the arrival time in such noisy scenarios is of particular interest. In this work, two new methods are presented to estimate the arrival times of AE signals which are based on Machine Learning. Inspired by great results in the field, two models are presented which are Deep Learning models - a subset of machine learning. They are based on Convolutional Neural Network (CNN) and Capsule Neural Network (CapsNet). The primary advantage of such models is that they do not require the user to pre-define selected features but only require raw data to be given and the models establish non-linear relationships between the inputs and outputs. The performance of the models is evaluated using AE signals generated by a custom ray-tracing algorithm by propagating them on an aluminium plate and compared to AIC. It was found that the relative error in estimation on the test set was < 5% for the models compared to around 45% of AIC. The testing process was further continued by preparing an experimental setup and acquiring real AE signals to test on. Similar performances were observed where the two models not only outperform AIC by more than a magnitude in their average errors but also they were shown to be a lot more robust as compared to AIC which fails in the presence of noise.

Design and acoustic characterization of a new listening room

Il presente lavoro tratta la progettazione e caratterizzazione di una nuova "listening room" ad acustica controllata partendo dai requisiti dettati dalle norme tecniche ITU-R BS 1116-1 e EBU/UER Tech. doc. 3276. Ad oggi è presente un'ampia letteratura, che tratta approcci per valutazione acustica delle sale di ascolto. Essa inizialmente era volta a trovare proporzioni ideali tra le dimensioni della camera, poi la ricerca si è spostata sull'elaborazione di modelli previsionali. Purtroppo tali metodi spesso non riescono a garantire le prestazioni desiderate, mentre le prove sperimentali dettate dalle norme risultano essere di comprovata validità. L'ambiente oggetto di studio è stato progettato all'interno dello spazio dei laboratori CIRI. La tecnologia costruttiva è frutto di uno studio approfondito, in particolare la scelta di fibre di poliestere termolegate, per il rivestimento delle pareti interne, è stata valutata attraverso misure in camera riverberante secondo UNI-EN-ISO 354. Si è poi seguita una metodologia iterativa che coinvolgesse messa in opera, misurazioni in situ e valutazione tramite confronto con i parametri consigliati dalle normative tecniche sopra citate. In quest'ottica sono state effettuate acquisizioni di risposte all'impulso monoaurali e dei livelli di pressione sonora per verificare la qualità di isolamento e il comportamento alle basse frequenze. La validazione restituisce indicazioni positive per gli utilizzi dell'ambiente ipotizzati e risulta compatibile con le stringenti richieste delle norme tecniche.

Main characteristics of the emission from elliptical galaxies

Elliptical galaxies are one of the most characteristic objects we can find in the sky. In order to unveil their properties, such as their structure or chemical composition, one must study their spectral emission. In fact they seem to behave rather differently when observed with different eyes. This is because their light is mainly brought by two different components: optical radiation arises from its stars, while the X emission is primarly due to a halo of extremely hot gas in which ellipticals seem to be embedded. After a brief classification, the two main processes linked to these phenomena will be described, together with the informations we can collect thanks to them. Eventually, we will take a quick look at the other regions of the electromagnetic spectrum.

Antarctic cloud spectral emission from ground-based measurements, a focus on far infrared signatures

The present work belongs to the PRANA project, the first extensive field campaign of observation of atmospheric emission spectra covering the Far InfraRed spectral region, for more than two years. The principal deployed instrument is REFIR-PAD, a Fourier transform spectrometer used by us to study Antarctic cloud properties. A dataset covering the whole 2013 has been analyzed and, firstly, a selection of good quality spectra is performed, using, as thresholds, radiance values in few chosen spectral regions. These spectra are described in a synthetic way averaging radiances in selected intervals, converting them into BTs and finally considering the differences between each pair of them. A supervised feature selection algorithm is implemented with the purpose to select the features really informative about the presence, the phase and the type of cloud. Hence, training and test sets are collected, by means of Lidar quick-looks. The supervised classification step of the overall monthly datasets is performed using a SVM. On the base of this classification and with the help of Lidar observations, 29 non-precipitating ice cloud case studies are selected. A single spectrum, or at most an average over two or three spectra, is processed by means of the retrieval algorithm RT-RET, exploiting some main IR window channels, in order to extract cloud properties. Retrieved effective radii and optical depths are analyzed, to compare them with literature studies and to evaluate possible seasonal trends. Finally, retrieval output atmospheric profiles are used as inputs for simulations, assuming two different crystal habits, with the aim to examine our ability to reproduce radiances in the FIR. Substantial mis-estimations are found for FIR micro-windows: a high variability is observed in the spectral pattern of simulation deviations from measured spectra and an effort to link these deviations to cloud parameters has been performed.