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.

High friction and acoustic surface for pavements: development of artificial engineered aggregates from recycled powders

There is a constant need to improve the infrastructure's quality and build new infrastructure with better designs. The risk of accidents and noise can be reduced by improving the surface properties of the pavement. The amount of raw material used in road construction is worrisome, as it is finite and due the waste produced. Environmentally-friendly roads construction, recycling might be the main way. Projects must be more environmentally-friendly, safer, and quieter. Is it possible to develop a safer, quieter and environmentally-friendly pavement surfaces? The hypothesis is: is it possible to create an Artificial Engineered Aggregate (AEA) using waste materials and providing it with a specific shape that can help to reduce the noise and increase the friction? The thesis presents the development of an AEA and its application as a partial replacement in microsurfacing samples. The 1st introduces the topic and provides the aim and objectives of the thesis. The 2nd chapter – presents a pavement solution to noise and friction review. The 3rd chapter - developing a mix design for a geopolymer mortar that used basalt powder. The 4th chapter is presented the physical-mechanical evaluation of the AEA. The 5th chapter evaluates the use of this aggregate in microsurfacing regarding the texture parameters. The 6th chapter, those parameter are used as an input to SPERoN® model, simulating their noise behavior of these solutions. The findings from this thesis are presented as partial conclusions in each chapter, to be closed in a final chapter. The main findings are: the DoE provided the tool to select the appropriate geopolymer mortar mix design; AEA had interesting results regarding the physical-mechanical tests; AEA in partial replacement of the natural aggregates in microsurfacing mixture proved feasible. The texture parameters and noise levels obtained in AEA samples demonstrate that it can serve as a HIFASP

Vibro-acoustic analysis and design optimization to improve comfort and sustainability of future passenger aircraft

Due to the interest of general public and the industrial stakeholders, new challenges and demands are rising in aircraft design. The sustainability is taking its place amongst more traditional design factors, such as safety, performances and costs. Sustainability is both environmental and economic, and among the factors contributing to economic sustainability, there is also passengers' comfort. In order to win these two challenges, they must be considered in the early stages of aircraft design. In this work, the focus is on emissions generation and acoustic comfort, aiming at reducing pollution and internal noise in the preliminary design phases. These results can be achieved with both unconventional aircraft configurations and advanced materials, which also require new numerical formulations to be assessed. In this research, on one hand, the windowless configuration for a commercial aircraft is studied with traditional preliminary design methods in order to achieve a weight reduction and consequently a return in terms of emissions and costs. On the other hand, a new class of insulating materials, the acoustic metamaterials, is applied on the passenger cabin lining panels. The complex kinematic behaviour of these advanced materials is studied through the Carrera's Unified Formulation, that enhances a wide class of powerful refined shell and beam theories with a unique formulation.

Vibro-acoustic analysis of additively manufactured acoustic metamaterials

In the last decade it emerged the interest in new types of acoustic insulating materials, called acoustic metamaterials. These materials are composed by a host and inclusions and are arranged periodically or non-periodically in sub-wavelength elements called meta-atoms. Their inclusions and internal geometries can be manipulated to tailor the acoustic properties, reducing weight, and increasing at the same time their efficiency. Thanks to the high absorbing characteristics that they can achieve, their usage is of particularly interest as material of the core in sandwich panels of aerospace structures to reduce vibrations and noise inside passengers aircraft’s cabin. In addition, since the low frequency signals are difficult to be damped with conventional materials, their usage can guarantee a high transmission loss at low frequencies, obtaining a positive benefit on passengers’ comfort. The performances and efficiency of these materials are enhanced thanks to the new additive manufacturing techniques opposed to the conventional ones uncapable to pro- duce such complex internal geometries. The aim of this work is to study, produce and redesign micro-perforated sandwich panels of a literature case study to achieve high performances in the low frequency range, e.g., below 2000 Hz. Some geometrical parameters, such as perforation ratio and diameter of holes, were considered to realize different models and see the differences in the sound transmission loss. The models were produced by means of Fused Deposition Modelling using an Acrylonitrile Butadiene Styrene (ABS Plus p430) material on a commercial additive manufacturing system. Finally, the frequency response analysis was carried out with Mul2 software, based on the Carrera’s Unified Formulation (CUF) to understand the acoustic and structural properties of the material employed, analyzing the plates’ displacements and the TL results.

Ambiências térmica, aérea e acústica para reprodutores suínos

The swine breeder rearing environment directly affects the animal's performance. This research had the objective of developing a thermal, aerial and acoustic environmental evaluation pattern for boar housing. The experiment was carried on a commercial swine farm in Salto County -SP, Brazil. Thermal, aerial and acoustic environment data of rearing conditions were registered. Data were statistically analyzed using as threshold the ideal housing environment that leads to animal welfare. Results showed that ambient temperature was around 70% beyond normal range, while air relative humidity, air speed and gases concentration were within threshold values. Noise level data besides being within normal range did not present large variation. In relation to the fuzzy logic analysis it was possible to build up a scenario which indicated that the best welfare indexes to male swine breeders happens when thermal comfort index are close to 80%, and noise level is lower than 40 dB. In the other hand the worst welfare index occur in the sector where the thermal comfort values are below 40% at the same time that the noise level is higher than 80 dB leading to inadequate conditions to the animal, and may directly interfere in the reproduction system performance.

Pre-processing for noise detection in gene expression classification data

Due to the imprecise nature of biological experiments, biological data is often characterized by the presence of redundant and noisy data. This may be due to errors that occurred during data collection, such as contaminations in laboratorial samples. It is the case of gene expression data, where the equipments and tools currently used frequently produce noisy biological data. Machine Learning algorithms have been successfully used in gene expression data analysis. Although many Machine Learning algorithms can deal with noise, detecting and removing noisy instances from the training data set can help the induction of the target hypothesis. This paper evaluates the use of distance-based pre-processing techniques for noise detection in gene expression data classification problems. This evaluation analyzes the effectiveness of the techniques investigated in removing noisy data, measured by the accuracy obtained by different Machine Learning classifiers over the pre-processed data.

Time-frequency and time-scale analysis of Barkhausen noise signals

Carrying out information about the microstructure and stress behaviour of ferromagnetic steels, magnetic Barkhausen noise (MBN) has been used as a basis for effective non-destructive testing methods, opening new areas in industrial applications. One of the factors that determines the quality and reliability of the MBN analysis is the way information is extracted from the signal. Commonly, simple scalar parameters are used to characterize the information content, such as amplitude maxima and signal root mean square. This paper presents a new approach based on the time-frequency analysis. The experimental test case relates the use of MBN signals to characterize hardness gradients in a AISI4140 steel. To that purpose different time-frequency (TFR) and time-scale (TSR) representations such as the spectrogram, the Wigner-Ville distribution, the Capongram, the ARgram obtained from an AutoRegressive model, the scalogram, and the Mellingram obtained from a Mellin transform are assessed. It is shown that, due to nonstationary characteristics of the MBN, TFRs can provide a rich and new panorama of these signals. Extraction techniques of some time-frequency parameters are used to allow a diagnostic process. Comparison with results obtained by the classical method highlights the improvement on the diagnosis provided by the method proposed.

CAN OLD GALAXIES AT HIGH REDSHIFTS AND BARYON ACOUSTIC OSCILLATIONS CONSTRAIN H(0)?

A new age-redshift test is proposed in order to constrain H(0) on the basis of the existence of old high-redshift galaxies (OHRGs). In the flat Lambda cold dark matter model, the value of H(0) is heavily dependent on the mass density parameter Omega(M) = 1- Omega(Lambda). Such a degeneracy can be broken through a joint analysis involving the OHRG and baryon acoustic oscillation signature. By assuming a galaxy incubation time, t(inc) = 0.8 +/- 0.4 Gyr, our joint analysis yields a value of H(0) = 71 +/- 4 km s(-1) Mpc(-1) (1 sigma) with the best-fit density parameter Omega(M) = 0.27 +/- 0.03. Such results are in good agreement with independent studies from the Hubble Space Telescope key project and recent estimates of the Wilkinson Microwave Anisotropy Probe, thereby suggesting that the combination of these two independent phenomena provides an interesting method to constrain the Hubble constant.

Kinetic ion effect on geodesic acoustic Alfven modes in tokamaks

Using a quasitoroidal set of coordinates with coaxial circular magnetic surfaces, the Vlasov equation is solved for collisionless plasmas, and the dielectric tensor is found for large aspect ratio tokamaks in a low frequency band. Taking into account q-profile and charge separation parallel electric field, it is found that the Alfven wave continuum is deformed by ion geodesic effects producing continuum minimum at the rational magnetic surfaces. Low frequency geodesic ion induced Alfven waves are found below the continuum minimum where collisionless damping has a gap for Maxwell distribution. In kinetic approach, the ion thermal motion defines the geodesic effect but the mode frequency is strongly corrected due to parallel motion of electrons.

Noise in resistively shunted Josephson junctions

We investigate the dynamics of a resistively shunted Josephson junction. We compute the Josephson frequency and the generalized impedances for a variety of the parameters, particularly with relevance to predicting the measurable effects of zero-temperature current noise in the resistor.

Extra phase noise from thermal fluctuations in nonlinear optical crystals

We show theoretically and experimentally that scattered light by thermal phonons inside a second-order nonlinear crystal is the source of additional phase noise observed in optical parametric oscillators. This additional phase noise reduces the quantum correlations and has hitherto hindered the direct production of multipartite entanglement in a single nonlinear optical system. We cooled the nonlinear crystal and observed a reduction in the extra noise. Our treatment of this noise can be successfully applied to different systems in the literature.

Role of noise in population dynamics cycles

Noise is an intrinsic feature of population dynamics and plays a crucial role in oscillations called phase-forgetting quasicycles by converting damped into sustained oscillations. This function of noise becomes evident when considering Langevin equations whose deterministic part yields only damped oscillations. We formulate here a consistent and systematic approach to population dynamics, leading to a Fokker-Planck equation and the associate Langevin equations in accordance with this conceptual framework, founded on stochastic lattice-gas models that describe spatially structured predator-prey systems. Langevin equations in the population densities and predator-prey pair density are derived in two stages. First, a birth-and-death stochastic process in the space of prey and predator numbers and predator-prey pair number is obtained by a contraction method that reduces the degrees of freedom. Second, a van Kampen expansion in the inverse of system size is then performed to get the Fokker-Planck equation. We also study the time correlation function, the asymptotic behavior of which is used to characterize the transition from the cyclic coexistence of species to the ordinary coexistence.

Missing levels in acoustic resonators

It is shown that the deviations of the experimental statistics of six chaotic acoustic resonators from Wigner-Dyson random matrix theory predictions are explained by a recent model of random missing levels. In these resonatorsa made of aluminum plates a the larger deviations occur in the spectral rigidity (SRs) while the nearest-neighbor distributions (NNDs) are still close to the Wigner surmise. Good fits to the experimental NNDs and SRs are obtained by adjusting only one parameter, which is the fraction of remaining levels of the complete spectra. For two Sinai stadiums, one Sinai stadium without planar symmetry, two triangles, and a sixth of the three-leaf clover shapes, was found that 7%, 4%, 7%, and 2%, respectively, of eigenfrequencies were not detected.

Scattering of sound waves by a canonical acoustic hole

This is a study of a monochromatic planar perturbation impinging upon a canonical acoustic hole. We show that acoustic hole scattering shares key features with black hole scattering. The interference of wave fronts passing in opposite senses around the hole creates regular oscillations in the scattered intensity. We examine this effect by applying a partial wave method to compute the differential scattering cross section for a range of incident wavelengths. We demonstrate the existence of a scattering peak in the backward direction, known as the glory. We show that the glory created by the canonical acoustic hole is approximately 170 times less intense than the glory created by the Schwarzschild black hole, for equivalent horizon-to-wavelength ratios. We hope that direct experimental observations of such effects may be possible in the near future.

Sensitivity to noise and ergodicity of an assembly line of cellular automata that classifies density

We investigate the sensitivity of the composite cellular automaton of H. Fuks [Phys. Rev. E 55, R2081 (1997)] to noise and assess the density classification performance of the resulting probabilistic cellular automaton (PCA) numerically. We conclude that the composite PCA performs the density classification task reliably only up to very small levels of noise. In particular, it cannot outperform the noisy Gacs-Kurdyumov-Levin automaton, an imperfect classifier, for any level of noise. While the original composite CA is nonergodic, analyses of relaxation times indicate that its noisy version is an ergodic automaton, with the relaxation times decaying algebraically over an extended range of parameters with an exponent very close (possibly equal) to the mean-field value.