Emotion Recognition in the Wild using Deep Neural Networks and Bayesian Classifiers

Group emotion recognition in the wild is a challenging problem, due to the unstructured environments in which everyday life pictures are taken. Some of the obstacles for an effective classification are occlusions, variable lighting conditions, and image quality. In this work we present a solution based on a novel combination of deep neural networks and Bayesian classifiers. The neural network works on a bottom-up approach, analyzing emotions expressed by isolated faces. The Bayesian classifier estimates a global emotion integrating top-down features obtained through a scene descriptor. In order to validate the system we tested the framework on the dataset released for the Emotion Recognition in the Wild Challenge 2017. Our method achieved an accuracy of 64.68% on the test set, significantly outperforming the 53.62% competition baseline.Comment: accepted by the Fifth Emotion Recognition in the Wild (EmotiW) Challenge 201

Non-destructive testing on aramid fibres for the long-term assessment of interventions on heritage structures

High strength fibre reinforced polymers (FRPs) are composite materials made of fibres such as carbon, aramid and/or glass, and a resin matrix. FRPs are commonly used for structural repair and strengthening interventions and exhibit high potential for applications to existing constructions, including heritage buildings. In regard to aramid fibres, uncertainties about the long-term behaviour of these materials have often made the designers reluctant to use them in structural engineering. The present study describes simple and non-destructive nonlinearity tests for assessing damage or degradation of structural properties in Kevlar fibres. This was obtained by using high precision measurements to detect small deviations in the dynamic response measured on fibres and ropes. The change in dynamic properties was then related to a damage produced by exposure of the sample to UV rays for a defined time period, which simulated long-term sun exposure. In order to investigate the sensitivity of such an approach to damage detection, non-linearity characterisation tests were conducted on aramid fibres in both damaged and undamaged states. With the purpose of carrying out dynamic tests on small fibre specimens, a dedicated instrumentation was designed and built in cooperation with the Metrology Laboratory of the Department of Electronics at the Politecnico di Torino

Video processing techniques for the contactless investigation of large oscillations

The experimental acquisition of large vibrations presents various technical difficulties. Especially in the case of geometric nonlinearities, dealing with very flexible, very light structures causes minimal variations in mass or stiffness to affect severely the dynamical response. Thus, sensors' added masses change the behaviour of the structure with respect to the unloaded condition. Moreover, the most common tools regularly employed for acquisition in vibration analysis - that is to say, laser vibrometers and accelerometers - are often designed with small amplitudes in mind. Their recordings are known to lack accuracy when the investigated structure undergoes large or very large motions, due to geometrical reasons. Image-based measurement techniques offer a valid solution to this problem. Here, an ensemble of three video processing techniques are benchmarked against each other and tested as viable options for the non-contact dynamic characterisation of slender beam-like structures. The methods have been applied to the case study of an aluminium spar for a highly-flexible airwing prototype and compared to the measurements recorded by a laser velocimeter and several Raspberry PI Inertial Measurement Units (IMUs), which also proved to be minimally invasive

A novel approach to damage localisation based on bispectral analysis and neural network

The normalised version of bispectrum, the so-called bicoherence, has often proved a reliable method of damage detection on engineering applications. Indeed, higher-order spectral analysis (HOSA) has the advantage of being able to detect non-linearity in the structural dynamic response while being insensitive to ambient vibrations. Skewness in the response may be easily spotted and related to damage conditions, as the majority of common faults and cracks shows bilinear effects. The present study tries to extend the application of HOSA to damage localisation, resorting to a neural network based classification algorithm. In order to validate the approach, a non-linear finite element model of a 4-meters-long cantilever beam has been built. This model could be seen as a first generic concept of more complex structural systems, such as aircraft wings, wind turbine blades, etc. The main aim of the study is to train a Neural Network (NN) able to classify different damage locations, when fed with bispectra. These are computed using the dynamic response of the FE nonlinear model to random noise excitation

Using video processing for the full-field identification of backbone curves in case of large vibrations

Nonlinear modal analysis is a demanding yet imperative task to rigorously address real-life situations where the dynamics involved clearly exceed the limits of linear approximation. The specific case of geometric nonlinearities, where the effects induced by the second and higher-order terms in the strain–displacement relationship cannot be neglected, is of great significance for structural engineering in most of its fields of application—aerospace, civil construction, mechanical systems, and so on. However, this nonlinear behaviour is strongly affected by even small changes in stiffness or mass, e.g., by applying physically-attached sensors to the structure of interest. Indeed, the sensors placement introduces a certain amount of geometric hardening and mass variation, which becomes relevant for very flexible structures. The effects of mass loading, while highly recognised to be much larger in the nonlinear domain than in its linear counterpart, have seldom been explored experimentally. In this context, the aim of this paper is to perform a noncontact, full-field nonlinear investigation of the very light and very flexible XB-1 air wing prototype aluminum spar, applying the well-known resonance decay method. Video processing in general, and a high-speed, optical target tracking technique in particular, are proposed for this purpose; the methodology can be easily extended to any slender beam-like or plate-like element. Obtained results have been used to describe the first nonlinear normal mode of the spar in both unloaded and sensors-loaded conditions by means of their respective backbone curves. Noticeable changes were encountered between the two conditions when the structure undergoes large-amplitude flexural vibrations

A computational methodology for assessing the time-dependent structural performance of electric road infrastructures

An infrastructure adapted to dynamic wireless recharging of electric vehicles is often referred to generically as Electric Road (“e-road”). E-roads are deemed to become essential components of future grid environments and smart city strategies. Several technologies already exist that propose different ways to integrate dynamic inductive charging systems within the infrastructure. One e-road solution uses a very thin rail with box-section made of fibre-reinforced polymer, inside which an electric current flows producing a magnetic field. In spite of the great interest and research generated by recharging technologies, the structural problems of e-roads, including vibrations and structural integrity in the short and/or long period, have received relatively little attention to date. This article presents a novel computational methodology for assessing the time-dependent structural performance of e-roads, including a recursive strategy for the estimation of the lifetime of surface layers. The article also reports some numerical findings about e-roads that will drive further numerical analyses and experimental studies on this novel type of infrastructure. Finally, numerical simulations have been conducted to compare an e-road with a traditional road (“t-road”), in terms of static, dynamic and fatigue behavior

Learning GAN-based Foveated Reconstruction to Recover Perceptually Important Image Features

A foveated image can be entirely reconstructed from a sparse set of samples distributed according to the retinal sensitivity of the human visual system, which rapidly decreases with increasing eccentricity. The use of Generative Adversarial Networks has recently been shown to be a promising solution for such a task, as they can successfully hallucinate missing image information. As in the case of other supervised learning approaches, the definition of the loss function and the training strategy heavily influence the quality of the output. In this work,we consider the problem of efficiently guiding thetraining of foveated reconstruction techniques such that they are more aware of the capabilities and limitations of the human visual system, and thus can reconstruct visually important image features. Our primary goal is to make the training procedure less sensitive to distortions that humans cannot detect and focus on penalizing perceptually important artifacts. Given the nature of GAN-based solutions, we focus on the sensitivity of human vision to hallucination in case of input samples with different densities. We propose psychophysical experiments, a dataset, and a procedure for training foveated image reconstruction. The proposed strategy renders the generator network flexible by penalizing only perceptually important deviations in the output. As a result, the method emphasized the recovery of perceptually important image features. We evaluated our strategy and compared it with alternative solutions by using a newly trained objective metric, a recent foveated video quality metric, and user experiments. Our evaluations revealed significant improvements in the perceived image reconstruction quality compared with the standard GAN-based training approach

A new testing machine for the dynamic characterization of high strength low damping fiber materials

This work describes a novel method for measuring the damping, the elastic modulus and the non-linear behavior of high strength low damping fiber materials such as para-aramids, silicon carbide (SiC) and carbon. The method is based on resonant response characterization of a spring-mass system excited by a sine-wave forcing term which is applied as a vertical force to the suspended mass. The damping is obtained from the measured resonance quality factor Q, the elasticity modulus is calculated from the resonance frequency, and the non-linear coefficient is obtained with the backbone approach from resonance profile variations as a function of the forcing term amplitude. It is argued that the method is very sensitive, to the point that a maximum excitation amplitude of the order of a few percent of resistance is sufficient to obtain an estimate of the non-linear coefficient. This claim is supported by experimental results. A testing machine is also discussed, which provides the necessary sensitivity at such small excitation amplitudes and the capability of evaluating very small damping values, as expected in high strength low damping fiber materials. The sensitivity is guaranteed by an optical position sensor with sub-micron resolution. To evaluate small damping values, particular care has been taken to ensure that energy dispersions in the generator are much smaller than energy dispersions in the fibers themselves. Examples of dynamic characterization are shown for para-aramid, silicon carbide, and carbon fibers

Occlusal Load Considerations in Implant-Supported Fixed Restorations

The advent of new technologies in the field of medicine and dentistry is creating improvements that lead clinicians to have materials and procedures able to improve patients' quality of life. The aim of this article is to evaluate occlusion load and its consequences on fixed implant-supported prosthesis. New materials have granted clinicians the possibility achieve great aesthetic results in dental prosthesis, and new procedures allow them to standardize and give precise and repeatable results, especially for the functional and long-term stability aspects of products. Some principles should be carefully evaluated and applied to every dental prosthesis; the evaluation of the forces and fitting of meso-structures to dental implants, an aspect that is often not well considered by clinicians, is the main focus of this article