SwimmerNET: Underwater 2D Swimmer Pose Estimation Exploiting Fully Convolutional Neural Networks

Professional swimming coaches make use of videos to evaluate their athletes' performances. Specifically, the videos are manually analyzed in order to observe the movements of all parts of the swimmer's body during the exercise and to give indications for improving swimming technique. This operation is time-consuming, laborious and error prone. In recent years, alternative technologies have been introduced in the literature, but they still have severe limitations that make their correct and effective use impossible. In fact, the currently available techniques based on image analysis only apply to certain swimming styles; moreover, they are strongly influenced by disturbing elements (i.e., the presence of bubbles, splashes and reflections), resulting in poor measurement accuracy. The use of wearable sensors (accelerometers or photoplethysmographic sensors) or optical markers, although they can guarantee high reliability and accuracy, disturb the performance of the athletes, who tend to dislike these solutions. In this work we introduce swimmerNET, a new marker-less 2D swimmer pose estimation approach based on the combined use of computer vision algorithms and fully convolutional neural networks. By using a single 8 Mpixel wide-angle camera, the proposed system is able to estimate the pose of a swimmer during exercise while guaranteeing adequate measurement accuracy. The method has been successfully tested on several athletes (i.e., different physical characteristics and different swimming technique), obtaining an average error and a standard deviation (worst case scenario for the dataset analyzed) of approximately 1 mm and 10 mm, respectively

Algorithms for Vision-Based Quality Control of Circularly Symmetric Components

Quality inspection in the industrial production field is experiencing a strong technological development that benefits from the combination of vision-based techniques with artificial intelligence algorithms. This paper initially addresses the problem of defect identification for circularly symmetric mechanical components, characterized by the presence of periodic elements. In the specific case of knurled washers, we compare the performances of a standard algorithm for the analysis of grey-scale image with a Deep Learning (DL) approach. The standard algorithm is based on the extraction of pseudo-signals derived from the conversion of the grey scale image of concentric annuli. In the DL approach, the component inspection is shifted from the entire sample to specific areas repeated along the object profile where the defect may occur. The standard algorithm provides better results in terms of accuracy and computational time with respect to the DL approach. Nevertheless, DL reaches accuracy higher than 99% when performance is evaluated targeting the identification of damaged teeth. The possibility of extending the methods and the results to other circularly symmetrical components is analyzed and discussed

On the use of Lagrange Multiplier State-Space Substructuring in dynamic substructuring analysis

In this article, the formulation of Lagrange Multiplier State-Space Substructuring (LM-SSS) is presented and extended to directly compute coupled displacement and velocity state-space models. The LM-SSS method is applied to couple and decouple state-space models established in the modal domain. Moreover, it is used together with tailored postprocessing procedures to eliminate the redundant states originated from the coupling and decoupling operations. This specific formulation of the LM-SSS approach made it possible to develop a tailored coupling form, named Unconstrained Coupling Form (UCF). UCF just requires the computation of a nullspace and does not rely on the selection of a subspace from a nullspace. By exploiting a numerical example, LM-SSS was compared with the Lagrange Multiplier Frequency Based Substructuring (LMFBS) approach, which is currently widely recognized as a reference approach. This was done both in terms of: a)coupled FRFs derived by coupling the state-space models of two substructures and b) decoupled FRFs derived by decoupling the state-space model of a component from the coupled model. LM-SSS showed to be suitable to compute minimal order coupled models and UCF turned out to have similar performance as other coupling forms already presented to the scientific community. As for the decoupling task, the FRFs derived from the LM-SSS approach perfectly matched those obtained by LM-FBS. Moreover, it was also demonstrated that the elimination of the redundant states originated from the decoupling operation was correctly performed. The approaches discussed were exploited on an experimental substructuring application. LM-SSS resulted to be a reliable SSS technique to perform coupling and decoupling operations with state-space models estimated from measured FRFs as well as to provide accurate minimal-order models

Erosion Prediction of Gas Turbine Compressor Blades Subjected to Water Washing Process

Technical BriefsBlade fouling is a relevant problem in turbomachinery applications. It affects both compressors and turbines. In the first case, fouling can be generated by the presence of dust, ashes or brackish air (in offshore applications). In turbines, fouling is mainly generated by residual of combustion process. Blade fouling generally leads to a reduction of the performance due to an increase on profile losses. Here we focus on the fouling due to salt deposition on naval/off-shore applications referring to machines that are part of the fleet of gas turbines manufacturers. In such applications, it is common to introduce on-line washing devices aiming at removing fouling from the early stages of the compressors. The water is sprayed upstream of the first rotor, it impacts on the rotor blades and thus dissolving the deposited salt. However, this procedure possibly leads to blade erosion and/or corrosion. A clear comprehension of the erosion mechanism is the main objective of the present work. To this end, we propose an integrated multi-phase CFD tool. The multi-phase flow is analyzed by adopting a one-way coupling, thus assuming water droplets to be drag by the carrier flow without influencing the main flow. The droplets are dispersed and tracked singularly by adopting a Lagrangian approach. As for the erosion, well-known and widely accepted models are used. The capability of a Lagrangian code, P-Track, developed and validated at the Department of Mechanical & Aerospace Engineering, Sapienza University in Rome, is presented. The code is able to predict the droplets trajectories, as well as to simulate the impact on the solid walls and the erosion mechanism. Simulations were performed using 25 and 100 ?m droplet size. Results, expressed in terms of normalized erosion rate, show the erosion patterns and erosive effect of the two size classes. Erosive capacity is proportional to droplet size, and the most eroded part of the blade is the leading edge, which is in qualitative agreement with measurements

Acoustic Attenuation of COVID-19 Face Masks: Correlation to Fibrous Material Porosity, Mask Breathability and Bacterial Filtration Efficiency

none7openMartarelli, Milena; Montalto, Luigi; Chiariotti, Paolo; Simoni, Serena; Castellini, Paolo; Battista, Gianmarco; Paone, NicolaMartarelli, Milena; Montalto, Luigi; Chiariotti, Paolo; Simoni, Serena; Castellini, Paolo; Battista, Gianmarco; Paone, Nicol

A Discrete-Continuous Method for Predicting Thermochemical Phenomena in a Cement Kiln and Supporting Indirect Monitoring

Thermochemical phenomena involved in cement kilns are still not well understood because of their complexity, besides technical difficulties in achieving direct measurements of critical process variables. This article addresses the problem of their comprehensive numerical prediction. The presented numerical model exploits Computational Fluid Dynamics and Finite Difference Method approaches for solving the gas domain and the rotating wall, respectively. The description of the thermochemical conversion and movement of the powder particles is addressed with a Lagrangian approach. Coupling between gas, particles and the rotating wall includes momentum, heat and mass transfer. Three-dimensional numerical predictions for a full-size cement kiln are presented and they show agreement with experimental data and benchmark literature. The quality and detail of the results are believed to provide a new insight into the functioning of a cement kiln. Attention is paid to the computational burden of the model and a methodology is presented for reducing the time-to-solution and paving the way for its exploitation in quasi-real-time, indirect monitoring

Stationary Wavelet Transform denoising in Pulsed Thermography: influence of camera resolution on defect detection

Denoising filters are widely used in image enhancement. However, they might induce severe blurring effects the lower is the resolution of the original image. When applied to a thermal image in Non-Destructive Testing (NDT), blurring could entail wrong estimation of defect boundaries and an overall reduction in defect detection performances. This contribution discusses the application of a wavelet-based denoising technique to a thermographic sequence obtained from a Pulsed Thermography testing, when using a high- resolution 1024x768 FPA infrared camera. Influence of denoising approach on data post- processed by Principal Component Analysis is discussed. Results indicate marked enhancement in defect detection, especially when compared to those obtained with a standard-resolution 320x240 FPA infrared camera

Erosion Prediction of Gas Turbine Compressor Blades Subjected to Water Washing Process

Technical BriefsBlade fouling is a relevant problem in turbomachinery applications. It affects both compressors and turbines. In the first case, fouling can be generated by the presence of dust, ashes or brackish air (in offshore applications). In turbines, fouling is mainly generated by residual of combustion process. Blade fouling generally leads to a reduction of the performance due to an increase on profile losses. Here we focus on the fouling due to salt deposition on naval/off-shore applications referring to machines that are part of the fleet of gas turbines manufacturers. In such applications, it is common to introduce on-line washing devices aiming at removing fouling from the early stages of the compressors. The water is sprayed upstream of the first rotor, it impacts on the rotor blades and thus dissolving the deposited salt. However, this procedure possibly leads to blade erosion and/or corrosion. A clear comprehension of the erosion mechanism is the main objective of the present work. To this end, we propose an integrated multi-phase CFD tool. The multi-phase flow is analyzed by adopting a one-way coupling, thus assuming water droplets to be drag by the carrier flow without influencing the main flow. The droplets are dispersed and tracked singularly by adopting a Lagrangian approach. As for the erosion, well-known and widely accepted models are used. The capability of a Lagrangian code, P-Track, developed and validated at the Department of Mechanical & Aerospace Engineering, Sapienza University in Rome, is presented. The code is able to predict the droplets trajectories, as well as to simulate the impact on the solid walls and the erosion mechanism. Simulations were performed using 25 and 100 ?m droplet size. Results, expressed in terms of normalized erosion rate, show the erosion patterns and erosive effect of the two size classes. Erosive capacity is proportional to droplet size, and the most eroded part of the blade is the leading edge, which is in qualitative agreement with measurements

Low-Cost and High-Performance Solution for Positioning and Monitoring of Large Structures

Systems for accurate attitude and position monitoring of large structures, such as bridges, tunnels, and offshore platforms are changing in recent years thanks to the exploitation of sensors based on Micro-ElectroMechanical Systems (MEMS) as an Inertial Measurement Unit (IMU). Currently adopted solutions are, in fact, mainly based on fiber optic sensors (characterized by high performance in attitude estimation to the detriment of relevant costs large volumes and heavy weights) and integrated with a Global Position System (GPS) capable of providing low-frequency or single-update information about the position. To provide a cost-effective alternative and overcome the limitations in terms of dimensions and position update frequency, a suitable solution and a corresponding prototype, exhibiting performance very close to those of the traditional solutions, are presented and described hereinafter. The solution leverages a real-time Kalman filter that, along with the proper features of the MEMS inertial sensor and Real-Time Kinematic (RTK) GPS, allows achieving performance in terms of attitude and position estimates suitable for this kind of application. The results obtained in a number of tests underline the promising reliability and effectiveness of the solution in estimating the attitude and position of large structures. In particular, several tests carried out in the laboratory highlighted high system stability; standard deviations of attitude estimates as low as 0.04 degrees were, in fact, experienced in tests conducted in static conditions. Moreover, the prototype performance was also compared with a fiber optic sensor in tests emulating actual operating conditions; differences in the order of a few hundredths of a degree were found in the attitude measurements

Stationary Wavelet Transform for denoising Pulsed Thermography data: optimization of wavelet parameters for enhancing defects detection

Innovative denoising techniques based on Stationary Wavelet Transform (SWT) have started being applied to Pulsed Thermography (PT) sequences, showing marked potentialities in improving defect detection. In this contribution, a SWT-based denoising procedure is performed on high and low resolution PT sequences. Samples under test are two composite panels with known defects. The denoising procedure undergoes an optimization step. An innovative criterion for selecting the optimal decomposition level in multi-scale SWT-based denoising is proposed. The approach is based on a comparison, in the wavelet domain, of the information content in the thermal image with noise propagated. The optimal wavelet basis is selected according to two performance indexes, respectively based on the probability distribution of the information content of the denoised frame, and on the Energy-to-Shannon Entropy ratio. After the optimization step, denoising is applied on the whole thermal sequence. The approximation coefficients at the optimal level are moved to the frequency domain, then low-pass filtered. Linear Minimum Mean Square Error (LMMSE) is applied to detail coefficients at the optimal level. Finally, Pulsed Phase Thermography (PPT) is performed. The performance of the optimized denoising method in improving the defect detection capability respect to the non-denoised case is quantified using the Contrast Noise Ratio (CNR) criterion