Análisis de la adhesión de recubrimientos del sistema Y2O3-Al2O3-SiO2 sobre sustratos de interés para la industria aeroespacial

En la industria aeroespacial se necesitan materiales ligeros que tengan unas altas prestaciones mecánicas combinadas con una baja densidad. El carburo de silicio, el carbono reforzado con fibra de carbono y el carburo de silicio reforzado con fibra de carbono son materiales que cumplen con estos requisitos, pero a altas temperaturas presentan problemas de oxidación. Una de las formas más efectivas de prevenir este fenómeno es la utilización de recubrimientos cerámicos, cuya correcta adhesión sobre los distintos sustratos es fundamental para garantizar su funcionamiento. En el caso del presente trabajo, se analiza la adhesión de recubrimientos vítreos del sistema Y2O3-Al2O3-SiO2 obtenidos mediante proyección térmica por llama oxiacetilénica. Para ello, se realizan ensayos de rayado a carga creciente analizando el tipo y la carga de fallo y su relación con las propiedades elásticas y mecánicas de los recubrimientos. Los resultados indican que la adhesión sobre los sustratos carburo de silicio y carburo de silicio reforzado con fibra de carbono es buena, mientras que el carbono reforzado con fibra de carbono no es un material adecuado para recubrir

Self-awareness in Intelligent Vehicles: Experience Based Abnormality Detection

The evolution of Intelligent Transportation System in recent times necessitates the development of self-driving agents: the self-awareness consciousness. This paper aims to introduce a novel method to detect abnormalities based on internal cross-correlation parameters of the vehicle. Before the implementation of Machine Learning, the detection of abnormalities were manually programmed by checking every variable and creating huge nested conditions that are very difficult to track. Nowadays, it is possible to train a Dynamic Bayesian Network (DBN) model to automatically evaluate and detect when the vehicle is potentially misbehaving. In this paper, different scenarios have been set in order to train and test a switching DBN for Perimeter Monitoring Task using a semantic segmentation for the DBN model and Hellinger Distance metric for abnormality measurements

Self-awareness in intelligent vehicles: Feature based dynamic Bayesian models for abnormality detection

The evolution of Intelligent Transportation Systems in recent times necessitates the development of self-awareness in agents. Before the intensive use of Machine Learning, the detection of abnormalities was manually programmed by checking every variable and creating huge nested conditions that are very difficult to track. This paper aims to introduce a novel method to develop self-awareness in autonomous vehicles that mainly focuses on detecting abnormal situations around the considered agents. Multi-sensory time-series data from the vehicles are used to develop the data-driven Dynamic Bayesian Network (DBN) models used for future state prediction and the detection of dynamic abnormalities. Moreover, an initial level collective awareness model that can perform joint anomaly detection in co-operative tasks is proposed. The GNG algorithm learns the DBN models\u2019 discrete node variables; probabilistic transition links connect the node variables. A Markov Jump Particle Filter (MJPF) is applied to predict future states and detect when the vehicle is potentially misbehaving using learned DBNs as filter parameters. In this paper, datasets from real experiments of autonomous vehicles performing various tasks used to learn and test a set of switching DBN models

Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation:The M&Ms Challenge

The emergence of deep learning has considerably advanced the state-of-the-art in cardiac magnetic resonance (CMR) segmentation. Many techniques have been proposed over the last few years, bringing the accuracy of automated segmentation close to human performance. However, these models have been all too often trained and validated using cardiac imaging samples from single clinical centres or homogeneous imaging protocols. This has prevented the development and validation of models that are generalizable across different clinical centres, imaging conditions or scanner vendors. To promote further research and scientific benchmarking in the field of generalizable deep learning for cardiac segmentation, this paper presents the results of the Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation (M&Ms) Challenge, which was recently organized as part of the MICCAI 2020 Conference. A total of 14 teams submitted different solutions to the problem, combining various baseline models, data augmentation strategies, and domain adaptation techniques. The obtained results indicate the importance of intensity-driven data augmentation, as well as the need for further research to improve generalizability towards unseen scanner vendors or new imaging protocols. Furthermore, we present a new resource of 375 heterogeneous CMR datasets acquired by using four different scanner vendors in six hospitals and three different countries (Spain, Canada and Germany), which we provide as open-access for the community to enable future research in the field