Мобильные установки для ручной и механизированной лазерной сварки и обработки металлов (Обзор)

Представлен обзор мобильных установок для ручной и механизированной лазерной сварки и обработки конструкционных металлов. Даны описания и краткие технические характеристики установки для очистки металлических поверхностей от краски и других загрязнений, переносных аппаратов для механизированной лазерной сварки стыковых и нахлесточных соединений, установки дл лазерной обработки и наплавки.The paper presents a review of mobile units for manual and mechanized laser welding and treatment of metals. Description and brief specification of a unit for cleaning metallic surfaces from paint and other contamination, portable units for mechanized laser welding of butt and overlap joints and of a laser surfacing unit are given

Spectral Graph Convolutions for Population-based Disease Prediction

Exploiting the wealth of imaging and non-imaging information for disease prediction tasks requires models capable of representing, at the same time, individual features as well as data associations between subjects from potentially large populations. Graphs provide a natural framework for such tasks, yet previous graph-based approaches focus on pairwise similarities without modelling the subjects' individual characteristics and features. On the other hand, relying solely on subject-specific imaging feature vectors fails to model the interaction and similarity between subjects, which can reduce performance. In this paper, we introduce the novel concept of Graph Convolutional Networks (GCN) for brain analysis in populations, combining imaging and non-imaging data. We represent populations as a sparse graph where its vertices are associated with image-based feature vectors and the edges encode phenotypic information. This structure was used to train a GCN model on partially labelled graphs, aiming to infer the classes of unlabelled nodes from the node features and pairwise associations between subjects. We demonstrate the potential of the method on the challenging ADNI and ABIDE databases, as a proof of concept of the benefit from integrating contextual information in classification tasks. This has a clear impact on the quality of the predictions, leading to 69.5% accuracy for ABIDE (outperforming the current state of the art of 66.8%) and 77% for ADNI for prediction of MCI conversion, significantly outperforming standard linear classifiers where only individual features are considered.Comment: International Conference on Medical Image Computing and Computer-Assisted Interventions (MICCAI) 201

Dynamique de l'or et d'autres minéraux lourds dans un profil d'altération cuirassé du Burkina Faso, Afrique de l'Ouest : intérêt pour l'interprétation de la mise en place des matériaux constituant les cuirasses de haut glacis

Le profil d'altération cuirassé de haut glacis, développé dans l'environnement du site aurifère de Gangaol, a subi des phases d'altération suffisamment intenses pour que des minéraux résistants, comme le zircon et l'or, présentent des traces de dissolution importantes. Dans l'horizon supérieur de la cuirasse, ces minéraux altérés coexistent avec des particules d'or conservant des formes primaires intactes et avec des sulfures sains. Au sein de cet horizon, les teneurs en particules d'or sont plus élevées dans la matrice que dans les nodules fortement indurés. Cela implique qu'à ce niveau, le cuirassement a affecté un matériau contenant des éléments de nature et de degré d'altération variés. L'absence d'or dans la partie médiane du profil confirme un certain degré d'allochtonie des matériaux parentaux de l'horizon supérieur de la cuirasse. (Résumé d'auteur

Off-Axis Nulling Transfer Function Measurement: A First Assessment

We want to study a polychromatic inverse problem method with nulling interferometers to obtain information on the structures of the exozodiacal light. For this reason, during the first semester of 2013, thanks to the support of the consortium PERSEE, we launched a campaign of laboratory measurements with the nulling interferometric test bench PERSEE, operating with 9 spectral channels between J and K bands. Our objective is to characterise the transfer function, i.e. the map of the null as a function of wavelength for an off-axis source, the null being optimised on the central source or on the source photocenter. We were able to reach on-axis null depths better than 10(exp 4). This work is part of a broader project aiming at creating a simulator of a nulling interferometer in which typical noises of a real instrument are introduced. We present here our first results

SPHERE: the exoplanet imager for the Very Large Telescope

Observations of circumstellar environments to look for the direct signal of exoplanets and the scattered light from disks has significant instrumental implications. In the past 15 years, major developments in adaptive optics, coronagraphy, optical manufacturing, wavefront sensing and data processing, together with a consistent global system analysis have enabled a new generation of high-contrast imagers and spectrographs on large ground-based telescopes with much better performance. One of the most productive is the Spectro-Polarimetic High contrast imager for Exoplanets REsearch (SPHERE) designed and built for the ESO Very Large Telescope (VLT) in Chile. SPHERE includes an extreme adaptive optics system, a highly stable common path interface, several types of coronagraphs and three science instruments. Two of them, the Integral Field Spectrograph (IFS) and the Infra-Red Dual-band Imager and Spectrograph (IRDIS), are designed to efficiently cover the near-infrared (NIR) range in a single observation for efficient young planet search. The third one, ZIMPOL, is designed for visible (VIR) polarimetric observation to look for the reflected light of exoplanets and the light scattered by debris disks. This suite of three science instruments enables to study circumstellar environments at unprecedented angular resolution both in the visible and the near-infrared. In this work, we present the complete instrument and its on-sky performance after 4 years of operations at the VLT.Comment: Final version accepted for publication in A&

Interpretation of Brain Morphology in Association to Alzheimer's Disease Dementia Classification Using Graph Convolutional Networks on Triangulated Meshes

We propose a mesh-based technique to aid in the classification of Alzheimer's disease dementia (ADD) using mesh representations of the cortex and subcortical structures. Deep learning methods for classification tasks that utilize structural neuroimaging often require extensive learning parameters to optimize. Frequently, these approaches for automated medical diagnosis also lack visual interpretability for areas in the brain involved in making a diagnosis. This work: (a) analyzes brain shape using surface information of the cortex and subcortical structures, (b) proposes a residual learning framework for state-of-the-art graph convolutional networks which offer a significant reduction in learnable parameters, and (c) offers visual interpretability of the network via class-specific gradient information that localizes important regions of interest in our inputs. With our proposed method leveraging the use of cortical and subcortical surface information, we outperform other machine learning methods with a 96.35% testing accuracy for the ADD vs. healthy control problem. We confirm the validity of our model by observing its performance in a 25-trial Monte Carlo cross-validation. The generated visualization maps in our study show correspondences with current knowledge regarding the structural localization of pathological changes in the brain associated to dementia of the Alzheimer's type.Comment: Accepted for the Shape in Medical Imaging (ShapeMI) workshop at MICCAI International Conference 202

Groupwise Structural Parcellation of the Cortex: A Sound Approach Based on Logistic Models

International audienceCurrent theories hold that brain function is highly related with long-range physical connections through axonal bundles, namely extrinsic connectivity. However, obtaining a groupwise cortical parcella-tion based on extrinsic connectivity remains challenging. Current par-cellation methods are computationally expensive; need tuning of several parameters or rely on ad-hoc constraints. Furthermore, none of these methods present a model for the cortical extrinsic connectivity. To tackle these problems, we propose a parsimonious model for the extrinsic con-nectivity and an efficient parcellation technique based on clustering of tractograms. Our technique allows the creation of single subject and groupwise parcellations of the whole cortex. The parcellations obtained with our technique are in agreement with anatomical and functional par-cellations in the literature. In particular, the motor and sensory cortex are subdivided in agreement with the human homunculus of Penfield. We illustrate this by comparing our resulting parcels with an anatomical atlas and the motor strip mapping included in the Human Connectome Project data