Исследование условий осаждения покрытий на основе нитрида хрома при работе магнетрона с горячей мишенью

В данной магистерской диссертации были исследованы механизмы формирования потоков атомарных частиц хрома, эмитируемых с поверхности горячей хромовой мишени в зависимости от мощности магнетрона. Была изучена кинетика поступления частиц на поверхность растущей пленки при формировании покрытий на основе CrN, а также проведен анализ потоков энергии и изменения температуры подложки в зависимости от мощности магнетрона. В результате исследований были получены зависимости скорости роста покрытий из хрома и на основе нитрида хрома от мощности магнетрона, которые показали, что за счет возникновения сублимации на поверхности мишени скорость осаждения покрытий увеличивается в несколько раз.In this master's thesis, the mechanisms of the formation of fluxes of atomic particles of chromium emitted from the surface of a hot chromium target were investigated, depending on the power of the magnetron. The kinetics of the arrival of particles on the surface of a growing film during the formation of coatings based on CrN was studied, and an analysis of energy fluxes and changes in the substrate temperature depending on the magnetron power was carried out. As a result of the studies, the dependences of the growth rate of chromium and chromium nitride coatings on the power of the magnetron were obtained, which showed that due to the occurrence of sublimation on the target surface, the deposition rate of the coatings increases several times

Self-supervised Hypergraphs for Learning Multiple World Interpretations

We present a method for learning multiple scene representations given a small labeled set, by exploiting the relationships between such representations in the form of a multi-task hypergraph. We also show how we can use the hypergraph to improve a powerful pretrained VisTransformer model without any additional labeled data. In our hypergraph, each node is an interpretation layer (e.g., depth or segmentation) of the scene. Within each hyperedge, one or several input nodes predict the layer at the output node. Thus, each node could be an input node in some hyperedges and an output node in others. In this way, multiple paths can reach the same node, to form ensembles from which we obtain robust pseudolabels, which allow self-supervised learning in the hypergraph. We test different ensemble models and different types of hyperedges and show superior performance to other multi-task graph models in the field. We also introduce Dronescapes, a large video dataset captured with UAVs in different complex real-world scenes, with multiple representations, suitable for multi-task learning.Comment: Accepted in ICCV 2023 Workshop

Semi-Supervised Learning for Multi-Task Scene Understanding by Neural Graph Consensus

We address the challenging problem of semi-supervised learning in the context of multiple visual interpretations of the world by finding consensus in a graph of neural networks. Each graph node is a scene interpretation layer, while each edge is a deep net that transforms one layer at one node into another from a different node. During the supervised phase edge networks are trained independently. During the next unsupervised stage edge nets are trained on the pseudo-ground truth provided by consensus among multiple paths that reach the nets' start and end nodes. These paths act as ensemble teachers for any given edge and strong consensus is used for high-confidence supervisory signal. The unsupervised learning process is repeated over several generations, in which each edge becomes a "student" and also part of different ensemble "teachers" for training other students. By optimizing such consensus between different paths, the graph reaches consistency and robustness over multiple interpretations and generations, in the face of unknown labels. We give theoretical justifications of the proposed idea and validate it on a large dataset. We show how prediction of different representations such as depth, semantic segmentation, surface normals and pose from RGB input could be effectively learned through self-supervised consensus in our graph. We also compare to state-of-the-art methods for multi-task and semi-supervised learning and show superior performance.Comment: Accepted at the 35th AAAI Conference on Artificial Intelligence (AAAI 2021

Оценка эмиссии метана автотранспортом мира

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Delayed propagation of derivatives in a two-dimensional aircraft design optimization problem

Introduction High-performance numerical simulations are among the indispensable tools used in the design and analysis of flight vehicles. We consider a particular computational fluid dynamics (CFD) simulation with the long-term aim to create an airfoil with the highest possible lift-over-drag ratio. A crucial preprocessing step of this design optimization problem consists in efficiently evaluating the derivatives of the lift-over-drag ratio with respect to some given geometric parameters characterizing the geometry of the underlying airfoil. Here, efficiency of the derivative computation is of major importance because the evaluation of the objective function includes the solution of the Navier-Stokes equations, representing a hard computational problem on its own. Therefore, one has to be careful when adding further computational work by the evaluation of derivatives. In a previous work [1], we used a technique called automatic differentiation (AD), whose basics are summarized in Se