Driver behaviour modelling and cognitive engineering tools development in order to assess driver sitation awareness

International audienceOur global objective is to define a framework for car driving behaviour analysis in order to assess driver's situation awareness. We present models, methods and software tools inspired from the "Experience Based Reasoning" theory coming from the field of artificial intelligence. It allows a construction of a representation of the driving activity including data collected in real driving situations as well as interpretations made on the driver's mental model of the situation, and permits a refinement of psychological theories

Modélisation de la microstructure des grains dans le silicium multicristallin pour le photovoltaïque

L'objectif de ce travail est d'approfondir et de mieux comprendre les mécanismes responsables de la formation et de la croissance de la structure des grains dans le silicium multicristallin pour des applications photovoltaïques. Lors de la solidification du silicium multicristallin, la sélection des grains, le contrôle de la distribution de leur taille et leur direction de croissance sont des paramètres importants pour obtenir un matériau de bonne qualité et homogène. Ces paramètres influencent directement le rendement de conversion des cellules photovoltaïques, au travers de la capture et de la recombinaison des porteurs de charges et des interactions avec les impuretés. La structure de grains dans le silicium photovoltaïque évolue au cours de la solidification : des grains vont disparaître, d'autres vont apparaître, d'autres vont grossir pour donner au final une structure composée de gros grains, de petits grains dénommés grits', de joints de grains, et de macles. Il est donc important de comprendre les relations entre les différents paramètres du procédé industriel et leur influence sur les phénomènes physico-chimiques qui se produisent lors de la croissance afin de pouvoir influer sur la structure de grains dans le silicium, et de prévoir ses propriétés. Dans une première étape, nous avons établi un modèle de développement des grains basé sur le type de croissance (facettée, rugueuse ou mixte), la cinétique de ces divers types de croissances, le phénomène de maclage et la sélection des grains, dont nous montrons qu'ils sont, avec la germination initiale, à l'origine de la taille et de la structure des grains. Ensuite, nous proposons une approche de modélisation numérique de l'évolution de la structure des grains au cours de la solidification. Cette méthode se base sur l'analyse dynamique bidimensionnelle du joint de grains au niveau de la ligne triple grain-grain-liquide (rugueuse, facettée) tout en prenant en compte les phénomènes produits à l'échelle macroscopique (le champ de température local) et microscopique (la cinétique des grains). Le modèle résulte du couplage thermique et des mécanismes cinétiques de croissance. Nous avons donc développé un modèle numérique de croissance des grains en 2 dimensions et nous l'avons introduit dans le code 2D-MiMSiS qui se déroule en 2 étapes : Premièrement, le calcul en régime transitoire de la solidification macroscopique d'un lingot de silicium nous permet d'obtenir le champ thermique dans le lingot et la position précise de l'interface solide-liquide à différents instants ainsi que sa vitesse, son orientation (sa forme) et les gradients de température dans le liquide et le solide. Deuxièmement, la modélisation de la croissance est basée sur la description géométrique des joints de grains qui dépend de la cinétique des grains qui les bordent. Elle suit des critères dépendants de la morphologie (rugueuse ou facettée) de l'interface. Elle s appuie sur le réseau d'isothermes du calcul thermique sans l'influencer dans un premier temps. Un des objectifs de ce modèle est de faire varier différents paramètres du procédé et d'en mesurer l'impact sur la structure cristalline finale. Des résultats de calculs 2D sont présentés et discutés par rapport à l'expérience.The objective of this work is to explore and better understand the mechanisms responsible for the formation and growth of the grain structure in polycrystalline silicon for photovoltaic applications. During the solidification of polycrystalline silicon for the selection of the grain, control the distribution of their size and direction of growth are important parameters to obtain a material of good quality and homogeneous. These parameters directly influence the conversion efficiency of solar cells, through the capture and recombination of charge carriers and interactions with impurities. Grain structure in silicon photovoltaic evolves during solidification: Grain will disappear, others will appear, others will grow to give the final structure composed of large grains, small grains called 'grits' grain boundaries and twins. It is therefore important to understand the relationship between the parameters of the industrial process, the physico-chemical phenomena that occur during the growth and structure of grains in the silicon to predict its properties. In a first step, we established a model of development based on the grain growth type (faceted, rough or mixed), the kinetics of the various growths, the phenomenon of twinning and the selection of grains, we show that they are, with the initial germination, originally of the size and structure of the grains. Then, we propose an approach to numerical modeling of the evolution of lala grain structure during solidification. This method is based on the two-dimensional dynamic analysis of the grain boundary at the triple line grain-grain-liquid (rough, faceted) taking into account the phenomena produced at the macroscopic scale (the local temperature field) and microscopic (kinetic grain). The resulting model of the thermal coupling mechanisms and growth kinetics. We have developed a numerical model of grain growth in two dimensions, and we have introduced in the 2D-code MiMSiS which takes place in two steps: First, the calculation of transient macroscopic solidification of an ingot of silicon allows us to obtain the temperature field in the ingot and the precise position of the solid-liquid interface at different times as well as its speed, direction ( form) and the thermal gradients in the liquid and the solid. Second, the growth model is based on the geometrical description of grain boundary which depends on the kinetics of grain that border. It follows dependent criteria of the rough morphology or faceted interface. It relies on a network of insulated thermal calculation without influence in the first place. One objective of this model is to vary the process parameters and to measure their impact on the final crystalline structure. 2D calculation results are presented and discussed in relation to the experience.SAVOIE-SCD - Bib.électronique (730659901) / SudocGRENOBLE1/INP-Bib.électronique (384210012) / SudocGRENOBLE2/3-Bib.électronique (384219901) / SudocSudocFranceF

Spatial Atomic Layer Deposition

In conventional atomic layer deposition (ALD), precursors are exposed sequentially to a substrate through short pulses while kept physically separated by intermediate purge steps. Spatial ALD (SALD) is a variation of ALD in which precursors are continuously supplied in different locations and kept apart by an inert gas region or zone. Film growth is achieved by exposing the substrate to the locations containing the different precursors. Because the purge step is eliminated, the process becomes faster, being indeed compatible with fast-throughput techniques such as roll-to-roll (R2R), and much more versatile and easier and cheap to scale up. In addition, one of the main assets of SALD is that it can be performed at ambient pressure and even in the open air (i.e., without using any deposition chamber at all), while not compromising the deposition rate. In the present chapter, the fundamentals of SALD and its historical development are presented. Then, a succinct description of the different engineering approaches to SALD developed to date is provided. This is followed by the description of the particular fluid dynamics aspects and the engineering challenges associated with SALD. Finally, some of the applications in which the unique assets of SALD can be exploited are described

Chapter Spatial Atomic Layer Deposition

In conventional atomic layer deposition (ALD), precursors are exposed sequentially to a substrate through short pulses while kept physically separated by intermediate purge steps. Spatial ALD (SALD) is a variation of ALD in which precursors are continuously supplied in different locations and kept apart by an inert gas region or zone. Film growth is achieved by exposing the substrate to the locations containing the different precursors. Because the purge step is eliminated, the process becomes faster, being indeed compatible with fast-throughput techniques such as roll-to-roll (R2R), and much more versatile and easier and cheap to scale up. In addition, one of the main assets of SALD is that it can be performed at ambient pressure and even in the open air (i.e., without using any deposition chamber at all), while not compromising the deposition rate. In the present chapter, the fundamentals of SALD and its historical development are presented. Then, a succinct description of the different engineering approaches to SALD developed to date is provided. This is followed by the description of the particular fluid dynamics aspects and the engineering challenges associated with SALD. Finally, some of the applications in which the unique assets of SALD can be exploited are described

Metallic Nanowire Percolating Network: From Main Properties to Applications

There has been lately a growing interest into flexible, efficient and low-cost transparent electrodes which can be integrated for many applications. This includes several applications related to energy technologies (photovoltaics, lighting, supercapacitor, electrochromism, etc.) or displays (touch screens, transparent heaters, etc.) as well as Internet of Things (IoT) linked with renewable energy and autonomous devices. This associated industrial demand for low-cost and flexible industrial devices is rapidly increasing, creating a need for a new generation of transparent electrodes (TEs). Indium tin oxide has so far dominated the field of TE, but indium’s scarcity and brittleness have prompted a search into alternatives. Metallic nanowire (MNW) networks appear to be one of the most promising emerging TEs. Randomly deposited MNW networks, for instance, can present sheet resistance values below 10 Ω/sq., optical transparency of 90% and high mechanical stability under bending tests. AgNW or CuNW networks are destined to address a large variety of emerging applications. The main properties of MNW networks, their stability and their integration in energy devices are discussed in this contribution

Chapter Metallic nanowire percolating networks: from main properties to applications

There has been lately a growing interest into flexible, efficient and low-cost transparent electrodes which can be integrated for many applications. This includes several applications related to energy technologies (photovoltaics, lighting, supercapacitor, electrochromism, etc.) or displays (touch screens, transparent heaters, etc.) as well as Internet of Things (IoT) linked with renewable energy and autonomous devices. This associated industrial demand for low-cost and flexible industrial devices is rapidly increasing, creating a need for a new generation of transparent electrodes (TEs). Indium tin oxide has so far dominated the field of TE, but indium’s scarcity and brittleness have prompted a search into alternatives. Metallic nanowire (MNW) networks appear to be one of the most promising emerging TEs. Randomly deposited MNW networks, for instance, can present sheet resistance values below 10 Ω/sq., optical transparency of 90% and high mechanical stability under bending tests. AgNW or CuNW networks are destined to address a large variety of emerging applications. The main properties of MNW networks, their stability and their integration in energy devices are discussed in this contribution

GAP: Differentially Private Graph Neural Networks with Aggregation Perturbation

International audienceIn this paper, we study the problem of learning Graph Neural Networks (GNNs) with Differential Privacy (DP). We propose a novel differentially private GNN based on Aggregation Perturbation (GAP), which adds stochastic noise to the GNN's aggregation function to statistically obfuscate the presence of a single edge (edge-level privacy) or a single node and all its adjacent edges (node-level privacy). Tailored to the specifics of private learning, GAP's new architecture is composed of three separate modules: (i) the encoder module, where we learn private node embeddings without relying on the edge information; (ii) the aggregation module, where we compute noisy aggregated node embeddings based on the graph structure; and (iii) the classification module, where we train a neural network on the private aggregations for node classification without further querying the graph edges. GAP's major advantage over previous approaches is that it can benefit from multi-hop neighborhood aggregations, and guarantees both edge-level and node-level DP not only for training, but also at inference with no additional costs beyond the training's privacy budget. We analyze GAP's formal privacy guarantees using Rényi DP and conduct empirical experiments over three real-world graph datasets. We demonstrate that GAP offers significantly better accuracy-privacy trade-offs than state-of-the-art DP-GNN approaches and naive MLP-based baselines. Our code is publicly available at https://github.com/sisaman/GAP

Hazy Al₂O₃-FTO Nanocomposites: A Comparative Study with FTO-Based Nanocomposites Integrating ZnO and S:TiO₂ Nanostructures

In this study, we report the use of Al₂O₃ nanoparticles in combination with fluorine doped tin oxide (F:SnO₂, aka FTO) thin films to form hazy Al₂O₃-FTO nanocomposites. In comparison to previously reported FTO-based nanocomposites integrating ZnO and sulfur doped TiO₂ (S:TiO₂) nanoparticles (i.e., ZnO-FTO and S:TiO₂-FTO nanocomposites), the newly developed Al₂O₃-FTO nanocomposites show medium haze factor HT of about 30%, while they exhibit the least loss in total transmittance Ttot. In addition, Al₂O₃-FTO nanocomposites present a low fraction of large-sized nanoparticle agglomerates with equivalent radius req > 1 μm; effectively 90% of the nanoparticle agglomerates show req < 750 nm. The smaller feature size in Al₂O₃-FTO nanocomposites, as compared to ZnO-FTO and S:TiO₂-FTO nanocomposites, makes them more suitable for applications that are sensitive to roughness and large-sized features. With the help of a simple optical model developed in this work, we have simulated the optical scattering by a single nanoparticle agglomerate characterized by bottom radius r₀, top radius r₁, and height h. It is found that r₀ is the main factor affecting the HT(λ), which indicates that the haze factor of Al₂O₃-FTO and related FTO nanocomposites is mainly determined by the total surface coverage of all the nanoparticle agglomerates present