DiffUTE: Universal Text Editing Diffusion Model

Diffusion model based language-guided image editing has achieved great success recently. However, existing state-of-the-art diffusion models struggle with rendering correct text and text style during generation. To tackle this problem, we propose a universal self-supervised text editing diffusion model (DiffUTE), which aims to replace or modify words in the source image with another one while maintaining its realistic appearance. Specifically, we build our model on a diffusion model and carefully modify the network structure to enable the model for drawing multilingual characters with the help of glyph and position information. Moreover, we design a self-supervised learning framework to leverage large amounts of web data to improve the representation ability of the model. Experimental results show that our method achieves an impressive performance and enables controllable editing on in-the-wild images with high fidelity. Our code will be avaliable in \url{https://github.com/chenhaoxing/DiffUTE}

Helium bubble nucleation in Laser Powder Bed Fusion processed 304L stainless steel

The interest in application of Additive Manufacturing (AM) to nuclear industry stems not only from the benefits of design freedom and shortened lead time, but also from the possibility of enhancing the performance through microstructure control. One of the most important requirements for in-core structural material in nuclear power plants is helium resistance. The Laser Powder Bed Fusion (LPBF) processed 304L stainless steel possesses strong defect sinks such as high densities of dislocation-surrounded sub-grains and dispersed nano-inclusions. In this work the LPBF processed 304L in as-built and solution-annealed conditions along with a conventionally rolled counterpart were implanted with 350\ua0keV He+\ua0ion at 300\ua0\ub0C to 0.24 dpa (displacement per atom). Transmission Electron Microscopy (TEM) observations indicate significantly higher helium resistance of the as-built LPBF 304L compared to the other two samples. The sink strengths in the three samples are calculated based on the measurements of the microstructural features using simplified equations for the correlation between microstructural characteristics and helium tolerance. Based on the calculation, the cellular sub-grains and the dispersed nano-inclusions are the primary and secondary contributors to the helium resistance of LPBF 304L steel

Développement d’un instrument MPE sans fil

Encadrée par Alain Pegatoquet équipe MCSOCMon stage de fin d’études ingénieur s’est déroulé d’Avril 2019 à Septembre 2019 au sein de l’équipe MCSOC du Laboratoire d’Electronique, Antennes et Télécommunications (LEAT). Ce stage s’inscrit dans le cadre du projet MPEi, dont le coordinateur est Gaël Navard du CRR (Conservatoire à Rayonnement Régional). L’ambition du projet MPEi est de créer un environnement complet pour la création d’instruments de musique d’expression polyphonique multidimensionnel (MPE) à bas coût. L’objectif est de déployer à terme des instruments numériques expressifs dans les collèges des Alpes Maritimes avec l’ambition de créer de véritables orchestres numériques pédagogiques. Parmi les partenaires du projet MPEi, le LEAT est responsable de la partie recherche et développement de ce nouvel instrument.L’objectif principal de mon stage est de mettre en oeuvre une gestion en 3 dimensions et polyphonique de l’instrument, mais également de participer à la conception d’une transmission sans fil des messages entre les différents instruments et une station de base centrale. J’ai eu ainsi la charge d’étudier et d’implémenter les fonctionnalités permettant un jeu en 3D et polyphonique (jouer avec plusieurs doigts en même temps) de l’instrument numérique. La partie transmission sans fil a été principalement effectuée par un ingénieur recruté sur ce projet. Lors de ces 6 mois de stage, j’ai réalisé principalement les tâches suivantes :— Gestion en 3D et polyphonique de l’instrument (programmation C++ multithreading).— Conception initiale d’un patch Pure Data pour la génération de l’audio à partir des données capteurs issus de l’instrument.— La communication entre le programme C++ de gestion de l’instrument et le patch Pure Data.— La participation à la conception d’un nouveau prototype matériel de l’instrument numérique.J’ai beaucoup appris grâce à ce stage. Cette expérience, enrichissante et complète, me sera utile dans la suite de ma carrièr

DESIGN AND FUNCTION OF SP-CARBON-LINKED COVALENT ORGANIC FRAMEWORKS

Ph.DDOCTOR OF PHILOSOPHY (FOS

Gust Response of Spanwise Morphing Wing by Simulation and Wind Tunnel Testing

The spanwise morphing wing can change its aerodynamic shape to suit its flight environment, thereby having the potential to improve the flight performance of the aircraft, especially in gusty conditions. To investigate the potential of morphing wings, the aerodynamic performance of a spanwise morphing wing with a flapping wingtip in a gust environment was analyzed in this paper. The aerodynamic characteristics of the morphing wing are hard to measure accurately, and thus a wind tunnel test was carried out to study the influences of morphing parameters, such as the morphing length, amplitude and frequency on the gust alleviation effect. The flow mechanism of the designed spanwise morphing wing was analyzed in detail by the instantaneous lift results of the wind tunnel test and the flow field results of the CFD method. The results have shown that with appropriate morphing parameters, the spanwise morphing wing designed in this paper can effectively achieve gust alleviation during flight. The conclusions obtained in this paper can be useful guidance for the design of morphing aircraft

MTCSNet: Mean Teachers Cross-Supervision Network for Semi-Supervised Cloud Detection

Cloud detection methods based on deep learning depend on large and reliable training datasets to achieve high detection accuracy. There will be a significant impact on their performance, however when the training data are insufficient or when the label quality is low. Thus, to alleviate this problem, a semi-supervised cloud detection method, named the mean teacher cross-supervision cloud detection network (MTCSNet) is proposed. This method enforces both consistency and accuracy on two cloud detection student network branches, which are perturbed with different initializations, for the same input image. For each of the two student branches, the respective teacher branches, used to generate high-quality pseudo labels, are constructed using an exponential moving average method (EMA). A pseudo one-hot label, produced by one teacher network branch, supervises the other student network branch with the standard cross-entropy loss, and vice versa. To incorporate additional prior information into the model, the presented method uses near-infrared bands instead of red bands as model inputs and injects strong data augmentations on unlabeled images fed into the student model. This induces the model to learn richer representations and ensure consistency constraints on the predictions of the same unlabeled image across different batches. To attain a more refined equilibrium between the supervised and semi-supervised loss in the training process, the proposed cloud detection network learns the optimal weights based on homoscedastic uncertainty, thus effectively exploiting the advantages of semi-supervised tasks and elevating the overall performance. Experiments on the SPARCS and GF1-WHU public cloud detection datasets show that the proposed method outperforms several state-of-the-art semi-supervised algorithms when only a limited number of labeled samples are available

Dynamic Analysis and Numerical Simulation of Arresting Hook Engaging Cable in Carried-Based UAV Landing Process

Carrier-based unmanned aerial vehicles (UAVs) require precise evaluation methods for their landing and arresting safety due to their high autonomy and demanding reliability requirements. In this paper, an efficient and accurate simulation method is presented for studying the arresting hook engaging arresting cable process. The finite element method and multibody dynamics (FEM-MBD) approach is employed. By establishing a rigid–flexible coupling model encompassing the UAV and arresting gear system, the simulation model for the engagement process is obtained. The model incorporates multiple coordinate systems to effectively capture the relative motion between the rigid and flexible components. The model considers the material properties, arresting gear system characteristics, and UAV state during engagement. Verification is conducted by comparing simulation results with experimental data from a referenced arresting hook rebound. Finally, simulations are performed under different touchdown points and roll angles of the UAV to analyze the stress distribution of the hook, center of gravity variations, and the tire touch and rollover cable response. The proposed rigid–flexible coupling arresting dynamics model in this paper enables the effective analysis of the dynamic behavior during the arresting hook engaging arresting cable process