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Design and Dimensioning of Natural Gas Pipelines with Hydrogen Injection
No full textThe global focus on reducing air pollution and dependence on fossil fuels has led to efforts to shift to renewable energy sources. Hydrogen is a promising alternative due to its high energy capacity and ability to regulate electricity production through electrolysis. In this context, the problem of designing and sizing natural gas pipelines with hydrogen injection is presented. The objective is to establish the network topology and diameter dimensions of each pipeline section for hydrogen distribution, in order to cover the demand at a minimum cost. To address the proposed problem, we consider the dimensioning as the selection of a diameter from a set of available measures, i.e., a discrete diameter approach, and we compare it with a continuous diameter approach from the literature, including a mixed integer nonlinear programming (MINLP) formulation of degree six. In our discrete diameter approach, we propose a non-convex quadratic (MIQLP) model, and we derive a mixed-integer quadratic convex relaxation (MIQCP). Finally, we adapt a Delta Change heuristic to this context. We implement several solution methods for a real case study in France. These include solving the dimensioning problem on a fixed Minimum Spanning Tree topology, considering both continuous and discrete diameters, employing the Delta Change heuristic for both cases, continuous and discrete, and solving the MIQCP relaxation problem. The strengths and weaknesses of each of these proposals are demonstrated through the study
On The Effect of Replacement Policies on The Security of Randomized Cache Architectures
No full textInternational audienceRandomizing the mapping of addresses to cache entries has proven to be an effective technique for hardening caches against contention-based attacks like Prime+Prome. While attacks and defenses are still evolving, it is clear that randomized caches significantly increase the security against such attacks. However, one aspect that is missing from most analyses of randomized cache architectures is the choice of the replacement policy. Often, only the random- and LRU replacement policies are investigated. However, LRU is not applicable to randomized caches due to its immense hardware overhead, while the random replacement policy is not ideal from a performance and security perspective.In this paper, we explore replacement policies for randomized caches. We develop two new replacement policies and evaluate a total of five replacement policies regarding their security against Prime+Prune+Probe attackers. Moreover, we analyze the effect of the replacement policy on the system's performance and quantify the introduced hardware overhead. We implement randomized caches with configurable replacement policies in software and hardware using a custom cache simulator, gem5, and the CV32E40P RISC-V core. Among others, we show that the construction of eviction sets with our new policy, VARP-64, requires over 25-times more cache accesses than with the random replacement policy while also enhancing overall performance
Unsupervised Motion Retargeting for Human-Robot Imitation
No full textInternational audienceThis early-stage research work aims to improve online human-robot imitation by translating sequences of joint positions from the domain of human motions to a domain of motions achievable by a given robot, thus constrained by its embodiment. Leveraging the generalization capabilities of deep learning methods, we address this problem by proposing an encoder-decoder neural network model performing domain-to-domain translation. In order to train such a model, one could use pairs of associated robot and human motions. Though, such paired data is extremely rare in practice, and tedious to collect. Therefore, we turn towards deep learning methods for unpaired domain-to-domain translation, that we adapt in order to perform human-robot imitation
On the use of the parabolic wave equation to model the electromagnetic propagation in maritime environment
No full textInternational audienceModeling the long-range propagation in a maritime environment is a challenging problem since many physical phenomena shall be accounted for, such as refraction, or waves. Here a model based on the parabolic wave equation solved with split-step wavelet that allows obtaining accurate simulations is proposed
Les pratiques managériales au cœur de la gestion de crise : la crise sanitaire dans le tourisme en Polynésie française
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Modelling of Buoyancy Based Actuation of an Inflatable Underwater Soft Robot
No full textInternational audienceThis paper presents a novel actuation method to change the buoyancy of an underwater soft robot by pumping denser and less dense liquids, than the liquid the robot is immersed in, into the robot to actively change the mass of the robot and cause it to experience a change in buoyancy. The technological research gap lies in the method of pumping lighter and heavier fluids into a soft robot to cause it to experience a change in mass and depth, which has not been explored before to the best of the author's knowledge. An analysis of the forces that are placed on the robotic system and the necessary equations to determine the force produced by a solution with a particular ratio of solute to solvent are presented. Preliminary experiments were conducted to test the buoyancy-based actuation method discussed in this paper by building a two link, soft, inflatable robot arm. This robot was shown to change the floatation height of its links when denser fluid was pumped into its links
Coherent phonons in incommensurate LaVS3 crystal
No full textInternational audienceIn this Letter, we investigate coherent phonon dynamics in the incommensurate LaVS3 crystal by femtosecond pump-probe spectroscopy. Two coherent phonon modes are systematically observed in the transient reflectivity, centered at 1.8 and 2.85 THz, respectively, while a third mode centered at 4.5 THz is observed only at high pump fluence. The experimental results obtained at two different polarization configurations as well as a comparison with recent theoretical results allow to assign the two main modes to the interlayer shearing mode and to an intralayer mode, respectively. Two possible assignments are discussed for the third mode, by invoking a possible emergence of nonlinear phonon processes
Time Consistency for Multistage Stochastic Optimization Problems under Constraints in Expectation
No full textInternational audienceWe consider sequences-indexed by time (discrete stages)-of families of multistage stochastic optimization problems. At each time, the optimization problems in a family are parameterized by some quantities (initial states, constraint levels.. .). In this framework, we introduce an adapted notion of time consistent optimal solutions, that is, solutions that remain optimal after truncation of the past and that are optimal for any values of the parameters. We link this time consistency notion with the concept of state variable in Markov Decision Processes for a class of multistage stochastic optimization problems incorporating state constraints at the final time, either formulated in expectation or in probability. For such problems, when the primitive noise random process is stagewise independent and takes a finite number of values, we show that time consistent solutions can be obtained by considering a finite dimensional state variable. We illustrate our results on a simple dam management problem
Statistical Linearization for Robust Motion Planning
No full textInternational audienceThe goal of robust motion planning consists of designing open-loop controls which optimally steer a system to a specific target region while mitigating uncertainties and disturbances which affect the dynamics. Recently, stochastic optimal control has enabled particularly accurate formulations of the problem. Nevertheless, despite interesting progresses, these problem formulations still require expensive numerical computations. In this paper, we start bridging this gap by leveraging statistical linearization. Specifically, through statistical linearization we reformulate the robust motion planning problem as a simpler deterministic optimal control problem subject to additional constraints. We rigorously justify our method by providing estimates of the approximation error, as well as some controllability results for the new constrained deterministic formulation. Finally, we apply our method to the powered descent of a space vehicle, showcasing the consistency and efficiency of our approach through numerical experiments
Classification of Gougerot-Sjögren Syndrome Based on Artificial Intelligence
No full textInternational audienceGougerot-Sjögren syndrome (GSS) is an incurable chronic autoimmune disease that involves an inflammatory process and lymphoproliferation that primarily affects the lacrimal and salivary glands. This disease mainly affects women (the ratio of affected women can be nine times higher than the ratio of affected men). According to an epidemiology study, GSS at different severity levels may affect between 0.1 and 5% of the total population. Usually, GSS detection is performed by biopsy. Some medical studies showed a correlation between biopsy results and the salivary gland ultrasonography (SGUS). On the other side, ultrasound imaging devices are widely used in various medical fields thanks to their noninvasive nature, safety and nonimpact on patients’ health. However, these grey images are affected by noise and artifacts. In our project, we developed an artificial intelligence approach to classify and detect GSS only based ultrasound imaging. Indeed, the salivary glands are made of tissue, with acinar, ductal, and myoepithelial cells. Some sonographic features are clearly identified for the detection of the primary GSS. Additionally, some patterns in the textures can help differentiate GSS with other diseases. So, we extracted specific features and then developed a learning scheme for deep neural networks based on joint training on classification and segmentation tasks. We obtained conclusive accuracy on the detection of GSS