Frederic Magoules

Shwetha Salimath, Francesca Bugiotti, Frederic Magoules

Metal forging is used to manufacture dies. We require the best set of input parameters for the process to be efficient. Currently, we predict the best parameters using the finite element method by generating simulations for the different initial conditions, which is a time-consuming process. In this paper, introduce a hybrid approach that helps in processing and generating new data simulations using a surrogate graph neural network model based on graph convolutions, having a cheaper time cost. We also introduce a hybrid approach that helps in processing and generating new data simulations using the model. Given a dataset representing meshes, our focus is on the conversion of the available information into a graph or point cloud structure. This new representation enables deep learning. The predicted result is similar, with a low error when compared to that produced using the finite element method. The new models have outperformed existing PointNet and simple graph neural network models when applied to produce the simulations.

Mengchen Wang, Nicolas Ferey, Patrick Bourdot et al.

This paper describes a work in progress about software and hardware architecture to steer and control an ongoing fluid simulation in a context of a serious game application. We propose to use the Lattice Boltzmann Method as the simulation approach considering that it can provide fully parallel algorithms to reach interactive time and because it is easier to change parameters while the simulation is in progress remaining physically relevant than more classical simulation approaches. We describe which parameters we can modify and how we solve technical issues of interactive steering and we finally show an application of our interactive fluid simulation approach of water dam phenomena.

Frederic Magoules, Qinmeng Zou

In this paper we present an original contactless human machine interface for driving car. The proposed framework is based on the image sent by a simple camera device, which is then processed by various computer vision algorithms. These algorithms allow the isolation of the user's hand on the camera frame and translate its movements into orders sent to the computer in a real time process. The optimization of the implemented algorithms on graphics processing unit leads to real time interaction between the user, the computer and the machine. The user can easily modify or create the interfaces displayed by the proposed framework to fit his personnel needs. A contactless driving car interface is here produced to illustrate the principle of our framework.

Frederic Magoules, Qinmeng Zou

This paper describes a global framework that enables contactless human machine interaction using computer vision and machine learning techniques. The main originality of our framework is that only a very simple image acquisition device, as a computer camera, is sufficient to establish a rich human machine interaction as traditional devices such as mouse or keyboard. This framework is based on well known computer vision techniques and efficient machine learning techniques are used to detect and track user hand gestures so the end user can control his computer using virtual interfaces with very simple gestures.