In this article, we propose an uncertainty decoding scheme for DNN-HMM hybrid systems based on numerical sampling. A finite set of samples is drawn from the estimated probability distribution of the acoustic features and subsequently passed through feature transformations/extensions and the deep neural network (DNN). Then, the nonlinearly-transformed feature samples are averaged at the output o...

In order to meet cost targets for hybrid electric (HEV), Plug-in hybrid electric (PHEV), and allelectric vehicles (EV), an improvement in the battery life cycle and safety is essential. One practical approach to improve performance is to use power electronics intensive cell equalizer, in conjunction with on-board energy storage devices. A new technique based on buck-boost topology to equalize a...

This paper presents a network planning application developed at THALES Research and Technology. The problem is to determine the successive evolutions to make in a GSM network so that all forecasted demands are satisfied and the global cost of investment and infrastructures maintenance is minimized. The application was modeled with a constraint solver over finite domains. The resolution algorith...

In order to establish autonomous behavior for technical systems, the well known trade-off between reactive control and deliberative planning has to be considered. Within this paper, we combine both principles by proposing a two-level hierarchical reinforcement learning scheme to enable the system to autonomously determine suitable solutions to new tasks. The approach is based on a behavior repr...

We present a framework for designing stable control schemes for systems with changing dynamics (SCD). Such systems form a subset of hybrid systems; their stabilization is therefore a problem in hybrid control. It is often difficult or even impossible to design a single controller that would stabilize a SCD. An appealing alternative are switching control schemes, where a different controller is ...

In this study, we consider a problem of estimating order flowtimes in two-stage hybrid flowshops, where orders arrive dynamically and various scheduling schemes can be used. To solve the problem, we devise several order flowtime estimation methods, and each method is specific to the scheduling scheme used in the shop. Whenever an order arrives, the flowtime of the order is estimated by using on...

The use of the high-order Flux Reconstruction (FR) spatial discretization scheme for LES on unstructured meshes is investigated. Simulations of the compressible Taylor-Green vortex at Re = 1600 demonstrate that the FR scheme has low numerical dissipation and accurately reproduces the turbulent energy cascade at low resolution, making it ideal for high-order LES. To permit the use of subgrid-sca...

An efficient and flexible parallel version of a Large-Eddy Simulation (LES) solver has been developed to simulate the compressible tip-flow of a rotor in hover. The solver employs an immersed-boundary technique in conjunction with a curvilinear structured grid and the dynamic model is used to model the subgrid scale (SGS) stress terms. The solver also employs a hybrid implicit-explicit time-dis...

A hybrid implicit-explicit scheme is developed for Eulerian hydrodynamics. The hybridi~atlon is a continuous switch and operates on each characteristic field separately. The explicit scheme is a version of the second-order Godunov scheme; the implicit method is only firstorder accurate in time but leads to Ii block tridiagonal matrix inversion for efficiency and is unconditionally stable for th...

A novel, parallel, high-order, central essentially non-oscillatory (CENO), cell-centered, finite-volume scheme is developed and applied to large-eddy simulation (LES) of turbulent premixed flames. The high-order CENO finite-volume scheme is applied to the solution of the Favre-filtered Navier-Stokes equations governing turbulent flows of a fully-compressible reactive mixture on three-dimensiona...