Stochastic Response of a Continuous System with Stochastic Surface Irregularities to an Accelerated Load

Stochastic Congestion Alleviation with a Trade-off between Demand Response Resources and Load Shedding

Under the smart grid environments, Demand Response Resources (DRRs) as power system resources can effectively participate in and improve performance of electric systems. Congestion management is one of the technical challenges in which DRRs can play a significant role. Previously, congestion management is applied without considering the power system uncertainties. Therefore, a stochastic conges...

Track Irregularities Stochastic Modeling

High speed trains are currently meant to run faster and to carry heavier loads, while being less energy consuming and still respecting the security and comfort certification criteria. To face these challenges, a better understanding of the interaction between the dynamic train behavior and the track geometry is needed. As during its lifecycle, the train faces a great variability of track condit...

Track Irregularities Stochastic Modelling

This paper presents a methodology to build representative railway track geometries thanks to a stochastic modelling. This modelling, which has to integrate the statistical and spatial variabilities and dependencies, is a key issue when using simulation for conception, maintenance or certification purposes, as the dynamic behaviour of the trains is mainly induced by the track geometry. The stoch...

Stochastic gradient method with accelerated stochastic dynamics

In this paper, we propose a novel technique to implement stochastic gradient methods, which are beneficial for learning from large datasets, through accelerated stochastic dynamics. A stochastic gradient method is based on mini-batch learning for reducing the computational cost when the amount of data is large. The stochasticity of the gradient can be mitigated by the injection of Gaussian nois...

Decision criteria in risk analysis : an application of stochastic dominance with respect to a function