1 Abstract. Multiperiod nancial optimization is usually based on a stochastic model for the possible market situations. There is a rich literature about modeling and estimation of continuous-state nancial processes, but little attention has been paid how to approximate such a process by a discrete-state scenario process and how to measure the pertaining approximation error. In this paper we sho...

We show that in the minimal three generation seesaw models for neutrinos, the presence of leptonic (Le+Lμ−Lτ )×S2 symmetry leads to one of the right handed neutrinos remaining massless. This state can then be identified with the sterile neutrino required for a simultaneous understanding of solar, atmospheric and LSND observations. We present a gauge model where the presence of higher dimensiona...

A brief overview of the requirements engineering, its history, and state of practice are given. A semi-formal method to structure the behavioral requirements for realtime embedded systems is presented. This method is based on a set of forms that contain both informal textbased descriptions and formally defined language constructs. After documentation of requirements into these forms, an algorit...

A multistage stochastic linear program (MSLP) is a model of sequential stochastic optimization where the objective and constraints are linear. When any of the random variables used in the MSLP are continuous, the problem is infinite dimensional. In order to numerically tackle such a problem we usually replace it with a finite dimensional approximation. Even when all the random variables have fi...

Data for optimization problems often comes from (deterministic) forecasts, but it is näıve to consider a forecast as the only future possibility. A more sophisticated approach uses data to generate alternative future scenarios, each with an attached probability. The basic idea is to estimate the distribution of forecast errors and use that to construct the scenarios. Although sampling from the ...

Abstract : This paper describes a novel technique for scenario generation aimed at closed loop stochastic nonlinear model predictive control. The key ingredient in the algorithm is the use of vector quantization methods. We also show how one can impose a tree structure on the resulting scenarios. Finally, we briefly describe how the scenarios can be used in large scale stochastic nonlinear mode...

We present a method to generate renewable scenarios using Bayesian probabilities by implementing the Bayesian generative adversarial network (Bayesian GAN), which is a variant of generative adversarial networks based on two interconnected deep neural networks. By using a Bayesian formulation, generators can be constructed and trained to produce scenarios that capture different salient modes in ...

start taking a few most applied scenarios from a traffic control centre, analysing each component and structure of the whole, and evaluating the impact of each component and some typical combinations, based on available monitoring systems. Carrying on such initial research on best practices, we build a dynamic simulation model, including these typical scenarios and evaluate the impact on traffi...

Serious games allow for adaptive and personalised forms of training; the nature and timing of learning activities can be tailored to the trainee’s needs and interests. Autonomous game-based training requires for the automatic selection of appropriate exercises for an individual trainee. This paper presents a framework for an automated scenario generation system. The underlying notion is that a ...