Dynamic Programming (DP) is known to be a standard optimization tool for solving Stochastic Optimal Control (SOC) problems, either over a finite or an infinite horizon of stages. Under very general assumptions, commonly employed numerical algorithms are based on approximations of the cost-to-go functions, by means of suitable parametric models built from a set of sampling points in the d-dimens...

Finding optimal policies for general partially observable Markov decision processes (POMDPs) is computationally difficult primarily due to the need to perform dynamic-programming (DP) updates over the entire belief space. In this paper, we first study a somewhat restrictive class of special POMDPs called almost-discernible POMDPs and propose an anytime algorithm called spaceprogressive value it...

In this paper we present an intelligent power controller for a vehicle power system that employs multiple power sources. In particular we focus on a vehicle power system architecture that is used in vehicles such as Mine Resistant Ambush Protected (MRAP) vehicle. These vehicles are designed to survive IED (Improvised Explosive Devices) attacks and ambushes. The power system has the following ma...

Stereo Vision has been an active research field that has produced a variety of different algorithms. Unfortunately most of the algorithms that produce superior results rely on non-linear optimization techniques that are very computationally expensive, and therefore not feasible to use for real-time applications such as tele-immersion. This paper will examine a number of real-time stereo algorit...

Dynamic Programming (DP) over tree decompositions is a well-established method to solve problems – that are in general NP-hard – efficiently for instances of small treewidth. Experience shows that (i) heuristically computing a tree decomposition has negligible runtime compared to the DP step; (ii) DP algorithms exhibit a high variance in runtime when using different tree decompositions; in fact...

This paper compares two optimal energy management methods for parallel hybrid electric vehicles using an Automatic Manual Transmission (AMT). A control-oriented model of the powertrain and vehicle dynamics is built first. The energy management is formulated as a typical optimal control problem to trade off the fuel consumption and gear shifting frequency under admissible constraints. The Dynami...

The aim of this study is to analyse dynamic programming in large scale, complex networks is more important in the fields of scientific and engineering. Recent applications needs the analysis of scale-free networks with many millions of nodes and edges; presenting a huge computational challenge. Employing distributed networks on-chip infrastructure presents a unique opportunity of delivering pow...

MOTIVATION Mass spectrometry allows sensitive, automated and high-throughput analysis of small molecules such as metabolites. One major bottleneck in metabolomics is the identification of 'unknown' small molecules not in any database. Recently, fragmentation tree alignments have been introduced for the automated comparison of the fragmentation patterns of small molecules. Fragmentation pattern ...

number: 007-0058 Adaptive Critics for Airline Revenue Management Abhijit Gosavi Department of Industrial and Systems Engineering University at Buffalo, SUNY 317 Bell Hall, Buffalo, NY 14260 [email protected] POMS 18th Annual Conference Dallas, Texas, U.S.A. May 4 to May 7, 2007. Abstract We present an approximate dynamic programming (DP) technique, called adaptive critic, for solving an airli...

For the general nonlinear processes control problem, the most rigorous approach is to use dynamic optimization. However, as the size of the problem grows, the Dynamic Programming (DP) approach is suffered from the burden of calculation which is called as 'Curse of Dimensionality'. To overcome this problem, a costapproximator can be used to obtain the optimal control input policy minimizing the ...