Search results for "Variable"
showing 10 items of 1674 documents
First-passage problem for nonlinear systems under Lévy white noise through path integral method
2016
In this paper, the first-passage problem for nonlinear systems driven by $$\alpha $$ -stable Levy white noises is considered. The path integral solution (PIS) is adopted for determining the reliability function and first-passage time probability density function of nonlinear oscillators. Specifically, based on the properties of $$\alpha $$ -stable random variables and processes, PIS is extended to deal with Levy white noises with any value of the stability index $$\alpha $$ . Application to linear and nonlinear systems considering different values of $$\alpha $$ is reported. Comparisons with pertinent Monte Carlo simulation data demonstrate the accuracy of the results.
Solving continuous models with dependent uncertainty: a computational approach
2013
This paper presents a computational study on a quasi-Galerkin projection-based method to deal with a class of systems of random ordinary differential equations (r.o.d.e.'s) which is assumed to depend on a finite number of random variables (r.v.'s). This class of systems of r.o.d.e.'s appears in different areas, particularly in epidemiology modelling. In contrast with the other available Galerkin-based techniques, such as the generalized Polynomial Chaos, the proposed method expands the solution directly in terms of the random inputs rather than auxiliary r.v.'s. Theoretically, Galerkin projection-based methods take advantage of orthogonality with the aim of simplifying the involved computat…
On the checking of g-coherence of conditional probability bounds
2003
We illustrate an approach to uncertain knowledge based on lower conditional probability bounds. We exploit the coherence principle of de Finetti and a related notion of generalized coherence (g-coherence), which is equivalent to the "avoiding uniform loss" property introduced by Walley for lower and upper probabilities. Based on the additive structure of random gains, we define suitable notions of non relevant gains and of basic sets of variables. Exploiting them, the linear systems in our algorithms can work with reduced sets of variables and/or constraints. In this paper, we illustrate the notions of non relevant gain and of basic set by examining several cases of imprecise assessments d…
A New Approach to the Stock Location Assignment Problem by Multidimensional Scaling and Seriation
1999
The problem of the best stock location assignment in a warehouse has a fundamental role while optimising picking activities. In the present paper, this problem has been faced by considering seven variables to compute similarity between items. In this context, the problem of the choice of the most adequate similarity (or dissimilarity) measure between units while applying Multidimensional Scaling (MDS), has been examined. Besides the right metric, the possibility of applying a Seriation algorithm has been also considered. By using both MDS and seriation not just a single target can be considered, but we are able to manage with a plenty of variables; on the contrary with techniques used in li…
Solution to nonlinear MHDS arising from optimal growth problems
2011
Abstract In this paper we propose a method for solving in closed form a general class of nonlinear modified Hamiltonian dynamic systems (MHDS). This method is used to analyze the intertemporal optimization problem from endogenous growth theory, especially the cases with two controls and one state variable. We use the exact solutions to study both uniqueness and indeterminacy of the optimal path when the dynamic system has not a well-defined isolated steady state. With this approach we avoid the linearization process, as well as the reduction of dimension technique usually applied when the dynamic system offers a continuum of steady states or no steady state at all.
A Highly Flexible Trajectory Model Based on the Primitives of Brownian Fields—Part II: Analysis of the Statistical Properties
2016
In the first part of our paper, we have proposed a highly flexible trajectory model based on the primitives of Brownian fields (BFs). In this second part, we study the statistical properties of that trajectory model in depth. These properties include the autocorrelation function (ACF), mean, and the variance of the path along each axis. We also derive the distribution of the angle-of-motion (AOM) process, the incremental traveling length process, and the overall traveling length. It is shown that the path process is in general non-stationary. We show that the AOM and the incremental traveling length processes can be modeled by the phase and the envelope of a complex Gaussian process with no…
Optimization of the objective function –surface quality by end-milling dimensional machining of some aluminum alloys
2019
Abstract In the aerospace industry, the milling of aluminum alloy parts is a machining process with the primary purpose of removing high volumes of material. Aluminum alloys are materials that have relatively good machinability, which helps the process because many of the components of the aircraft are of high dimensions. These parts have many pockets more or less deep, and the removal by cutting off about 90% of the initial volume of the workpiece is a matter of consideration. The manufacturing process is protracted and involves long semi-finishing and finishing operations, so it is recommended that any researcher who begins and finishes an experimental study should do it base on a specifi…
An Introduction to Kernel Methods
2009
Machine learning has experienced a great advance in the eighties and nineties due to the active research in artificial neural networks and adaptive systems. These tools have demonstrated good results in many real applications, since neither a priori knowledge about the distribution of the available data nor the relationships among the independent variables should be necessarily assumed. Overfitting due to reduced training data sets is controlled by means of a regularized functional which minimizes the complexity of the machine. Working with high dimensional input spaces is no longer a problem thanks to the use of kernel methods. Such methods also provide us with new ways to interpret the cl…
Solving dynamic memory allocation problems in embedded systems with parallel variable neighborhood search strategies
2015
International audience; Embedded systems have become an essential part of our lives, thanks to their evolution in the recent years, but the main drawback is their power consumption. This paper is focused on improving the memory allocation of embedded systems to reduce their power consumption. We propose a parallel variable neighborhood search algorithm for the dynamic memory allocation problem, and compare it with the state of the art. Computational results and statistical tests applied show that the proposed algorithm produces significantly better outcomes than the previous algorithm in shorter computing time.
Time-Dependent Multiple Depot Vehicle Routing Problem on Megapolis Network under Wardrop's Traffic Flow Assignment
2018
In this work multiple depot vehicle routing problem is considered in case of variable travel times between nodes on a metropolis network. This variant of the classic multiple depot vehicle routing problem is motivated by the fact that in urban contexts variable traffic conditions play an essential role and can not be ignored in order to perform a realistic optimization. Time-travel matrices corresponding to each period of planning horizon were formed by solving the traffic assignment problem in conjunction with shortest path problem. Routing problem instances include from 20 to 100 customers randomly chosen from a road network of Saint-Petersburg. The results demonstrate that taking into ac…