Transport/Storage Problem Identified

2020 ◽  
Author(s):  
Keyword(s):  
Storage Problem

The Minimum -Storage Problem: Complexity, Approximation, and Experimental Analysis

2016 ◽  
Vol 15 (7) ◽  
pp. 1797-1811 ◽  
Author(s):  
Gianlorenzo DAngelo ◽  
Daniele Diodati ◽  
Alfredo Navarra ◽  
Cristina M. Pinotti
Keyword(s):  
Experimental Analysis ◽  
Problem Complexity ◽  
Storage Problem

scholarly journals Storage Efficient Trajectory Clustering and k-NN for Robust Privacy Preserving Spatio-Temporal Databases

Algorithms ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 266 ◽  
Author(s):  
Elias Dritsas ◽  
Andreas Kanavos ◽  
Maria Trigka ◽  
Spyros Sioutas ◽  
Athanasios Tsakalidis
Keyword(s):  
Privacy Preservation ◽  
Dimensional Space ◽  
Research Work ◽  
Temporal Databases ◽  
Mobile Object ◽  
Linear Component ◽  
Trajectory Data ◽  
Spatio Temporal ◽  
Storage Problem ◽  
The One

The need to store massive volumes of spatio-temporal data has become a difficult task as GPS capabilities and wireless communication technologies have become prevalent to modern mobile devices. As a result, massive trajectory data are produced, incurring expensive costs for storage, transmission, as well as query processing. A number of algorithms for compressing trajectory data have been proposed in order to overcome these difficulties. These algorithms try to reduce the size of trajectory data, while preserving the quality of the information. In the context of this research work, we focus on both the privacy preservation and storage problem of spatio-temporal databases. To alleviate this issue, we propose an efficient framework for trajectories representation, entitled DUST (DUal-based Spatio-temporal Trajectory), by which a raw trajectory is split into a number of linear sub-trajectories which are subjected to dual transformation that formulates the representatives of each linear component of initial trajectory; thus, the compressed trajectory achieves compression ratio equal to M : 1 . To our knowledge, we are the first to study and address k-NN queries on nonlinear moving object trajectories that are represented in dual dimensional space. Additionally, the proposed approach is expected to reinforce the privacy protection of such data. Specifically, even in case that an intruder has access to the dual points of trajectory data and try to reproduce the native points that fit a specific component of the initial trajectory, the identity of the mobile object will remain secure with high probability. In this way, the privacy of the k-anonymity method is reinforced. Through experiments on real spatial datasets, we evaluate the robustness of the new approach and compare it with the one studied in our previous work.

Characteristics of Hydrogen Sorption, Solubility and Diffusivity in Graphites and Carbon Nanomaterials: Relevance to the On-Board Storage Problem

2006 ◽  
Vol 249 ◽  
pp. 143-146 ◽  
Author(s):  
Yuriy S. Nechaev ◽  
G.A. Filippov
Keyword(s):  
Experimental Data ◽  
Automotive Industry ◽  
Carbon Nanostructures ◽  
Carbon Nanomaterials ◽  
Hydrogen Sorption ◽  
Diffusion Parameters ◽  
Theoretical Investigations ◽  
Storage Problem ◽  
Kinetic Diffusion ◽  
Sorption Parameters

Results of experimental and theoretical investigations on hydrogen sorption by various carbon nanostructures, including fullerenes, single-walled and multi-walled nanotubes, nanofibers and nanographite-based composites are surveyed. Results of a thermodynamic analysis of the most significant experimental data are presented. The emphasis is placed on the studies reporting the extremum sorption parameters. The thermodynamic and kinetic (diffusion) parameters and equations describing the sorption processes are refined. The prospects of the applications of novel carbon nanomaterials for hydrogen storage in automotive industry are discussed.

scholarly journals Energy and Uncertainty: Models and Algorithms for Complex Energy Systems

AI Magazine ◽  
2014 ◽  
Vol 35 (3) ◽  
pp. 8-21 ◽  
Author(s):  
Warren Powell
Keyword(s):  
Learning Community ◽  
Value Function ◽  
Optimization Problems ◽  
Energy Systems ◽  
Modeling Framework ◽  
Different Types ◽  
Case Application ◽  
Storage Problem ◽  
Function Approximations ◽  
Uncertainty Models

The problem of controlling energy systems (generation, transmission, storage, investment) introduces a number of optimization problems which need to be solved in the presence of different types of uncertainty. We highlight several of these applications, using a simple energy storage problem as a case application. Using this setting, we describe a modeling framework based around five fundamental dimensions which is more natural than the standard canonical form widely used in the reinforcement learning community. The framework focuses on finding the best policy, where we identify four fundamental classes of policies consisting of policy function approximations (PFAs), cost function approximations (CFAs), policies based on value function approximations (VFAs), and lookahead policies. This organization unifies a number of competing strategies under a common umbrella.

On a general storage problem and its approximating solution

1981 ◽  
Vol 13 (03) ◽  
pp. 567-602 ◽  
Author(s):  
N. M. H. Smith ◽  
G. F. Yeo
Keyword(s):  
Hermite Polynomial ◽  
Conditional Density ◽  
Extinction Time ◽  
Two Dimensional ◽  
Conditional Density Function ◽  
Approximating Procedure ◽  
The Matrix ◽  
Storage Problem ◽  
Polynomial Expansions

A GI/G/r(x) store is considered with independently and identically distributed inputs occurring in a renewal process, with a general release rate r(·) depending on the content. The (pseudo) extinction time, or the content, just before inputs is a Markov process which can be represented by a random walk on and below a bent line; this results in an integral equation of the form gn +1(y) = ∫ l(y, w)gn (w) dw with l(y, w) a known conditional density function. An approximating solution is found using Hermite or modified Hermite polynomial expansions resulting in a Gram–Charlier or generalized Gram–Charlier representation, with the coefficients being determined by a matrix equation. Evaluation of the elements of the matrix involves two-dimensional numerical integration for which Gauss–Hermite–Laguerre integration is effective. A number of examples illustrate the quality of the approximating procedure against exact and simulated results.

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