33 research outputs found
Linear approach to the orbiting spacecraft thermal problem
We develop a linear method for solving the nonlinear differential equations
of a lumped-parameter thermal model of a spacecraft moving in a closed orbit.
Our method, based on perturbation theory, is compared with heuristic
linearizations of the same equations. The essential feature of the linear
approach is that it provides a decomposition in thermal modes, like the
decomposition of mechanical vibrations in normal modes. The stationary periodic
solution of the linear equations can be alternately expressed as an explicit
integral or as a Fourier series. We apply our method to a minimal thermal model
of a satellite with ten isothermal parts (nodes) and we compare the method with
direct numerical integration of the nonlinear equations. We briefly study the
computational complexity of our method for general thermal models of orbiting
spacecraft and conclude that it is certainly useful for reduced models and
conceptual design but it can also be more efficient than the direct integration
of the equations for large models. The results of the Fourier series
computations for the ten-node satellite model show that the periodic solution
at the second perturbative order is sufficiently accurate.Comment: 20 pages, 11 figures, accepted in Journal of Thermophysics and Heat
Transfe
A measure of centrality based on the spectrum of the Laplacian
We introduce a family of new centralities, the k-spectral centralities.
k-Spectral centrality is a measurement of importance with respect to the
deformation of the graph Laplacian associated with the graph. Due to this
connection, k-spectral centralities have various interpretations in terms of
spectrally determined information.
We explore this centrality in the context of several examples. While for
sparse unweighted networks 1-spectral centrality behaves similarly to other
standard centralities, for dense weighted networks they show different
properties. In summary, the k-spectral centralities provide a novel and useful
measurement of relevance (for single network elements as well as whole
subnetworks) distinct from other known measures.Comment: 12 pages, 6 figures, 2 table
Regularity of Edge Ideals and Their Powers
We survey recent studies on the Castelnuovo-Mumford regularity of edge ideals
of graphs and their powers. Our focus is on bounds and exact values of and the asymptotic linear function , for in terms of combinatorial data of the given graph Comment: 31 pages, 15 figure
Elementary landscape decomposition of the 0-1 unconstrained quadratic optimization
Journal of Heuristics, 19(4), pp.711-728Landscapes’ theory provides a formal framework in which combinatorial optimization problems can be theoretically characterized as a sum of an especial kind of landscape called elementary landscape. The elementary landscape decomposition of a combinatorial optimization problem is a useful tool for understanding the problem. Such decomposition provides an additional knowledge on the problem that can be exploited to explain the behavior of some existing algorithms when they are applied to the problem or to create new search methods for the problem. In this paper we analyze the 0-1 Unconstrained Quadratic Optimization from the point of view of landscapes’ theory. We prove that the problem can be written as the sum of two elementary components and we give the exact expressions for these components. We use the landscape decomposition to compute autocorrelation measures of the problem, and show some practical applications of the decomposition.Spanish Ministry of Sci- ence and Innovation and FEDER under contract TIN2008-06491-C04-01 (the M∗ project). Andalusian Government under contract P07-TIC-03044 (DIRICOM project)
Nodal Domain Theorems and Bipartite Subgraphs
The Discrete Nodal Domain Theorem states that an eigenfunction of the k-th largest eigenvalue of a generalized graph Laplacian has at most k (weak) nodal domains. We show that the number of strong nodal domains cannot exceed the size of a maximal induced bipartite subgraph and that this bound is sharp for generalized graph Laplacians. Similarly, the number of weak nodal domains is bounded by the size of a maximal bipartite minor. (author's abstract)Series: Preprint Series / Department of Applied Statistics and Data Processin
CAMPways: constrained alignment framework for the comparative analysis of a pair of metabolic pathways
Motivation: Given a pair of metabolic pathways, an alignment of the pathways corresponds to a mapping between similar substructures of the pair. Successful alignments may provide useful applications in phylogenetic tree reconstruction, drug design and overall may enhance our understanding of cellular metabolism. \ud
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Results: We consider the problem of providing one-to-many alignments of reactions in a pair of metabolic pathways. We first provide a constrained alignment framework applicable to the problem. We show that the constrained alignment problem even in a primitive setting is computationally intractable, which justifies efforts for designing efficient heuristics. We present our Constrained Alignment of Metabolic Pathways (CAMPways) algorithm designed for this purpose. Through extensive experiments involving a large pathway database, we demonstrate that when compared with a state-of-the-art alternative, the CAMPways algorithm provides better alignment results on metabolic networks as far as measures based on same-pathway inclusion and biochemical significance are concerned. The execution speed of our algorithm constitutes yet another important improvement over alternative algorithms
Kalman prediction based proportional fair resource allocation for a solar powered wireless downlink
Optimization of a Wireless Sensor Network (WSN) downlink with an energy harvesting transmitter (base station) is considered. The base station (BS), which is attached to the central controller of the network, sends control information to the gateways of individual WSNs in the downlink. This paper specifically addresses the case where the BS is supplied with solar energy. Leveraging the daily periodicity inherent in solar energy harvesting, the schedule for delivery of maintenance messages from the BS to the nodes of a distributed network is optimized. Differences in channel gain from the BS to sensor nodes make it a challenge to provide service to each of them while efficiently spending the harvested energy. Based on PTF (Power-Time-Fair), a close-to-optimal solution for fair allocation of harvested energy in a wireless downlink proposed in previous work, we develop an online algorithm, PTF-On, that operates two algorithms in tandem: A prediction algorithm based on a Kalman filter that operates on solar irradiation measurements, and a modified version of PTF. PTF-On can predict the energy arrival profile throughout the day and schedule transmission to nodes to maximize total throughput in a proportionally fair way