Scalable Approach to Uncertainty Quantification and Robust Design of Interconnected Dynamical Systems

Development of robust dynamical systems and networks such as autonomous aircraft systems capable of accomplishing complex missions faces challenges due to the dynamically evolving uncertainties coming from model uncertainties, necessity to operate in a hostile cluttered urban environment, and the distributed and dynamic nature of the communication and computation resources. Model-based robust design is difficult because of the complexity of the hybrid dynamic models including continuous vehicle dynamics, the discrete models of computations and communications, and the size of the problem. We will overview recent advances in methodology and tools to model, analyze, and design robust autonomous aerospace systems operating in uncertain environment, with stress on efficient uncertainty quantification and robust design using the case studies of the mission including model-based target tracking and search, and trajectory planning in uncertain urban environment. To show that the methodology is generally applicable to uncertain dynamical systems, we will also show examples of application of the new methods to efficient uncertainty quantification of energy usage in buildings, and stability assessment of interconnected power networks

Extinction calculations of multi-sphere polycrystalline graphitic clusters - A comparison with the 2175 AA peak and between a rigorous solution and discrete-dipole approximations

Certain dust particles in space are expected to appear as clusters of individual grains. The morphology of these clusters could be fractal or compact. In this paper we study the light scattering by compact and fractal polycrystalline graphitic clusters consisting of touching identical spheres. We compare three general methods for computing the extinction of the clusters in the wavelength range 0.1 - 100 micron, namely, a rigorous solution (Gerardy & Ausloos 1982) and two different discrete-dipole approximation methods -- MarCODES (Markel 1998) and DDSCAT (Draine & Flatau 1994). We consider clusters of N = 4, 7, 8, 27,32, 49, 108 and 343 particles of radii either 10 nm or 50 nm, arranged in three different geometries: open fractal (dimension D = 1.77), simple cubic and face-centred cubic. The rigorous solution shows that the extinction of the fractal clusters, with N < 50 and particle radii 10 nm, displays a peak within 2% of the location of the observed interstellar extinction peak at ~4.6 inverse micron; the smaller the cluster, the closer its peak gets to this value. By contrast, the peak in the extinction of the more compact clusters lie more than 4% from 4.6 inverse micron. At short wavelengths (0.1 - 0.5 micron), all the methods show that fractal clusters have markedly different extinction from those of non-fractal clusters. At wavelengths > 5 micron, the rigorous solution indicates that the extinction from fractal and compact clusters are of the same order of magnitude. It was only possible to compute fully converged results of the rigorous solution for the smaller clusters, due to computational limitations, however, we find that both discrete-dipole approximation methods overestimate the computed extinction of the smaller fractal clusters.Comment: Corrections added in accordance with suggestions by the referee. 12 pages, 12 figures. Accepted for publication in Astronomy & Astrophysic

A Density-Based Approach to the Retrieval of Top-K Spatial Textual Clusters

Keyword-based web queries with local intent retrieve web content that is relevant to supplied keywords and that represent points of interest that are near the query location. Two broad categories of such queries exist. The first encompasses queries that retrieve single spatial web objects that each satisfy the query arguments. Most proposals belong to this category. The second category, to which this paper's proposal belongs, encompasses queries that support exploratory user behavior and retrieve sets of objects that represent regions of space that may be of interest to the user. Specifically, the paper proposes a new type of query, namely the top-k spatial textual clusters (k-STC) query that returns the top-k clusters that (i) are located the closest to a given query location, (ii) contain the most relevant objects with regard to given query keywords, and (iii) have an object density that exceeds a given threshold. To compute this query, we propose a basic algorithm that relies on on-line density-based clustering and exploits an early stop condition. To improve the response time, we design an advanced approach that includes three techniques: (i) an object skipping rule, (ii) spatially gridded posting lists, and (iii) a fast range query algorithm. An empirical study on real data demonstrates that the paper's proposals offer scalability and are capable of excellent performance