a crack depth in mm, same as x â measurement system response, 1 – R– A(x) system model for evolving crack depth x A(n) linearised system model at measurement set n B(x) measurement model giving normalised energy ratio R– as a function of crack depth x B(n) linearised measurement model at measurement set n B(R) inverse measurement model giving crack depth x as a function of normalised energy rat...

We prove almost sure convergence of the maximum degree in an evolving tree model combining local choice and preferential attachment. At each step in the growth of the graph, a new vertex is introduced. A fixed, finite number of possible neighbors are sampled from the existing vertices with probability proportional to degree. Of these possibilities, the new vertex attaches to the vertex from the...

From 22.10.06 to 27.10.06, the Dagstuhl Seminar 06431 Scalable Data Management in Evolving Networks was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of semi...

Community structure in networks has been investigated from many viewpoints, usually with the same end result: a community detection algorithm of some kind. Recent research offers methods for combining the results of such algorithms into timelines of community evolution. This paper investigates community detection and tracking from the data fusion perspective. We avoid the kind of hard calls mad...

In this paper a new approach called evolving principal component clustering is applied to a data stream. Regions of the data described by linear models are identified. The method recursively estimates the data variance and the linear model parameters for each cluster of data. It enables good performance, robust operation, low computational complexity and simple implementation on embedded comput...

Many applications of contemporary science involve multiscale dynamics, which are typically characterized by the time and space scale separation of patterns of motion, with fewer slowly evolving variables and much larger set of faster evolving variables. This time-space scale separation causes direct numerical simulation of the evolution of the dynamics to be computationally expensive, due both ...