Density-based Clustering Analysis Research Articles

Real-Space Analysis of Branch Point Motion in Architecturally Complex Polymers

By means of large-scale molecular dynamics simulations, we investigate branch point motion in pure branched polymers and in mixtures of stars and linear chains. We perform a purely geometrical density-based cluster analysis of the branch point trajectories and identify regions of strong localization (traps). Our results demonstrate that the branch point motion can be described as the motion over a network of traps at the time scales corresponding to the reptation regime. Residence times within the traps are broadly distributed, even extending to times much longer than the side-arm relaxation time. The distributions of distances between consecutively visited traps are very similar for all the investigated branched polymers, even though tube dilation is much stronger in the star/linear mixtures than in the pure branched systems. Our analysis suggests that the diffusivity of the branch point introduced by hierarchical models must be understood as a parameter to account for the effective friction associated w...

Spatial Cluster Analysis of Bursting Pipes in Water Supply Networks

Urban water supply network is a hidden underground asset; therefore, it is difficult to make a direct diagnosis towards the faults of pipe network by means of the detecting technology. Based on the historical data of the rupture of water supply pipe network, a spatial cluster analysis will be carried out towards the historical data of spatial fault in water supply pipeline, using density- based cluster analysis and the DBSCAN algorithm. K-neighborhood graph is well-used in optimizing the input parameter radius of neighborhood (Eps) and density (MinPts) and classifying the historical data of the rupture of pipeline. According to the pipe network failure point coordinates (X,Y), adopting density-based cluster analysis, a danger level classification will be researched without knowing the reasons of the rupture of pipe network, which lays the foundation for finding out pipeline rupture in local areas in the future. Meanwhile, a noise analysis will be processed in terms of the historical data of pipeline rupture in water supply network, avoiding the ineffective data caused by recording errors and regular analysis. This research focuses on the reasons that result in the rupture of water supply network, which plays a significant role in the identification of high-density and high-danger zones in water supply networks.

Analysis of clustering in three-dimensional grain fabric

Sedimentological analysis of grain fabric has paid scant attention to grain shape. However, the information of grain orientation is inseparable from that of shape in three-dimensional fabric analysis. Not only should the dominant major-axis orientations be recognized, but so should the dominant combinations of shapes and orientations of grains. The aim of this paper is to demonstrate that such combinations can be identified by density-based cluster analysis in a five-dimensional parameter space, where a point represents a specific combination of the shape and orientation of a grain approximated by a triaxial ellipsoid. We tested the present method using an artificial data set. The data were successfully classified into correct groups. Next, we applied it to a data set obtained by X-ray computed microtomography from some 5000 sand grains deposited in an experimental flume. We show that triaxial grains, which have principal axes with distinctive radii, have major axes in the paleocurrent orientation and roller-shaped grains in the transverse orientation. Both grain types have vertical minor axes. Those orientations are not preconditioned for statistical analysis but are recognized as significant; this suggests the potential of the method in three-dimensional fabric analysis for applications in sedimentology. The method can be applied to the statistical processing of ellipsoidal objects including, for example, deformed grains, stress ellipsoids, and magnetic susceptibility ellipsoids.