6 research outputs found
Clustering and Non-Gaussian Behavior in Granular Matter
We investigate the properties of a model of granular matter consisting of
Brownian particles on a line subject to inelastic mutual collisions. This model
displays a genuine thermodynamic limit for the mean values of the energy and
the energy dissipation. When the typical relaxation time associated with
the Brownian process is small compared with the mean collision time
the spatial density is nearly homogeneous and the velocity probability
distribution is gaussian. In the opposite limit one has
strong spatial clustering, with a fractal distribution of particles, and the
velocity probability distribution strongly deviates from the gaussian one.Comment: 4 pages including 3 eps figures, LaTex, added references, corrected
typos, minimally changed contents and abstract, to published in
Phys.Rev.Lett. (tentatively on 28th of October, 1998
Bifurcations of a driven granular system under gravity
Molecular dynamics study on the granular bifurcation in a simple model is
presented. The model consists of hard disks, which undergo inelastic
collisions; the system is under the uniform external gravity and is driven by
the heat bath. The competition between the two effects, namely, the
gravitational force and the heat bath, is carefully studied. We found that the
system shows three phases, namely, the condensed phase, locally fluidized
phase, and granular turbulent phase, upon increasing the external control
parameter. We conclude that the transition from the condensed phase to the
locally fluidized phase is distinguished by the existence of fluidized holes,
and the transition from the locally fluidized phase to the granular turbulent
phase is understood by the destabilization transition of the fluidized holes
due to mutual interference.Comment: 35 pages, 17 figures, to be published in PR
Controlling the temperature of one-dimensional systems composed of elastic and inelastic particles
Controlling the temperature of one-dimensional systems composeed of elastic and inelastic particles
Automatic Graph-Based clustering for security logs
Computer security events are recorded in several log files. It is necessary to cluster these logs to discover security threats, detect anomalies, or identify a particular error. A problem arises when large quantities of security log data need to be checked as existing tools do not provide sufficiently sophisticated grouping results. In addition, existing methods need user input parameters and it is not trivial to find optimal values for these. Therefore, we propose a method for the automatic clustering of security logs. First, we present a new graph-theoretic approach for security log clustering based on maximal clique percolation. Second, we add an intensity threshold to the obtained maximal clique to consider the edge weight before proceeds to the percolations. Third, we use the simulated annealing algorithm to optimize the number of percolations and intensity threshold for maximal clique percolation. The entire process is automatic and does not need any user input. Experimental results on various real-world datasets show that the proposed method achieves superior clustering results compared to other methods