Coronal fuzziness modelled with pulse-heated multistranded loop systems

Coronal active regions are observed to get fuzzier and fuzzier (i.e. more and more confused and uniform) in harder and harder energy bands or lines. We explain this evidence as due to the fine multi-temperature structure of coronal loops. To this end, we model bundles of loops made of thin strands, each heated by short and intense heat pulses. For simplicity, we assume that the heat pulses are all equal and triggered only once in each strand at a random time. The pulse intensity and cadence are selected so as to have steady active region loops ($\sim 3$ MK), on the average. We compute the evolution of the confined heated plasma with a hydrodynamic loop model. We then compute the emission along each strand in several spectral lines, from cool ($\leq 1$ MK), to warm ($2-3$ MK) lines, detectable with Hinode/EIS, to hot X-ray lines. The strands are then put side-by-side to construct an active region loop bundle. We find that in the warm lines ($2-3$ MK) the loop emission fills all the available image surface. Therefore the emission appears quite uniform and it is difficult to resolve the single loops, while in the cool lines the loops are considerably more contrasted and the region is less fuzzy. The main reasons for this effect are that, during their evolution, i.e. pulse heating and slow cooling, each strand spends a relatively long time at temperatures around $2-3$ MK, and that it has a high emission measure during that phase, so the whole region appears more uniform or smudged. We make the prediction that the fuzziness should be reduced in the hot UV and X-ray lines.Comment: 27 pages, 14 figure

Intelligent Diagnosis and Smart Detection of Crack in a Structure from its Vibration Signatures

In recent years, there has been a growing interest in the development of structural health monitoring for vibrating structures, especially crack detection methodologies and on-line diagnostic techniques. In the current research, methodologies have been developed for damage detection of a cracked cantilever beam using analytical, fuzzy logic, neural network and fuzzy neuro techniques. The presence of a crack in a structural member introduces a local flexibility that affects its dynamic response. For finding out the deviation in the vibrating signatures of the cracked cantilever beam the local stiffness matrices are taken into account. Theoretical analyses have been carried out to calculate the natural frequencies and mode shapes of the cracked cantilever beam using local stiffness matrices. Strain energy release rate has been used for calculating the local stiffness of the beam. The fuzzy inference system has been designed using the first three relative natural frequencies and mode shapes as input parameters. The output from the fuzzy controller is relative crack location and relative crack depth. Several fuzzy rules have been developed using the vibration signatures of the cantilever beam. A Neural Network technique using multi layered back propagation algorithm has been developed for damage assessment using the first three relative natural frequencies and mode shapes as input parameters and relative crack location and relative crack depth as output parameters. Several training patterns are derived for designing the Neural Network. A hybrid fuzzy-neuro intelligent system has been formulated for fault identification. The fuzzy controller is designed with six input parameters and two output parameters. The input parameters to the fuzzy system are relative deviation of first three natural frequencies and first three mode shapes. The output parameters of the fuzzy system are initial relative crack depth and initial relative crack location. The input parameters to the neural controller are relative deviation of first three natural frequencies and first three mode shapes along with the interim outputs of fuzzy controller. The output parameters of the fuzzy-neuro system are final relative crack depth and final relative crack location. A series of fuzzy rules and training patterns are derived for the fuzzy and neural system respectively to predict the final crack location and final crack depth.To diagnose the crack in the vibrating structure multiple adaptive neuro-fuzzy inference system (MANFIS) methodology has been applied. The final outputs of the MANFIS are relative crack depth and relative crack location. Several hundred fuzzy rules and neural network training patterns are derived using natural frequencies, mode shapes, crack depths and crack locations. The proposed research work aims to broaden the development in the area of fault detection of dynamically vibrating structures. This research also addresses the accuracy for detection of crack location and depth with considerably low computational time. The objective of the research is related to design of an intelligent controller for prediction of damage location and severity in a uniform cracked cantilever beam using AI techniques (i.e. Fuzzy, neural, adaptive neuro-fuzzy and Manfis)

Searching for network modules

When analyzing complex networks a key target is to uncover their modular structure, which means searching for a family of modules, namely node subsets spanning each a subnetwork more densely connected than the average. This work proposes a novel type of objective function for graph clustering, in the form of a multilinear polynomial whose coefficients are determined by network topology. It may be thought of as a potential function, to be maximized, taking its values on fuzzy clusterings or families of fuzzy subsets of nodes over which every node distributes a unit membership. When suitably parametrized, this potential is shown to attain its maximum when every node concentrates its all unit membership on some module. The output thus is a partition, while the original discrete optimization problem is turned into a continuous version allowing to conceive alternative search strategies. The instance of the problem being a pseudo-Boolean function assigning real-valued cluster scores to node subsets, modularity maximization is employed to exemplify a so-called quadratic form, in that the scores of singletons and pairs also fully determine the scores of larger clusters, while the resulting multilinear polynomial potential function has degree 2. After considering further quadratic instances, different from modularity and obtained by interpreting network topology in alternative manners, a greedy local-search strategy for the continuous framework is analytically compared with an existing greedy agglomerative procedure for the discrete case. Overlapping is finally discussed in terms of multiple runs, i.e. several local searches with different initializations.Comment: 10 page