Search

Additional file 1. Methods in selection of 8 IRLPs.

A cognitive modelling based new inversion method, the successive differential evolution (DE-S) algorithm, is proposed to estimate the Q factor and velocity from the zero-offset vertical seismic profile (VSP) record for oil-gas reservoir exploration. The DE algorithm seeks optimal solutions by simulating the natural species evolution processes and makes the individuals become optimal. This algorithm is suitable for the high-dimensional nonseparable model space where the inversion leads to recognition and prediction of hydrocarbon reservoirs. The viscoelastic medium is split into layers whose thicknesses equal to the space between two successive VSP geophones, and the estimated parameters of each layer span the related subspace. All estimated parameters span to a high dimensional nonseparable model space. We develop bottom-up workflow, in which the Q factor and the velocity are estimated using the DE algorithm layer by layer. In order to improve the inversion precision, the crossover strategy is discarded and we derive the weighted mutation strategy. Additionally, two kinds of stopping criteria for effective iteration are proposed to speed up the computation. The new method has fast speed, good convergence and is no longer dependent on the initial values of model parameters. Experimental results on both synthetic and real zero-offset VSP data indicate that this method is noise robust and has great potential to derive reliable seismic attenuation and velocity, which is an important diagnostic tool for reservoir characterization.

NSFC [11171228, 10801111, 10971171]; fundamental Research Funds for the Central University [2010121006]; Natural Science Foundation of Fujian Province of China [2010J05011]

The authors study the Cauchy problem of the magnetohydrodynamic equations for viscous compressible barotropic flows in two or three spatial dimensions with vacuum as far field density. For two spatial dimensions, we establish the global existence and uniqueness of strong solutions (which may be of possibly large oscillations) provided the smooth initial data are of small total energy, and obtain some a priori decay with rates (in large time) for the pressure, the spatial gradient of both the velocity field and the magnetic field. Moreover, for three spatial dimensions case, some similar decay rates are also obtained., arXiv admin note: substantial text overlap with arXiv:1310.1673, arXiv:1004.4749, arXiv:1207.3746, arXiv:1107.4655 by other authors

The infiltration kinetics of liquid metal into a fibrous preform was analyzed theoretically on the basis of Darcy's law and the assumptions that the porous preform can be treated as “bundles of capillaries” and that the liquid flow penetrating the preform is laminar. Experiments in infiltration kinetics were carried out with respect to an Al–1.5 Mg aluminium alloy/alumina short fiber preform system. It was revealed that an incubation time is required to activate infiltration, that there is a parabolic dependence of the infiltrated depth on the infiltration time for an infiltration process under constant external pressure, and that the infiltration rate can be well predicted by the equation derived by the theoretical model proposed in the present paper. Both the incubation time and the infiltration rate are influenced significantly by the applied pressure.

Image segmentation is an important processing step in many image, video and computer vision applications. Artificial Immune Systems (AIS) is a diverse area of research that attempts to bridge the divide between immunological and engineering. In this paper, we present a threshold method based on granular immune algorithm (GIA) for image segmentation, which includes granular hierarchy and immunological mechanism. Based on two granular hierarchies, the method can not only execute multi-point parallel search from local to global searching field but also find better solutions with small generation and mean numbers of function values. So this method has better performance in stabilization and convergence that GA. Our experimental results indicate that the proposed method here is very suitable for image segmentation

In recent years, there has been a growth of investigation into the use of artificial immune system as a source of inspiration and metaphor for computational tasks. In this paper, we present a fault diagnosis algorithm based on artificial immune mechanism that can execute multi-point parallel search from local to global searching field and increase the diversity of antigen population and the ability of searching maximum. At last, we apply this algorithm to the fault diagnosis system. Simulation studies show that the proposed method is feasible and recognize the fault correctly.