Nonlinearly correlated failure analysis and autonomic prediction for distributed systems

In order to achieve failure prediction without manual intervention for distributed systems, a novel failure feature analysis and extraction approach to automate failure prediction is proposed. Compared with the traditional methods which focus on building heuristic rules or models, the autonomic prediction approach analyzes the nonlinear correlation of failure features by recognizing failure patterns. Failure data are sorted according to the nonlinear correlation and failure signature is proposed for autonomic prediction. In addition, the Manifold Learning algorithm named supervised locally linear embedding is applied to achieve feature extraction. Based on the runtime monitoring of failure metrics, the experimental results indicate that the proposed method has better performance in terms of both correlation recognition precision and feature extraction quality and thus it can be used to design efficient autonomic failure prediction for distributed systems.

From Nothing to Something Ⅱ： Nonlinear Systems via Consistent Correlated Bang

The Chinese ancient sage Laozi said that everything comes from ＇nothing＇. In the work [Chin. Phys. Lett. 30？（2013）？080202], infinitely many discrete integrable systems have been obtained from nothing via simple principles （Dao）. In this study, a new idea, the consistent correlated bang, is introduced to obtain nonlinear dynamic systems including some integrable ones such as the continuous nonlinear Schr？dinger equation, the （potential） Korteweg de Vries equation, the （potential） Kadomtsev–Petviashvili equation and the sine-Gordon equation. These nonlinear systems are derived from nothing via suitable ＇Dao＇, the shifted parity, the charge conjugate, the delayed time reversal, the shifted exchange, the shifted-parity-rotation and so on.

Coherence estimation algorithm using Kendall's concordance measurement on seismic data

The coherence method is always used to describe the discontinuity and heterogeneity of seismic data. In traditional coherence methods, a linear correlation coefficient is always used to measure the relationship between two random variables （i.e., between two seismic traces）. However, mathematically speaking, a linear correlation coefficient cannot be applied to describe nonlinear relationships between variables. In order to overcome this limitation of liner correlation coefficient. We proposed an improved concordance measurement algorithm based on Kendall＇s tau. That mainly concern the sensitivity of the liner correlation coefficient and concordance measurements on the waveform. Using two designed numerical models tests sensitivity of waveform similarity affected by these two factors. The analysis of both the numerical model results and real seismic data processing suggest that the proposed method, combining information divergence measurement, can not only precisely characterize the variations of waveform and the heterogeneity of an underground geological body, but also does so with high resolution. In addition, we verified its effectiveness by the actual application of real seismic data from the north of China.

A MNCIE method for registration of ultrasound images

A new approach to the problem of registration of ultrasound images is presented, using a concept of Nonlinear Correlation Information Entropy (NCIE) as the matching criterion. The proposed method applies NCIE to measure the correlation degree between the image intensities of corresponding voxel in the floating and reference images. Registration is achieved by adjustment of the relative position until NCIE between the images is maximized. However, unlike mutual information (MI), NCIE varies in the closed interval [0, 1], and around the extremum it varies sharply, which makes it possible that thresholds of NCIE can be used to boost the search for the registration transformation. Using this feature of NCIE, we combine the downhill simplex searching algorithm to register the ultrasound images. The simulations are conducted to testify the effectiveness and rapidity of the proposed registration method, in which the ultrasound floating images are aligned to the reference images with required registration accuracy. Moreover, the NCIE based method can overcome local minima problem by setting thresholds and can take care of the differences in contrast between the floating and reference images.

Dynamic tensile characterization of pig skin

The strain-rate dependent response of porcine skin oriented in the fiber direction is explored under tensile loading. Quasi-static response was obtained at strain rates in the range of 10-3s-1to 25 s-1. Characterization of the response at even greater strain rates is accomplished by measuring the spatio-temporal evolution of the particle velocity and strain in a thin strip subjected to high speed impact loading that generates uniaxial stress conditions. These experiments indicate the formation of shock waves; the shock Hugoniot that relates particle velocity to the shock velocity and the dynamic stress to dynamic strain is obtained directly through experimental measurements, without any assumptions regarding the constitutive properties of the material.