Fragile phase stability in (1-x)Pb(Mg1/3Nb2/3O3)-xPbTiO3 crystals: A comparisons of [001] and [110] field-cooled phase diagrams

Phase diagrams of [001] and [110] field-cooled (FC) (1-x)Pb(Mg1/3Nb2/3O3)-xPbTiO3 or PMN-xPT crystals have been constructed, based on high-resolution x-ray diffraction data. Comparisons reveal several interesting findings. First, a region of abnormal thermal expansion above the dielectric maximum was found, whose stability range extended to higher temperatures by application of electric field (E). Second, the rhombohedral (R) phase of the ZFC state was replaced by a monoclinic MA in the [001] FC diagram, but with monoclinic MB in the [110] FC. Third, the monoclinic MC phase in ZFC and [001] FC diagram was replaced by an orthorhombic (O) phase in the [110] FC. Finally, in the [001] FC diagram, the phase boundary between tetragonal (T) and MA was extended to lower PT contents (x=0.25); whereas in the [110] FC diagram, this extended region was entirely replaced by the O phase. These results clearly demonstrate that the phase stability of PMN-xPT crystals is quite fragile, depending not only on modest changes in E, but also on the direction along which that E is applied.Comment: 13 pages, 8 figures, 1 tabl

A multimodel fusion engine for filtering webpages

© 2013 IEEE. Fusing multiple existing models for filtering webpages can mitigate the shortcomings of individual filtering models. To provide an engine for such fusion, we propose a multimodel fusion engine for filtering webpages for the extraction of target webpages. This engine can handle large datasets of webpages crawled from websites and supports five individual filtering models and the fusion of any two of them. There are two possible fusion methods: one is to simultaneously satisfy the conditions of both individual models, and the other is to satisfy the conditions of one of the two individual models. We present the functions, architecture, and software design of the proposed engine. We use recall ratio (RR) and precision ratio (PR) as the evaluation indices of the filtering models and propose rules describing how PR and RR change when individual models are fused. We use 200 000 webpages collected by crawling the popular online shopping website 'http://www.jd.com' as the experimental dataset to verify these rules. The experimental results show that two-model fusion can improve either PR or RR. Thus, the proposed engine has good practical value for engineering applications

Numerical simulations of negative-index refraction in wedge-shaped metamaterials

A wedge-shaped structure made of split-ring resonators (SRR) and wires is numerically simulated to evaluate its refraction behavior. Four frequency bands, namely, the stop band, left-handed band, ultralow-index band, and positive-index band, are distinguished according to the refracted field distributions. Negative phase velocity inside the wedge is demonstrated in the left-handed band and the Snell's law is conformed in terms of its refraction behaviors in different frequency bands. Our results confirmed that negative index of refraction indeed exists in such a composite metamaterial and also provided a convincing support to the results of previous Snell's law experiments.Comment: 18 pages, 6 figure

Shared Nearest-Neighbor Quantum Game-Based Attribute Reduction with Hierarchical Coevolutionary Spark and Its Application in Consistent Segmentation of Neonatal Cerebral Cortical Surfaces

© 2012 IEEE. The unprecedented increase in data volume has become a severe challenge for conventional patterns of data mining and learning systems tasked with handling big data. The recently introduced Spark platform is a new processing method for big data analysis and related learning systems, which has attracted increasing attention from both the scientific community and industry. In this paper, we propose a shared nearest-neighbor quantum game-based attribute reduction (SNNQGAR) algorithm that incorporates the hierarchical coevolutionary Spark model. We first present a shared coevolutionary nearest-neighbor hierarchy with self-evolving compensation that considers the features of nearest-neighborhood attribute subsets and calculates the similarity between attribute subsets according to the shared neighbor information of attribute sample points. We then present a novel attribute weight tensor model to generate ranking vectors of attributes and apply them to balance the relative contributions of different neighborhood attribute subsets. To optimize the model, we propose an embedded quantum equilibrium game paradigm (QEGP) to ensure that noisy attributes do not degrade the big data reduction results. A combination of the hierarchical coevolutionary Spark model and an improved MapReduce framework is then constructed that it can better parallelize the SNNQGAR to efficiently determine the preferred reduction solutions of the distributed attribute subsets. The experimental comparisons demonstrate the superior performance of the SNNQGAR, which outperforms most of the state-of-the-art attribute reduction algorithms. Moreover, the results indicate that the SNNQGAR can be successfully applied to segment overlapping and interdependent fuzzy cerebral tissues, and it exhibits a stable and consistent segmentation performance for neonatal cerebral cortical surfaces