A novel iterative approach for mapping local singularities from geochemical data

International audienceThere are many phenomena in nature, such as earthquakes, landslides, floods, and large-scale mineralization that are characterized by singular functions exhibiting scale invariant properties. A local singularity analysis based on multifractal modeling was developed for detection of local anomalies for mineral exploration. An iterative approach is proposed in the current paper for improvement of parameter estimations involved in the local singularity analysis. The advantage of this new approach is demonstrated with de Wijs's zinc data from a sphalerite-quartz vein near Pulacayo in Bolivia. The semivariogram method was used to illustrate the differences between the raw data and the estimated data by the new algorithm. It has been shown that the outcome of the local singularity analysis consists of two components: singularity component characterized by local singularity index and the non-singular component by prefractal parameter

An iterative initial-points refinement algorithm for categorical data clustering

The original k-means clustering algorithm is designed to work primarily on numeric data sets. This prohibits the algorithm from being directly applied to categorical data clustering in many data mining applications. The k-modes algorithm [Z. Huang, Clustering large data sets with mixed numeric and categorical value, in: Proceedings of the First Pacific Asia Knowledge Discovery and Data Mining Conference. World Scientific, Singapore, 1997, pp. 21–34] extended the k-means paradigm to cluster categorical data by using a frequency-based method to update the cluster modes versus the k-means fashion of minimizing a numerically valued cost. However, as is the case with most data clustering algorithms, the algorithm requires a pre-setting or random selection of initial points (modes) of the clusters. The differences on the initial points often lead to considerable distinct cluster results. In this paper we present an experimental study on applying Bradley and Fayyad\u27s iterative initial-point refinement algorithm to the k-modes clustering to improve the accurate and repetitiveness of the clustering results [cf. P. Bradley, U. Fayyad, Refining initial points for k-mean clustering, in: Proceedings of the 15th International Conference on Machine Learning, Morgan Kaufmann, Los Altos, CA, 1998]. Experiments show that the k-modes clustering algorithm using refined initial points leads to higher precision results much more reliably than the random selection method without refinement, thus making the refinement process applicable to many data mining applications with categorical data

Application of local singularity in prospecting potential oil/gas Targets

International audienceTogether with generalized self-similarity and the fractal spectrum, local singularity analysis has been introduced as one part of the new 3S principle and technique for mineral resource assessment based on multifractal modeling, which has been demonstrated to be useful for anomaly delineation. Local singularity is used in this paper to characterize the property of multifractal distribution patterns of geochemical indexes to delineate potential areas for oil/gas exploration using the advanced GeoDAS GIS technology. Geochemical data of four oil/gas indexes, consisting of acid-extracted methane (SC1), ethane (SC2), propane (SC3), and secondary carbonate (?C), from 9637 soil samples amassed within a large area of 11.2×104 km2 in the Songpan-Aba district, Sichuan Province, southwestern China, were analyzed. By eliminating the interference of geochemical oil/gas data with the method of media-modification and Kriging, the prospecting area defined by the local singularity model is better identified and the results show that the subareas with higher singularity exponents for the four oil/gas indexes are potential targets for oil/gas exploration. These areas in the shape of rings or half-rings are spatially associated with the location of the known producing drilling well in this area. The spatial relationship between the anomalies delineated by oil/gas geochemical data and distribution patterns of local singularity exponents is confirmed by using the stable isotope of ?13C

A Novel Simulator of Nonstationary Random MIMO Channels in Rayleigh Fading Scenarios

For simulations of nonstationary multiple-input multiple-output (MIMO) Rayleigh fading channels in time-variant scattering environments, a novel channel simulator is proposed based on the superposition of chirp signals. This new method has the advantages of low complexity and implementation simplicity as the sum of sinusoids (SOS) method. In order to reproduce realistic time varying statistics for dynamic channels, an efficient parameter computation method is also proposed for updating the frequency parameters of employed chirp signals. Simulation results indicate that the proposed simulator is effective in generating nonstationary MIMO channels with close approximation of the time-variant statistical characteristics in accordance with the expected theoretical counterparts

A specific type of cyclin-like F-box domain gene is involved in the cryogenic autolysis of \u3ci\u3eVolvariella volvacea\u3c/i\u3e

Cryogenic autolysis is a typical phenomenon of abnormal metabolism in Volvariella volvacea. Recent studies have identified 20 significantly upregulated genes via high-throughput sequencing of the mRNAs expressed in the mycelia of V. volvacea after cold exposure. Among these significantly upregulated genes, 15 annotated genes were used for functional annotation cluster analysis. Our results showed that the cyclin-like F-box domain (FBDC) formed the functional cluster with the lowest P-value. We also observed a significant expansion of FBDC families in V. volvacea. Among these, the FBDC3 family displayed the maximal gene expansion in V. volvacea. Gene expression profiling analysis revealed only one FBDC gene in V. volvacea (FBDV1) that was significantly up-regulated, which is located in the FBDC3 family. Comparative genomics analysis revealed the homologous sequences of FBDV1 with high similarity were clustered on the same scaffold. However, FBDV1 was located far from these clusters, indicating the divergence of duplicated genes. Relative time estimation and rate test provided evidence for the divergence of FBDV1 after recent duplications. Real-time RT-PCR analysis confirmed that the expression of the FBDV1 was significantly up-regulated (P , 0.001) after cold-treatment of V. volvacea for 4 h. These observations suggest that the FBDV1 is involved in the cryogenic autolysis of V. volvacea

A Non-Stationary VVLC MIMO Channel Model for Street Corner Scenarios

In recent years, the application potential of visible light communication (VLC) technology as an alternative and supplement to radio frequency (RF) technology has attracted people's attention. The study of the underlying VLC channel is the basis for designing the VLC communication system. In this paper, a new non-stationary geometric street corner model is proposed for vehicular VLC (VVLC) multiple-input multiple-output (MIMO) channel. The proposed model takes into account changes in vehicle speed and direction. The category of scatterers includes fixed scatterers and mobile scatterers (MS). Based on the proposed model, we derive the channel impulse response (CIR) and explore the statistical characteristics of the VVLC channel. The channel gain and root mean square (RMS) delay spread of the VVLC channel are studied. In addition, the influence of velocity change on the statistical characteristics of the model is also investigated. The proposed channel model can guide future vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) optical communication system design

Map-based Channel Modeling and Generation for U2V mmWave Communication

Unmanned aerial vehicle (UAV) aided millimeter wave (mmWave) technologies have a promising prospect in the future communication networks. By considering the factors of three-dimensional (3D) scattering space, 3D trajectory, and 3D antenna array, a non-stationary channel model for UAV-to-vehicle (U2V) mmWave communications is proposed. The computation and generation methods of channel parameters including interpath and intra-path are analyzed in detail. The inter-path parameters are calculated in a deterministic way, while the parameters of intra-path rays are generated in a stochastic way. The statistical properties are obtained by using a Gaussian mixture model (GMM) on the massive ray tracing (RT) data. Then, a modified method of equal areas (MMEA) is developed to generate the random intra-path variables. Meanwhile, to reduce the complexity of RT method, the 3D propagation space is reconstructed based on the user-defined digital map. The simulated and analyzed results show that the proposed model and generation method can reproduce non-stationary U2V channels in accord with U2V scenarios. The generated statistical properties are consistent with the theoretical and measured ones as well

Coherent interface strengthening of ultrahigh pressure heat-treated Mg-Li-Y alloys.

Achieving good strength-ductility of Mg alloys has always been a crucial issue for the widespread applications of Mg-based structural materials. Herein, an unexpected double-stage strengthening phenomenon was discovered in Mg-8Li-1Y(wt.%) alloys through high pressure (6 GPa) heat treatments over a range of 700-1300°C. Attractively, the yield strength values are improved remarkably without losing their ductility. The low temperature strengthening mechanism is mainly driven by the formation of large-volume nanoscale contraction twins. In contrast, the high-temperature strengthening reason is ascribed to the presence of densely nano-sized stacking faults. Both coherent interfaces contribute effectively to high mechanical strength without any tradeoff in ductility