Identification of the anomaly component using BEMD combined with PCA from element concentrations in the Tengchong tin belt, SW China

Concentration of elements or element groups in a geological body is the result of multiple stages of rockforming and ore-forming geological processes.An ore-forming element group can be identified by PCA(principal component analysis)and be separated into two components using BEMD(bi-dimensional empirical mode decomposition):(1)a high background component which represents the ore-forming background developed in rocks through various geological processes favorable for mineralization(i.e.magmatism,sedimentation and/or metamorphism);(2)the anomaly component which reflects the oreforming anomaly that is overprinted on the high background component developed during mineralization.Anomaly components are used to identify ore-finding targets more effectively than ore-forming element groups.Three steps of data analytical procedures are described in this paper;firstly,the application of PCA to establish the ore-forming element group;secondly,using BEMD on the o re-forming element group to identify the anomaly components created by different types of mineralization processes;and finally,identifying ore-finding targets based on the anomaly components.This method is applied to the Tengchong tin-polymetallic belt to delineate ore-finding targets,where four targets for Sn(W)and three targets for Pb-Zn-Ag-Fe polymetallic mineralization are identified and defined as new areas for further prospecting.It is shown that BEMD combined with PCA can be applied not only in extracting the anomaly component for delineating the ore-finding target,but also in extracting the residual component for identifying its high background zone favorable for mineralization from its oreforming element group.

Classification of hyperspectral image based on BEMD and SVM

As a powerful tool for image processing,bi-dimensional empirical mode decomposition (BEMD) covers a wide range of applications. In this paper,we explore a novel hyperspectral classification algorithm which integrates BEMD and support vector machine (SVM) . By virtue of BEMD,the selected hyperspectral bands are decomposed into several bi-dimensional intrinsic mode functions (BIMFs) ,which reflect the essential properties of hyperspectral image. We further make full use of SVM,which is a supervised classification tool widely accepted,to classify the suitable sum of BIMFs. Experimental results indicate that though the proposed method has no advantage in computing time,it exhibits higher classification accuracy and stability than the classical SVM.

Performance analysis of rate-adaptive cooperative MAC based on wireless local area networks

Bi-dimensional Markov chain model based on cooperative medium access control （MAC） of wireless local area networks （WLAN） is considered to reflect system performance accurately. Two basic factors that affect the analysis results are station retry limits and non-saturated transmit probability. A uniform solution considering both factors is proposed. To prove the theoretical analysis, a cooperative MAC （CoopMAC） topology is established and the simulation model is enhanced by changing the cooperative table to the nodes＇ memory with more information added. Meanwhile, the three-way handshake scheme is modified and a handshake threshold is set based on the packet size. Simulation results show the performance analytical model is accurate, and the rate-adaptive cooperative MAC protocol significantly improves the network performance in terms of non-saturated system throughput and delay.

Computational Study of Scission Neutrons in Low-Energy Fission:Stationary and Time-Dependent Approaches

The emission of scission neutrons from fissioning nuclei is of high practical interest.To study this process we have used the sudden approximation and also a more realistic approach that takes into account the scission dynamics.Numerically,this implies the solution of the bi-dimensional Schr¨odinger equation,both stationary and time-dependent.To describe axially symmetric extremely deformed nuclear shapes,we have used the Cassini parametrization.The Hamiltonian is discretized by using finite difference approximations of the derivatives.The main computational challenges are the solution of algebraic eigenvalue problems and of linear systems with large sparse matrices.We have employed appropriate procedures(Arnoldi and bi-conjugate gradients).The numerical solutions have been used to evaluate physical quantities,like the number of emitted neutrons per scission event,the primary fragments’excitation energy and the distribution of the emission points.