Dependence Tree Structure Estimation via Copula

We propose an approach for dependence tree structure learning via copula. A nonparametric algorithm for copula estimation is presented. Then a Chow-Liu like method based on dependence measure via copula is proposed to estimate maximum spanning bivariate copula associated with bivariate dependence relations. The main advantage of the approach is that learning with empirical copula focuses on dependence relations among random variables, without the need to know the properties of individual variables as well as without the requirement to specify parametric family of entire underlying distribution for individual variables. Experiments on two real-application data sets show the effectiveness of the proposed method.

High coercivity Nd-Ce-Fe-B nanostructured ribbons prepared from melt spinning technique

The Ce-substituted(Nd1-xCex)12.2 Fe81.6 B6.2(x=0.0, 0.2, 0.4, 0.6) nanocrystalline ribbons were prepared by annealing amorphous ribbons from melt spinning. It is found that all ribbons are in a multiphase state consisting of a-Fe phase, Nd(Ce)-rich phases and RE2 Fe14 B(RE = Nd, Ce) phases. However, the coercivity of all annealed ribbons can reach a considerably high value without doping any heavy rare earth or other coercivity enhanced elements. A strong intergranular exchange coupling appears in these nanocrystalline ribbons. The Nd12.2 Fe81.6 B6.2 ribbons with multiphase have a coercivity of about 11.3 k Oe, and the coercivity decreases slightly with increasing Ce content. A coercivity of 7.5 kOe can be obtained when60 at% of Nd is replaced by Ce(x = 0.6) due to the grain refinement and the strong intergranular exchange coupling. This provides a practical approach of fabricating high coercivity Ce-substituted Nd-Fe-B materials.

Noninvasive Human-Prosthesis Interfaces for Locomotion Intent Recognition:A Review

The lower-limb robotic prostheses can provide assistance for amputees’daily activities by restoring the biomechanical functions of missing limb(s).To set proper control strategies and develop the corresponding controller for robotic prosthesis,a prosthesis user’s intent must be acquired in time,which is still a major challenge and has attracted intensive attentions.This work focuses on the robotic prosthesis user’s locomotion intent recognition based on the noninvasive sensing methods from the recognition task perspective(locomotion mode recognition,gait event detection,and continuous gait phase estimation)and reviews the state-ofthe-art intent recognition techniques in a lower-limb prosthesis scope.The current research status,including recognition approach,progress,challenges,and future prospects in the human’s intent recognition,has been reviewed.In particular for the recognition approach,the paper analyzes the recent studies and discusses the role of each element in locomotion intent recognition.This work summarizes the existing research results and problems and contributes a general framework for the intent recognition based on lower-limb prosthesis.