Recent developments in the structural design and optimization of ITER neutral beam manifold

This paper describes a new design of the neutral beam manifold based on a more optimized support system.A proposed alternative scheme has presented to replace the former complex manifold supports and internal pipe supports in the final design phase.Both the structural reliability and feasibility were confirmed with detailed analyses.Comparative analyses between two typical types of manifold support scheme were performed.All relevant results of mechanical analyses for typical operation scenarios and fault conditions are presented.Future optimization activities are described,which will give useful information for a refined setting of components in the next phase.

Spontaneous Emergence of Physical Structures and Observable Formations: Fluctuations, Waves, Turbulent Pulsations and So on

As it is known, the closed inexact exterior form and associated closed dual form make up a differential-geometrical structure. Such a differential-geometrical structure describes a physical structure, namely, a pseudostructure on which conservation laws are fulfilled (A closed dual form describes a pseudostructure. And a closed exterior form, as it is known, describes a conservative quantity, since the differential of closed form is equal to zero). It has been shown that closed inexact exterior forms, which describe physical structures, are obtained from the equations of mathematical physics. This process proceeds spontaneously under realization of any degrees of freedom of the material medium described. Such a process describes an emergence of physical structures and this is accompanied by an appearance of observed formations such as fluctuations, waves, turbulent pulsations and so on.

Global Inference Preserving Projection for Semi-supervised Discriminant Analysis

Semi-supervised dimensionality reduction is an important research area for data classification.A new linear dimensionality reduction approach,global inference preserving projection(GIPP),was proposed to perform classification task in semi-supervised case.GIPP provided a global structure that utilized the underlying discriminative knowledge of unlabeled samples.It used path-based dissimilarity measurement to infer the class label information for unlabeled samples and transformd the discriminant algorithm into a generalized eigenequation problem.Experimental results demonstrate the effectiveness of the proposed approach.

Face recognition based on subset selection via metric learning on manifold

With the development of face recognition using sparse representation based classification(SRC), many relevant methods have been proposed and investigated. However, when the dictionary is large and the representation is sparse, only a small proportion of the elements contributes to the l1-minimization. Under this observation,several approaches have been developed to carry out an efficient element selection procedure before SRC. In this paper, we employ a metric learning approach which helps find the active elements correctly by taking into account the interclass/intraclass relationship and manifold structure of face images. After the metric has been learned, a neighborhood graph is constructed in the projected space. A fast marching algorithm is used to rapidly select the subset from the graph, and SRC is implemented for classification. Experimental results show that our method achieves promising performance and significant efficiency enhancement.

Ranking with Adaptive Neighbors

Retrieving the most similar objects in a large-scale database for a given query is a fundamental building block in many application domains, ranging from web searches, visual, cross media, to document retrievals. Stateof-the-art approaches have mainly focused on capturing the underlying geometry of the data manifolds. Graphbased approaches, in particular, define various diffusion processes on weighted data graphs. Despite success,these approaches rely on fixed-weight graphs, making ranking sensitive to the input affinity matrix. In this study,we propose a new ranking algorithm that simultaneously learns the data affinity matrix and the ranking scores.The proposed optimization formulation assigns adaptive neighbors to each point in the data based on the local connectivity, and the smoothness constraint assigns similar ranking scores to similar data points. We develop a novel and efficient algorithm to solve the optimization problem. Evaluations using synthetic and real datasets suggest that the proposed algorithm can outperform the existing methods.