DBLAR:A DISTANCE-BASED LOCATION-AIDED ROUTING FOR MANET

In location-aided routing of Mobile Ad hoc NETworks(MANET),nodes mobility and the inaccuracy of location information may result in constant flooding,which will reduce the network performance.In this paper,a Distance-Based Location-Aided Routing(DBLAR) for MANET has been proposed.By tracing the location information of destination nodes and referring to distance change between nodes to adjust route discovery dynamically,the proposed routing algorithm can avoid flooding in the whole networks.Besides,Distance Update Threshold(DUT) is set up to reach the balance between real-time ability and update overhead of location information of nodes,meanwhile,the detection of relative distance vector can achieve the goal of adjusting forwarding condition.Simulation results reveal that DBLAR performs better than LAR1 in terms of packet successful delivery ratio,average end-to-end delay and routing-load,and the set of DUT and relative distance vector has a significant impact on this algorithm.

Exploiting multi-context analysis in semantic image classification

As the popularity of digital images is rapidly increasing on the Internet, research on technologies for semantic image classification has become an important research topic. However, the well-known content-based image classification methods do not overcome the so-called semantic gap problem in which low-level visual features cannot represent the high-level semantic content of images. Image classification using visual and textual information often performs poorly since the extracted textual features are often too limited to accurately represent the images. In this paper, we propose a semantic image classification ap- proach using multi-context analysis. For a given image, we model the relevant textual information as its multi-modal context, and regard the related images connected by hyperlinks as its link context. Two kinds of context analysis models, i.e., cross-modal correlation analysis and link-based correlation model, are used to capture the correlation among different modals of features and the topical dependency among images induced by the link structure. We propose a new collective classification model called relational support vector classifier (RSVC) based on the well-known Support Vector Machines (SVMs) and the link-based cor- relation model. Experiments showed that the proposed approach significantly improved classification accuracy over that of SVM classifiers using visual and/or textual features.

Energy and rotation-dependent stereodynamics of H(~2S) + NH(a^1?) → H_2(X^1Σ_g^+) + N(~2D) reaction

Quasi-classical trajectory calculations are performed to study the stereodynamics of the H（~2S） ＋ NH（a^1？） →H_2（X^1Σ_g~＋） ＋ N（~2D） reaction based on the first excited state NH_2（1~2A＇） potential energy surface reported by Li et al.[Li Y Q and Varandas A J C 2010 J. Phys. Chem. A 114 9644] for the first time. We observe the changes of differential cross-sections at different collision energies and different initial reagent rotational excitations. The influence of collision energy on the k-k＇ distribution can be attributed to a purely impulsive effect. Initial reagent rotational excitation transforms the reaction mechanism from insertion to abstraction. The effect of initial reagent rotational excitations on k-k＇ distribution can be explained by the rotational excitation enlarging the rotational rate of reagent NH in the entrance channel to reduce the probability of collision between incidence H atom and H atom of target molecular. We also investigate the changes of vector correlations and find that the rotational angular momentum vector j＇ of the product H_2 is not only aligned, but also oriented along the y axis. The alignment parameter, the disposal of total angular momentum and the reaction mechanism are all analyzed carefully to explain the polarization behavior of the product rotational angular moment.

Aero-engine fault diagnosis applying new fast support vector algorithm

A new fast learning algorithm was presented to solve the large-scale support vector machine ( SVM ) training problem of aero-engine fault diagnosis.The relative boundary vectors ( RBVs ) instead of all the original training samples were used for the training of the binary SVM fault classifiers.This pruning strategy decreased the number of final training sample significantly and can keep classification accuracy almost invariable.Accordingly , the training time was shortened to 1 / 20compared with basic SVM classifier.Meanwhile , owing to the reduction of support vector number , the classification time was also reduced.When sample aliasing existed , the aliasing sample points which were not of the same class were eliminated before the relative boundary vectors were computed.Besides , the samples near the relative boundary vectors were selected for SVM training in order to prevent the loss of some key sample points resulted from aliasing.This can improve classification accuracy effectively.A simulation example to classify 5classes of combination fault of aero-engine gas path components was finished and the total fault classification accuracy reached 96.1%.Simulation results show that this fast learning algorithm is effective , reliable and easy to be implemented for engineering application.