Vanishing of quasi-invariant generalized functions

Determination of quasi-invariant generalized functions is important for a variety of problems in representation theory, notably character theory and restriction problems. In this note, we review some new and easy-to-use techniques to show vanishing of quasi-invariant generalized functions, developed in the recent work of the authors (Uniqueness of Ginzburg-Rallis models: the Archimedean case, Trans. Amer. Math. Soc. 363, (2011), 2763-2802). The first two techniques involve geometric notions attached to submanifolds, which we call metrical properness and unipotent $\chi$-incompatibility. The third one is analytic in nature, and it arises from the first occurrence phenomenon in Howe correspondence. We also highlight how these techniques quickly lead to two well-known uniqueness results, on trilinear forms and Whittaker models.Comment: Contribution to Proceedings of International Conference on Geometry, Number Theory, and Representation Theory, October 10-12, 2012, Inha University, Kore

A Multi-Classification Method of Improved SVM-based Information Fusion for Traffic Parameters Forecasting

With the enrichment of perception method, modern transportation system has many physical objects whose states are influenced by many information factors so that it is a typical Cyber-Physical System (CPS). Thus, the traffic information is generally multi-sourced, heterogeneous and hierarchical. Existing research results show that the multi-sourced traffic information through accurately classifying in the process of information fusion can achieve better parameters forecasting performance. For solving the problem of traffic information accurately classification, via analyzing the characteristics of the multi-sourced traffic information and using redefined binary tree to overcome the shortcomings of the original SVM (Support Vector Machine) classification in information fusion, a multi-classification method using improved SVM in information fusion for traffic parameters forecasting is proposed. The experiment was conducted to examine the performance of the proposed scheme and the results reveal that the method can get more accurate and practical outcomes.  </p

An incident detection method considering meteorological factor with fuzzy logic

To improve the performance of automatic incident detection algorithm under extreme weather conditions, this paper introduces an innovative method to quantify the relationship between multiple weather parameters and the occurrence of traffic incident as the meteorological influencing factor, and combines the factor with traffic parameters to improve the effect of detection. The new algorithm consists of two modules: meteorological influencing factor module and incident detection module. The meteorological influencing factor module based on fuzzy logic is designed to determine the factor. On the basis of learning vector quantization (LVQ) neural network, the new incident detection module uses the factor and traffic parameters to detect incidents. The algorithm is tested with data collected from a typical freeway in Chongqing, China. Also, the performance of the algorithm is evaluated by the common criteria of detection rate (DR), false alarm rate (FAR) and mean time to detection (MTTD). The experiments conducted on the field data study the influence of different algorithm architectures exerted on the detection performance. In addition, comparative experiments are performed. The experimental results have demonstrated that the proposed algorithm has higher DR, lower FAR than the contrast algorithms, and the proposed algorithm has better potential for the application of freeway automatic incident detection

Stabilization of switched neural networks with time-varying delay via bumpless transfer control

This paper investigates the stabilization of switched neural networks with time-varying delay. In order to overcome the drawback that the classical switching state feedback controller may generate the bumps at switching time, a new switching feedback controller which can smooth effectively the bumps is proposed. According to mode-dependent average dwell time, new exponential stabilization results are deduced for switched neural networks under the proposed feedback controller. Based on a simple corollary, the procedures which are used to calculate the feedback control gain matrices are also obtained. Two simple numerical examples are employed to demonstrate the effectiveness of the proposed results.Peer reviewe

Uniqueness of Bessel models: the archimedean case

In the archimedean case, we prove uniqueness of Bessel models for general linear groups, unitary groups and orthogonal groups.Comment: 22 page