A test for stability robustness of linear time‐varying systems utilizing the linear time‐invariant ν‐gap metric

A stability robustness test is developed for internally stable, nominal, linear time‐invariant (LTI) feedback systems subject to structured, linear time‐varying uncertainty. There exists (in the literature) a necessary and sufficient structured small gain condition that determines robust stability in such cases. In this paper, the structured small gain theorem is utilized to formulate a (sufficient) stability robustness condition in a scaled LTI ν‐gap metric framework. The scaled LTI ν‐gap metric stability condition is shown to be computable via linear matrix inequality techniques, similar to the structured small gain condition. Apart from a comparison with a generalized robust stability margin as the final part of the stability test, however, the solution algorithm implemented to test the scaled LTI ν‐gap metric stability robustness condition is shown to be independent of knowledge about the controller transfer function (as opposed to the LMI feasibility problem associated with the scaled small gain condition which is dependent on knowledge about the controller). Thus, given a nominal plant and a structured uncertainty set, the stability robustness condition presented in this paper provides a single constraint on a controller (in terms of a large enough generalized robust stability margin) that (sufficiently) guarantees to stabilize all plants in the uncertainty set. Copyright © 2008 John Wiley & Sons, Ltd.     

Measuring robustness of computing systems

System builders are becoming increasingly interested in robust design. We believe that a methodology for generating robustness metrics will help the robust design research efforts and, in general, is an important step in the efforts to create robust computing systems. The purpose of the research in this paper is to quantify the robustness of a resource allocation, with the eventual objective of setting a standard that could easily be instantiated for a particular computing system to generate a robustness metric. The paper exposes how not considering the uncertainties can result in gross overestimation of the system capacity, and shows a method for reducing the impact of uncertainty, and even use it to our advantage if the uncertainties are correlated. We present our theoretical foundation for a robustness metric and give its instantiation for a particular system.     

Controller certification

A modern problem from aerospace control involves the certification of a large set of potential controllers with either a single plant or a fleet of potential plant systems, with both plants and controllers being MIMO and, for the moment, linear. Experiments on a limited number of controller/plant pairs should establish the stability and a certain level of margin of the complete set. We consider this certification problem for a set of controllers and provide algorithms for selecting an efficient subset for testing. This is done for a finite set of candidate controllers and, at least for SISO plants, for compact infinite set. In doing this, the ν-gap metric will be the main tool. Computational examples are given, including one of certification of an aircraft engine controller. The overarching aim is to introduce truly MIMO margin calculations and to understand their efficacy in certifying stability over a set of controllers and in replacing legacy single-loop gain and phase margin calculations.     

一种基于二次误差测度的三维累进网格生成算法

研究了一类三维累进网格生成算法,在网格分辨率固定的前提下,以期获得较高质量的三维模型;基于二次误差模型,提出了基于累进网格生成的改进算法;引入了边界约束条件,提出了二阶邻域指标。实验证明,该算法输出的模型质量优于文献中的算法。     

逻辑代数[0,1]上的度量结构

在4种逻辑代数中分别建立了逻辑度量,讨论了其性质,并对它们之间的关系进行了详尽地讨论;又对4个逻辑度量空间的结构及其性质进行了详细地讨论,并得到一些好的结果。     

面向移动Agent的软件度量工具的设计

利用软件度量工具对软件的各类质量属性度量,对于提高程序的质量有重要意义。在分析面向移动Agent的度量指标的基础上,设计并实现一种基于移动Agent的软件度量工具,该工具通过度量Agent与系统中其它Agent进行交互来获取度量所需信息并对其进行加工处理,用户可以通过度量Agent提供的接口查询度量的结果。最后给出度量指标与度量特征之间关系的实验结果。     

Weakly supervised temporal action localization with proxy metric modeling

Temporal localization is crucial for action video recognition. Since the manual annotations are expensive and time-consuming in videos, temporal localization with weak video-level labels is challenging but indispensable. In this paper, we propose a weakly-supervised temporal action localization approach in untrimmed videos. To settle this issue, we train the model based on the proxies of each action class. The proxies are used to measure the distances between action segments and different original action features. We use a proxy-based metric to cluster the same actions together and separate actions from backgrounds. Compared with state-of-the-art methods, our method achieved competitive results on the THUMOS14 and ActivityNet1.2 datasets.     

基于间隙度量的多模型过热汽温预测控制

针对火电机组主汽温被控对象所具有的大惯性、大迟延以及其随机组负荷变化而发生的参数时变性,为解决传统串级控制方案在机组深度调峰情况下控制品质恶化的问题,提出了一种基于间隙度量的多模型过热汽温预测控制策略。引入间隙度量理论来确定各模型输出的权值。首先选取若干个典型工况设计局部预测控制器;再运用间隙度量理论,计算各个局部预测控制器的输出权值,进而加权求出控制器的输出值。仿真研究表明,该方法可以很好地应对机组负荷变化时被控对象参数变化的问题;与使用常规的串级控制以及单模型预测控制方案相比,主汽温的动态和稳态性能得到了改善。     

结合注意力与批特征擦除的行人重识别模型

在行人重识别过程中,图像局部遮挡会造成识别准确率下降。提出一种结合注意力和批特征擦除的网络（ABFE-Net）模型,旨在学习具有辨别力的全局特征和局部细粒度特征,提高图像局部遮挡条件下行人特征的表达能力。将轻量级注意力模块嵌入到ResNet-50中自主学习每个通道的权重,通过强化有用特征和抑制无关特征增强网络特征的学习能力,提取行人更具辨别力的全局特征。对于深层特征使用批特征擦除方法,随机擦除同一批次特征图的相同区域,使得网络关注剩余的局部细粒度特征。将两种特征融合得到更加全面的行人特征表示,对其进行相似性度量并排序,得到行人重识别的结果。实验结果表明,与HA-CNN、PCB等方法相比,ABFE-Net模型在Market1501和DukeMTMC-reID数据集上的Rank-1和mAP分别达到94.4%、85.9%和88.3%、75.1%,能够明显增强行人特征的辨别性,提高行人重识别效果。     

Semisupervised dimensionality reduction for hyperspectral images based on the combination of semisupervised learning and metric learning

This paper presents a semisupervised dimensionality reduction (DR) method based on the combination of semisupervised learning (SSL) and metric learning (ML) (CSSLML-DR) in order to overcome some existing limitations in HSIs analysis. Specifically, CSSML focuses on the difficulties of high dimensionality of hyperspectral images (HSIs) data, the insufficient number of labelled samples and inappropriate distance metric. CSSLML aims to learn a local metrics under which the similar samples are pushed as close as possible, and simultaneously, the different samples are pulled away as far as possible. CSSLML constructs two local-reweighted dynamic graphs in an iterative two-steps approach: L-step and V-step. In L-step, the local between-class and within-class graphs are updated. In V-step, the transformation matrix and the reduced space are updated. The algorithm is repeated until a stopping criterion is satisfied. Experimental results on two well-known hyperspectral image data sets demonstrate the superiority of CSSLML algorithm compared to some traditional DR methods.