Stochastic algorithms for robustness of control performances

In recent years, there has been a growing interest in developing statistical learning methods to provide approximate solutions to “difficult” control problems. In particular, randomized algorithms have become a very popular tool used for stability and performance analysis as well as for design of control systems. However, as randomized algorithms provide an efficient solution procedure to the “intractable” problems, stochastic methods bring closer to understanding the properties of the real systems. The topic of this paper is the use of stochastic methods in order to solve the problem of control robustness: the case of parametric stochastic uncertainty is considered. Necessary concepts regarding stochastic control theory and stochastic differential equations are introduced. Then a convergence analysis is provided by means of the Chernoff bounds, which guarantees robustness in mean and in probability. As an illustration, the robustness of control performances of example control systems is computed.     

Fidelity-guaranteed robustness enhancement of blind-detection watermarking schemes

Watermarking performance enhancement has always been a difficult task since the performance metrics of watermarking systems, i.e., fidelity, robustness, and payload size, inherently conflict with each other. Nowadays, most watermarking schemes hide payloads according to predefined rules or empirical perceptual models. Therefore, the performance of digital watermarking schemes can be determined only passively. In this study, a genetic algorithm-based framework for watermarking performance enhancement is proposed. Watermarked results having better robustness, guaranteed fidelity, and fixed payload size can be obtained. Existing blind-detection watermarking schemes can be improved significantly by incorporating the proposed framework. In addition, the proposed framework has many desirable advantages such as asymmetric embedding/detection overhead, easy integration with existing data-hiding schemes, and direct control over fidelity and robustness.     

Robust resource allocation in a cluster based imaging system

Recently there has been an increased demand for imaging systems in support of high-speed digital printing. The required increase in performance in support of such systems can be accomplished through an effective parallel execution of image processing applications in a distributed cluster computing environment. The output of the system must be presented to a raster based display at regular intervals, effectively establishing a hard deadline for the production of each image. Failure to complete a rasterization task before its deadline will result in an interruption of service that is unacceptable. The goal of this research was to derive a metric for measuring robustness in this environment and to design a resource allocation heuristic capable of completing each rasterization task before its assigned deadline, thus, preventing any service interruptions. We present a mathematical model of such a cluster based raster imaging system, derive a robustness metric for evaluating heuristics in this environment, and demonstrate using the metric to make resource allocation decisions. The heuristics are evaluated within a simulation of the studied raster imaging system. We clearly demonstrate the effectiveness of the heuristics by comparing their results with the results of a resource allocation heuristic commonly used in this type of system.     

Design and analysis of robust fault detection filter using LMI tools

In this paper, the robust fault detection filter design problem for linear time invariant (LTI) systems with unknown inputs and modeling uncertainties is studied. The basic idea of our study is to formulate the robust fault detection filter design as a H_∞ model-matching problem. A solution of the optimal problem is then presented via a linear matrix inequality (LMI) formulation. The main results include the formulation of robust fault detection filter design problems, the derivation of a sufficient condition for the existence of a robust fault detection filter and construction of a robust fault detection filter based on the iterative of LMI algorithm.     

一种基于改进谱熵的语音端点检测方法

针对常规谱熵端点检测法在非平稳噪声环境下检测效果差的缺陷,提出了一种基于子带谱熵幅度积参数的语音端点检测方法。该方法利用非平稳信号处理技术将语音信号的时域分析和频域分析相结合,在常规谱熵的基础上计算出子带谱熵,再结合时域中的短时平均幅度进行端点检测。仿真结果表明,与常规谱熵端点检测算法和短时平均幅度算法相比,该方法在各种噪声环境下的检测效果都比较好,鲁棒性增强,其有效性得到验证。     

基于支持向量机与结构相似度的图像数字水印算法*

针对空域数字水印算法鲁棒性差、难以抵抗较强的攻击的问题,本文提出一种基于支持向量机SVM与结构相似度SSIM的自适应图像数字水印算法。本文利用结构相似度算法SSIM计算不同图像子块的最大水印嵌入强度,通过回归性支持向量机建立不同图像子块与最大水印嵌入强度的相关性模型,实现了根据不同图像子块预测水印嵌入强度。本文在现有基于图像邻域像素之间相关性的时空域数字水印算法的基础上,选取图像中的图像子块进行水印嵌入,通过修改子块中心位置像素值,进行水印嵌入与提取。本文提出的算法在确保水印算法具有较好的透明性的基础上,提高了水印算法的鲁棒性。实验结果表明,该算法在保持较好透明性的基础上,对于JPEG压缩、噪声、中值滤波等攻击具有较强的抵抗能力。     

Robustness analysis of genetic regulatory networks affected by model uncertainty

A fundamental problem in systems biology consists of investigating robustness properties of genetic regulatory networks (GRNs) with respect to model uncertainty. This paper addresses this problem for GRNs where the coefficients are rationally affected by polytopic uncertainty, and where the saturation functions are not exactly known. First, it is shown that a condition for ensuring that the GRN has a globally asymptotically stable equilibrium point for all admissible uncertainties can be obtained in terms of a convex optimization problem with linear matrix inequalities (LMIs), hence generalizing existing results that mainly consider only the case of GRNs where the coefficients are linearly affected by the uncertainty and the regulatory functions are in SUM form. Second, the problem of estimating the worst-case convergence rate of the trajectories to the equilibrium point over all admissible uncertainties is considered, and it is shown that a lower bound of this rate can be computed by solving a quasi-convex optimization problem with LMIs. Third, the paper considers the problem of estimating the set of uncertainties for which the GRN has a globally asymptotically stable equilibrium point. This problem is addressed, first, by showing how one can compute estimates with fixed shape by solving a quasi-convex optimization problem with LMIs, and second, by deriving a procedure for computing estimates with variable shape. Numerical examples illustrate the use of the proposed techniques.     

On the robustness of Type-1 and Interval Type-2 fuzzy logic systems in modeling

Research on the robustness of fuzzy logic systems (FLSs), an imperative factor in the design process, is very limited in the literature. Specifically, when a system is subjected to small deviations of the sampling points (operating points), it is of great interest to find the maximum tolerance of the system, which we refer to as the system’s robustness. In this paper, we present a methodology for the robustness analysis of interval type-2 FLSs (IT2 FLSs) that also holds for T1 FLSs, hence, making it more general. A procedure for the design of robust IT2 FLSs with a guaranteed performance better than or equal to their T1 counterparts is then proposed. Several examples are performed to demonstrate the effectiveness of the proposed methodologies. It was concluded that both T1 and IT2 FLSs can be designed to achieve robust behavior in various applications, and preference one or the other, in general, is application-dependant. IT2 FLSs, having a more flexible structure than T1 FLSs, exhibited relatively small approximation errors in the several examples investigated. The methodologies presented in this paper lay the foundation for the design of FLSs with robust properties that will be very useful in many practical modeling and control applications.     

Robust network planning in nonuniform traffic scenarios

Robustness to the environmental variations is an important feature of any reliable communication network. This paper reports on a network theory approach to the design of such networks where the environmental changes are traffic fluctuations, topology modifications, and changes in the source of external traffic. Motivated by the definition of betweenness centrality in network science, we introduce the notion of traffic-aware betweenness (TAB) for data networks, where usually an explicit (or implicit) traffic matrix governs the distribution of external traffic into the network. We use the average normalized traffic-aware betweenness, which is referred to as traffic-aware network criticality (TANC), as our main metric to quantify the robustness of a network. We show that TANC is directly related to some important network performance metrics, such as average network utilization and average network cost. We prove that TANC is a linear function of end-to-end effective resistances of the graph. As a result, TANC is a convex function of link weights and can be minimized using convex optimization techniques. We use semi-definite programming method to study the properties of the optimization problem and derive useful results to be employed for robust network planning purposes.     

Robust estimators and tests for bivariate copulas based on likelihood depth

Estimators and tests based on likelihood depth for one-parametric copulas are given. For the Gaussian and Gumbel copulas, it is shown that the maximum depth estimators are biased. They can be corrected and the new estimators are robust against contamination. For testing, simplicial likelihood depth is considered. Because of the bias of the maximum depth estimator, simplicial likelihood depth is not a degenerated U-statistic so that easily asymptotic α-level tests can be derived for arbitrary hypotheses. Tests are in particular investigated for the one-sided alternatives. Simulation studies for the Gaussian and Gumbel copulas show that the power of the first test is rather good, but the latter one has to be improved, which is also done here. The new tests are robust against contamination.