Stochastic reliable control of a class of networked control systems with actuator faults and input saturation

The stochastic reliable control problem for networked control systems (NCSs) subject to actuator failure and input saturation is investigated in this paper. In order to get the relationship between the maximum allowable consecutive packet dropouts, the packet dropout probability, the actuator failure matrix and the input saturation, a packet dropout probability dependent condition is given via linear matrix inequality (LMI) technology. Then, a suitable reliable controller is designed to ensure the closed-loop system to be exponentially mean square stable against actuator failures and input saturation. Finally, numerical examples are provided to show the effectiveness of the proposed method.     

Fixed-time Consensus Tracking for Second-order Leader-follower Multi-agent Systems with Nonlinear Dynamics Under Directed Topology

In this paper, we address the fixed-time consensus tracking problem of second-order leader-follower multi-agent systems with nonlinear dynamics under directed topology. The consensus tracking algorithm consists of distributed observer and observer-based decentralized controller. The fixed-time distributed observer guarantees that each follower estimates the leader’s state under directed topology within a fixed time, where the upper bound of convergence time is independent on the initial conditions. The fixed-time decentralized controller makes each follower converge to the leader’s state in fixed time via tracking the distributed observer’s state and overcome the nonlinear dynamics without adding linear control terms. Finally, the numerical example is provided to illustrate the effectiveness of the results.

     

基于复合混沌动力系统的图像加密算法设计

本文通过对部分数字化混沌图像加密算法的分析研究,设计了一个基于复合离散混沌动力系统的对称图像加密算法。该对称图像加密算法充分利用混沌变化的特性,以Logistic映射和Chebyshev映射生成的混沌序列为初始序列,结合对图像的切割、拉伸、折叠变换,充分打乱图像像素间的相关性,使得针对这两个混沌映射的现有分析方法不再有效,并且可以实现较高的加密速度。实验结果证明了算法的有效性和计算速度。     

基于信息增量矩阵的故障诊断方法

主元分析(Principal component analysis, PCA)是一种常用的故障检测方法,由于特征提取不准确, 在用于故障诊断时常存在误报率和漏报率较高的现象.为此,本文首先介绍了基于全局的协方差矩阵的信息增量矩阵的故障诊断方法,虽然相比PCA方法它能有效减少误报率和漏报率, 但随着采样样本的增加,会因计算得到的阈值越来越不具代表性和计算量较大等原因而影响该方法的性能.然后,建立了基于局部数据的移动窗口协方差矩阵的信息增量矩阵的故障诊断方法, 以克服上述方法中存在的不足. 该方法主要通过定义局部协方差矩阵、局部信息增量矩阵、局部信息增量均值、 局部动态阈值、异常检测与判定等过程完成.最后,通过两个数值仿真例子来验证PCA方法、 基于全局的协方差矩阵的信息增量矩阵方法以及本文方法在故障误报和漏报方面的检测效能. 实验结果表明,本文方法具有最好的检测性能.     

冲突证据融合的优化方法

多数研究者认为, 用修改数据模型(证据体)的方法来解决冲突证据组合问题较为合理. 然而, 已有的基于修改数据模型的方法仅考虑如何提高冲突证据组合结果的聚焦程度. 实际上, 它们并没有考虑如何通过修正来消减证据之间的冲突. 显然, 若融合结果由冲突证据组合得到, 那么其可信性必然较低且会给随后的融合过程带来较大的风险. 针对此问题, 沿用折扣系数法, 该文基于证据距离准则提出了一种折扣系数(可靠度)优化学习模型, 优化过程同时考虑提高聚焦程度和消减冲突, 通过使折扣修正后组合结果的基本概率赋值(Basic probability assignment, BPA)与直言BPA (Categorical BPA, CBPA)之间的距离最小来寻优, 其中证据可靠度大小的序关系作为约束条件, 它依据证据的虚假度确定. 典型算例验证了所提方法比现有的一些组合方法, 在聚焦能力和冲突消减两方面都更合理.     

松潘—甘孜造山带南东段羊房沟石英闪长岩时代、成因及地质意义

羊房沟石英闪长岩出露于松潘—甘孜造山带南东段,该地区岩浆活动较强烈。LA-ICP-MS锆石U-Pb测年获得石英闪长岩年龄为(205.3±1.3) Ma,形成时代为晚三叠世。石英闪长岩SiO_2为58.01%～59.34%,Al_2O_3为14.90%～15.77%,K_2O/Na_2O比值为1.69～2.13,显示高钾低钠特点,里特曼指数为1.68～2.12,在K_2O-Na_2O关系图上落入钾玄岩系列,A/CNK为0.76～0.91,属于准铝质岩石;稀土元素总量(∑REE=183.08×10~(-6)～283.08×10~(-6)),为轻稀土元素富集、重稀土相对亏损的右倾型。δEu为0.77～1.00,负铕异常明显。在微量元素原始地壳标准化蛛网图上,岩石富集Rb、Ba、Th等大离子亲石元素、LREE和不相容元素(如U),相对亏损Nb、Ti、P等高场强元素,指示具有岛弧岩浆特征,Rb/Sr、Nb/Ta比值介于壳幔之间,反应具有壳幔岩浆混合成因。结合区域地质背景,认为羊房沟石英闪长岩形成于晚三叠世洋壳俯冲阶段的岛弧环境。     

Topset-to-forest rollover trajectories as reliable predictors of sediment-volume partitioning into deep-lake areas

Topset-to-forest rollover trajectories and their relation to sediment-and sand-budget partitioning into deep-lake areas are far from being well understood,as co...     

Classifier combination based on confidence transformation

This paper investigates the effects of confidence transformation in combining multiple classifiers using various combination rules. The combination methods were tested in handwritten digit recognition by combining varying classifier sets. The classifier outputs are transformed to confidence measures by combining three scaling functions (global normalization, Gaussian density modeling, and logistic regression) and three confidence types (linear, sigmoid, and evidence). The combination rules include fixed rules (sum-rule, product-rule, median-rule, etc.) and trained rules (linear discriminants and weighted combination with various parameter estimation techniques). The experimental results justify that confidence transformation benefits the combination performance of either fixed rules or trained rules. Trained rules mostly outperform fixed rules, especially when the classifier set contains weak classifiers. Among the trained rules, the support vector machine with linear kernel (linear SVM) performs best while the weighted combination with optimized weights performs comparably well. I have also attempted the joint optimization of confidence parameters and combination weights but its performance was inferior to that of cascaded confidence transformation-combination. This justifies that the cascaded strategy is a right way of multiple classifier combination.     

Class-specific feature polynomial classifier for pattern classification and its application to handwritten numeral recognition

The polynomial classifier (PC) that takes the binomial terms of reduced subspace features as inputs has shown superior performance to multilayer neural networks in pattern classification. In this paper, we propose a class-specific feature polynomial classifier (CFPC) that extracts class-specific features from class-specific subspaces, unlike the ordinary PC that uses a class-independent subspace. The CFPC can be viewed as a hybrid of ordinary PC and projection distance method. The class-specific features better separate one class from the others, and the incorporation of class-specific projection distance further improves the separability. The connecting weights of CFPC are efficiently learned class-by-class to minimize the mean square error on training samples. To justify the promise of CFPC, we have conducted experiments of handwritten digit recognition and numeral string recognition on the NIST Special Database 19 (SD19). The digit recognition task was also benchmarked on two standard databases USPS and MNIST. The results show that the performance of CFPC is superior to that of ordinary PC, and is competitive with support vector classifiers (SVCs).