基于 SOLT 校准技术的S参数测量不确定度评定磁

目前,常用的 S 参数测量不确定度评定方法中均未考虑 S 参数相关性的问题,论文给出了一种2GHz ～18GHz 频率范围内 S 参数测量不确定度的评定方法,该方法从校准件的定义出发,基于 SOLT 校准技术对矢量网络分析仪进行自校准,将校准件引入的不确定度通过矢量网络分析仪传递给被测件（DUT ）,通过使用蒙特卡洛仿真的方法（MCM ）得到矢量网络分析仪测量 DUT S 参数的测量不确定度,评定过程中考虑了相关性问题,提高了 S 参数测量不确定度的可靠性和合理性。     

Modeling and robust discrete LQ repetitive control of electrically driven robots

Discrete linear quadratic control has been efciently applied to linear systems as an optimal control.However,a robotic system is highly nonlinear,heavily coupled and uncertain.To overcome the problem,the robotic system can be modeled as a linear discrete-time time-varying system in performing repetitive tasks.This modeling motivates us to develop an optimal repetitive control.The contribution of this paper is twofold.For the frst time,it presents discrete linear quadratic repetitive control for electrically driven robots using the mentioned model.The proposed control approach is based on the voltage control strategy.Second,uncertainty is efectively compensated by employing a robust time-delay controller.The uncertainty can include parametric uncertainty,unmodeled dynamics and external disturbances.To highlight its ability in overcoming the uncertainty,the dynamic equation of an articulated robot is introduced and used for the simulation,modeling and control purposes.Stability analysis verifes the proposed control approach and simulation results show its efectiveness.     

Spatial and functional relationships between air conduits and blood capillaries in the pulmonary gas exchange tissue of adult and developing chickens

The documented data regarding the three-dimensional structure of the air capillaries (ACs), the ultimate sites of gas exchange in the avian lung is contradictory. Further, the mode of gas exchange, described as cross-current has not been clearly elucidated. We studied the temporal and spatial arrangement of the terminal air conduits of the chicken lung and their relationship with the blood capillaries (BCs) in embryos as well as the definitive architecture in adults. Several visualization techniques that included corrosion casting, light microscopy as well as scanning and transmission electron microscopy were used. Two to six infundibulae extend from each atrium and give rise to numerous ACs that spread centrifugally. Majority of the ACs are tubular structures that give off branches, which anastomose with their neighboring cognates. Some ACs have globular shapes and a few are blind-ending tapering tubes. During inauguration, the luminal aspects of the ACs are characterized by numerous microvillus-like microplicae, which are formed during the complex processes of cell attenuation and canalization of the ACs. The parabronchial exchange BCs, initially inaugurated as disorganized meshworks, are reoriented via pillar formation to lie predominantly orthogonal to the long axes of the ACs. The remodeling of the retiform meshworks by intussusceptive angiogenesis essentially accomplishes a cross-current system at the gas exchange interface in the adults, where BCs form ring-like patterns around the ACs, thus establishing a cross-current system. Our findings clarify the mode of gas exchange in the parabronchial mantle and illuminate the basis for the functional efficiency of the avian lung.     

Dynamic Output Feedback Consensualization of Uncertain Swarm Systems with Time Delays

Consensus analysis and design problems of high‐dimensional discrete‐time swarm systems in directed networks with time delays and uncertainties are dealt with by using output information. Two subspaces are introduced, namely a consensus subspace and a complement consensus subspace. By projecting the state of a swarm system onto the two subspaces, a necessary and sufficient condition for consensus is presented, and based on different influences of time delays and uncertainties, an explicit expression of the consensus function is given which is very important in applications of swarm systems. A method to determine gain matrices of consensus protocols is proposed. Numerical simulations are presented to demonstrate theoretical results.     

一类不确定线性系统圆盘极点鲁棒可靠配置

针对一类不确定线性系统,基于线性矩阵不等式（LMI）,给出状态反馈鲁棒控制器的设计算法。利用比离散故障模型更具有实际意义的连续故障模型,给出确定状态反馈鲁棒可靠控制器存在的充分条件,通过求解LMI,确定鲁棒可靠控制器的参数矩阵。所给出的鲁棒可靠控制器,不仅可以保持不确定闭环系统的极点保持在圆盘内,而且能够抵御执行器故障对圆盘极点的影响。一个数值例子说明所给出方法的有效性及可行性。     

CDDL:动态描述逻辑的不确定性扩展

本体(Ontology)是语义Web中共享知识的形式化建模工具,其逻辑基础是描述逻辑.动态描述逻辑(DDL)具有同时表示静态和动态知识的优势.本文针对语义Web需要处理不确定性动态知识的需求,利用云模型对DDL进行不确定性扩展,提出了一种能够有效实现不确定性静态和动态知识进行表示和推理的不确定性动态描述逻辑CDDL.与...     

子空间聚类改进算法研究综述

高维数据聚类是聚类技术的难点和重点,子空间聚类是实现高维数据集聚类的有效途径。CLIQUE算法是最早提出的基于密度和网格的子空间聚类算法,自动子空间聚类算法的实用性和高效性,带来了子空间聚类算法的空前发展。深入分析CLIQUE算法的优点和局限性;介绍了一些近几年提出的子空间聚类算法,并针对CLIQUE算法的局限性作了改进,聚类的效率和精确性得到了提高;最后对子空间聚类算法的发展趋势进行了讨论。     

A new graphical visualization of n-dimensional Pareto front for decision-making in multiobjective optimization

New challenges in engineering design lead to multiobjective (multicriteria) problems. In this context, the Pareto front supplies a set of solutions where the designer (decision-maker) has to look for the best choice according to his preferences. Visualization techniques often play a key role in helping decision-makers, but they have important restrictions for more than two-dimensional Pareto fronts. In this work, a new graphical representation, called Level Diagrams, for n-dimensional Pareto front analysis is proposed. Level Diagrams consists of representing each objective and design parameter on separate diagrams. This new technique is based on two key points: classification of Pareto front points according to their proximity to ideal points measured with a specific norm of normalized objectives (several norms can be used); and synchronization of objective and parameter diagrams. Some of the new possibilities for analyzing Pareto fronts are shown. Additionally, in order to introduce designer preferences, Level Diagrams can be coloured, so establishing a visual representation of preferences that can help the decision-maker. Finally, an example of a robust control design is presented - a benchmark proposed at the American Control Conference. This design is set as a six-dimensional multiobjective problem.     

Limit load and shakedown analysis of plastic structures under stochastic uncertainty

Problems from plastic analysis are based on the convex, linear or linearised yield/strength condition and the linear equilibrium equation for the stress (state) vector. In practice one has to take into account stochastic variations of several model parameters. Hence, in order to get robust maximum load factors, the structural analysis problem with random parameters must be replaced by an appropriate deterministic substitute problem. A direct approach is proposed based on the primary costs for missing carrying capacity and the recourse costs (e.g. costs for repair, compensation for weakness within the structure, damage, failure, etc.). Based on the mechanical survival conditions of plasticity theory, a quadratic error/loss criterion is developed. The minimum recourse costs can be determined then by solving an optimisation problem having a quadratic objective function and linear constraints. For each vector a(·) of model parameters and each design vector x, one obtains then an explicit representation of the “best” internal load distribution F^∗. Moreover, also the expected recourse costs can be determined explicitly. Consequently, an explicit stochastic nonlinear program results for finding a robust maximal load factor μ^∗. The analytical properties and possible solution procedures are discussed.     

Sampled-data based average consensus with measurement noises: convergence analysis and uncertainty principle

In this paper, sampled-data based average-consensus control is considered for networks consisting of continuous-time first-order integrator agents in a noisy distributed communication environment. The impact of the sampling size and the number of network nodes on the system performances is analyzed. The control input of each agent can only use information measured at the sampling instants from its neighborhood rather than the complete continuous process, and the measurements of its neighbors’ states are corrupted by random noises. By probability limit theory and the property of graph Laplacian matrix, it is shown that for a connected network, the static mean square error between the individual state and the average of the initial states of all agents can be made arbitrarily small, provided the sampling size is sufficiently small. Furthermore, by properly choosing the consensus gains, almost sure consensus can be achieved. It is worth pointing out that an uncertainty principle of Gaussian networks is obtained, which implies that in the case of white Gaussian noises, no matter what the sampling size is, the product of the steady-state and transient performance indices is always equal to or larger than a constant depending on the noise intensity, network topology and the number of network nodes.