无界Petri网的可达树的综述

Petri网自提出以来得到了学术界和工业界的广泛关注. Petri网系统的可达性是最基本性质之一.系统的其他相关性质都可以通过可达性进行分析.利用等价的有限可达树来研究无界Petri网可达性,依然是一个开放性问题.该研究可以追溯到40年前,但由于问题本身的复杂性和难度太大,直到最近20年,经过国内外诸多学者的不懈努力,才逐渐取得了一些阶段性的成果和部分突破.本文回顾了近40年来国内外学者为彻底解决该问题作出的贡献.重点对4种开创性的研究成果展开讨论,分别为有限可达树、扩展可达树、改进可达树及新型改进可达树.探讨了今后无界Petri网可达性问题的研究方向.     

一种空域和频域相结合的遥感图像亚像素配准方法

扩展相位相关配准方法只适用于图像间相对运动小于图像尺寸的50%甚至30%的情况,其配准精度随着图像间运动增大而降低且易受边缘效应和混叠效应的影响.针对这种情况,本文做了如下改进:在使用相位相关法之前使用空间域方法粗配准,根据粗配准结果提取重叠区域形成两幅新图像,再对两幅新图像使用扩展相位相关进行精配准,解决了原方法只适用于图像间相对运动较小且精度随相对运动增大而降低的问题;为减弱边缘效应对配准精度的影响,通过实验评价了不同加窗方法,并给与了最优的建议;为减弱混叠效应对配准精度的影响,通过实验讨论了理想低通滤镜的截止频率的取值方法.实验证明这种方法不仅拓展了相位相关的使用范围而且在很大程度上提高了配准的精度.     

GJK算法的一种特殊情形的形式化验证和应用

计算几何算法经常用于机器人避碰运动规划等安全攸关领域,对这些算法进行正确性证明非常重要.用形式化方法对算法进行验证是一种十分有效的手段,尤其是定理证明的方法用严格的数学公理和定理推理证明逻辑模型的性质,对所验证的性质而言是完备的.基于GJK算法设计了计算空间两条线段间距离的算法,用定理证明器HOL4对其相关的定义和定理进行形式化定义和证明,进而基于霍尔逻辑完成形式化表示和证明,对该算法的正确性实现了形式化验证.最后,给出了这一经过验证的算法在双臂机器人无碰撞运动规划中的应用.     

Mooring system diagnosis and structural reliability control for position moored vessels

Early diagnosis and fault-tolerant control are essential for safe operation of floating platforms where mooring systems maintain vessel position and must withstand environmental loads. This paper considers two critical faults, line breakage and loss of a buoyancy element and employs vector statistical change detection for timely diagnosis of faults. Diagnosis design is scrutinised and a procedure is proposed based on specified false alarm probability and estimation of the distribution of the test statistics on which change detection is based. A structural reliability index is applied for monitoring the safety level of each mooring line and a set-point chasing algorithm accommodates the effects of line failure, as an integral part of the reliability-based set-point chasing control algorithm. The feasibility of the diagnosis and of the fault-tolerant control strategy is verified in model basin tests.     

Experimental investigation of stochastic parafoil guidance using a graphics processing unit

Control of autonomous systems subject to stochastic uncertainty is a challenging task. In guided airdrop applications, random wind disturbances play a crucial role in determining landing accuracy and terrain avoidance. This paper describes a stochastic parafoil guidance system which couples uncertainty propagation with optimal control to protect against wind and parameter uncertainty in the presence of impact area obstacles. The algorithm uses real-time Monte Carlo simulation performed on a graphics processing unit (GPU) to evaluate robustness of candidate trajectories in terms of delivery accuracy, obstacle avoidance, and other considerations. Building upon prior theoretical developments, this paper explores performance of the stochastic guidance law compared to standard deterministic guidance schemes, particularly with respect to obstacle avoidance. Flight test results are presented comparing the proposed stochastic guidance algorithm with a standard deterministic one. Through a comprehensive set of simulation results, key implementation aspects of the stochastic algorithm are explored including tradeoffs between the number of candidate trajectories considered, algorithm runtime, and overall guidance performance. Overall, simulation and flight test results demonstrate that the stochastic guidance scheme provides a more robust approach to obstacle avoidance while largely maintaining delivery accuracy.     

Supervised Distance Preserving Projections: Applications in the quantitative analysis of diesel fuels and light cycle oils from NIR spectra

In this work, we discuss a recently proposed approach for supervised dimensionality reduction, the Supervised Distance Preserving Projection (SDPP) and, we investigate its applicability to monitoring material's properties from spectroscopic observations. Motivated by continuity preservation, the SDPP is a linear projection method where the proximity relations between points in the low-dimensional subspace mimic the proximity relations between points in the response space. Such a projection facilitates the design of efficient regression models and it may also uncover useful information for visualisation. An experimental evaluation is conducted to show the performance of the SDPP and compare it with a number of state-of-the-art approaches for unsupervised and supervised dimensionality reduction. The regression step after projection is performed using computationally light models with low maintenance cost like Multiple Linear Regression and Locally Linear Regression with k-NN neighbourhoods. For the evaluation, a benchmark and a full-scale calibration problem are discussed. The case studies pertain the estimation of a number of chemico-physical properties in diesel fuels and in light cycle oils, starting from near-infrared spectra. Based on the experimental results, we found that the SDPP leads to parsimonious projections that can be used to design light and yet accurate estimation models.     

Synchronization of a class of nonlinear network flow systems

In this paper, we consider the synchronization of a class of nonlinear network flow systems. Motivated by air distribution problem in air conditioning and mechanical ventilation (ACMV) systems, we propose a class of coupled nonlinear multi‐agent systems that can model a wide class of network flow systems, including air flow in ACMV systems, water flow in irrigation systems, traffic flow in transportation systems, and so on. Then we consider the synchronization problem for the class of nonlinear multi‐agent systems and propose cooperative controllers for the system. Based on graph theory, we derive conditions on the initial values of the state and the control input such that synchronization can be achieved. An application to air ventilation is provided to demonstrate the effectiveness of the cooperative controllers. Copyright © 2015 John Wiley & Sons, Ltd.     

Stochastic consensus control with finite frequency specification for Markov jump networks

A stochastic finite frequency consensus protocol for directed networks with Markov jump topologies and external disturbances is proposed in this paper. By introducing the frequency band information into consensus control design, the disagreement dynamics of interconnected networks asymptotically converge to zero with an improved level of disturbance attenuation in the specific frequency band. In addition, the new model transformation approach is presented by exploring certain features of Laplacian matrix in real Jordan form, which leads to more generality of the designed protocol. A numerical example validates the potential of the developed results. Copyright © 2015 John Wiley & Sons, Ltd.     

Dispersion Constraint Based Non-negative Sparse Coding Model

Based on advantages of basic non-negative sparse coding (NNSC) model, and considered the prior class constraint of image features, a novel NNSC model is discussed here. In this NNSC model, the sparseness criteria is selected as a two-parameter density estimation model and the dispersion ratio of within-class and between-class is used as the class constraint. Utilizing this NNSC model, image features can be extracted successfully. Further, the feature recognition task by using different classifiers can be implemented well. Simulation results prove that our NNSC model proposed is indeed effective in extracting image features and recognition task in application.