基于相似度的多粒度不确定语言群决策方法

针对具有多粒度不确定语言评价信息的多属性群决策问题,提出了一种基于区间二元语义信息处理和矢量相似度的群决策方法,弥补了基于距离测度的决策方法易造成信息混淆的不足。该方法首先使用二元语义转换函数对多粒度区间语言评价信息进行一致化处理;然后通过建立使备选方案对正理想解相似度最大、负理想解相似度最小的最优化模型来获得相应的属性权重;最后利用区间二元语义的集结算子对评价信息进行加权集成,并通过优序数排序法实现对各方案的排序。实例分析说明了该方法的可行性和有效性。     

云环境下基于协同推荐的信任评估与服务选择

“模糊、自治”的云计算环境中,服务品类繁多、质量参差不齐,用户主体难以进行可信赖的服务选择。在用户交互经验的基础上,结合现实人际交易模式,提出了一种基于协同推荐的综合信任量化评估模型。模型引入了时间衰减、权重两类动态因子,设计了多元化混合协同推荐算法来实现用户之间的有效协作,帮助用户正确选择可信云服务。为了验证模型的可行性,设计出一个分布式的原型系统,对模型的用户满意度和服务选择质量进行仿真实验。仿真结果表明,该模型能够更快地提高平均服务满意度,更有效地抑制恶意服务,而且随着交互次数的增长,服务选择质量也会不断提高。     

不确定数据聚类的U-PAM算法和UM-PAM算法的研究

UK-means算法在处理不确定数据时对孤立点非常敏感,而且事先必须已知不确定数据的分布函数或概率密度,然而这在实际中往往很难获得。因此,针对UK-means在处理不确定测量数据时的不足,首先提出了基于区间数的PAM不确定聚类算法——U-PAM,该算法用区间数和标准差合理地描述了不确定测量数据的不确定性,进而完成有效的聚类;其次,针对海量不确定测量数据难以聚类的问题,基于U-PAM聚类算法,采用抽样技术提出了处理海量不确定测量数据的算法——UM-PAM算法,该算法先抽样,对样本数据聚类,然后再总体聚类;最后,基于U-PAM算法和CH聚类的有效性指标函数对聚类结果进行分析,以确定最佳聚类数。实验理论表明,所提算法聚类效果明显。     

一种基于量子密码的卫星网络窃听攻击检测方法

鉴于窃听攻击是对卫星网络实施各类高级攻击行为的基础,结合量子密码在未来卫星网络的应用趋势,提出了一种基于量子密码的卫星网络窃听攻击检测方法。首先,基于卫星网络节点在空间分布上的分层特点,构建了层簇式的卫星网络窃听攻击检测模型。实际检测过程中,相邻卫星节点检测到窃听攻击威胁时,将相关预警信息经簇首节点融合后通过安全信道传送到地面控制中心,随后再根据地面控制中心反馈的安全链路构建方案构建节点间的安全通信链路。最后,对方案的安全性及有效性进行了分析。相关成果可为进一步深入开展卫星网络安全防护技术研究打下一定的基础。     

Generating strictly binary trees at random based on convex polygon triangulations

A set of maximal non-intersecting diagonals can decompose a polygon into triangles, and the edges and diagonals can be converted into the external and internal nodes of a strictly binary tree. This paper gives algorithms to generate all types of triangulations and triangulations at random. Based on that, this paper gives an algorithm to generate strictly binary trees at random. The experimental results show that the numbers of various shapes of strictly binary trees generated are nearly equal. The algorithm to generate strictly binary trees at random can be transformed to the algorithm to randomly generate binary trees.     

Optimal replacement policy for a two-dissimilar-component cold standby system with different repair actions

In this paper, a cold standby repairable system consisting of two dissimilar components and one repairman is studied. When failures occur, the repair of both component 1 and component 2 are not ‘as good as new’. The consecutive operating times of component 1 after repair constitute a decreasing geometric process, while the repair times of component 1 are independent and identically distributed. For component 2, its failure is rectified by minimal repair, and the repair time is negligible. Component 1 has priority in use when both components are good. The replacement policy N is based on the failure number of component 1. Under policy N, we derive the explicit expression of the long-run average cost rate C(N) as well as the average number of repairs of component 2 before the system replaced. The optimal replacement policy N*, which minimises the long-run average cost rate C(N), is obtained theoretically. If the failure rate r(t) of component 2 is increasing, the existence and uniqueness of the optimal policy N* is also proved. Finally, a numerical example is given to validate the developed theoretical model. Some sensitivity analyses are provided to show the influence of some parameters, such as the costs for replacement and repair, and the parameters of the lifetime and repair time distributions of both components, to the optimal replacement policy N* and corresponding average cost rate C(N*).     

Subspace-based continuous-time identification of fractional order systems from non-uniformly sampled data

This work focuses on the identification of fractional commensurate order systems from non-uniformly sampled data. A novel scheme is proposed to solve such problem. In this scheme, the non-uniformly sampled data are first complemented by using fractional Laguerre generating functions. Then, the multivariable output error state space method is employed to identify the relevant system parameters. Moreover, an in-depth property analysis of the proposed scheme is provided. A numerical example is investigated to illustrate the effectiveness of the proposed method.     

Fault-tolerant control of delta operator systems with actuator saturation and effectiveness loss

This paper studies the problem of robust fault-tolerant control against the actuator effectiveness loss for delta operator systems with actuator saturation. Ellipsoids are used to estimate the domain of attraction for the delta operator systems with actuator saturation and effectiveness loss. Some invariance set conditions used for enlarging the domain of attraction are expressed by linear matrix inequalities. Discussions on system performance optimisation are presented in this paper, including reduction on computational complexity, expansion of the domain of attraction and disturbance rejection. Two numerical examples are given to illustrate the effectiveness of the developed techniques.     

Synchronisation of a fractional-order chaotic system using finite-time input-to-state stability

The issue of synchronisation for a fractional-order chaotic system with uncertainties and disturbance is studied in this paper. The finite-time input-to-state stable theory of fractional-order dynamical system is presented for the first time. A linear feedback controller is proposed to achieve synchronisation of this fractional-order system and guarantee the bounded state error for any bounded interference in finite time. Since the chaotic system displays special dynamical behaviours as invariable Lyapunov exponent spectrums and controllable signal amplitude, one can achieve complete synchronisation and projective synchronisation by only adjusting the system parameter. Numerical simulations are shown to verify the feasibility of the presented synchronisation scheme.     

Space‐Time Co‐Segmentation of Articulated Point Cloud Sequences

Consistent segmentation is to the center of many applications based on dynamic geometric data. Directly segmenting a raw 3D point cloud sequence is a challenging task due to the low data quality and large inter‐frame variation across the whole sequence. We propose a local‐to‐global approach to co‐segment point cloud sequences of articulated objects into near‐rigid moving parts. Our method starts from a per‐frame point clustering, derived from a robust voting‐based trajectory analysis. The local segments are then progressively propagated to the neighboring frames with a cut propagation operation, and further merged through all frames using a novel space‐time segment grouping technqiue, leading to a globally consistent and compact segmentation of the entire articulated point cloud sequence. Such progressive propagating and merging, in both space and time dimensions, makes our co‐segmentation algorithm especially robust in handling noise, occlusions and pose/view variations that are usually associated with raw scan data.