Editorial

Nowadays control systems can find applications in many areas, like aerospace, motion tracking, chemical engineering, physics, biology, economics. To improve control performance is a perpetual goal. With recent progresses of computing technologies, better control performance can be achieved by more judicious control strategies based on more precise and more complicated, such as time-varying, nonlinear, models. Another trend to improve control performance is built upon the divide-and-conquer philosophy, i.e., a complicated control task is cooperatively accomplished by multiple controllers/agents, instead of a single super-powerful controller. This trend is made possible due to great advances in communication, which enable the information exchange among agents and may unite the less powerful agents. The cost for such control performance improvement is higher spatial complexity of control systems. Although high model complexity and spatial complexity can be physically handled by current powerful controllers/agents, the lack of efficient analysis and synthesis methods prevents the performance improvement of control systems with high model or/and spatial complexity. As we know, conventional control methods are mainly developed under the assumptions of linear, time-invariant and centralized models. In order to resolve the issues due to the aforementioned two types of complexities, new methods are expected, which is exactly the major aim of the present special issue.     

A social tag clustering method based on common co-occurrence group similarity

Social tagging systems are widely applied in Web 2.0. Many users use these systems to create, organize, manage, and share Internet resources freely. However, many ambiguous and uncontrolled tags produced by social tagging systems not only worsen users’ experience, but also restrict resources’ retrieval efficiency. Tag clustering can aggregate tags with similar semantics together, and help mitigate the above problems. In this paper, we first present a common co-occurrence group similarity based approach, which employs the ternary relation among users, resources, and tags to measure the semantic relevance between tags. Then we propose a spectral clustering method to address the high dimensionality and sparsity of the annotating data. Finally, experimental results show that the proposed method is useful and efficient.     

Algorithm of gaussian sum filter based on high-order UKF for dynamic state estimation

In this work we consider the state estimation problem in nonlinear/non-Gaussian systems. A new version of Gaussian sum estimation algorithm is developed here based on high-order unscented Kalman filter (HUKF). A sigma point selection method, high-order unscented transformation (HUT) technique is proposed for the HUKF, which can approximate the Gaussian distributions more accurately. We present the systematic formulation of Gaussian filters and develop efficient and accurate numerical integration of the optimal filter. We then go on to extend the use of the HUKF to discrete-time, nonlinear systems with additive, possibly non-Gaussian noise. The resulting filtering algorithm, called the Gaussian sum high-order unscented Kalman filter (GS-HUKF) approximates the predicted and posterior densities as a finite number of weighted sums of Gaussian densities. It is corroborated in the theoretical analysis and the simulation that the proposed Gaussian sum HUKF has integrated advantages with respect to computational accuracy and time complexity for nonlinear non-Gaussian filtering problems.     

基于网络层次分析法的煤矿应急救援能力评价

针对煤矿应急救援能力评价指标之间相互依存和反馈的关系,构建了基于网络层次分析法和模糊综合评价法的煤矿应急救援能力评价模型;对该模型进行实证研究,借助超级决策软件和专家打分制实现了各评价指标的赋权和评分,并据此对不同煤矿的应急救援能力进行评价。实证结果表明,该模型具有较好的可行性和有效性,对煤矿应急救援能力评价研究起到了一定的借鉴作用。     

烟草行业IT治理原型系统设计与实现

该文分析了烟草行业信息化面临的现状,指出IT治理是解决当前问题的有效办法,重点研究论述了烟草行业原型系统的软件设计与实现过程,并就一些关键问题作了深入分析与研究。     

数据挖掘常用分类算法研究

数据库、数据仓库以及其他存储信息库中潜藏着很多与商业、科学研究等活动的决策有关的数据和知识。对于数据挖掘中的数据分析,通常有两种常见的方法,即分类和预测,首先对数据库中的数据进行分类归纳,然后根据分类规则可以得到比较有价值的数据,然后我们可以根据这个数据来预测得到一些包含未来趋势的信息。在常见的分类算法中,决策树算法是一个有着很好扩展性的算法,可以应用到大型数据库中,可以对多种数据类型进行处理,分类模式容易转化为分类规则,结果也十分的浅显易懂易于理解。该文主要先介绍了几种常用的分类算法,然后具体介绍决策树算法的过程以及在分类算法实际应用中的优缺点。     

基于形态分量分析的图像去噪算法研究

由于信号采集,信号传输等原因,随机噪声对信号有着很大的影响,甚至降低信号质量。传统的去噪方法无法自动的在去除随机噪声,和保存有用信息之间做出最佳选择。在本文中,我们展示了一种以相对无损的方法从有用信息中去除所有明显的随机噪声。该文设计的模型是建立在一种如下的理论假设之上:原始图像信号是由有用信号和随机噪声信号组成的,而这两者在形态学上是具有不同表示的。基于他们形态学上的不同性,这两种分量可以分别以不同的字典来稀疏的表示,接下来就是把各自分量给分离出来,去噪完成之后再以分量重建信号。     

基于DSP的自适应光纤/千兆网接口设计

本文以DSP芯片C6455为应用平台,介绍了一种自适应的光纤和网络接口通信设计方法。系统具有两个光纤和网络接口,可以自动识别所插入的设备,在不断电情况下可以任意交换接口,无须用户参与。文中详细介绍了该系统的软硬件设计方法。