Nonlinear aircraft sensor fault reconstruction in the presence of disturbances validated by real flight data

This paper proposes an approach for Inertial Measurement Unit sensor fault reconstruction by exploiting a ground speed-based kinematic model of the aircraft flying in a rotating earth reference system. Two strategies for the validation of sensor fault reconstruction are presented: closed-loop validation and open-loop validation. Both strategies use the same kinematic model and a newly-developed Adaptive Two-Stage Extended Kalman Filter to estimate the states and faults of the aircraft. Simulation results demonstrate the effectiveness of the proposed approach compared to an approach using an airspeed-based kinematic model. Furthermore, the major contribution is that the proposed approach is validated using real flight test data including the presence of external disturbances such as turbulence. Three flight scenarios are selected to test the performance of the proposed approach. It is shown that the proposed approach is robust to model uncertainties, unmodeled dynamics and disturbances such as time-varying wind and turbulence. Therefore, the proposed approach can be incorporated into aircraft Fault Detection and Isolation systems to enhance the performance of the aircraft.     

基于线性表示的协同入侵检测模型

针对现有入侵检测算法误报率较高和鲁棒性较差的问题,提出一种基于线性表示的协同入侵检测模型,按照网络协议产生3类基于K-均值聚类算法的检测代理,以协同的方式对其相应网络数据做出决策。实验对比结果表明,该协同模型具有较高的检测率和较低的误警率。     

Safe driving in a green world: A review of driver performance benchmarks and technologies to support ‘smart’ driving

Road transport is a significant source of both safety and environmental concerns. With climate change and fuel prices increasingly prominent on social and political agendas, many drivers are turning their thoughts to fuel efficient or ‘green’ (i.e., environmentally friendly) driving practices. Many vehicle manufacturers are satisfying this demand by offering green driving feedback or advice tools. However, there is a legitimate concern regarding the effects of such devices on road safety – both from the point of view of change in driving styles, as well as potential distraction caused by the in-vehicle feedback. In this paper, we appraise the benchmarks for safe and green driving, concluding that whilst they largely overlap, there are some specific circumstances in which the goals are in conflict. We go on to review current and emerging in-vehicle information systems which purport to affect safe and/or green driving, and discuss some fundamental ergonomics principles for the design of such devices. The results of the review are being used in the Foot-LITE project, aimed at developing a system to encourage ‘smart’ – that is safe and green – driving.     

A Bayesian solution to the multiple composite hypothesis testing for fault diagnosis in dynamic systems

This paper is concerned with model-based isolation and estimation of additive faults in discrete-time linear Gaussian systems. The isolation problem is stated as a multiple composite hypothesis testing on the innovation sequence of the Kalman filter (KF) that considers the system operating under fault-free conditions. Fault estimation is carried out, after isolating a fault mode, by using the Maximum a Posteriori (MAP) criterion. An explicit solution is presented for both fault isolation and estimation when the parameters of the fault modes are assumed to be realizations of specific random variables (RV).     

Active fault tolerant control of discrete event systems using online diagnostics

The aim of this paper is to deal with the problem of fault tolerant control in the framework of discrete event systems modeled as automata. A fault tolerant controller is a controller able to satisfy control specifications both in nominal operation and after the occurrence of a fault. This task is solved by means of a parameterized controller that is suitably updated on the basis of the information provided by online diagnostics: the supervisor actively reacts to the detection of a malfunctioning component in order to eventually meet degraded control specifications. Starting from an appropriate model of the system, we recall the notion of safe diagnosability as a necessary step in order to achieve fault tolerant control. We then introduce two new notions: (i) “safe controllability”, which represents the capability, after the occurrence of a fault, of steering the system away from forbidden zones and (ii) “active fault tolerant system”, which is the property of safely continuing operation after faults. Finally, we show how the problem can be solved using a general control architecture based on the use of special kind of diagnoser, called “diagnosing controller”, which is used to safely detect faults and to switch between the nominal control policy and a bank of reconfigured control policies. A simple example is used to illustrate the new notions and the control architecture introduced in the paper.     

基于免疫网络的异态电磁信号监测模型

针对人工免疫系统运用于信号密度适中的日常异态信号监测时,系统对检测器数量要求过高的问题,本文对现有的模型进行了改进,提出了基于免疫网络的异态电磁信号监测模型。模型引入数据正向压缩和检测器相互抑制的机制,使得系统对检测器数量的要求明显降低。本文采用实际无线电监测数据对模型进行了检验,并与基于阴性选择的模型进行对比,验证了该模型在信号密度适中的日常监测中可大幅减少对检测器数量的要求,提高了监测效率。     

安全仪表系统在高含硫天然气净化厂的建立

高含硫天然气净化厂的安全仪表系统关键技术通过充分的实践检验和完善,它将实现特大型高含硫净化厂自动监控和安全联锁关断功能,实现迅速监测,准确判断,及时处理,使生产装置最快的速度进入安全状态,不需人工动态操作。系统的S O S E功能对系统本身的故障或触发的事件进行记录,以利于运行故障的快速分析,为装置在事故状态下的安全提供保证。     

DIDAPPER:具有认知能力的分布式入侵检测系统

近年来,随着网络安全问题日益突出,入侵检测也越来越受到关注。目前,研究入侵检测的课题很多,侧重点也各不相同。该文介绍的ＤＩＤＡＰＰＥＲ系统是一种具有认知能力的分布式入侵检测系统。分布式结构、认知能力和知识的共享是ＤＩＤＡＰＰＥＲ系统的重要特点。流量标本和ＩＰ陷阱是ＤＩＤＡＰＰＥＲ系统所提出的新概念。ＤＩＤＡＰＰＥＲ的分布式三级结构使得它适合于检测大规模网络自动攻击行为,而且有较强的可扩展性和高效性。     

用有限状态图来识别系统入侵

文章首先对系统入侵的状态转换进行了研究,通过使用有限状态图的分析方法,分析识别了常见黑客攻击的特征,包括ROOT用户攻击、TCPSYNFLOODING、IP分片攻击等等;最后根据对系统入侵的状态转换图的研究和对攻击特征的分析设计了一个入侵检测系统。     

View-based virtual learning and recognition of 3D object using view model obtained by motion-stereo

View-based approach for learning and recognition of 3D object and its pose detection was proved to be affective and efficient, except its high learning cost. In this research, we propose a virtual learning approach which generates learning samples of views of an object from its 3D view model obtained by motion-stereo method. From the generated learning sample views, features of high-order autocorrelation are extracted, and discriminant feature spaces for object recognition and pose detection are built. Recognition experiments on real objects are carried out to show the effectiveness of the proposed method. Caihua Wang, Ph.D.: He received his B.S. in mathematics and M.E. in electronic engineering from Renmin University of China, Beijing, China in 1983 and 1986, and his Ph. D. from Shizuoka University, Hamamatsu, Japan in 1996. He is a JST domestic fellow and is doing his post doctoral research at Electrotechnical Laboratory. His research interests are computer vision and image processing. He is a member of IEICE and IPSJ. Katsuhiko Sakaue, Ph.D.: He received the B.E., M.E., and Ph.D. degrees all in electronic engineering from University of Tokyo, in 1976, 1978 and 1981, respectively. In 1981, he joined the Electrotechnical Laboratory, Ministry of International Trade and Industry, and engaged in researches in image processing and computer vision. He received the Encouragement Prize in 1979 from IEICE, and the Paper Award in 1985 from Information.