基于任务和角色的分布式工作流安全模型

针对现有基于角色访问控制的缺陷和分布式工作流管理系统的特性,在传统的基于角色的访问控制模型中引入任务集(Tasks)、任务实例集(TaskInstances)和任务上下文(TaskContext)的概念,将传统的user role permission权限赋予结构修改为user role task permission权限赋予结构,建立了基于任务和角色的访问控制模型,给出了其形式化定义。该模型解决了传统的基于角色访问控制中的动态适应性差和最小权限约束假象的问题,用于分布式工作流管理系统,提高了安全性、实用性。     

雷达组网区域性防御区优化部署

对雷达网的费效比、数量、类型方面的优化部署进行了讨论。针对自卫压制式干扰或随队压制式干扰,给出一种采用不同频率的雷达利用不同位置进行优化部署的方法,并利用数据融合技术对这种部署方法进行讨论。     

谱分析在惯性平台试验数据处理中的应用

为了获取更多有效信息,将谱分析方法应用于惯性平台试验数据的定性分析。首先对试验数据进行差分处理得到平稳数据,再利用快速傅里叶变换就能得到数据的频谱。对某次试验数据进行处理后得到的频谱图进行了分析,结果表明此方法简单、有效。     

基于对象petri网的雷达组网系统应用效能研究

建立了复杂环境下雷达网防空作战系统OPDL模型,并嵌入相关的数学模型,对雷达网在防空作战中的应用效能进行了仿真分析,给出了定量的分析结论。研究表明,雷达组网技术的运用有效提高了防空武器系统的作战能力,为深入分析研究雷达网的作战效能奠定了基础。     

Appointment scheduling at a multidisciplinary outpatient clinic using stochastic programming

The purpose of this paper is to investigate the problem of constructing an appointment template for scheduling patients at a specific type of multidisciplinary outpatient clinic called an integrated practice unit (IPU). The focus is on developing and solving a stochastic optimization model for a back pain IPU in the face of random arrivals, an uncertain patient mix, and variable service times. The deterministic version of the problem is modeled as a mixed integer program with the objective of minimizing a weighted combination of clinic closing time (duration) and total patient waiting time (length of stay). A two‐stage stochastic program is then derived to account for the randomness and the sequential nature of the decisions. Although it was not possible to solve the two‐stage problem for even a limited number of scenarios, the wait‐and‐see (WS) problem was sufficiently tractable to provide a lower bound on the stochastic solution. The introduction of valid inequalities, limiting indices, and the use of special ordered sets helped to speed up the computations. A greedy heuristic was also developed to obtain solutions much more quickly. Out of practical considerations, it was necessary to develop appointment templates with time slots at fixed intervals, which are not available from the WS solution. The first to be derived was the expected value (EV) template that is used to find the expected value of the EV solution (EEV). This solution provides an upper bound on the objective function value of the two‐stage stochastic program. The average gap between the EEV and WS solutions was 18%. Results from extensive computational testing are presented for the EV template and for our adaptation of three other templates found in the literature. Depending on the relative importance of the two objective function metrics, the results demonstrate the trade‐off that exists between them. For the templates investigated, the “closing time” ranged from an average of 235 to 275 minutes for a 300‐minute session, while the corresponding “total patient time in clinic” ranged from 80 to 71 minutes.     

Robust cubature Kalman filter method for the nonlinear alignment of SINS

Nonlinear initial alignment is a significant research topic for strapdown inertial navigation system(SINS).Cubature Kalman filter(CKF)is a popular tool for nonlinear initial alignment.Standard CKF assumes that the statics of the observation noise are pre-given before the filtering process.Therefore,any unpredicted outliers in observation noise will decrease the stability of the filter.In view of this problem,improved CKF method with robustness is proposed.Multiple fading factors are introduced to rescale the obser-vation noise covariance.Then the update stage of the filter can be autonomously tuned,and if there are outliers exist in the observations,the update should be less weighted.Under the Gaussian assumption of KF,the Mahalanobis distance of the innovation vector is supposed to be Chi-square distributed.Therefore a judging index based on Chi-square test is designed to detect the noise outliers,determining whether the fading tune are required.The proposed method is applied in the nonlinear alignment of SINS,and vehicle experiment proves the effective of the proposed method.     

A cooperative interference resource allocation method based on improved firefly algorithm

To deal with the radio frequency threat posed by modern complex radar networks to aircraft, we researched the unmanned aerial vehicle (UAV) formations radar countermeasures, aiming at the solution of radar jamming resource allocation under system countermeasures. A jamming resource allocation method based on an improved firefly algorithm (FA) is proposed. Firstly, the comprehensive factors affecting the level of threat and interference efficiency of radiation source are quantified by a fuzzy comprehensive evaluation. Besides, the interference efficiency matrix and the objective function of the allocation model are determined to establish the interference resource allocation model. Finally, A mutation operator and an adaptive heuristic are integtated into the FA algorithm, which searches an interference resource allocation scheme. The simulation results show that the improved FA algorithm can compensate for the deficiencies of the FA algorithm. The improved FA algorithm provides a more sci-entific and reasonable decision-making plan for aircraft mission allocation and can effectively deal with the battlefield threats of the enemy radar network. Moreover, in terms of convergence accuracy and speed as well as algorithm stability, the improved FA algorithm is superior to the simulated annealing algorithm (SA), the niche genetic algorithm (NGA), the improved discrete cuckoo algorithm (IDCS), the mutant firefly algorithm (MFA), the cuckoo search and fireflies algorithm (CSFA), and the best neighbor firefly algorithm (BNFA).     

Disruption management in production planning

We study the problem of recovering a production plan after a disruption, where the disruption may be caused by incidents such as power failure, market change, machine breakdown, supply shortage, worker no‐show, and others. The new recovery plan we seek after has to not only suit the changed environment brought about by the disruption, but also be close to the initial plan so as not to cause too much customer unsatisfaction or inconvenience for current‐stage and downstream operations. For the general‐cost case, we propose a dynamic programming method for the problem. For the convex‐cost case, a general problem which involves both cost and demand disruptions can be solved by considering the cost disruption first and then the demand disruption. We find that a pure demand disruption is easy to handle; and for a pure cost disruption, we propose a greedy method which is provably efficient. Our computational studies also reveal insights that will be helpful to managing disruptions in production planning. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005.     

A chessboard model of the U‐boat war in the Atlantic with applications to signals intelligence

This paper uses a simple Monte Carlo model to analyze the influence of signals intelligence on the Second World War's Battle of the Atlantic. The principle measure of effectiveness is the number of U‐boat days of attack to which convoys were subjected. A secondary measure is the number of convoyed ships sunk. The model is validated against historical data and then used to explore the effectiveness of the two sides' signals intelligence. Allied use of signals intelligence is shown to have been capable of completely offsetting German use of signals intelligence, and then some. © 2003 Wiley Periodicals, Inc. Naval Research Logistics, 2005     

雷达隐身材料技术研究

隐身技术普遍应用于军事领域,首先介绍了雷达隐身技术的基本原理,重点阐述了雷达隐身材料新技术的发展及应用,并且提出隐身材料技术的几点构想。