DDoS主动防御建模仿真研究

建模仿真是研究网络攻防的一种行之有效的手段。在研究DDo S主动防御理论模型的基础上,基于多Agent建模思想建立了DDo S主动防御多Agent模型;基于网络仿真软件OMNe T++建立了DDo S主动防御仿真模型;针对网络仿真软件操作复杂等问题,采用MFC开发工具对OMNe T++进行二次开发,构建了具有友好界面的DDo S主动防御仿真验证演示系统。进行了DDo S攻击与主动防御仿真实验。实验结果证明了所建模型的正确性和仿真验证演示系统的可用性。为进一步研究网络安全仿真提供了模型和平台支持。     

物流系统出库行为动力学的统计特征分析

首先对某大型物流基地产品出库的间隔分布进行分析,发现物流基地内各不同仓库的钢材产品出库间隔均具有显著阵发特征,出库时间间隔较为一致地服从幂指数为2.5的幂律分布,而个体的出库行为则不具有阵发特征。进一步地,对物流系统的阵发特征产生机理,物流系统的节假日及内部作业等问题进行探讨。最后,利用重极标差方法,发现物流系统产品出库量普遍服从分形布朗运动,HURST指数大于0.5。结果表明物流基地的产品出库量不仅受当前市场价格的引导,且具有长记忆性,与过去的出库量密切相关。     

考虑搬运时间的多品种、小批量混流制造系统批量加工模式的优化与资源调度

对带搬运时间的混流制造系统批量加工模式的优化与资源调度问题进行研究. 建立批量工件的加工与搬运时间模型,用于求解生产周期和优化搬运次数. 在此基础上,分析工件排序方案的决策准则,用于确定工件最优投产顺序. 设计互助组优化算法,其可对最优作业排序方案所需的搬运设备进行调度. 研究结果表明: 本文的研究方法可有效求解带搬运时间的批量工件的生产周期,能优化批量工件的搬运时间和次数,可获得工件的最优排序方案,并能合理调度搬运设备资源.     

基于随机共振的惯性传感器信号实时处理方法

根据惯性传感器信号处理特点,研究其基于随机共振的信号实时处理方法。首先,从实时性的角度出发,确定随机共振数值算法--龙格库塔法,并对其进行相应改进,实现实时处理。然后,研究单稳系统中各个参数对信号恢复结果的影响,确定系统处理的较佳参数。最终的仿真结果表明,静态信号的零漂值得到了较大改善,而动态信号的信噪比最大可提高约20 dB。同时,为了进一步验证算法,在数字信号处理硬件平台上实现算法,采样频率为5 000 Hz,结果完全能够满足惯性传感器信号处理的要求。因此,所提算法能够有效进行惯性传感器信号实时处理,为随机共振理论在惯性传感器信号处理中的应用提供了重要参考。     

Optimized LDPC differential-encoded 16QAM scheme for high-speed transmission systems

The 16-ary quadrature amplitude modulation （16QAM） is a high spectral efficient scheme for high-speed transmission systems. To remove the phase ambiguity in the coherent detection system, differential-encoded 16QAM （DE-16QAM） is usually used, however, it will cause performance degradation about 3 dB as compared to the conventional 16QAM. To overcome the performance loss, a serial concatenated system with outer low density parity check （LDPC） codes and inner DE-16QAM is proposed. At the receiver, joint iterative differential demodulation and decoding （ID） is carried out to approach the maximum likelihood performance. Moreover, a genetic evolution algorithm based on the extrinsic information transfer chart is proposed to optimize the degree distribution of the outer LDPC codes. Both theoretical analyses and simulation results indicate that this algorithm not only compensates the performance loss, but also obtains a significant performance gain, which is up to 1 dB as compared to the conventional non-DE-16QAM.     

信息物理融合系统可信软件形式化建模与分析

从多Agent系统的角度,以Petri网和π演算为语义基础,建立了一种信息物理融合系统（cyber-physical systems,CPS）可信软件形式化模型（high-confidence software formal model,HCSFM）. HCSFM以Petri网形象地描述CPS可信软件静态结构模型及动态行为,用Petri网分析方法和支持工具对模型进行分析和验证; 利用π演算刻画CPS可信软件中Agent的加入、退出、更新和体系结构重配置等动态演化机制,并研究Agent的演化策略及演化后CPS的一致性,确保动态演化后CPS软件能正常交互,从而为CPS软件设计提供可信保障. 通过HCSFM在无人驾驶车辆编队CPS中的应用,表明HCSFM可以有效地对CPS可信软件进行建模和分析.     

基于效用的公司证券定价与资本结构选择

考虑到市场的不完备性,本文基于效用无差别定价原理,研究公司证券主观价值,分析最优资本结构,计算最优破产触发水平. 本文发现：1)对于给定的债券发行量,随着非系统风险增加,股权主观价值逐渐减小,财务杠杆逐渐增大,这一结果与完备市场相反;2) 与完备市场下资本结构相比,非完备市场下最优财务杠杆较小,债权的收益率溢价较大;3)股权的系统风险和非系统风险溢价随着公司收益流增加而减少;4) 投资者的风险态度和非系统风险对破产选择、股权和债权的主观价值有显著影响.     

灰色Verhulst预测模型的病态特性

为揭示灰色Verhulst模型的建模精度在原始序列存在微小扰动下的变化规律,采用矩阵谱条件数作为研究工具对该模型系数矩阵谱条件数的取值进行分类讨论. 研究结果表明,当且仅当系统原始数据序列第一项为大于零的常数,其他所有项数据皆为0时,建立灰色Verhulst模型才会呈现严重病态性,而采用已呈现特定规律的数据序列构建灰色Verhulst预测模型毫无实际意义. 研究结论认为,灰色Verhulst模型并不存在严重病态性.     

White noise estimation for discrete-time systems with random delay and packet dropout

This paper is concerned with the optimal and suboptimal deconvolution problems for discrete-time systems with random delayed observations. When the random delay is known online, i.e., time stamped, the random delayed system is reconstructed as an equivalent delay-free one by using measurement reorganization technique, and then an optimal input white noise estimator is presented based on the stochastic Kahnan filtering theory. However, tb_e optimal white-noise estimator is timevarying, stochastic, and doesn＇t converge to a steady state in general. Then an alternative suboptimal input white-noise estimator with deterministic gains is developed under a new criteria. The estimator gain and its respective error covariance-matrix information are derived based on a new suboptimal state estimator. It can be shown that the suboptimal input white-noise estimator converges to a steady-state one under appropriate assumptions.     

Group consensus for multiple networked Euler-Lagrange systems with parametric uncertainties

In this paper,a group consensus problem is investigated for multiple networked agents with parametric uncertainties where all the agents are governed by the Euler-Lagrange system with uncertain parameters.In the group consensus problem,the agents asymptotically reach several different states rather than one consistent state.A novel group consensus protocol and a time-varying estimator of the uncertain parameters are proposed for each agent in order to solve the couple-group consensus problem.It is shown that the group consensus is reachable even when the system contains the uncertain parameters.Furthermore,the multi-group consensus is discussed as an extension of the couple-group consensus,and then the group consensus with switching topology is considered.Simulation results are finally provided to validate the effectiveness of the theoretical analysis.