Dynamic Event-Triggered Control for Nonlinear Systems: A Small-Gain Approach

An event-triggered control(ETC) system transmits data packages and updates control inputs only when the predefined criterion is satisfied. In this way, network communication and computing resources are scheduled more reasonably in contrast to the traditional periodic sampling strategy.Small-gain approach proposed in recent literatures is a new modeling method to deal with nonlinear ETC systems. Different from traditional ETC models, stability criteria are proposed in the form of input to state stability(ISS) gain to design the triggering mechanisms. This paper introduces additional dynamic variables in this model and proposes a small-gain based dynamic event-triggered strategy.Sufficient conditions to guarantee the stability of the system are derived with the help of cyclic-smallgain theorem and Zeno behaviors are avoided to ensure the feasibility of this method in practical applications. Numerical simulations are given to demonstrate the effectiveness of our approach.     

Credit Scoring Based on the Set-Valued Identification Method

Credit scoring is one of the key problems in financial risk managements. This paper studies the credit scoring problem based on the set-valued identification method, which is used to explain the relation between the individual attribute vectors and classification for the credit worthy and credit worthless lenders. In particular, system parameters are estimated by the set-valued identification algorithm based on a given recognition criteria. In order to illustrate the efficiency of the proposed method, practical experiments are conducted for credit card applicants of Australia and credit card holders from Taiwan, respectively. The empirical results show that the set-valued model has a higher prediction accuracy on both small and large numbers of data set compared with logistic regression model. Furthermore, parameters estimated by the set-valued identification method are more stable,which provide a meaningful and logical explanation for extracting factors that influence the borrowers' credit scorings.     

一种基于工作流网的游戏作弊检测系统

游戏作弊对网络游戏危害很大,目前反作弊技术的瓶颈是效率低并且过多依赖于签名匹配。提出一种高效且不依赖于签名匹配的游戏作弊检测系统,用工作流网对作弊行为建模,通过流程树对作弊行为模型进行融合,在不影响检测结果的前提下节省存储空间。用热门网游的作弊软件对系统进行实验,存储空间降低率达到约60%,证明了该系统框架是可行、有效的。     

时变海场景双基SAR回波实时模拟方法研究

基于时变海场景双站散射机理,结合双基合成孔径雷达(SAR,Synthetic Aperture Radar)成像几何模型,提出了一种时变海场景双基SAR射频回波信号实时模拟方法。针对时变海场景的复杂耦合散射,采用多径近似理论建立了双基SAR海面目标回波信号模型,提取了时变海面双基SAR系统响应函数,结合高性能计算技术、同步动态随机存取(SDRAM)技术以及全系统交换设计,实现了时变海场景双基SAR射频回波信号实时模拟,最后仿真测试结果验证了该方法的有效性。     

城市疫情传播和复工强度建模及相关性分析

以多源大数据为基础构建模型,分析全国34个典型城市因疫情导致的人口迁徙变化和人力缺口,并通过迁徙基数推算其他各城市的复工强度。仿真结果证明SEIR仓室模型能够较好地模拟此次疫情发展趋势,利用其估计各城市内部新型冠状病毒感染肺炎的基本再生数,结合人力缺口对复工强度进行回顾性的矩阵分析,以总结我国此次抗疫经验。相关性分析阶段对K-means无监督聚类后的城市集群进行回归分析,结果表明对于大部分城市而言,复工强度的大小与其人力缺口、基本再生数以及人均GDP水平有明显的相关关系。     

自适应的无线融断网络路由算法研究

无线融断网络具有长延时、高动态拓扑、链路不稳定等特点,伴随着网络联接缺乏源端到目的端的持续性,为解决网络通信困难的问题,将多头绒泡菌的智能性和自适应性引入,提出一种自适应的无线融断网络路由算法。搭建无线融断网络模型,推导出链路容量的数学表达;设计下一跳节点的选择策略和最佳路由选择策略,实现在融断网络环境下的数据尽力投递。仿真实验表明,该算法能够在网络开销率较低的情况下,达到良好的数据投递,且平均延迟小。     

基于博弈论的化工园区合作巡逻策略研究

化工园区的生产活动对周围的大气环境和居民健康构成了巨大威胁,需要园区管理团队对化学生产过程和气体排放进行严格监管以保证空气质量,对各化工厂进行巡逻是一种有效的方法。基于博弈论研究多辆巡逻车的合作巡逻策略,提出了贪婪部署算法确定巡逻车的初始部署,采取静态分区的合作方法将化工园区划分成多个小区域,在化工厂和巡逻车之间构建巡逻博弈模型,通过MultiLPs算法计算得到博弈的Stackelberg均衡解。选取Antwerp化工园区进行案例研究,结果表明巡逻车可以将收益提高8.08%~35.24%。     

我国证券投资基金羊群行为及其对股价影响的实证研究

建立CCK校正模型，分别检验我国证券投资基金羊群行为及股市整体羊群行为是否存在；在CCK校正模型的基础上建立虚拟变量模型，以检验基金羊群行为与股市整体羊群行为二者的显著程度是否相同，从而证明基金羊群行为能否引起股市整体价格的波动。研究结果表明：我国基金羊群行为与股市整体羊群行为均显著存在，且二者的显著程度基本相等，主要是基金在整个股票市场中所占份额较少所致，因此基金羊群行为不会对股市整体价格波动产生大的影响。     

一种基于需求景气分析和“幼稚预测”思想的需求预测模型

根据汽车生产企业和宾馆企业需求预测问题的基本特点,利用需求时间序列及其变化曲线,提出了一种基于需求景气分析和"幼稚"预测思想的定量预测模型,即首先划分产品的景气变化模式,然后寻找当前点状态的最佳历史匹配,并据此给出需求预测值.本文给出了模型的相关定义及算法,并以宾馆和汽车生产企业的实际需求数据为应用对象,检验了该模型的预测性能.试验结果表明,该方法具有较理想的预测精度.     

Nuclear Norm Subspace System Identification and Its Application on a Stochastic Model of Plague

The discrete-time model of plague is deduced by zero-order holder based on the continuoustime model. Due to the existence of stochastic disturbances, the stochastic model is given corresponding to the discrete-time model. The state estimation and noise reduction of the stochastic model are achieved by designing Kalman filter. Nuclear norm minimization is to structure the low-rank matrix approximation instead of the singular value decomposition in the process of subspace system identification. According to the plague data from the World Health Organization, the system matrices and noise intensity of the model are identified. Simulations are carried out to show the higher approximation capability of the proposed method.