DOS攻击下不确定双边遥操作系统的控制

研究了存在定常时延和模型参数不确定的双边遥操作系统在受到拒绝服务攻击（denial of service，DOS）时的稳定性问题。利用位置误差结构，设计了基于事件触发的控制器，并利用自适应律估计机械臂模型中的未知参数。通过Lyapunov函数理论证明了双边遥操作系统的稳定性，并证明了基于事件触发的控制器不存在Zeno行为。通过数值仿真验证了基于事件触发的自适应控制器能够使遥操作系统在DOS攻击下保持稳定。     

对氮掺杂二氧化钛抗菌剂中氮元素含量测定的不确定度评定

利用有机元素分析仪，对氮掺杂二氧化钛中氮元素的含量进行了测定，评定了其测量不确定度。分析了测定过程中的不确定度的来源，并对不确定度的各个分量进行了评定，给出了测定结果的合成不确定度和扩展不确定度。结果表明：不确定度可表示为（1.21±0.16）%，k=2，其中校准曲线对不确定度的影响最大。     

基于信息决策的最优保守度配电网恢复策略

考虑到自然灾害的极端不确定性，本文提出了一种基于信息决策的最优保守度配电网恢复策略。首先将信息缺口决策理论应用于拓扑不确定性，将配电线路发生自然灾害后的停电状态视为一个不确定参数。进一步提出了一个鲁棒性函数，用于确定配电线路中断的风险规避区域及其总受损长度。然后通过线性优化模型实现最优拓扑重构方法，以确保在遭受破坏性自然灾害后提供预定水平的负荷，并且提出了一种新的保守度选择算法来调整模型的输入。实验结果证明优化的分布式电源资源配置、拓扑重构和线路加固方案能显著提高配电网的可靠性和恢复力。     

ICP-AES基体匹配法测定高导电铜中的微量元素

用ICP-AES基体匹配法测定了高导电铜中Ni，Be，Mg，Co和Si等微量元素含量，同时对所测元素相对不确定度进行了考察.发现以质量比计，元素Ni，Mg，Be，Co，Si的含量分别为2.13，0.088，0，0，0.22 mg/kg；相对不确定度主要由测量精密度和回收率2个因素决定.     

火焰原子吸收分光光度法测定地表水中铜含量的不确定度评定

根据GB 7475—87《水质铜、锌、铅、镉的测定火焰原子吸收分光光度法》建立地表水样测定的数学模型,并根据JJF 1059.1—2012《测量不确定度评定与表示》的要求,计算测量过程中的不确定度分量,最终得出扩展不确定度.经检测计算,该地表水样品中的铜含量为1.53 mg/L,其扩展不确定度为0.036 mg/L,包含因子k=2,地表水样品中铜含量的测量结果应报告为(1.53±0.036) mg/L, k=2.结果表明,火焰原子吸收分光光度法测定地表水样中铜含量的不确定度主要来源于标准曲线拟合、标准溶液配制和样品重复测量.     

双重不确定冷贮备系统的可靠性分析

运用不确定理论研究了无充足样本的冷贮备冗余系统。假设系统所有元件的寿命服从具有不确定参数且相互独立的不确定分布,基于可靠度函数和平均寿命建立了3类冗余系统可靠性的数学模型:转换开关完全可靠型、转换开关离散型、转换开关连续型的冷贮备系统。此外,针对元件寿命为独立同分布的特殊情况进行了分析,给出数值算例,进一步印证模型的合理性。     

新不确定度量下的冲突证据融合

针对证据高度冲突的情况下,DS证据理论在实际应用中出现错误融合结果的问题,提出基于不确定度的冲突解决方法.首先分析现有的不确定度量方法的不足,研究基本概率赋值本身所包含的信息,提出一种新的对证据不确定性度量的方法;依据新的不确定度量,结合证据距离生成权重来修正待组合证据体,再利用Dempster规则完成证据组合,可有效解决证据高度冲突情况下的信息融合问题,最后通过算例验证了该方法的有效性.     

采用Bootstrap抽样的靖远黄河大桥 模态参数识别不确定性量化

提出一种基于Bootstrap抽样的模态参数识别不确定性量化方法,从整体和局部的角度评价模态参数识别结果的可靠性.首先,基于动力测试的加速度时程数据,采用协方差驱动随机子空间(SSI-COV)法识别不同测试组的模态参数;引入Bootstrap抽样方法,对多组模态参数识别结果进行B次重复抽样,得到Bootstrap样本数据,并通过其概率统计特征值衡量整体不确定性.然后,对单个测试组中不同时间段的识别结果进行重复抽样,分析并量化单个测试组的模态参数识别的不确定性.最后,以靖远黄河大桥试验数据为例,对靖远黄河大桥竖向单个及多个测试组下的模态参数进行不确定性量化.结果表明:不同测试组识别的前3阶固有频率的均值分别为1.553 9,1.720 6,2.165 2,方差分别为0.076 1,0.042 9,0.096 5;单个测试组识别的前3阶固有频率的均值分别为1.526 5,1.788 0,2.306 0,方差分别为0.015 3,0.049 6,0.018 2;文中方法识别的固有频率值总体较为稳定.     

Multi-model ensembles in climate science: Mathematical structures and expert judgements

Projections of future climate change cannot rely on a single model. It has become common to rely on multiple simulations generated by Multi-Model Ensembles (MMEs), especially to quantify the uncertainty about what would constitute an adequate model structure. But, as Parker points out (2018), one of the remaining philosophically interesting questions is: “How can ensemble studies be designed so that they probe uncertainty in desired ways?” This paper offers two interpretations of what General Circulation Models (GCMs) are and how MMEs made of GCMs should be designed. In the first interpretation, models are combinations of modules and parameterisations; an MME is obtained by “plugging and playing” with interchangeable modules and parameterisations. In the second interpretation, models are aggregations of expert judgements that result from a history of epistemic decisions made by scientists about the choice of representations; an MME is a sampling of expert judgements from modelling teams. We argue that, while the two interpretations involve distinct domains from philosophy of science and social epistemology, they both could be used in a complementary manner in order to explore ways of designing better MMEs.     

Forecasting and nowcasting emerging market GDP growth rates: The role of latent global economic policy uncertainty and macroeconomic data surprise factors

In this paper, we assess the predictive content of latent economic policy uncertainty and data surprise factors for forecasting and nowcasting gross domestic product (GDP) using factor-type econometric models. Our analysis focuses on five emerging market economies: Brazil, Indonesia, Mexico, South Africa, and Turkey; and we carry out a forecasting horse race in which predictions from various different models are compared. These models may (or may not) contain latent uncertainty and surprise factors constructed using both local and global economic datasets. The set of models that we examine in our experiments includes both simple benchmark linear econometric models as well as dynamic factor models that are estimated using a variety of frequentist and Bayesian data shrinkage methods based on the least absolute shrinkage operator (LASSO). We find that the inclusion of our new uncertainty and surprise factors leads to superior predictions of GDP growth, particularly when these latent factors are constructed using Bayesian variants of the LASSO. Overall, our findings point to the importance of spillover effects from global uncertainty and data surprises, when predicting GDP growth in emerging market economies.