Modeling,estimation, and analysis of epidemics over networks: An overview

We present and discuss a variety of mathematical models that have been proposed to capture the dynamic behavior of epidemic processes. We first present traditional group models for which no underlying graph structures are assumed, thus implying that instantaneous mixing between all members of a population occurs. Then we consider models driven by similar principles, but involving non-trivial networks where spreading occurs between connected nodes. We present stability analysis results for selected models from both classes, as well as simple least squares approaches for estimating the spreading parameters of the virus from data for each basic networked model structure. We also provide some simulation models. The paper should serve as a succinct, accessible guide for systems and control research efforts toward understanding and combating COVID-19 and future pandemics.     

Recursive cointegration analytics for adaptive monitoring of nonstationary industrial processes with both static and dynamic variations

Conventional adaptive monitoring strategies detect anomalies in time-varying process by frequently updating models, which requires high computation complexity and may falsely include abnormal samples. Cointegration analysis (CA) based monitoring strategies can be implemented with less model updating since they are developed based on the extracted long-term equilibrium relationship. However, once the cointegration relationship changes, the previous CA model cannot accurately reflect the operation status of future nonstationary process. In this study, an adaptive monitoring scheme based on recursive CA is proposed to address the aforementioned issues for nonstationary processes. First, a recursive strategy is developed for CA to effectively update the monitoring model. After that, three monitoring statistics are developed to reflect the operation status of the industrial process with representation of both static deviation and dynamic fluctuation. Finally, an adaptive monitoring strategy is constructed based on the proposed recursive CA using the aforementioned monitoring statistics. Experimental results of two real industrial processes show that the adaptive monitoring strategy based on recursive CA can effectively adapt to normal process changes without frequent model updating.     

Fast tunable gradient RBF networks for online modeling of nonlinear and nonstationary dynamic processes

Since most real-world processes exhibit both nonlinear and time-varying characteristics, there exists a need for accurate and efficient models that can adapt in nonstationary environments. Also for adaptive control purpose, it is vital that an adaptive model has a fixed small model size. In this paper, we propose an adaptive tunable gradient radial basis function (GRBF) network for online modeling of nonlinear dynamic processes, which meets these practical requirements. Specifically, a compact GRBF model is constructed by the orthogonal least squares algorithm in training, which is capable of modeling variations of local mean and trend in the data well. During online operation, the adaptive GRBF model tacks the time-varying process’s dynamics by replacing a worst performing node with a new node which encodes the current new data. By exploiting the local predictor property of the GRBF node, the new node optimization can be done extremely efficiently. The proposed approach combining the advantages of both the GRBF network structure and fast tunable node mechanism is capable of tracking the time-varying nonlinear dynamics accurately and effectively. Extensive simulation results demonstrate that the proposed fast tunable GRBF network significantly outperforms the existing state-of-the-art methods, in terms of both adaptive modeling accuracy and online computational complexity.     

利用Ka波段云雷达研究青藏高原对流云和降水的垂 直结构及微观物理特征

对第三次青藏高原大气科学试验的Ka波段毫米波云雷达功率谱和雨滴谱仪资料提出了数据处理和质量控制方法,并计算了常用物理量和国内运用很少的物理量(谱偏度、谱峰度、粒子平均下落末速度、大气垂直速度).应用这些物理量对2015年7月16日青藏高原(那曲)一次对流云-降水的垂直结构和微观物理过程进行了研究分析.结果表明:(1)青藏高原地区的降霰对流云在16:00-17:00发展到最强,具有和低海拔地区冰雹云相类似的结构;(2)同一对流云中,地面降霰前后,谱偏度由"正-负-正-负"结构变为负偏度为主,谱峰度由负值转为零值附近,云内粒子更趋于球形;(3)对流云中,强上升气流(≥6 m/s)贯穿-17～-7℃的过冷水层,冰晶与过冷水撞冻和淞附增长形成较大的霰,反之,上升气流较弱(≤4 m/s),淞附增长较弱,霰粒子较小;(4)对流云中,冰晶和霰的融化出现在环境0℃层上方的300 m区域内.     

BPMN 2.0过程模型的语义和分析

BPMN 2.0已成为了建模业务过程事实上的标准。BPMN 2.0过程模型中建模元素的混用会产生控制流方面的语义错误。首先,建立了BPMN 2.0过程模型到工作流网的映射,并使用Petri网来形式定义过程模型的语义;其次,借助Petri网的分析技术,使用这种定义的语义对BPMN 2.0过程模型进行了合理性分析。实验结果表明,这种形式化可以识别BPMN 2.0过程模型中的语义错误。     

Information‐driven robotic sampling in the coastal ocean

Efficient sampling of coastal ocean processes, especially mechanisms such as upwelling and internal waves and their influence on primary production, is critical for understanding our changing oceans. Coupling robotic sampling with ocean models provides an effective approach to adaptively sample such features. We present methods that capitalize on information from ocean models and in situ measurements, using Gaussian process modeling and objective functions, allowing sampling efforts to be concentrated to regions with high scientific interest. We demonstrate how to combine and correlate marine data from autonomous underwater vehicles, model forecasts, remote sensing satellite, buoy, and ship‐based measurements, as a means to cross‐validate and improve ocean model accuracy, in addition to resolving upper water‐column interactions. Our work is focused on the west coast of Mid‐Norway where significant influx of Atlantic Water produces a rich and complex physical–biological coupling, which is hard to measure and characterize due to the harsh environmental conditions. Results from both simulation and full‐scale sea trials are presented.     

基于网络结构搜索的工业过程自动故障诊断方法

针对现有基于深度神经网络的工业过程故障诊断方法存在网络结构设计烦琐及参数寻优耗时等问题,提出了一种基于网络结构搜索的工业过程自动故障诊断方法(automatic fault diagnosis, AutoFD),该方法采用AutoFD网络结构搜索算法,来自动完成卷积神经网络的网络结构设计和网络参数寻优。在此基础上,首先通过在原始数据上施加操作生成新通道;接着利用表现预测加速获取通道适应性排序的过程;然后依据通道适应性排序,通过表现预测来快速选取最优卷积通道数;最终根据最优卷积通道来搜索表现最优的多通道卷积神经网络模型用于工业过程自动故障诊断。采用田纳西—伊斯曼(Tennessee Eastman, TE)工业过程和数值系统对提出方法进行验证,结果表明该方法可以实现网络结构自动设计及网络参数的自动寻优,并且具有优良的故障诊断性能。     

Decentralised output feedback control of Markovian jump interconnected systems with unknown interconnections

The problem of decentralised output feedback control is addressed for Markovian jump interconnected systems with unknown interconnections and general transition rates (TRs) allowed to be unknown or known with uncertainties. A class of decentralised dynamic output feedback controllers are constructed, and a cyclic-small-gain condition is exploited to dispose the unknown interconnections so that the resultant closed-loop system is stochastically stable and satisfies an H_∞ performance. With slack matrices to cope with the nonlinearities incurred by unknown and uncertain TRs in control synthesis, a novel controller design condition is developed in linear matrix inequality formalism. Compared with the existing works, the proposed approach leads to less conservatism. Finally, two examples are used to illustrate the effectiveness of the new results.     

Variance and Convergence Analysis of Monte Carlo Line and Segment Sampling

Recently researchers have started employing Monte Carlo‐like line sample estimators in rendering, demonstrating dramatic reductions in variance (visible noise) for effects such as soft shadows, defocus blur, and participating media. Unfortunately, there is currently no formal theoretical framework to predict and analyze Monte Carlo variance using line and segment samples which have inherently anisotropic Fourier power spectra. In this work, we propose a theoretical formulation for lines and finite‐length segment samples in the frequency domain that allows analyzing their anisotropic power spectra using previous isotropic variance and convergence tools. Our analysis shows that judiciously oriented line samples not only reduce the dimensionality but also pre‐filter C⁰ discontinuities, resulting in further improvement in variance and convergence rates. Our theoretical insights also explain how finite‐length segment samples impact variance and convergence rates only by pre‐filtering discontinuities. We further extend our analysis to consider (uncorrelated) multi‐directional line (segment) sampling, showing that such schemes can increase variance compared to unidirectional sampling. We validate our theoretical results with a set of experiments including direct lighting, ambient occlusion, and volumetric caustics using points, lines, and segment samples.     

将军山渡槽缺陷及除险加固技术

本文浅述了将军山渡槽槽身渗水、混凝土表面碳化、槽墩及腹拱和拱波裂缝处理、河床段槽墩防冲处理及主拱圈抗震加固等运用的新技术和新材料,分析了新材料在运用中出现的新特性和优缺点,论述遇到的新难题,提出了切实可行的技术措施方案,克服了施工中各种困难,加固效果明显,为今后类似工程加固积累经验和提供借鉴。