中国东北冷涡背景下连续发生的中尺度对流系统的组织演变特征个例分析

使用雷达、地面加密观测、探空数据和ERA5再分析数据研究2009年7月22日0400—2400 UTC的21小时内东北冷涡后部在京津地区连续发生的4次中尺度对流系统的组织形态演变和中尺度环境特征。研究结果表明, 在东北冷涡后部稳定的西北气流背景下, 由于东北冷涡后部对流层中层西北气流中的浅槽、其在对流层低层发展的低槽和低涡以及对流层低层高压脊西北部的西南暖湿气流与冷池之间复杂的相互作用, 导致4次过程中强对流的组织形态各异。第一次过程受冷涡西侧一个浅槽锋生影响, 在河北北部形成西南?东北走向弱对流线, 对流线位于北京北部的对流发展较强, 移动迅速, 发展为超级单体和弓形回波, 其冷池出流和西南暖湿气流辐合形成西北?东南走向的后部增生型组织形态, 横贯京津地区。第二次过程是第一次过程位于北京南部的冷池出流触发, 形成超级单体, 之后受第一次过程冷池向西出流的影响, 产生西南?东北走向的后部增生型对流线。第三次过程发生在第一个浅槽造成对流层低层低涡发展的环境下, 低涡西侧的偏北风与低层高压脊北部的偏南风在冷池上面辐合, 造成多条平行的西北?东南走向的后部增生型对流线, 产生列车效应, 造成天津的强降水。第四次过程由冷涡西南部的又一个浅槽锋生和冷涡在天津北部调整出的切变线共同触发, 两个初始的西南?东北走向对流线合并形成一条西南?东北走向的线状对流, 最后南侧的对流发展为弓形回波。4次过程中出现的弓型回波部分还具有弓箭回波结构特征。     

基于融合模型的单相接地故障类型辨识方法

在中国6~66 kV的中低压配电网中,单相接地故障约占配电网故障总数的80%。由于发生单相接地故障时仅由系统的对地电容引起很小的接地故障电流,故障特征不明显,并且不同类型的单相接地故障之间的特征区分度不高,造成了对其类型辨识的难度增大。对此,提出了一种融合特征分解和深度学习思想的单相接地故障类型辨识方法。首先,基于希尔伯特黄变换（HHT,hilbert-huang transform）对配电网采集到的故障录波数据进行初步处理,使不同故障类型间的区分度更高;其次设计深度学习模型ResNet18学习故障事件的复杂非线性特征,从而辨识出故障类型结果。通过国内某真型试验场采集到的录波数据进行验证,证明了本文提出的综合辨识方法能准确识别出多种单相接地故障类型,可为后续制定有针对性的故障处理措施提供可靠依据。     

基于自适应未知输入观测器的多故障快速重构

针对一类非线性函数中耦合执行器故障的非线性动态系统, 提出一种基于自适应未知输入观测器的多故障快速重构方法, 通过引入比例项提高故障重构的快速性。首先, 将执行器故障进行解耦处理并构建包含传感器故障的增广系统。然后, 综合H_∞性能指标给出状态估计误差的稳定性证明。接着, 将观测器增益矩阵的求解转化为受线线矩阵不等式约束的非线性优化问题, 并实现执行器故障和传感器故障的多故障重构。最后, 结合单关节柔性机器人算例仿真验证了所提方法的有效性。     

武器装备体系组合规划的高维多目标优化决策

针对武器装备体系组合规划问题中存在多类相互冲突的高维多目标问题(目标数 ≥ 5),提出了一种三阶段的集成优化决策方法. 首先运用目的规划技术将高维多目标问题转换为一般多目标优化模型(目标数 ≤ 3); 然后提出一种多目标差分进化算法,用于搜索属于决策者关心区间的非劣解集; 最后提出基于预测优化的理想点算法,可生成精确满足决策者偏好的最佳折衷解. 通过某侦察装备体系组合规划示例,证明了各算法模块的优势和该方法的整体有效性,可为武器装备发展和顶层规划提供决策支持.     

两阶段串联可修系统的统计过程控制与视情维修整合研究

针对两阶段串联可修系统,考虑随机偏移的情况,研究统计过程控制（SPC）与视情维修整合的问题. 首先,应用均值-残差联合控制图监控系统的质量状态,根据质量状态采取相应的维修策略,在此基础上,分析SPC与视情维修策略整合的可能场景,并根据全概率公式给出了每个场景发生的概率;其次,考虑人力成本、生产率以及停时损失,根据更新理论构建了SPC与视情维修整合的数学模型;然后,针对具体实例,将构建的模型与单独的维修策略模型和采用经验法则选取控制图的设计参数模型进行了比较分析,结果表明构建的模型在节约成本方面有着明显的优势;最后,运用部分析因设计对过程参数、时间参数和成本参数进行了敏感性分析.     

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.     

Reduced K-best sphere decoding algorithm based on minimum route distance and noise variance

This paper focuses on reducing the complexity of K-best sphere decoding （SD） algorithm for the detection of uncoded multi-ple input multiple output （MIMO） systems. The proposed algorithm utilizes the threshold-pruning method to cut nodes with partial Euclidean distances （PEDs） larger than the threshold. Both the known noise value and the unknown noise value are considered to generate the threshold, which is the sum of the two values. The known noise value is the smal est PED of signals in the detected layers. The unknown noise value is generated by the noise power, the quality of service （QoS） and the signal-to-noise ratio （SNR） bound. Simulation results show that by considering both two noise values, the proposed algorithm makes an efficient reduction while the performance drops little.     

Measuring and locating zones of chaos and irregularity

The new measures computed here are the spectral detrended fluctuation anatysls （sDFA） and spectral multi-taper method （sMTM）. sDFA applies the standard detrended fluctuation analysis （DFA） algorithm to power spectra, sMTM exploits the minute increases in the broadband response, typical of chaotic spectra approaching optimal values. The authors chose the Brusselator, Lorenz, and Duffing as the proposed models to measure and locate chaos and severe irregularity. Their series of chaotic parametric responses in short time-series is advantageous. Where cycles have only a limited number of slow oscillations such as for systems biology and medicine. It is difficult to create, locate, or monitor chaos. From 50 linearly increasing starting points applied to the chaos target function （CTF）; the mean percentage increases in Kolmogorov-Sinai entropy （KS-Entropy） for the proposed chosen models; and p-values when the models were compared statistically by Kruskal-Wallis and ANOVA1 test with distributions assumed normal are Duffing （CTF： 31%： p 〈0.03）; Lorenz （CTF： 2%： p 〈0.03）, and I3russelator （CTF： 8%： p 〈0.01）. Principal component analysis （PCA） is applied to assess the significance of the objective functions for tuning the chaotic response. From PCA the conclusion is that CTF is the most beneficial objective function overall delivering the highest increases in mean KS-Entropy.     

Further results on limitations of sampled-data feedback

To control continuous-time uncertain dynamical systems with sampled data-feedback is prevalent today,but the sampling rate is usually not allowed to be arbitrarily fast due to various physical and/or computational constrains.In this paper,the authors examine the limitations of sampled-data feedback control for a class of uncertain systems in continuous-time,with sampling rate not necessary fast enough and with the unknown system structure confined to a set of functions with both linear and nonlinear growth.The limitations of the sampled-data feedback control for the uncertain systems are established quantitatively,which extends the existing related results in the literature.     

Consensus design for continuous-time multi-agent systems with communication delay

This paper investigates a consensus design problem for continuous-time first-order multiagent systems with uniform constant communication delay.Provided that the agent dynamic is unstable and the diagraph is undirected,sufficient conditions are derived to guarantee consensus.The key technique is the adoption of historical input information in the protocol.Especially,when agent＇s own historical input information is used in the protocol design,the consensus condition is constructed in terms of agent dynamic,communication delay,and the eigenratio of the network topology.Simulation result is presented to validate the effectiveness of the theoretical result.