Robust method to provide exponential convergence of model parameters solving linear time-invariant plant identification problem

The scope of this research is a problem of parameters identification of a linear time-invariant plant, which (1) input signal is not frequency-rich, (2) is subjected to initial conditions and external disturbances. The memory regressor extension (MRE) scheme, in which a specially derived differential equation is used as a filter, is applied to solve the above-stated problem. Such a filter allows us to obtain a bounded regressor value, for which a condition of the initial excitation (IE) is met. Using the MRE scheme, the recursive least-squares method with the forgetting factor is used to derive an adaptation law. The following properties have been proved for the proposed approach. If the IE condition is met, then: (1) the parameter error of identification is bounded and converges to zero exponentially (if there are no external disturbances) or to a set (in the case of them) with an adjustable rate, (2) the parameters adaptation rate is a finite value. The above-mentioned properties are mathematically proved and demonstrated via simulation experiments.     

电商平台产品共同购买网络的影响因素研究

共同购买网络的推荐系统应用越加广泛，仅基于网络内生结构变量研究其共同购买链接的经济意义已有局限，故加入网络口碑这一外生变量，进行更全面的分析。采用了社会网络方法中指数随机图模型进行建模，主要围绕产品销售量、产品入度、差评率和评论数4个方面因素，探究其对共同购买网络中共同购买链接形成的影响。结果显示，销售量、产品入度和评论数对共同购买链接形成的影响呈正比关系，而差评率则会显著地降低产品共同购买的几率。该指数随机图构建出的共同购买网络为在线电商平台管理网络口碑和推荐系统优化设计提供有益参考。     

Cloud resource prediction model based on triple exponential smoothing method and temporal convolutional network

The container cloud represented by Docker and Kubernetes has the advantages of less additional resource overhead and shorter start-up and destruction time.However there are still resource management issues such as over-supply and under-supply.In order to allow the Kubernetes cluster to respond “in advance” to the resource usage of the applications deployed on it,and then to schedule and allocate resources in a timely,accurate and dynamic manner based on the predicted value,a cloud resource prediction model based on triple exponential smoothing method and temporal convolutional network was proposed,based on historical data to predict future demand for resources.To find the optimal combination of parameters,the parameters were optimized using TPOT thought.Experiments on the CPU and memory of the Google dataset show that the model has better prediction performance than other models.     

Asynchronous control for two‐dimensional hidden Markovian jump systems with partly known mode observation conditional probabilities

This study focuses on the asynchronous control problem for two‐dimensional discrete‐time hidden Markovian jump systems where the mode observation conditional probability matrix is partly known. Considering the original system modes are invisible, the observed modes emitted from an observer serve as an alternative for stability analysis and controller design where a mode observation conditional probability matrix is constructed to characterize the emission between system modes and observed modes. Specially, only partly known information of the mode observation conditional probability matrix is accessible. With the introduction of the free‐connection weighting matrices, the asymptotic mean square stability criterion is firstly derived based on Lyapunov method. This introduction provides a further degree of relaxation and less conservatism is therefore achieved. Secondly, we present synthesis conditions for asynchronous state feedback controller design given in terms of a set of interconnected linear matrix inequalities. Moreover, cluster concept based on the partitions of observed modes is adopted which helps to decrease the number of controllers and simplify the design complexity. A numerical example, regarding the cases with and without clustering of the observed modes, is presented to illustrate the effectiveness of the proposed method.     

石蜡与石蜡/膨胀石墨熔化性能的实验研究

为改善相变储能过程中石蜡(PA)的熔化性能，向PA中添加少量膨胀石墨(EG)制备了4种配比的石蜡/膨胀石墨复合相变材料(PA-EG)。通过热物性分析筛选出合适配比的PA-EG，并对其和PA在水平管壳式相变储能单元中的熔化过程进行了实验研究。根据相变材料的温度场变化以及加权法计算得到的熔化分数变化，对比分析了添加EG前后PA的熔化性能，并探究了加热温度对相变材料熔化性能的影响。结果表明，PA-EG3的热导率比PA高了7倍，且两者的相变温度和潜热相差不大。PA-EG3熔化过程中的自然对流效应弱于PA，但是较高的热导率能够显著改善相变储能单元中下部的熔化，使得其整体熔化速度快于PA。当加热温度为80℃时，PA-EG3的熔化过程比PA缩短了78.16%。此外，降低加热温度会使PA和PA-EG3的完全熔化时间都显著增加，但相同条件下PA-EG3的增加幅度更小。     

Stabilisation of Schrödinger equation in dynamic boundary feedback with a memory-typed heat equation

In this work, we study the dynamic behaviour for a heat equation with exponential polynomial kernel memory to be a controller for a Schrödinger system. By introducing some new variables, the time-variant system is transformed into a time-invariant one. Remarkably, the resolvent of the closed-loop system operator is not compact anymore. The residual spectrum is shown to be empty and the continuous spectrum consisting of finite isolated points are obtained. It is shown that the sequence of generalised eigenfunctions forms a Riesz basis for the Hilbert state space. This deduces the spectrum-determined growth condition for the C ₀-semigroup, and the exponential stability is then established.     

支持规避设计的海量专利质量评价模型

专利规避设计需从现有专利中遴选具有核心竞争力的目标专利，以提升技术起点。针对设计领域的海量专利信息，提出了专利质量多指标主客观综合评价模型。分析专利书目信息与专利质量存在的正相关关系，甄选存活期、权利项数、同族专利数、引证数和被引证数等书目信息指标，构建了专利质量多指标综合评价指标体系，提升了评价方法的科学性和可操作性；提出将德尔菲法、层次分析法和均方差决策法相结合的主客观赋权法，确定专利质量评价指标权重，既保证了评价的权威性，又减小了个人偏见与从众妥协等因素的影响；建立了基于质量评价模型遴选目标专利的过程模型，并通过应用实例验证了该模型对遴选具有核心竞争力专利的有效性。     

基于加权法的砾类土含水率检测

砾类土在工程建设中有着广泛的应用,其含水率是一项非常重要的指标,但现有测试方法耗时较长。以砂砾石料的砾类土为试验对象,基于加权法思想,将饱和面干含水率替代法和虚拟比重法进行结合,开展砾类土含水率快速检测方法的研究,并对现有的饱和面干含水率测定操作进行一定的改进和优化。采用多种数学方法(绝对误差、均方偏差平方和,相关曲线的相关系数和F显著性检验分析法)对加权法和烘干法的结果进行对比分析。通过试验统计数据分析,证实加权法与烘干法的测定结果基本一致。该分析结果为现场土料含水率快速测定提供了一种新的思路和手段。     

Stabilisation of stochastic differential equations driven by G-Brownian motion via aperiodically intermittent control

ABSTRACT

As an emerging field, the G-Itô stochastic calculus plays an important role in describing the model uncertainty. Many interesting works have been done on stochastic differential equations driven by G-Brownian motion (G-SDEs). Among the theories and applications of G-SDEs, the stability is the vital important one. In this paper, we investigate the stabilisation for G-SDEs based on G-Lyapunov function and aperiodically adaptive intermittent controller. As an application, the sufficient conditions are established for the stabilisation of stochastic Cohen–Grossberg neural networks driven by G-Brownian motion (G-SCGNNs). Finally, an example is provided to illustrate the obtained results.