汽油气相色谱模拟馏程的应用

介绍了用AC8612气相色谱仪对六个汽油组分馏程分析的应用情况,结果表明:该方法的重复性和准确性都较好,与D86相比,结果满足方法的再现性要求,但对于差值大于3℃的点,应定期进行修正,以使结果更准确,该方法用于炼油厂各装置汽油组分馏出口的日常质量监测分析,可以提高分析效率并减少劳动强度。     

一种磁控管用扼流线圈仿真设计

采用电路仿真软件仿真滤波组件S参数曲线,观察曲线随器件参数的变化;介绍利用三维全波电磁仿真软件HFSS简化设计流程的方法,并与测试结果对比。结果表明:利用3D仿真软件在满足器件设计精度的同时可以简化线圈的设计流程。     

媒体融合背景下高校学生记者队伍培养策略

媒体融合发展是高校开展新闻宣传工作的必然要求，针对高校学生记者队伍建设中存在的问题，提出要以“选修课+线上学习资源+校内培训”为抓手，学习相关专业学科知识；以学校融媒体中心为基础，为学生记者提供实践平台；深化学生记者对媒体融合传播理念的认识，不断提升互联网思维和创新能力。     

基于云网PoP的弹性边缘网络设计思路探讨

移动边缘云是公司“云+5G”双引擎战略的最佳契合点,边缘网络是发挥移动云“大云”产品和5G网络融合优势,实现云网统筹、构建运营商“连接+计算”核心能力的关键。运营商传统接入网存在云网割裂、分段入云和组网复杂等突出问题,难以适应边缘业务敏捷交付要求。本文通过深入分析边缘云业务特征和技术架构,对标业界主流云商建设实践,研究基于云网PoP网的边缘网络建设思路,创新性提出云网一体化规划设计和建设交付流程变革,基于云网POP统一网络和业务锚点,构建Overlay和Underlay融合双层加速网络架构,探索Spine-leaf化的新型城域接入网实现L3下沉和弹性扩容等方法,实现“云+网+应用”一体化敏捷交付的边缘网络能力。     

LPNN-based approach for LASSO problem via a sequence of regularized minimizations

In compressive sampling theory, the least absolute shrinkage and selection operator (LASSO) is a representative problem. Nevertheless, the non-differentiable constraint impedes the use of Lagrange programming neural networks (LPNNs). We present in this article the -LPNN model, a novel algorithm that tackles the LASSO minimization together with the underlying theory support. First, we design a sequence of smooth constrained optimization problems, by introducing a convenient differentiable approximation to the non-differentiable -norm constraint. Next, we prove that the optimal solutions of the regularized intermediate problems converge to the optimal sparse signal for the LASSO. Then, for every regularized problem from the sequence, the -LPNN dynamic model is derived, and the asymptotic stability of its equilibrium state is established as well. Finally, numerical simulations are carried out to compare the performance of the proposed -LPNN algorithm with both the LASSO-LPNN model and a standard digital method.     

Novel model reference adaptive control architecture using semi‐initial excitation‐based switched parameter estimator

This paper is a generalization of the recently developed techniques of initial excitation (IE)–based adaptive control with an introduction to the definition of semi‐initial excitation (semi‐IE), a still more relaxed notion than IE. Classical adaptive controllers typically ensure Lyapunov stability of the extended error dynamics (tracking error + parameter estimation error) and asymptotic tracking, while requiring a stringent condition of persistence of excitation (PE) for parameter convergence. Of late, the authors have proposed a new adaptive control architecture, which guarantees parameter convergence under the online‐verifiable IE condition leading to exponential stability of the extended error dynamics. In earlier works, it has been established that the IE condition is significantly milder than the classical PE condition. The current work further slackens the excitation condition by proposing the concept of semi‐IE. The proposed adaptive controller is proved to ensure convergence of the parameter estimation error to a lower‐dimensional manifold under the weaker semi‐IE condition, while the stronger condition of IE guarantees convergence of the parameter estimation error to zero. The designed algorithm is shown to improve transient response of tracking error sufficiently in contrast to conventional adaptive controllers.     

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.