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。     

中医药膳与特膳食品产业融合发展思辨

中医药膳与特膳食品一样,均能为特定人群系统定量地提供特殊或针对性的生理物质与营养成分,从而改善特定人群的营养基础代谢,减少不必要的代谢负担。文章提出在健康中国战略背景下加强特膳食品与中医药膳产业融合发展的必要性及发展的瓶颈,并指出两者融合发展的有效路径。     

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.     

基于压力和流量守恒收敛准则的微织构端面机械密封数值计算

目前在研究微织构端面机械密封时,离散求解雷诺方程时采用的压力收敛准则,并不能完全保证得到准确的泄漏率。提出基于压力收敛和内外径流量守恒收敛双重收敛准则的微织构端面机械密封数值计算方法,针对圆柱形微孔端面机械密封,基于考虑JFO边界条件的二维雷诺方程,建立含一个计算域的数值计算模型,并采用不同的收敛准则对开启力和泄漏率进行数值计算。结果表明:仅使用压力收敛准则不能显式判断计算是否已经满足质量守恒,不能准确计算泄漏率;仅使用内外径流量守恒收敛准则可能出现迭代初始阶段就满足收敛条件,导致错误结果;使用压力收敛准则和内外径流量守恒收敛准则相结合的双重收敛准则,可保证泄漏率计算的正确性。