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。     

Robust optimal operation of continuous catalytic reforming process under feedstock uncertainty

The continuous catalytic regenerative (CCR) reforming process is one of the most significant sources of hydrogen production in the petroleum refining process. However, the fluctuations in feedstock composition and flow rate could significantly affect both product distribution and energy consumption. In this study, a robust deviation criterion based multi-objective optimization approach is proposed to perform the optimal operation of CCR reformer under feedstock uncertainty, with simultaneous maximization of product yields and minimization of energy consumption. Minimax approach is adopted to handle these uncertain objectives, and the Latin hypercube sampling method is then used to calculate these robust deviation criteria. Multi-objective surrogate-based optimization methods are next introduced to effectively solve the robust operational problem with high computational cost. The level diagram method is finally utilized to assist in multi-criteria decision-making. Two robust operational optimization problems with different objectives are solved to demonstrate the effectiveness of the proposed method for robust optimal operation of the CCR reforming process under feedstock uncertainty.     

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。     

Progressive failure analysis of notched composite plate by utilizing macro mechanics approach

In this study, gradual and sudden reduction methods were combined to simulate a progressive failure in notched composite plates using a macro mechanics approach. Using the presented method, a progressive failure is simulated based on a linear softening law prior to a catastrophic failure, and thereafter, sudden reduction methods are employed for modeling a progressive failure. This combination method significantly reduces the computational cost and is also capable of simultaneously predicting the first and last ply failures (LPFs) in composite plates. The proposed method is intended to predict the first ply failure (FPF), LPF, and dominant failure modes of carbon/epoxy and glass/epoxy notched composite plates. In addition, the effects of mechanical properties and different stacking sequences on the propagation of damage in notched composite plates were studied. The results of the presented method were compared with experimental data previously reported in the literature. By comparing the numerical and experimental data, it is revealed that the proposed method can accurately simulate the failure propagation in notched composite plates at a low computational cost.     

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.     

Phase-change regulation criterion based on size-dependent lattice distortion rate and born theory for VO2 nanomaterials

Vanadium dioxide (VO₂) is a promising thermally induced phase transition material because of the abrupt changes in electrical and optical properties. However, the high phase transition temperature of VO₂ and its unspecified modulation relationship need to be resolved urgently. Herein, we proposed a simple and precise regulation criterion for VO₂ materials based on size-dependent lattice distortion rate and Born theory. The results indicated that the application of a tensile stress changed the elastic properties of the VO₂, which promoted VO₂ phase transition, and regulated the phase transition temperature. Moreover, the specific modulation relationship between the stress and phase transition temperature of VO₂ was confirmed experimentally. These results show that our criterion provides theoretical guidance to regulate VO₂ thermally induced phase transition materials.