谁在主导中日韩的国际科学合作?

[目的/意义]为了探究中日韩的国际科学合作中的主导地位及变化趋势。[方法/过程]以"中日"、"中韩"和"中日韩"国际论文合著现象为分析对象,通过科学计量的方法和手段,从重要作者、高影响力论文和资助者三个维度来分析近40年来中日韩科学合作背后的主导力量。[结果/结论]研究结果发现中日科学合作呈现出日本主导趋势由强到弱,而中国主导趋势渐强并超过日本;中韩科学合作呈现双方共同主导的特征;在三国合作中,中国主导的论文影响力较强,但随着论文影响力的增大,中国显示出的主导优势却在减弱;中国作为科研资助者极大地推动了三国的科学合作。     

金融集聚、技术创新与高技术产业升级研究

分析金融集聚实现高技术产业升级的影响机理,即通过促进高技术企业形成外部规模经济效应推动技术创新的作用机制,以及通过降低高技术企业交易成本推动高技术创新的作用机制,并运用2001—2019年我国31个省份的面板数据,采用动态面板广义矩估计(GMM)方法探索这一影响机理。研究表明:(1)金融集聚显著促进了高技术产业升级;(2)变更高技术产业高级化替换变量、金融集聚替换变量,这一影响效果依然稳健;(3)金融集聚均通过显著提高高技术企业规模效应和显著降低高技术企业交易成本进而实现技术创新;(4)金融集聚也通过显著提高技术创新实现高技术产业升级。研究结论的政策启示包括:持续深化金融供给侧结构性改革,有力推动高技术产业集聚;推动金融集聚为高技术产业专业化、多样化聚集模式提供资金支持;不断提升高技术企业生产效率以及培养高层次技术发明人才和技术创新团队。     

科技论文网络发表的发展及研究现状分析

本文首先阐明科技论文网络发表的含义、形式等基本问题,然后分别阐述了科技论文网络发表的几个主要载体.最后对中外科技论文网络发表的研究现状进行了比较,得出了中国学者对科技论文网络发表的关注度和研究深度仍不及英美发达国家、我国对开放存取的研究仍局限在图书情报界、开放存取在我国还有待于尽快从理论研究走向实际应用等结论.     

Stabilization of a class of nonlinear parametrized systems by a flexible switching adaptive backstepping

In this paper, we consider global adaptive feedback control of nonlinear systems with unknown parameters entering nonlinearly. Such unknown parameters are also not required to lie in a known compact set. Unlike previous results, our proposed adaptive controller is a new double dynamical switching-type controller in which the controller parameter is tuned in a flexible switching manner via a monotonically decreasing switching logic and the controller combines the traditional adaptive theorem with the switching scheme perfectly. Global stability results of the closed-loop system have been proved.     

Multi-lagged-input iterative dynamic linearization based data-driven adaptive iterative learning control

This article presents a multi-lagged-input based data-driven adaptive iterative learning control (M-DDAILC) method for nonlinear multiple-input-multiple-output (MIMO) systems by virtue of multi-lagged-input iterative dynamic linearization (IDL). The original nonlinear and non-affine MIMO system is equivalently transformed into a linear input-output incremental counterpart without loss of dynamics. The proposed learning law utilizes the desired trajectory to cancel the influence from iteration-by-iteration variations, as well as additional multi-lagged inputs to improve control performance. The developed iterative estimation law is more effective and also makes estimation of the unknown parameters easier because the dynamics for each parameter to represent are decreased by dividing the system into multiple components in the multi-lagged-input IDL formulation. Moreover, the proposed M-DDAILC does not need an explicit and accurate model. It is proved to be iteratively convergent with rigorous analysis. Both a numerical example and a practical application to a permanent magnet linear motor are provided to verify the validity and applicability of the proposed method.     

The filtering based parameter identification for bilinear-in-parameter systems

This paper discusses the parameter estimation for a class of bilinear-in-parameter systems with colored noise. By utilizing the filtering technique, we derive the relationship between the filtered output and the measurement output and obtain two linear regressive sub-models. A filtering based multi-innovation stochastic gradient algorithm is derived for interactively identifying each sub-model. The proposed algorithm avoids the estimation of correlated noise and improves the parameter estimation accuracy by making full use of the measurement data. The numerical simulation results indicate that the proposed algorithm has higher estimation accuracy than the hierarchical multi-innovation stochastic gradient algorithm.     

Consensus disturbance rejection with event-triggered communications

This paper considers the consensus disturbance rejection problem of networks of linear agents with event-triggered communications in the presence of matched disturbances. Based on the disturbance observer, distributed event-based consensus protocols are proposed and constructed for both the cases of neutrally stable and general linear agents. Under the proposed event-based consensus protocol, it is shown that the consensus errors are asymptotically stable and the Zeno behavior can be excluded. Compared to the previous related works, our main contribution is that the proposed event-based protocol can achieve consensus and meanwhile reject disturbance, without the need of continuous communications among neighboring agents. For the case of neutrally stable agents, the event-based protocol is fully distributed, using only the local information of each agent and its neighbors. Simulation results are presented to illustrate the effectiveness of the theoretical results.     

Adaptive distributed convex optimization for multi-agent and its application in flocking behavior

This paper studies adaptive optimization problem of continuous-time multi-agent systems. Multi-agents with second-order dynamics are considered. Each agent is equipped with a time-varying cost function which is known only to an individual agent. The objective is to make multi-agents velocities minimize the sum of local functions by local interaction. First, a distributed adaptive algorithm is presented, in which each agent depends only on its own velocity and neighbors velocities. It is indicated that all agents can track the optimal velocity. Then we apply the distributed adaptive algorithm to flocking of multi-agents. It is proved that all agents can track the optimal trajectory. The agents will maintain connectivity and avoid the inter-agent collision. Finally, two simulations are included to illustrate the results.     

Robust self-triggered MPC with fast convergence for constrained linear systems

In this paper, a robust self-triggered model predictive control (MPC) scheme is proposed for linear discrete-time systems subject to additive disturbances, state and control constraints. To reduce the amount of computation on controller sides, MPC optimization problems are only solved at certain sampling instants which are determined by a novel self-triggering mechanism. The main idea of the self-triggering mechanism is to choose inter-sampling times by guaranteeing a fast decrease in optimal costs. It implies a fast convergence of system states to a compact set where it is ultimately bounded and a reduction of computation times to stabilize the system. Once the state enters a terminal region, the system can be stabilized to a robust invariant set by a state feedback controller. Robust constraint satisfaction is ensured by utilizing the worst-case set-valued predictions of future states in such a way that recursive feasibility is guaranteed for all possible realisations of disturbances. In the case where a priority is given to reducing communication costs rather than improvement in control performance in a neighborhood of the origin, a feedback control law based on nominal state predictions is designed in the terminal region to avoid frequent feedback. Performances of the closed-loop system are demonstrated by a simulation example.