ATSP:时态网络可视化的自适应时间片划分方法

现有的时态网络可视化方法大多采用等量时间片来可视化网络的演变，不利于时态模式的快速挖掘和发现。为此，根据时态网络固有的特征提出自适应时间片划分方法（Adaptive Time Slice Partition method，ATSP）。在时态网络的两种表示方式（基于事件的表示方式和基于快照的表示方式）的基础上，构建了ATSP的基础模型，同时提出了一种改进模型用来描述事件间隔时间服从长尾分布的时态网络。为了实现时间片的不等量划分，针对探索任务的不同提出了基于时态模式的ATSP规则和基于中心节点的ATSP规则，并提出了实现算法--层次划分算法（Hierarchical Partition algorithm，HP）和增量划分算法（Incremental Partition algorithm，IP）。实验结果表明，ATSP方法比传统的时间片划分方法更能准确地表示网络的时态特征，且该方法应用于可视化时，能有效归纳并展示网络的特征，明显提高了视觉分析的效率。     

马尔可夫跳变线性系统最优控制的研究现状与进展

马尔可夫跳变线性系统（MJLS）是一种具有多个模态的随机系统，系统在各个模态之间的跳变转移由一组马尔可夫链来决定。MJLS模型因其在表示过程中可以产生突变而更能精确的描述实际工程应用中的系统。近年来，MJLS的最优控制问题成为了研究的热点，动态规划、极大值原理以及线性矩阵不等式等成为了解决此类问题的主流方法。本文对MJLS最优控制领域的研究现状进行了综述。分别对一般情况下、带有噪声的情况下、带有时滞的情况下以及某些特定情况下的MLJS最优控制问题的国内外研究现状进行论述。最后进行了总结并提出MJLS最优控制领域未来值得关注的研究方向。     

基于区块链技术的公益时间银行系统

在现有的时间银行系统中，时间币的发行功能和结算功能完全集中到一个中心节点上。这种极度中心化的功能结构，不仅存在容易发生中心节点单点失效、数据容易被篡改等信息安全问题，还存在着时间币的发行和流通缺乏透明度以及时间币的结算依赖中心化的结算机构等问题。针对这些问题，提出了一种基于区块链技术的解决方法。首先，将时间币的发行功能和结算功能从中心节点上分离出来；然后，利用具有分布式去中心化、集体维护和不可篡改等特性的区块链技术，将分离出来的发行功能逐步去中心化，将分离出来的结算功能去中心化，形成公益时间链（PWTB）；最终，PWTB利用区块链技术以去中心化的方式将时间银行系统由单个节点维护账本变成由集体维护一个分布式的共享账本，使时间币的发行和流通公开透明，时间币的结算不依赖某个中心化的节点。安全分析表明所设计的PWTB能够实现安全的信息传输与存储，以及数据的共享。     

Usability of a cloud-based collaborative learning framework to improve learners’ experience

Computer-Supported Collaborative Learning (CSCL) is concerned with how Information and Communication Technology (ICT) might facilitate learning in groups which can be co-located or distributed over a network of computers such as Internet. CSCL supports effective learning by means of communication of ideas and information among learners, collaborative access of essential documents, and feedback from instructors and peers on learning activities. As the cloud technologies are increasingly becoming popular and collaborative learning is evolving, new directions for development of collaborative learning tools deployed on cloud are proposed. Development of such learning tools requires access to substantial data stored in the cloud. Ensuring efficient access to such data is hindered by the high latencies of wide-area networks underlying the cloud infrastructures. To improve learners’ experience by accelerating data access, important files can be replicated so a group of learners can access data from nearby locations. Since a cloud environment is highly dynamic, resource availability, network latency, and learner requests may change. In this paper, we present the advantages of collaborative learning and focus on the importance of data replication in the design of such a dynamic cloud-based system that a collaborative learning portal uses. To this end, we introduce a highly distributed replication technique that determines optimal data locations to improve access performance by minimizing replication overhead (access and update). The problem is formulated using dynamic programming. Experimental results demonstrate the usefulness of the proposed collaborative learning system used by institutions in geographically distributed locations.     

Two-agent two-machine flowshop scheduling with learning effects to minimize the total completion time

We study a two-agent scheduling problem in a two-machine permutation flowshop with learning effects. The objective is to minimize the total completion time of the jobs from one agent, given that the maximum tardiness of the jobs from the other agent cannot exceed a bound. We provide a branch-and-bound algorithm for the problem. In addition, we present several genetic algorithms to obtain near-optimal solutions. Computational results indicate that the algorithms perform well in either solving the problem or efficiently generating near-optimal solutions.     

Design of disturbance observer based on adaptive-neural control for large-scale time-delay systems in the presence of actuator fault and unknown dead zone

This article presents an adaptive neural compensation scheme for a class of large-scale time delay nonlinear systems in the presence of unknown dead zone, external disturbances, and actuator faults. In this article, the quadratic Lyapunov–Krasovskii functionals are introduced to tackle the system delays. The unknown functions of the system are estimated by using radial basis function neural networks. Furthermore, a disturbance observer is developed to approximate the external disturbances. The proposed adaptive neural compensation control method is constructed by utilizing a backstepping technique. The boundedness of all the closed-loop signals is guaranteed via Lyapunov analysis and the tracking errors are proved to converge to a small neighborhood of the origin. Simulation results are provided to illustrate the effectiveness of the proposed control approach.     

Delay compensated control of the Stefan problem and robustness to delay mismatch

This paper presents a control design for the one‐phase Stefan problem under actuator delay via a backstepping method. The Stefan problem represents a liquid‐solid phase change phenomenon which describes the time evolution of a material's temperature profile and the interface position. The actuator delay is modeled by a first‐order hyperbolic partial differential equation (PDE), resulting in a cascaded transport‐diffusion PDE system defined on a time‐varying spatial domain described by an ordinary differential equation (ODE). Two nonlinear backstepping transformations are utilized for the control design. The setpoint restriction is given to guarantee a physical constraint on the proposed controller for the melting process. This constraint ensures the exponential convergence of the moving interface to a setpoint and the exponential stability of the temperature equilibrium profile and the delayed controller in the norm. Furthermore, robustness analysis with respect to the delay mismatch between the plant and the controller is studied, which provides analogous results to the exact compensation by restricting the control gain.     

Real‐time monitoring by proton relaxometry of radical polymerization reactions of acrylamide in aqueous solution

The potential of time‐domain nuclear magnetic resonance (TD‐NMR) for the real‐time monitoring of solution radical polymerizations is demonstrated. A model system composed of a redox‐pair initiator system, acrylamide as monomer and water as solvent was investigated. A second‐generation continuous wave free precession technique was employed to measure the longitudinal relaxation time constant (T₁) of the samples throughout the polymerization reactions. This parameter was shown to be sensitive to the reactant feed free‐radical enhancement of the water molecule relaxation time, making it a good probe to monitor monomer conversion in real time in an automated, non‐destructive fashion. It was found that the T₁ value was better than the transverse relaxation time constant (T₂) for describing the evolution of the polymerization reactions, due to its greater sensitivity to paramagnetic effects. The TD‐NMR signal variation observed was linked to the formation, propagation and termination steps of the radical polymerization kinetics scheme. These first results may contribute to the application of real‐time monitoring of radical polymerization reactions employing low‐cost and robust TD‐NMR spectrometers. © 2018 Society of Chemical Industry     

MG410采煤机滚筒辅助叶片板的增设与应用

针对MG170/410-WD滚筒式采煤机在大倾角、仰角、“三软”煤层使用中遗漏在机道中的浮煤多、装煤效率低等问题,通过添加螺旋板数量对采煤机滚筒进行改造。改造后,在滚筒转速不变的情况下,提高抛煤效率,缩短了工作面割煤和工作面空顶时间,提高了装煤效率,从源头上降低了顶板管理的安全风险。