Controlling the multi‐plant networked system with external perturbations via adaptive model‐based event‐triggered strategy

This paper concerns the multi‐plant networked control system with external perturbations by applying the adaptive model‐based event‐triggered control strategy. Compared with existing works, we introduce multiple plants topology in order to smooth the information exchanges among different plants. Gained insight into the adaptive model features in the view of event‐triggered thought, the event‐triggered rules, determining when the control inputs update, are obtained using the Lyapunov technique to reduce the communication cost. By designing some adaptive updating laws combined with the concept of event‐triggered, the unknown parameters in uncertain multi‐plant networked control systems are real‐time online estimated and adjusted with respect to the relevant nominal systems at event‐triggered instants. To avoid Zeno phenomena, a lower bound of event‐triggered execution interval is discussed. Furthermore, given the external perturbations, gain stability theory is introduced to analyze the stability of multi‐plant networked control systems with bounded perturbations, and then the sufficient conditions related to the multiple plants topology structure are derived. Finally, a numerical simulation is provided to illustrate the effectiveness of our theoretic results.     

RXDNFuse: A aggregated residual dense network for infrared and visible image fusion

This study proposes a novel unsupervised network for IR/VIS fusion task, termed as RXDNFuse, which is based on the aggregated residual dense network. In contrast to conventional fusion networks, RXDNFuse is designed as an end-to-end model that combines the structural advantages of ResNeXt and DenseNet. Hence, it overcomes the limitations of the manual and complicated design of activity-level measurement and fusion rules. Our method establishes the image fusion problem into the structure and intensity proportional maintenance problem of the IR/VIS images. Using comprehensive feature extraction and combination, RXDNFuse automatically estimates the information preservation degrees of corresponding source images, and extracts hierarchical features to achieve effective fusion. Moreover, we design two loss function strategies to optimize the similarity constraint and the network parameter training, thus further improving the quality of detailed information. We also generalize RXDNFuse to fuse images with different resolutions and RGB scale images. Extensive qualitative and quantitative evaluations reveal that our results can effectively preserve the abundant textural details and the highlighted thermal radiation information. In particular, our results form a comprehensive representation of scene information, which is more in line with the human visual perception system.     

差分准对立学习多目标蚁狮算法

为解决多目标优化问题，对经典的蚁狮算法进行改进，提出了基于差分进化的准对立学习多目标蚁狮算法（DEQOMALO）。该算法针对蚁狮算法易陷入局部最优的不足，一方面，该算法引用差分进化的思想，充分利用种群和精英蚁狮的信息对原算法中蚂蚁个体的位置更新方式进行改进；另一方面采用反向学习策略对蚂蚁种群进行优化，将原种群个体和其准对立个体进行混合并择优作为新的种群，大大增加种群的多样性。选取典型的标准测试函数，将提出的算法与原始蚁狮算法以及其他传统进化策略优化的蚁狮算法进行比较。实验结果表明，改进算法在收敛性和分布性上均有很大程度的提升，在解决双目标优化问题上具有较好的鲁棒性和有效性。     

适老化智能产品的设计与开发策略

研究适老化智能产品的设计与开发策略。以互联网 大数据时代为背景,通过比较分析国内外的适老化智能产品,发现企业未来的发展方向更加趋向于产品研发技术的软硬件融合。基于我国智能技术、适老化产品的市场现状和潜在的消费需求、开发价值,提出适老化智能产品的设计方法与开发策略。在互联网 大数据的渗透下,加速了我国传统制造业的转型升级,智能化与情感化将成为未来适老化产品开发的两大趋势。智能产品开发能够满足老年群体的基本物质需求与精神情感需求,解决空巢老年人的孤独感,因此其市场开发潜力较大。功能定制式与情感体验式的设计方法是针对老年人的生理特征而进行的产品开发方式,能够满足老年消费市场的多样化需求。     

Mathematical formulation and heuristic algorithms for optimisation of auto-part milk-run logistics network considering forward and reverse flow of pallets

The operational planning of distribution network for automotive industry is complex with many conditions to consider, including heterogeneous fleet, enforcing the feasibility of 3D-packing of pallets into vehicles to address the vehicle's capacity in terms of weight and volume, compatibility of orders in a vehicle, returning empty pallets from assembly-plants backwards to suppliers, and delivery time windows. A mathematical model (MILP) is proposed that takes account of these conditions to minimise total transportation costs. The network structure can be a combination of direct shipment and milk-run for both forward and reverse flow of pallets. The model is solved optimally for small-size problems. For solving larger problems, a heuristic algorithm (in two versions) is proposed that uses a similarity measure to generate a reasonable list of orders. Best/first-fit strategies are employed to generate a feasible solution with the aid of a relaxed version of the proposed MILP. Improvement heuristics are also designed. Unlike most of existing constructive heuristics, our aim for developing the heuristic approach is to force routing decision, with all of its considerations, being made optimal. We also use the proposed best-fit strategy in the body of grouping evolution strategy (GES) algorithm to attain an effective meta-heuristic approach. The effectiveness of heuristics is tested on generated instances which demonstrates they are optimal for small-size problems. They are also tested on the data of daily auto-parts shipments gathered from the largest Iranian automobile company. Results demonstrate there exists a significant potential for cost saving through milk-run strategy compared with the direct shipping strategy.     

四轮轮毂电机驱动电动汽车电液复合制动平顺性控制策略

液压制动与电机再生制动的时域响应差异导致电动汽车在制动模式切换时产生冲击感，影响驾驶员驾驶感受和乘坐舒适性。以四轮轮毂电机驱动电动汽车为研究对象，提出一种基于分层架构的电液复合制动平顺性控制策略。针对"高压蓄能器+电机泵"式电子液压制动系统（EHB），上层控制器提出基于模糊控制的轮缸压力控制策略；针对制动模式切换过程中产生的冲击，下层控制器提出包括液压介入预测模块和电机制动补偿模块的电液复合制动平顺性控制策略。通过Simulink-AMESim联合仿真平台进行仿真试验验证。结果表明，轮缸压力控制策略能够保证轮缸液压力较好地追随目标压力，且稳态误差不超过2%；电液复合制动平顺性控制策略能够有效提高制动系统的响应速度，同时显著降低制动模式切换时的冲击，能提升车辆制动平顺性和乘坐舒适性。     

换热网络优化中换热单元生成频次的影响分析及策略改进

换热单元的生成频次对换热网络优化有直接的影响，分析其机理可以指导求解算法的改进。本文采用节点非结构模型及强制进化随机游走算法，通过设置不同的换热单元生成概率与生成个数以改变换热单元的生成频次，观察并记录个体在优化过程中产生的年综合费用变化情况。发现不同生成频次下整型变量和连续变量的相对优化频率发生了变化，从而改变了整体优化过程，并且与个体当前优化状态不适应的生成频次会造成相对优化频率不平衡、优化结果较差。基于此，本文提出了一种具有生成参数动态调节策略的换热单元生成方式，在优化过程中根据个体状态实时调整换热单元的生成概率与生成个数，平衡整型变量与连续变量的相对优化频率。最后，采用15SP、10SP和20SP算例进行验证，换热网络的年综合费用较文献结果分别下降了1.06%、0.16%、0.68%，验证了该策略使得换热单元的生成频次更为合理，有效地提高了算法的优化效率。     

高聚物的新型3D打印材料开发进展

综述了一般3D打印技术的概念、产业及其发展。重点介绍了高聚物在3D打印材料中的应用。持续跟踪结果表明:一批新型高聚物的3D打印材料推向市场,一批国际知名化工公司也在积极介入3D打印业务。     

基于网络学习行为聚类分析的在线课堂优化策略研究

利用聚类分析法对网络学习行为分类，从而挖掘不同类型学习者在学习行为和学习效果间的差异。以大学计算机公共课《数据库技术及应用》为例，综合学习者学习行为类别差异、在线课程内容组织以及课程特点等要素为在线课堂进一步发展提供优化策略。     

Integrating Variable Reduction Strategy With Evolutionary Algorithms for Solving Nonlinear Equations Systems

Nonlinear equations systems (NESs) are widely used in real-world problems and they are difficult to solve due to their nonlinearity and multiple roots. Evolutionary algorithms (EAs) are one of the methods for solving NESs, given their global search capabilities and ability to locate multiple roots of a NES simultaneously within one run. Currently, the majority of research on using EAs to solve NESs focuses on transformation techniques and improving the performance of the used EAs. By contrast, problem domain knowledge of NESs is investigated in this study, where we propose the incorporation of a variable reduction strategy (VRS) into EAs to solve NESs. The VRS makes full use of the systems of expressing a NES and uses some variables (i.e., core variable) to represent other variables (i.e., reduced variables) through variable relationships that exist in the equation systems. It enables the reduction of partial variables and equations and shrinks the decision space, thereby reducing the complexity of the problem and improving the search efficiency of the EAs. To test the effectiveness of VRS in dealing with NESs, this paper mainly integrates the VRS into two existing state-of-the-art EA methods (i.e., MONES and DR-JADE) according to the integration framework of the VRS and EA, respectively. Experimental results show that, with the assistance of the VRS, the EA methods can produce better results than the original methods and other compared methods. Furthermore, extensive experiments regarding the influence of different reduction schemes and EAs substantiate that a better EA for solving a NES with more reduced variables tends to provide better performance.