碳酸镉转化制备氢氧化镉及产物光催化降解性

用碳酸钠溶液直接混合硫酸镉溶液制备了沉淀碳酸镉,再以沉淀碳酸镉为基体,氢氧化钠溶液为转化剂,通过阴离子交换制备出氢氧化镉纳米材料。采用X射线衍射仪（XRD）和场发射扫描电子显微镜（FESEM）对沉淀碳酸镉和转化产物的结构和形貌进行了表征。结果表明,沉淀碳酸镉的结构遭到破坏,转化完全所得到的氢氧化镉的形貌完全不同于沉淀碳酸镉,为片状组装的不规则球体。紫外光照射下光催化降解罗丹明B染料测试表明,转化产物比沉淀碳酸镉具有更好的光催化降解性能,转化时间不同的产物表现出不同的降解效率。当20 mg的转化产物分散到50 mL 8 mg/L的罗丹明B染料中,在黑暗环境中磁力搅拌吸附120 min达吸附平衡,开启300 W紫外汞灯后,效率最高的转化产物可在180 min内将染料完全降解。     

大口径管道直管段制作技术应用

针对大口径管道施工过程中存在施工周期长,施工难度大,工程成本高等问题,提出一种新型优化管段分段技术和管段组对方案。结果表明:该制作技术能保证大口管道安装施工顺利进行;有效地解决现有大口径套管安装过程中的技术难题。该方法具有创新性、实用性、可靠性等优点,在工程建设的许多领域,具有推广应用价值。     

基于SARIMA-GRNN-SVM的短期商业电力负荷组合预测方法

针对短期商业电力负荷预测准确性与周期难以满足现有电力现货市场的问题,提出了一种基于SARIMAGRNN-SVM(seasonal autoregressive integrated moving average-generalized regression neural network-support vector machine)的商业电力负荷组合预测模型。首先,对商业电力负荷变化的周期规律与随机因素的复杂影响进行了分析;然后,结合以上分析,选用SARIMA和GRNN为单一预测模型对商业电力负荷进行预测,并利用SVM进行组合,实现日前商业电力负荷预测;最后,通过某商业综合体的电力负荷数据进行验证。所提组合预测模型较单一预测模型拥有更优的预测精度与鲁棒性,可以为短期商业电力负荷预测提供借鉴。     

电子束诱导沉积钴微米线及其铁磁性

通过电子束诱导沉积的方法制备了钴(Co)微米线,并利用扫描电子显微镜(SEM)、原子力/磁力显微镜(AFM/MFM)以及物性测量系统(PPMS)等手段对Co微米线的沉积尺寸、微结构、铁磁性和电学性质进行了测试和分析。研究结果表明:Co微米线轮廓清晰、均匀性好。在不同的沉积条件下,微米线的实际长度与设定长度基本一致;实际宽度数据呈类梯形分布,半高宽是设定值的2~10倍;实际厚度低于设定厚度的60%。沉积电流对Co微米线的铁磁特性有重要影响。当沉积电流大于0.5 nA时,样品呈现出良好的铁磁特性。另外,电学性能测试结果显示Co微米线呈现绝缘特性。成功制备了室温铁磁绝缘Co微米线,这将有助于深入开展微纳尺度的结构与器件的研究和应用。     

Multi-objective particle swarm optimisation based integrated production inventory routing planning for efficient perishable food logistics operations

Sustainable and efficient food supply chain has become an essential component of one’s life. The model proposed in this paper is deeply linked to people's quality of life as a result of which there is a large incentive to fulfil customer demands through it. This proposed model can enhance food quality by making the best possible food quality accessible to customers, construct a sustainable logistics system considering its environmental impact and ensure the customer demand to be fulfilled as fast as possible. In this paper, an extended model is examined that builds a unified planning problem for efficient food logistics operations where four important objectives are viewed: minimising the total expense of the system, maximising the average food quality along with the minimisation of the amount of CO₂ emissions in transportation along with production and total weighted delivery lead time minimisation. A four objective mixed integer linear programming model for intelligent food logistics system is developed in the paper. The optimisation of the formulated mathematical model is proposed using a modified multi-objective particle swarm optimisation algorithm with multiple social structures: MO-GLNPSO (Multi-Objective Global Local Near-Neighbour Particle Swarm Optimisation). Computational results of a case study on a given dataset as well as on multiple small, medium and large-scale datasets followed by sensitivity analysis show the potency and effectiveness of the introduced method. Lastly, there has been a scope for future study displayed which would lead to the further progress of these types of models.     

大口径光学元件装夹转运结构设计及分析

为了实现大口径光学元件的安全装夹、转运,通过光学元件开槽与不开槽两种装夹方式的分析,得出开槽夹紧转运方式将带来微裂纹、应力集中、成本高等缺陷,提出了利用摩擦力克服光学零件的重力和惯性力的低应力装夹转运方案。通过对光学元件低应力夹紧结构设计,并利用有限元分析方法,得到不开槽装夹方式下,光学元件的最大主应力为1.11 MPa,最大切应力为0.73 MPa,远低于光学元件破坏的强度极限,且受力均匀,无应力集中现象。     

A Global Maximum Error Controller Based Nonlinearity Aware TPWL Method for Reducing Nonlinear Systems

Abstract

Model order reduction is a common practice to reduce large order systems so that their simulation and control become easy. Nonlinearity aware trajectory piecewise linear is a variation of trajectory piecewise linearization technique of order reduction that is used to reduce nonlinear systems. With this scheme, the reduced approximation of the system is generated by weighted sum of the linearized and reduced sub-models obtained at certain linearization points on the system trajectory. This scheme uses dynamically inspired weight assignment that makes the approximation nonlinearity aware. Just as weight assignment, the process of linearization points selection is also important for generating faithful approximations. This article uses a global maximum error controller based linearization points selection scheme according to which a state is chosen as a linearization point if the error between a current reduced model and the full order nonlinear system reaches a maximum value. A combination that not only selects linearization points based on an error controller but also assigns dynamic inspired weights is shown in this article. The proposed scheme generates approximations with higher accuracies. This is demonstrated by applying the proposed method to some benchmark nonlinear circuits including RC ladder network and inverter chain circuit and comparing the results with the conventional schemes.     

小尺度交通信号灯的检测与状态识别

交通信号灯识别包括检测和状态识别，在智能交通系统中发挥重要作用。基于YOLOv3算法提出了一种交通信号灯检测与状态识别模型。针对交通信号灯相较于交通场景中其他目标具有尺度小的特性进行了算法的设计：降低骨干网络的下采样倍率以增加小尺度目标的特征描述能力；通过增大特征图的尺度来改进多尺度特征融合；引入广义交并比作为检测任务的损失函数来改进目标边界框的回归效果。同时，根据交通信号灯本身的特性，使用颜色和形状约束的方法对信号灯进行状态识别和类别验证。最后在公开的Bosch交通信号灯数据集上和实际的城区道路进行了实验验证。实验结果表明，所提出的算法能够提升交通信号灯识别的精度和召回率，识别准确率可以达到90%左右。     

300M超高强钢在中性盐雾环境中的腐蚀行为及机制

摘要：为了研究300M超高强钢在中性盐雾环境中的腐蚀行为及腐蚀机制，采用失重法，宏观、微观腐蚀形貌分析，三维表面轮廓分析及电化学分析的研究方法，来表征腐蚀实验现象并进行分析。结果表明：300M超高强钢在中性盐雾环境中的腐蚀产物为FeOOH、Fe2O3、Fe(OH)3和Fe3O4；腐蚀速率随着腐蚀时间逐渐降低，腐蚀后期（72h）腐蚀速率降低50%；腐蚀初期以点蚀为主，点蚀坑通过横向扩展，逐渐发展为后期的均匀腐蚀，腐蚀表面形貌呈沟壑状；外腐蚀层对基体的保护能力很弱，Cr元素在锈层靠近基体的一侧偏聚使内腐蚀层具有一定的抗腐蚀性。     

Airline planning and scheduling: Models and solution methodologies

The airline industry is a representative industry with high cost and low profitability. Therefore, airlines should carefully plan their schedules to ensure that overall profit is maximized. We review the literature on airline planning and scheduling and focus on mathematical formulations and solution methodologies. Our research framework is anchored on three major problems in the airline scheduling, namely, fleet assignment, aircraft routing, and crew scheduling. General formulation, widely used solution approaches, and important extensions are presented for each problem and integrated problems. We conclude the review by identifying promising areas for further research.