基于M-AFSA的MPRM逻辑电路面积优化

现有基于传统智能优化算法的MPRM电路面积优化算法存在效果差的问题。由于MPRM电路面积优化属于组合优化问题，先提出一种多策略协同进化人工鱼群算法（MAFSA），该算法引入基于反向学习的种群初始化策略，以提高种群多样性及初始种群解的质量；引入觅食与追尾交互性策略，以加强人工鱼个体之间的信息交流、提高所提算法的收敛速度；引入自适应扰动策略，以增加人工鱼个体位置变异的随机性、避免所提算法陷入局部最优。此外，提出一种MPRM逻辑电路面积优化方法，利用所提算法来搜索电路面积最小的最佳极性。基于北卡罗莱纳州微电子中心（MCNC）Benchmark电路的实验结果表明：与遗传算法相比，所提算法优化电路平均面积百分比最高为57.24%，平均为39.57%；与人工鱼群算法相比，所提算法优化电路平均面积百分比最高为33.53%，平均为14.54%；与改进的人工鱼群算法相比，所提算法优化电路平均面积百分比最高为30.25%，平均为13.86%。     

A geographical and operational deep graph convolutional approach for flight delay prediction

Flight delay prediction has attracted great interest in civil aviation community due to its significant role in airline planning, flight scheduling, airport operation, and passenger service. Flight delay is affected by numerous factors and irregularly propagates in air transportation networks owing to flight connectivity, which brings critical challenges to accurate flight delay prediction. In recent years, Graph Convolutional Networks (GCNs) have become popular in flight delay prediction due to the advantage in extracting complicated relationships. However, most of the existing GCN-based methods have failed to effectively capture the spatial–temporal information in flight delay prediction. In this paper, a Geographical and Operational Graph Convolutional Network (GOGCN) is proposed for multi-airport flight delay prediction. The GOGCN is a GCN-based spatial–temporal model that improves node feature representation ability with geographical and operational spatial–temporal interactions in a graph. Specifically, an operational aggregator is designed to extract global operational information based on the graph structure, while a geographical aggregator is developed to capture the similar nature among spatially close airports. Extensive experiments on a real-world dataset demonstrate that the proposed approach outperforms the state-of-the-art methods with a satisfying accuracy improvement.     

导航接收机抗干扰算法综述

近年来,导航接收机抗干扰技术发展迅速,成为导航领域的研究重点。介绍了抗干扰技术的发展历程,分析总结了目前主要的抗干扰方法及算法特点,并讨论了未来关键需求和发展方向,对接收机抗干扰技术的研究和工程应用有一定意义。     

新型微光机电系统惯性测量技术研究进展

微惯性测量单元(MIMU)是惯性导航系统(INS)的核心组件，亦是构建微定位导航授时（μ-PNT）系统的重要组成部分。当前成熟的微惯性测量单元主要基于微机电系统（MEMS）实现，其性能逐渐难以满足新型无人驾驶车、无人飞行器以及制导弹药、航空航天器等军民用领域对高精确惯性导航的需求。近年来,各种新型微惯性测量技术相继被提出，以期望突破微惯性测量单元的性能与尺寸、质量、功耗（SWaP）之间相互制约的关键技术难题。系统总结了近年来国内外在常规MEMS惯性测量技术以及新型微光机电系统（MOEMS）惯性测量、微腔光力惯性测量、量子精密测量等几类新型惯性测量技术方面的研究进展，展望了未来新型高精度惯性测量技术的发展趋势，并提出了一种基于腔光力系统的量子增强型惯性测量技术构想。     

基于可重构标定板的激光与视觉联合标定方法

目前,激光雷达与视觉传感器的联合标定方法包含动态在线标定与静态离线标定两大类.动态在线标定对标定的环境有较高的要求,且标定结果不稳定;静态离线标定通常采用标定板,因而对标定板的要求较高,手动选择与线拟合都易引入或放大误差,故提出了一种基于可重构标定板的激光雷达与视觉传感器的联合标定方法.首先,对传统的标定板进行拆分与重构,采用类条形码的方式自动识别激光雷达特征点,并添加相机校验机制以缓解标定过程中相机识别的不稳定性带来的误差.最后,通过与双目相机标定,采用重投影的方式对外参标定结果进行可视化主观评价与客观量化评价.实验表明,该标定方法的均方根误差(RMSE)为1.275cm,重投影后的误差为3.2个像素.