Proximity-Induced Fully Ferromagnetic Order with Eightfold Magnetic Anisotropy in Heavy Transition Metal Oxide CaRuO3

Ferromagnetic materials with a strong spin-orbit coupling (SOC) have attracted much attention in recent years because of their exotic properties and potential applications in energy-efficient spintronics. However, such materials are scarce in nature. Here, a proximity-induced paramagnetic to ferromagnetic transition for the heavy transition metal oxide CaRuO₃ in (001)-(LaMnO₃/CaRuO₃) superlattices is reported. Anomalous Hall effect is observed in the temperature range up to 180 K. Maximal anomalous Hall conductivity and anomalous Hall angle are as large as ∼15 Ω⁻¹ cm⁻¹ and ∼0.93%, respectively, by one to two orders of magnitude larger than those of the typical 3d ferromagnetic oxides such as La_0.67Sr_0.33MnO₃. Density functional theory calculations indicate the existence of avoid band crossings in the electronic band structure of the ferromagnetic CRO layer, which enhances Berry curvature thus strong anomalous Hall effects. Further evidences from polarized neutron reflectometry show that the CaRuO₃ layers are in a fully ferromagnetic state (∼0.8 μ_B/Ru), in sharp contrast to the proximity-induced canted antiferromagnetic state in 5d oxides SrIrO₃ and CaIrO₃ (∼0.1 μ_B/Ir). More than that, the magnetic anisotropy of the (001)-(LaMnO₃/CaRuO₃) superlattices is eightfold symmetric, showing potential applications in the technology of multistate data storage.     

基于模型预测—中枢模式发生器的六足机器人轨迹跟踪控制

为了模仿动物卓越的运动能力和环境适应能力,提出了六足仿生机器人的轨迹跟踪控制方法。首先建立了机器人的运动学模型,接着通过转向参数将机器人的速度和角速度与中枢模式发生器（CPG）参数结合起来,设计了转换函数。然后通过转换函数将模型预测控制器和CPG网络结合起来,提出了基于CPG的模型预测控制器（MPC-CPG）,并证明了其稳定性。最后对机器人跟踪圆周轨迹和直线轨迹进行了仿真和实验。实验表明,在有初始误差的条件下,机器人在MPC-CPG控制器的作用下能够快速地消除位置误差和航向角误差,跟踪上参考轨迹。轨迹跟踪的位置误差始终保持在-0.1～0.1 m,航向角误差保持在-27?～20?。在MPC-CPG控制器的作用下,机器人不仅具有较高的轨迹跟踪精度,同时还表现出良好的运动平滑性和协调性,进一步验证了所提出的MPC-CPG控制器的有效性。     

用于行人轨迹预测的场景限制时空图卷积网络

目的 针对行人轨迹预测问题,已有的几种结合场景信息的方法基于合并操作通过神经网络隐式学习场景与行人运动的关联,无法直观地解释场景对单个行人运动的调节作用。除此之外,基于图注意力机制的时空图神经网络旨在学习全局模式下行人之间的社会交互,在人群拥挤场景下精度不佳。鉴于此,本文提出一种场景限制时空图卷积神经网络（scene-constrained spatial-temporal graph convolutional neural network,Scene-STGCNN）。方法 Scene-STGCNN由运动模块、基于场景的微调模块、时空卷积和时空外推卷积组成。运动模块以时空图卷积提取局部行人时空特征,避免了时空图神经网络在全局模式下学习交互的局限性。基于场景的微调模块将场景信息嵌入为掩模矩阵,用来调节运动模块生成的中间运动特征,具备实际场景下的物理解释性。通过最小化核密度估计下真实轨迹的负对数似然,增强Scene-STGCNN输出的多模态性,减少预测误差。结果 实验在公开数据集ETH （包含ETH和HOTEL）和UCY （包含UNIV、ZARA1和ZARA2）上与其他7种主流方法进行比较,就平均值而言,相对于性能第2的模型,平均位移误差（average displacement error,ADE）值减少了12%,最终位移误差（final displacement error,FDE）值减少了9%。在同样的数据集上进行了消融实验以验证基于场景的微调模块的有效性,结果表明基于场景的微调模块能有效建模场景对行人轨迹的调节作用,从而减小算法的预测误差。结论 本文提出的场景限制时空图卷积网络能有效融合场景和行人运动,在学习局部模式下行人交互的同时基于场景特征对轨迹特征做实时性调节,相比于其他主流方法,具有更优的性能。     

周期激励下广义离散Duffing系统的多稳态分析

具有硬边界的动基座双自由度动力学系统在受到基座周期性激励与随机冲击扰动时,振子与边界碰撞后的强非线性特性导致系统产生复杂的混沌行为.本文基于Soft Actor-Critic强化学习框架,构建了同时实现振动控制与基座运动跟随的智能算法,研究了宽频域范围内含有硬边界约束的动基座双自由度系统的动力学控制效果.通过构建包含相对位移、控制力的复合奖励函数,实现动力学系统精度较高的轨迹跟踪与振动抑制.结果表明,该算法可以实现频率范围跨2个数量级(0.01Hz到1Hz)的有效振动控制,并通过与PID控制方法的比较,展现了该方法在复杂环境中的稳定性与泛化性.     

基于被动声信号解算方法的高速目标轨迹测量

声学测量作为新一代外场测量系统的重要组成部分,为实现全天候、全地形下目标的有效轨迹测量起到重要作用。现有外场测量系统多依靠光、雷达等手段对目标进行定位追踪,轨迹测量,但光学轨迹测量精度极易受自然光线的影响,而雷达轨迹测量精度易被复杂电磁环境干扰,无法实现所有时段各类目标的轨迹测量。本文所提出的声学轨迹测量方法,对于超音速目标其测量精度优于10m,对于高亚音速目标测量精度优于10m。声学测量手段的出现弥补了外场观测手段的空缺,极大程度丰富了外场的观测维度。     

基于光电成像技术的运动员垂直弹跳高度测试

针对运动员弹跳过程持续时间短,弹跳位置相对不固定,弹跳高度测试操作复杂的问题,提出一种基于线阵光电成像技术的运动员垂直弹跳高度测试方法;首先构建大视场垂直弹跳高度测试系统,线阵CCD相机进行实时获取,得到目标弹跳轨迹图像;然后对目标弹跳轨迹图像进行图像分析和处理,高精度提取弹跳特征点,从而获得运动员垂直弹跳的运动时间;最后依据运动学公式,实现运动员垂直弹跳高度的准确测试;进行多组实验并分析实验数据,结果表明在1500mm的弹跳测试范围内,实验相对误差范围在3%以内,证明了该方法的可行性。     

Spatial-temporal analysis of safety risks in trajectories of construction workers based on complex network theory

Understanding the traffic patterns of construction workers on high-risk construction sites is important for implementing behaviour-based safety management. However, safety risks in worker trajectories are a complex system with high uncertainty. This resulted in few studies focusing on analysing the spatial–temporal risk in workers' trajectories from a systematic perspective. This study designs a new framework to explore the spatial–temporal patterns of safety risks in the trajectories of construction workers based on complex network theory. First, an integrated site safety risk classification method by combining hazard sources and group trajectory distribution is developed to accurately describe the spatial distribution of site risks. Second, a new complex network chronnet is used to construct complex networks containing risk information for spatial–temporal analysis. Finally, key risk areas and risk transition patterns are identified through the analysis of network measures. The study also developed a computational program that allows the network to be constructed and analysed in real-time. The feasibility and effectiveness of the method are verified through a case study. The results show that the method can reveal the risk distribution at the micro level, and explore the risk clustering and transition features in worker trajectories at the macro level. The study allows for an accurate analysis of dynamic risk patterns in construction workers' trajectories from a systematic perspective. It can also provide theoretical and practical support for personalized and adaptive behaviour-based safety management for construction workers.     

采用固定时间观测器的翼伞控制方法与应用

针对翼伞–火箭一子级柔性结构系统的控制难题,提出了一种采用固定时间观测器的改进矢量场轨迹跟踪控制方法.首先,设计了在线风场估计方法对未知风场进行了估计;其次,为了保证该控制系统在气动参数不确定、子系统铰连耦合等因素影响下的快速稳定性,提出了一种固定时间干扰观测器用来估计无需知道干扰上边界的复合干扰,并用于补偿控制系统的输入;然后,设计了一个同时保证侧偏距、偏航角以及偏航角速率稳定的横侧向控制器,并利用Lyapunov理论证明了闭环翼伞控制系统的收敛特性.最后,通过数值仿真及硬件在环实验验证了所提出方法的有效性及应用价值.     

Exploring the tidal effect of urban business district with large-scale human mobility data

Business districts are urban areas that have various functions for gathering people, such as work, consumption, leisure and entertainment. Due to the dynamic nature of business activities, there exists significant tidal effect on the boundary and functionality of business districts. Indeed, effectively analyzing the tidal patterns of business districts can benefit the economic and social development of a city. However, with the implicit and complex nature of business district evolution, it is non-trivial for existing works to support the fine-grained and timely analysis on the tidal effect of business districts. To this end, we propose a data-driven and multi-dimensional framework for dynamic business district analysis. Specifically, we use the large-scale human trajectory data in urban areas to dynamically detect and forecast the boundary changes of business districts in different time periods. Then, we detect and forecast the functional changes in business districts. Experimental results on real-world trajectory data clearly demonstrate the effectiveness of our framework on detecting and predicting the boundary and functionality change of business districts. Moreover, the analysis on practical business districts shows that our method can discover meaningful patterns and provide interesting insights into the dynamics of business districts. For example, the major functions of business districts will significantly change in different time periods in a day and the rate and magnitude of boundaries varies with the functional distribution of business districts.     

基于PID控制算法的变电站用锄草机器人设计

目前,国内的变电站内锄草作业主要靠人工完成,锄草质量受锄草工人技术的娴熟程度影响明显。此外在锄草过程中,锄草工人有可能会由于自身不小心造成与变电站内的高压设备产生接触,有人身触电的风险,由此可见锄草自动化势在必行。本文结合国内外锄草机器人的研究现状,阐述了研发此款锄草机器人的目的和现实意义;通过对LEGO（乐高）模块组装锄草机器人的可行性进行分析论证后,对锄草机器人的结构进行了设计;对锄草机器人的锄草轨迹进行了规划设计,实现了半圆曲面和平面的锄草移动作业,并对机器人小车不规则曲线的运动轨迹采用PID控制算法进行了设计。