边缘AI的霜雪识别系统设计与实现

为提高霜雪识别系统的识别率,开展了基于边缘AI的霜雪识别系统设计。综合海思嵌入式处理器Hi3559A高算力、低功耗特点,硬件部分采用模块复用的方法降低功耗和资源消耗,软件部分,从模型训练、模型量化、模型部署入手对MobileNetV2图像分类网络和ISP自适应处理算法进行改进,最终识别率99.7%,预处理时间0.3 s,图像分类时间0.8 s,模组平均功率2 W,鲁棒性好、性能稳定。由此可见Hi3559A智能相机模组可有效纠正雪深探测仪的疑误数据。     

基于改进LMD和能量相对熵的主动配电网故障定位方法

针对配电网发生单相接地故障且分布式电源（distributed generations, DGs）大量接入后,配电网结构和运行方式复杂多变、故障后电气量不明显、故障特征弱等特点,提出一种基于改进局部均值分解（improved local mean decomposition, ILMD）和能量相对熵的主动配电网故障定位方法。首先,利用镜像延拓将信号在两端延拓,消除LMD存在的端点效应,同时,在信号中加入自适应噪声,克服LMD存在的模态混叠问题,对各区段暂态零序电流进行ILMD分解;然后对分解后所有乘积函数（product function, PF）的能量相对熵进行计算,所有PF分量的相对能量熵之和即为区段间的相对能量熵,通过所设置的定位判据,对故障区段进行判断。仿真结果表明,所提算法在10 kV小电流接地系统和改进的IEEE33节点系统中,在不同的仿真条件下故障定位结果准确率高,验证了所提方法的准确性。     

基于模型参考模糊自适应的电液位置伺服系统鲁棒控制研究

针对电液位置伺服系统存在的参数变化及外力扰动，引入模型参考模糊自适应机构，构成模型参考模糊自适应控制，即MRFAC，仿真实验表明，与传统PID相比，该方法可以有效抑制参数扰动及外力干扰，具有很强的鲁棒性。     

神经网络在电液位置伺服系统中的应用

设计了基于BP神经网络的自适应PID控制器，该控制器充分利用了经典PID控制算法简单的特点，又利用了神经网络良好的自适应和抗干扰能力，通过神经网络的学习和对系统的在线辨识，自适应调整PID参数，使控制系统对参数变化表现出良好的鲁棒性和控制性能，并通过仿真证明了所设计系统的合理性。     

注塑流动与传热分析的自适应隐式控制体积法

在注塑流动与传热分析中采用自适应隐式控制体积法,利用自动控制的参数识别方法来自动调节时间步长,在每个时间步长流动将向前推进大约半个单元大小的距离,厚度方向的温度采用契比雪夫配点法进行计算,压力方程的求解方法是预条件共轭梯度法。计算结果表明,自适应隐式控制体积法的时间复杂度是结点数目的平方,而且1h之内可以求解10000结点规模的问题。     

Analysis and tuning of adaptive generalized predictive control

The adaptive Generalized Predictive Controller (Clarke et al., 1987a, b) is capable of controlling plants with variable dead-time, unknown model orders and unstable poles and zeros. This paper shows how the GPC control law can be written in an equivalent general linear transfer function form which simplifies closed-loop (eg. root locus) analysis. Three recommended strategies for selecting the design parameters during the commissioning stage allow the user to adjust the closed-loop speed of response on-line using only a single active tuning parameter. Experimental runs confirm the ability of adaptive GPC to provide a consistent closed-loop response in spite of large process changes.     

水泥回转窑的智能控制

文章介绍了PIT Navigator对水泥熟料生产自动化控制的实现,基于神经网络的自学习、自适应控制元件,将相关的过程数据记录下来,随后借助数据关联性进行分析,在预测和自学习过程模型(模型预测控制)中处理这些数据,并计算过程参数的实时预测,使用这些工具,可以实现更稳定的窑况,同时提高产量和降低能耗。     

水资源适应性利用理论及其在治水实践中的应用前景

水系统受气候变化和人类活动的影响,一直在不断地变化,因此水资源的开发利用也应适应这种变化,而不能按照固定的控制阈值或约束条件来进行。当然,也不是被动的、随意的去适应。在对水系统及其与环境变化关系剖析的基础上,分析了水资源适应性利用的原理与模式;基于对环境变化下水资源适应性利用机理的认识,提出水资源适应性利用理论的框架体系,阐述了其基本理念和关键内容;基于对我国现代治水实践的分析,分析了其暗含的水资源适应性利用理论的内容,以及我国治水实践中存在的问题及应用水资源适应性利用理论解决这些问题的可能途径。研究认为,水资源适应性利用理论对水资源合理开发、综合利用、科学管理具有重要的指导作用,为环境变化下水资源利用研究与实践奠定理论基础。     

Modeling and global trajectory tracking control for an over-actuated MAV

This paper deals with an original micro aerial vehicle (MAV) design, the Omnicopter MAV. It has two central coaxial rotors with fixed-pitch propellers and three perimeter mounted ducted fans with servo motors performing thrust vectoring. Compared with traditional rotary wing MAVs that have inherent underactuation, the Omnicopter possesses some advantages in mobility, for example, lateral translation with zero attitude and hover with nonzero attitude. The trajectory tracking control design, global stability analysis, and control allocation are demonstrated through numerical simulation. The advantage of zero attitude translation is illustrated through experimental results.     

HMM-based state classification of a user with a walking support system using visual PCA features

The improvement of safety and dependability in systems that physically interact with humans requires investigation with respect to the possible states of the user’s motion and an attempt to recognize these states. In this study, we propose a method for real-time visual state classification of a user with a walking support system. The visual features are extracted using principal component analysis and classification is performed by hidden Markov models, both for real-time fall detection (one-class classification) and real-time state recognition (multi-class classification). The algorithms are used in experiments with a passive-type walker robot called “RT Walker” equipped with servo brakes and a depth sensor (Microsoft Kinect). The experiments are performed with 10 subjects, including an experienced physiotherapist who can imitate the walking pattern of the elderly and people with disabilities. The results of the state classification can be used to improve fall-prevention control algorithms for walking support systems. The proposed method can also be used for other vision-based classification applications, which require real-time abnormality detection or state recognition.