测量电视的自动调焦系统实现

自动调焦系统是保证测量电视获得被测目标清晰图像的一个关键部分,快速有效地实现自动调焦,有助于提高整个系统的性能。本论文提出了一种基于图像处理技术的自动调焦方法,采用Sobel边缘检测算子来检测图像的边缘点数,并根据该值判断是否聚焦,同时采用自适应搜索步长的登山算法控制调焦镜头组得到清晰图像。实验证明,使用本论文算法可对复杂条件下的目标实现自动调焦,增强了测量电视的适用性。     

基于DSP技术的摄像头自动调焦系统的设计

利用CMOS图像传感芯片,经过TMS320VC5509A处理获取图像,对获取的每一帧图像进行清晰度评价算法判别,得到最佳的对焦评价值,以便后续的控制摄像头快速准确完成自动对焦任务.本系统可以应用于各种嵌入式系统中,具有实用性和先进性.采用了基于Sobel算子的差分平方和作为图像清晰度评价函数.具有较好的单峰性,能够快速准确地聚焦,满足实时性的要求.     

某进口型导弹红外导引头光学系统调焦分析

针对某进口型导弹红外导引头维护和修理过程中的光学系统调焦问题进行了理论分析,建立了调焦理论模型,并通过与试验对比,验证了该调焦模型的可行性。     

移动测量系统的三维数据采集与建模

介绍了一种基于距离数据重构立体图像的三维户外信息移动采集系统。该系统在车辆顶端安置两个激光扫描测距仪,分别采集横向和纵向数据。这种移动测量系统通过代价函数的计算和距离剖面匹配使得重建图像的精度和效率大大提高。     

一种航空铆钉自动检测系统研制

研究并实现了一种图像识别的铆钉自动尺寸检测系统,主要介绍了系统的工作流程和原理,给出了系统硬件组成,并在对CCD相机的采集图像清晰度评价函数分析基础上,提出了一种适合多种铆钉检测的自动调焦方法。通过对图像滤波处理、边缘检测算法的论证分析,建立了适合铆钉检测的图像特征提取方案,最后给出了系统主要功能界面。     

基于多尺度 3D-2D卷积神经网络的高光谱图像分类

设计提出了 1种针对高光谱图像分类任务的 3D-MSCNN模型。在 PCA降维的基础上,利用 3D空谱特征提 取网络和 2D多尺度特征提取网络实现高光谱图像特征提取,充分发挥高光谱图像空谱信息价值,增强对不同尺度 地表覆盖的表达能力。最后,利用 Softmax分类损失函数实现高光谱图像分类任务。实验结果表明,本文算法在 In. dian Pines和 Pavia University数据集上都取得了较好的分类效果。与 CD-CNN、3D-CNN、SS-Net和 HybirdSN等方法相 比,本文算法能够有效提升总体精度、平均精度和 Kappa系数等客观评价指标。     

考虑执行机构饱和的迎角跟踪控制

设计提出了 1种针对高光谱图像分类任务的 3D-MSCNN模型。在 PCA降维的基础上,利用 3D空谱特征提取网络和 2D多尺度特征提取网络实现高光谱图像特征提取,充分发挥高光谱图像空谱信息价值,增强对不同尺度地表覆盖的表达能力。最后,利用 Softmax分类损失函数实现高光谱图像分类任务。实验结果表明,本文算法在 In. dian Pines和 Pavia University数据集上都取得了较好的分类效果。与 CD-CNN、3D-CNN、SS-Net和 HybirdSN等方法相比,本文算法能够有效提升总体精度、平均精度和 Kappa系数等客观评价指标。     

基于像素点对误差累加的最佳阈值选取与一种边界检测新算法

本文研究存在背景噪声和干扰的情况下图像的有效分割及边界检测的问题。提出了新的图像分割准则函数和梯度二值化算法。算法兼顾了分割效果和处理的实时性，通过编程在实际图像中的应用，证明了算法的有效性和优越性。     

基于改进ORB-GMS-SPHP算法的快速图像拼接方法

针对传统图像拼接算法存在拼接速度慢、图像拼接有色差等问题，提出了一种基于ORB-GMS-SPHP算法的大视场多图像拼接方法。该方法首先利用高斯函数构建尺度空间，将高斯尺度空间划分为多个网格，在每个网格内借助FAST算法提取尺度空间特征点，使用BRIEF算法提取描述符并匹配，得到更加均匀分布的特征点；然后使用运动网格统计算法筛选匹配点；最后采用SPHP算法融合图像重叠区域，从而得到完整的拼接图像。将改进的ORB-GMS-SPHP算法与现有的传统特征点匹配算法在特征点匹配精度和特征点匹配速度进行对比与评价，验证了该方法特征点匹配速度快、精度高，并且可以保留更多的正确匹配点的特点。将该拼接方法与传统拼接方法在拼接速度、图像配准均方误差RMSE以及视觉主观判断拼接色差进行对比与评价，验证了该拼接方法具有较快的拼接速度、更高的拼接精度和无明显色差。该方法在2 736像素×3 648像素图像中,特征点匹配时间降低至6.463 s，图像配准精度RMSE降低至3.87。实验证明该方法特征点匹配速度快、精度高,且拼接精度高、无明显色差。     

基于图像化变差函数的性能数据异常判别

为提升发动机性能监控的智能化水平,实现性能数据的高效利用,提出了基于图像化变差函数的发动机性能数据异常判别方法。通过研究发动机性能数据的标准化修正方法和图像转化方法,将数值型表示的发动机性能数据转化为发动机性能图像。通过引入变差函数理论,采用4方向的变差函数值表示性能特征值,融合不同时刻不同参数的性能数据。在提取发动机性能图像关键特征点的基础上,定义性能图像间的差异距离,实现基于变差函数的发动机性能图像异常判别方法,从而实现对发动机性能状态的判别。选用若干组实际发动机性能数据对方法进行验证。验证结果表明:该方法运算高效,实现了高维性能数据的降维和对性能图像运行状态的分类,从而判别发动机性能数据的运行状态。     

基于ARX模型的笼型异步电机控制策略研究

针对笼型异步电机控制系统中时变、非线性的特点,提出了以ARX模型为核心。模型参考自适应控制策略为基础的笼型异步电机的控制策略。所提出的控制策略对于电机参数变化和系统干扰具有较好的鲁棒性。通过仿真和样机试验,对所提出的控制策略进行了验证。     

Robustness evaluation method for unmanned aerial vehicle swarms based on complex network theory

Unmanned Aerial Vehicle (UAV) swarms have been foreseen to play an important role in military applications in the future, wherein they will be frequently subjected to different disturbances and destructions such as attacks and equipment faults. Therefore, a sophisticated robustness evaluation mechanism is of considerable importance for the reliable functioning of the UAV swarms. However, their complex characteristics and irregular dynamic evolution make them extremely challenging and uncertain to evaluate the robustness of such a system. In this paper, a complex network theory-based robustness evaluation method for a UAV swarming system is proposed. This method takes into account the dynamic evolution of UAV swarms, including dynamic reconfiguration and information correlation. The paper analyzes and models the aforementioned dynamic evolution and establishes a comprehensive robustness metric and two evaluation strategies. The robustness evaluation method and algorithms considering dynamic reconfiguration and information correlation are developed. Finally, the validity of the proposed method is verified by conducting a case study analysis. The results can further provide some guidance and reference for the robust design, mission planning and decision-making of UAV swarms.     

多重化双向Buck/Boost变换器控制策略仿真研究

多重化双向Buck/Boost变换器常作为能量交换接口用于储能系统中。采用基本建模法对三重化双向Buck/Boost变换器建立交流小信号模型,基于变换器的开环特性,设计了电压、电流补偿网络,在此基础上提出一种新的基于前馈校正的复合双闭环控制策略。为验证所提控制策略的正确性进行了MATLAB仿真研究。仿真结果表明:该控制策略可为三重化双向Buck/Boost变换器提供良好的动、稳态性能,同时对大范围的负载变化等扰动均具有很好的鲁棒性。     

区域导航无人机空中基准站的多源信息融合容错导航系统研究

作为卫星导航系统的补充和备份,区域导航服务系统近年来得到较大发展。在基于无人机的区域导航服务系统中,无人机自身的定位精度对区域导航服务系统的可靠运行有直接的影响。针对无人机导航传感器及系统的容错和可靠性问题,设计了具有针对性和自优化功能的多源信息融合容错导航方案,提出了一种优化的基于矢量分配形式的自适应联邦滤波算法。通过对每个状态量设计不同的信息分配系数,实现传感器量测噪声的动态优化调整,有效减小了传感器故障对融合导航系统的影响,提高了无人机导航系统的鲁棒性。验证分析表明,该方法可以减小子滤波器故障信息对融合导航系统联邦滤波全局估计的影响,避免了故障子滤波器在信息重置过程中对系统造成的污染,提高和保障了无人机空中基准站多源信息融合导航系统的稳定性和可靠性。     

基于扩张状态观测器的永磁同步电机自抗扰无源控制

针对传统永磁同步电机滑模控制存在的抖振以及系统抗扰动鲁棒性差问题,提出一种基于扩张状态观测器的永磁同步电机自抗扰无源控制方法。转速外环设计自适应滑模控制器,采用扩张状态观测器对系统干扰项进行观测,用其进行前馈补偿。电流内环将无源控制与自抗扰控制相融合,得到dq旋转坐标系下的电压给定。新型控制方法可有效抑制系统抖振,增强系统鲁棒性。试验结果验证了所提控制方法的有效性和实用性。     

A strong robustness open-circuit fault diagnosis strategy for novel fault-tolerant electric drive system based on d-q-axis current signal

To diagnose the Open-Circuit (OC) fault in the novel fault-tolerant electric drive system, based on d-q-axis current signal, a strong robustness diagnosis strategy is proposed and investigated. Fewer independent power supplies and converters are required in the novel fault-tolerant electric drive system based on Dual-Winding Permanent Magnet Motor (DWPMM), and the system’s reliability, usage ratio and power density have been improved compared to the conventional fault-tolerant motor drive system. However, the novel fault-tolerant electric drive system has the OC fault diagnostic false alarms issue when load changes suddenly or under light-load condition. And it lacks the research on the diagnostic method when the system encounters intermittent OC fault in power switches. By theory derivation, simulation and experimental verification, it can be concluded that the proposed strong robustness OC fault diagnosis strategy based on d-q-axis current signal can overcome the OC fault diagnostic false alarms issue when load changes suddenly or under light-load condition. And it can detect and locate the OC fault of single-phase winding in real time, and diagnose the intermittent OC fault of power switches.     

基于时间反转和降阶Keystone的SAR-GMTI快速聚焦方法

对合成孔径雷达（SAR）地面运动目标聚焦技术进行了研究。针对现有运动目标聚焦方法存在的问题,提出了一种基于时间反转和降阶Keystone的SAR地面运动目标检测（SAR-GMTI）快速聚焦方法。首先,根据目标的机动特性建立了3阶距离模型;其次,针对目标多普勒中心模糊引起的多普勒谱分裂现象,结合所提时间反转处理（TRP）和降阶Keystone变换处理估计出运动目标2阶参数。此后,构造2阶相位补偿函数补偿运动目标的2阶距离徙动和多普勒徙动,从而完成运动目标的聚焦。同时,所提方法没有任何参数搜索过程,降低了计算复杂度。最后,仿真实验验证了所提算法的正确性和有效性。     

Drogue detection for autonomous aerial refueling based on convolutional neural networks

Drogue detection is a fundamental issue during the close docking phase of autonomous aerial refueling(AAR). To cope with this issue, a novel and effective method based on deep learning with convolutional neural networks(CNNs) is proposed. In order to ensure its robustness and wide application, a deep learning dataset of images was prepared by utilizing real data of ‘‘Probe and Drogue" aerial refueling, which contains diverse drogues in various environmental conditions without artificial features placed on the drogues. By employing deep learning ideas and graphics processing units(GPUs), a model for drogue detection using a Caffe deep learning framework with CNNs was designed to ensure the method's accuracy and real-time performance. Experiments were conducted to demonstrate the effectiveness of the proposed method, and results based on real AAR data compare its performance to other methods, validating the accuracy, speed, and robustness of its drogue detection ability.     

Robustness analysis metrics for worldwide airport network: A comprehensive study

Robustness of transportation networks is one of the major challenges of the 21st century. This paper investigates the resilience of global air transportation from a complex network point of view, with focus on attacking strategies in the airport network, i.e., to remove airports from the sys-tem and see what could affect the air traffic system from a passenger's perspective. Specifically, we identify commonalities and differences between several robustness measures and attacking strate-gies, proposing a novel notion of functional robustness: unaffected passengers with rerouting. We apply twelve attacking strategies to the worldwide airport network with three weights, and eval-uate three robustness measures. We find that degree and Bonacich based attacks harm passenger weighted network most. Our evaluation is geared toward a unified view on air transportation net-work attack and serves as a foundation on how to develop effective mitigation strategies.     

A high-safety active/passive hybrid control approach for compressor surge based on nonlinear model predictive control

Surge active control can expand the stable operating range of the compressor. However, the difficulty of flow measurement, dynamic uncertainty disturbance, actuator delay characteristics, hard constraints of control variable, and system security measures have not been fully considered in the existing active control system, which significantly hinders its engineering application. Therefore, a nonlinear model predictive surge active control method is first presented based on flow estimator designed by using a continuous-time Kalman filter for dealing with the hard constraint of control variable and the impact of actuator delay of compression system with dynamic uncertainty. Then, a high-safety active/surge passive hybrid control strategy is designed, dominated by the surge active control and supplemented by the surge passive control, to ensure the compression system’s safe and stable operation. Lastly, the simulation results suggest that the flow estimator accurately estimates the compressor flow. When considering the delay impact of the actuators and sensors and measurement noise on the system, the proposed method exhibits stronger robustness than the existing methods. The active/surge passive hybrid control strategy can successfully ensure the compression system's safe and stable operation. This paper is of high practical significance for the engineering application of future compressor surge active control technologies.