Adaptive fixed-time prescribed performance control of vehicular platoons with unknown dead-zone and actuator saturation

This article focuses on the problem of fixed-time prescribed performance platoon control for heterogeneous vehicles with unknown dead-zone and actuator saturation. First, an equivalent transformation is developed to approximate the actuator nonlinearity (i.e., dead-zone and saturation), which reduces the complexity of controller design. Then, to guarantee the tracking errors converge to the predetermined region in the given time, a modified prescribed performance function is presented. Based on this, a novel adaptive sliding mode control scheme is developed in the context of fixed-time theory, which is proved to be capable of ensuring individual vehicle stability and string stability in fixed time. In addition, under the proposed control scheme, the convergence time is independent of initial conditions of the system. Finally, numerical simulations are carried out to demonstrate the effectiveness of the proposed control scheme.     

Robust integrated sequential covariance intersection fusion Kalman filters and their convergence and stability for networked sensor systems with five uncertainties

For networked sensor systems (NSSs) with hard and soft sensors including five uncertainties, two universal approaches of solving the robust fusion estimation problems are presented. It includes an integrated sequential covariance intersection (SCI) fusion minimax robust Kalman filtering approach with cross-covariance information and a generalized Lyapunov equation approach with four pairs of Lyapunov equations. Applying them, the robust local and SCI fused time-varying and steady-state Kalman filters are presented in the sense that their actual estimation error variances are guaranteed to have the corresponding minimal upper bounds. The equivalent batch SCI fusers are also presented. Their robustness and accuracy relations are proved, and the sensitivity of the SCI fuser with respect to the fused orders of sensors is analyzed. Applying the dynamic error system analysis method and the dynamic variance error system analysis method, a new convergence and absolute asymptotic stability theory of robust fusion Kalman filtering is presented. The classical Kalman filtering convergence and stability theory is developed. Compared with the original covariance intersection fuser, they significantly reduced the computational complexity and burden. Compared with the optimal and conservative SCI fusers, they significantly improved the robust accuracies. They are suitable to deal with asynchronous or random delayed data and are suitable for real-time applications. A simulation applied to the two-mass spring damper mechanical system shows their effectiveness.     

可见光与红外图像融合质量评价指标分析

目的 客观评价作为图像融合的重要研究领域,是评价融合算法性能的有力工具。目前,已有几十种不同类型的评价指标,但各应用领域包括可见光与红外图像融合,仍缺少统一的选择依据。为了方便比较不同融合算法性能,提出一种客观评价指标的通用分析方法并应用于可见光与红外图像融合。方法 将可见光与红外图像基准数据集中的客观评价指标分为两类,分别是基于融合图像的评价指标与基于源图像和融合图像的评价指标。采用Kendall相关系数分析融合指标间的相关性,聚类得到指标分组;采用Borda计数排序法统计算法的综合排序,分析单一指标排序和综合排序的相关性,得到一致性较高的指标集合;采用离散系数分析指标均值随不同算法的波动程度,选择充分体现不同算法间差异的指标;综合相关性分析、一致性分析及离散系数分析,总结具有代表性的建议指标集合。结果 在13对彩色可见光与红外和8对灰度可见光与红外两组图像源中,分别统计分析不同图像融合算法的客观评价数据,得到可见光与红外图像融合的建议指标集(标准差、边缘保持度),作为融合算法性能评估的重要参考。相较于现有方法,实验覆盖20种融合算法和13种客观评价指标,并且不依赖主观评价结果。结论...     

小目标检测研究综述

随着计算机视觉和人工智能技术的快速发展,目标检测受到了更加广泛的关注。由于小目标像素占比小、语义信息少、易受复杂场景干扰以及易聚集遮挡等问题,导致小目标检测一直是目标检测领域中的一大难点。目前,视觉的小目标检测在生活的各个领域中日益重要。为了进一步促进小目标检测的发展,提高小目标检测的精度与速度,优化其算法模型,本文针对小目标检测中存在的问题,梳理了国内外研究现状及成果。首先,分别从小目标可视化特征、目标分布情况和检测环境等角度对小目标检测的难点进行了分析,同时从数据增强、超分辨率、多尺度特征融合、上下文语义信息、锚框机制、注意力机制以及特定的检测场景等方面系统总结了小目标检测方法,并整理了在框架结构、损失函数、预测和匹配机制等方面发展的较为成熟的单阶段小目标检测方法。其次,本文对小目标检测的评价指标以及可用于小目标检测的各类数据集进行了详细介绍,并针对部分经典的小目标检测方法在MSCOCO(Microsoft common objects in context)、VisDrone2021(vision meets drones2021)和Tsinghua-Tencent100K等数据...     

Circularly polarized patch antenna with simultaneously wide frequency tuning range and high gain performance

A tunable circularly polarized square patch antenna with parasitic elements is designed for a wide frequency tuning range and high gain characteristics. The proposed antenna is constructed by one main patch and four semi-elliptic parasitic units. By loading four varactor diodes and adjusting their capacitance values, the tunable feature is performed to reallocate the corresponding working frequency. Moreover, the diagonal corners of the antenna are cut and loaded with varactor diodes, which provide the appropriate perturbation between the two orthogonality modes of the antenna, so as to ensure the circular polarization characteristic in the entire operating tuning band. The experimental results demonstrate that the reflection coefficient and axial ratio are less than −13 dB and 3 dB, respectively. The proposed antenna features a relatively wide continuously tuning range of 24% within 1.9-2.3 GHz and a stable gain of over 7 dBi with a radiation efficiency of above 85%.     

掺烧煤质差异较大煤种对“W”火焰炉运行参数的影响

针对掺烧煤质差异较大煤种时锅炉运行参数变化不确定的问题,以一台660 MW“W”火焰炉为研究对象、100%热耗率验收工况为锅炉负荷条件,研究在燃用现行煤种义马烟煤（烟煤）的基础上,按照不同比例掺烧金竹山无烟煤（无烟煤）对锅炉运行参数的影响。利用数值模拟得到2种煤化程度差异较大煤种按照不同比例掺烧时,锅炉有效利用热、烟气速度和温度以及NOx、SOx、CO产量的变化情况。模拟结果表明:锅炉在以高负荷运行时,65%无烟煤+35%烟煤的掺烧比例可以提高锅炉使用寿命,降低炉膛受热面发生腐蚀的概率,但对煤粉的着火和稳燃无促进作用;50%无烟煤+50%烟煤的掺烧比例可以满足锅炉额定效率,同时能显著降低发电成本,但炉膛受热面的腐蚀情况会较为严重。     

基于DeblurGAN的运动模糊图像盲复原算法研究

DeblurGAN方法利用条件生成对抗网络解决了端到端的图像去模糊问题,但存在图像边缘细节恢复不足以及鲁棒性不高的问题,针对此问题,提出一种基于DeblurGAN的运动模糊图像盲复原方法。在生成网络中,采用多尺度卷积核神经网络提取特征,并使用级联空洞卷积扩大神经元的感受野;采用自适配归一化方法代替原来生成器中使用的实例归一化方法。其次,引入了梯度图像L1损失,结合对抗损失和感知损失,将其作为图像去模糊的正则约束,使得生成图像的边缘特征更加清晰。实验结果表明,提出方法复原的图像峰值信噪比数值较DeblurGAN算法高出5.4%,结构相似性指标高出1%;在主观上清晰化效果较好,且消除了网格效应。     

自适应加权的多特征融合ECT图像重建算法

为了解决电容层析成像(electrical cpacitance tomography, ECT)图像重建中电容值与介电常数这一非线性病态问题,提出一种自适应加权的多特征融合(adaptive weighted multi-feature fusion, AWMF) ECT图像重建算法,利用网络模型拟合电容张量和介电常数的非线性映射关系。首先,在网络模型中采用密集卷积网络(DenseNet),不仅缓解梯度消失现象,还融合不同通道的特征信息;添加挤压激励网络(squeeze excitation network, SENet)自适应调整特征通道的权重,用以提取不同通道的关键特征,提高重建图像的精度。其次,构建树形聚合结构（tree aggregation structure network, TASN）网络模块,扩大感受野并提取丰富的多尺度特征信息,消除普通卷积所带来的伪影现象。在COMSOL5.3软件上建模仿真后,通过MATLAB2014a对图像进行重建。实验结果表明,重建图像误差系数降低至0.0256,相关系数提高至0.9717,与传统算法和CNN算法相比,具有更高的图像重建质量。     

On measuring network robustness for weighted networks

Knowledge and Information Systems - Network robustness measures how well network structure is strong and healthy when it is under attack, such as vertices joining and leaving. It has been widely...