Durable Integrated K-Metal Anode with Enhanced Mass Transport through Potassiphilic Porous Interconnected Mediator

K-metal batteries have become one of the promising candidates for the large-scale energy storage owing to the virtually inexhaustible and widely potassium resources. The uneven K⁺ deposition and dendrite growth on the anode causes the batteries prematurely failure to limit the further application. An integrated K-metal anode is constructed by cold-rolling K metal with a potassiphilic porous interconnected mediator. Based on the experimental results and theoretical calculations, it demonstrates that the potassiphilic porous interconnected mediator boosts the mass transportation of K-metal anode by the K affinity enhancement, which decreases the concentration polarization and makes a dendrite-free K-metal anode interface. The interconnected porous structure mitigates the internal stress generated during repetitive deposition/stripping, enabling minimized the generation of electrode collapse. As a result, a durable K-metal anode with excellent cycling ability of exceed 1, 000 h at 1 mA cm⁻²/1 mAh cm⁻² and lower polarization voltage in carbonate electrolyte is obtained. This proposed integrated anode with fast K⁺ kinetics fabricated by a repeated cold rolling and folding process provides a new avenue for constructing a high-performance dendrites-free anode for K-metal batteries.     

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.     

基于可再分发抗合谋编码的低失真水印方案

在大数据的时代背景下,数据潜在的价值使其成为重要的财富之一。数据的非法篡改、修正和非法分发给追踪数据的泄密源头带来巨大挑战。数字指纹技术可应用在数据泄密溯源领域,即在数据中嵌入一串能够唯一标识用户信息的序列,当数据发生泄密后,提取其中蕴含的数字指纹,追踪到泄密的叛逆者。多个用户对数据合谋攻击并泄密数据,从而毁坏其中嵌入的指纹信息达到逃脱追责的目的,抗合谋编码能够解决此问题。针对现有的数字指纹编码无法满足数据再分发需求、数字指纹嵌入造成较大数据失真的问题,本文使用BIBD（Balanced incomplete block design）作为外码,码字扩展后的C码作为内码,构建一种可再分发的抗合谋指纹编码RD-ACC（Redistributable anti-collusion fingerprint coding）。在此基础上,提出一种基于多目标优化的数据库指纹算法,能够在较小的数据库失真情况下保证数字指纹较高的鲁棒性,提取出的RD-ACC能够有效抵抗组内、组间多用户合谋攻击。实验结果表明,该算法能够在较小的数据失真下实现数据的再分发操作,并且能抵抗合谋攻击进行泄密溯源。     

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...     

Development,integration, and field evaluation of an autonomous citrus-harvesting robot

Citrus harvesting is a labor-intensive and time-intensive task. As the global population continues to age, labor costs are increasing dramatically. Therefore, the citrus-harvesting robot has attracted considerable attention from the business and academic communities. However, robotic harvesting in unstructured and natural citrus orchards remains a challenge. This study aims to address some challenges faced in commercializing citrus-harvesting robots. We present a fully integrated, autonomous, and innovative solution for citrus-harvesting robots to overcome the harvesting difficulties derived from the natural growth characteristics of citrus. This solution uses a fused simultaneous localization and mapping algorithm based on multiple sensors to perform high-precision localization and navigation for the robot in the field orchard. Besides, a novel visual method for estimating fruit poses is proposed to cope with the randomization of citrus growth orientations. Further, a new end-effector is designed to improve the success and conformity rate of citrus stem cutting. Finally, a fully autonomous harvesting robot system has been developed and integrated. Field evaluations showed that the robot could harvest citrus continuously with an overall success rate of 87.2% and an average picking time of 10.9 s/fruit. These efforts provide a solid foundation for the future commercialization of citrus-harvesting robots.     

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%.     

基于纳什议价的多微网综合能源系统分布式低碳优化运行策略

针对当前多微网能源交易中未充分考虑供能侧与需求侧的互动关系,且随着碳交易市场的全面发展,多主体在碳交易机制下能源交易时的利益交互关系需深入挖掘等问题,提出一种基于纳什议价的多微网综合能源系统分布式低碳优化运行策略。首先,综合考虑多微网综合能源系统能源交易中各微网运营商以及用户的利益诉求,基于纳什议价理论构建碳交易机制下多个微网运营商与代表整个多微网用户侧利益的负荷聚合商的合作运行模型。其次,在证明所提纳什议价模型可以最大化社会效益的基础上,将原问题转化为易于求解的两个子问题,运用交替方向乘子法先后对两个子问题进行分布式求解,在交互有限信息的基础上达到博弈均衡,充分保证了各交易主体的信息隐私。最后,通过算例对比证明了所提方法在减少碳排放、提升个体效益和社会效益上的有效性,并深入分析了在碳交易机制下多主体合作运行减少碳排放的综合原因。     

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。