Fully distributed consensus for fuzzy multiagent systems with jointly connected topology

This paper investigates the problem of fully distributed consensus for polynomial fuzzy multiagent systems (MASs) under jointly connected topologies. First, a polynomial fuzzy modeling method is presented to characterize the error dynamics that is constructed by one leader and multiple followers. Then, using the relative state information and the agents' dynamics, a distributed adaptive protocol is designed to guarantee that MASs under jointly connected topologies can achieve consensus in a fully distributed fashion. Utilizing the Lyapunov technique, a relaxed sufficient criterion is proposed to ensure consensus for fuzzy MASs under jointly connected topologies. Moreover, the adaptive coupling weights between neighboring agents can converge to certain values. The derived condition is transformed into a sum-of-squares form, which can be solved numerically. We provide an example to illustrate the proposed distributed adaptive consensus technique's validity.     

Finite-time optimal tracking control for a class of nonlinear systems with prescribed performance

In this paper, a finite-time optimal tracking control scheme based on integral reinforcement learning is developed for partially unknown nonlinear systems. In order to realize the prescribed performance, the original system is transformed into an equivalent unconstrained system so as to a composite system is constructed. Subsequently, a modified nonlinear quadratic performance function containing the auxiliary tracking error is designed. Furthermore, the technique of experience replay is used to update the critic neural network, which eliminates the persistent of excitation condition in traditional optimal methods. By combining the prescribed performance control with the finite-time optimization control technique, the tracking error is driven to a desired performance in finite time. Consequently, it has been shown that all signals in the partially unknown nonlinear system are semiglobally practical finite-time stable by stability analysis. Finally, the provided comparative simulation results verify the effectiveness of the developed control scheme.     

融合双注意力机制的人群计数算法

针对背景复杂、遮挡、人群分布不均等人群计数常见问题,提出了一种结合联合损失的空间-通道双注意力机制卷积神经网络模型(joint loss-based space-channel dual attention network, JL-SCDANet).该网络前端进行图像粗粒度特征提取,中间加入空间注意力机制以及通道注意力机制突出图像重点区域,后端使用可加大感受野且不丢失图像分辨率的空洞卷积提取深层二维特征.此外,该模型结合联合损失函数进行训练,以增强模型的鲁棒性.为了验证模型的改进效果,在3个公共数据集(ShanghaiTech Part B、mall和UCF＿CC＿50)上分别进行了对比实验,在ShanghaiTech Part B数据集中平均绝对误差(MAE)和均方误差(MSE)分别达到了8.13和13.13;在mall数据集中MAE、MSE达到了1.78和2.28;在UCF＿CC＿50数据集中MAE、MSE分别达到了182.12和210.24,实验结果证明了该网络在提高人数统计准确率上的有效性.     

基于实时视频感知的虚拟体育交互系统

针对疫情常态化背景下,传统体育项目受场地、器材等限制,市场上相关产品价格昂贵、可扩展性不足等问题,提出了一种基于实时视频感知的虚拟体育交互系统.该系统设计视频数据采集模块和人体关节点提取模块,结合OpenPose获取人体的关节点坐标,实时捕捉人体手势以及肢体动作.动作语义理解模块包括运动动作理解和绘图动作理解.前者根据运动中肢体关节点的相对位置关系,识别运动动作语义.后者将手腕部关节点绘图动作轨迹生成为草图图像,使用AlexNet进行识别分类,解析为对应的绘制动作语义.该模型在边缘端设备的分类准确率为98.83%.采用基于Unity设计的草图游戏应用作为可视化交互界面,实现在虚拟场景中的运动交互.该系统使用实时视频感知交互方式实现居家运动健身,无需其他的外部设备,具有更强的参与度和趣味性.     

融合语音、脑电和人脸表情的多模态情绪识别

本文提出了一种多模态情绪识别方法, 该方法融合语音、脑电及人脸的情绪识别结果来从多个角度综合判断人的情绪, 有效地解决了过去研究中准确率低、模型鲁棒性差的问题. 对于语音信号, 本文设计了一个轻量级全卷积神经网络, 该网络能够很好地学习语音情绪特征且在轻量级方面拥有绝对的优势. 对于脑电信号, 本文提出了一个树状LSTM模型, 可以全面学习每个阶段的情绪特征. 对于人脸信号, 本文使用GhostNet进行特征学习, 并改进了GhostNet的结构使其性能大幅提升. 此外, 我们设计了一个最优权重分布算法来搜寻各模态识别结果的可信度来进行决策级融合, 从而得到更全面、更准确的结果. 上述方法在EMO-DB与CK+数据集上分别达到了94.36%与98.27%的准确率, 且提出的融合方法在MAHNOB-HCI数据库的唤醒效价两个维度上分别得到了90.25%与89.33%的准确率. 我们的实验结果表明, 与使用单一模态以及传统的融合方式进行情绪识别相比, 本文提出的多模态情绪识别方法有效地提高了识别准确率.     

融合多向学习的混沌麻雀搜索算法

对于原始麻雀搜索算法（SSA）在迭代过程中表现出的种群多样性减小,易陷入局部最优等问题,提出一种融合多向学习的混沌麻雀搜索算法（MSSA）。利用Hénon混沌映射初始化种群,增加麻雀种群的多样性,扩大可行解的搜索范围,为全局寻优奠定基础;采用多向学习策略增加麻雀跟随者探索未知领域的机会,平衡算法的局部开发性能和全局搜索能力;当算法陷入局部最优时,引用遗传算法中的变异策略依据动态的变异概率对当前最优个体进行扰动变异;将MSSA算法应用到无线传感器网络节点覆盖优化问题。数值实验结果与Wilcoxon秩和检验结果均表明MSSA算法在收敛精度与收敛速度等方面具有更明显的优势。     

基于MFE-BERT与FNNAttention的心理医学知识图谱构建

针对心理医学领域文本段落冗长、数据稀疏、知识散乱且规范性差的问题, 提出一种基于多层级特征抽取能力预训练模型(MFE-BERT)与前向神经网络注意力机制(FNNAttention)的心理医学知识图谱构建方法. MFE-BERT在BERT模型基础上将其内部所有Encoder层特征进行合并输出, 以获取包含更多语义的特征向量, 同时对两复合模型采用FNNAttention机制强化词级关系, 解决长文本段落语义稀释问题. 在自建的心理医学数据集中, 设计MFE-BERT-BiLSTM-FNNAttention-CRF和MFE-BERT-CNN-FNNAttention复合神经网络模型分别进行心理医学实体识别和实体关系抽取, 实体识别F1值达到93.91%, 实体关系抽精确率达到了89.29%, 通过融合文本相似度与语义相似度方法进行实体对齐, 将所整理的数据存储在Neo4j图数据库中, 构建出一个含有3652个实体, 2396条关系的心理医学知识图谱. 实验结果表明, 在MFE-BERT模型与FNNAttention机制的基础上构建心理医学知识图谱切实可行, 提出的改进模型所搭建的心理医学知识图谱可以更好地应用于心理医学信息管理中, 为心理医学数据分析提供参考.     

Direct Freeform Laser Fabrication of 3D Conformable Electronics

3D conformable electronic devices on freeform surfaces show superior performance to the conventional, planar ones. They represent a trend of future electronics and have witnessed exponential growth in various applications. However, their potential is largely limited by a lack of sophisticated fabrication techniques. To tackle this challenge, a new direct freeform laser (DFL) fabrication method enabled by a 5-axis laser processing platform for directly fabricating 3D conformable electronics on targeted arbitrary surfaces is reported. Accordingly, representative laser-induced graphene (LIG), metals, and metal oxides are successfully fabricated as high-performance sensing and electrode materials from different material precursors on various types of substrates for applications in temperature/light/gas sensing, energy storage, and printed circuit board for circuit. Last but not the least, to demonstrate an application in smart homes, LIG-based conformable strain sensors are fabricated and distributed in designated locations of an artificial tree. The distributed sensors have the capability of monitoring the wind speed and direction with the assistance of well-trained machine-learning models. This novel process will pave a new and general route to fabricating 3D conformable electronic devices, thus creating new opportunities in robotics, biomedical sensing, structural health, environmental monitoring, and Internet of Things applications.     

分数阶时滞非线性多智能体系统的自适应控制

针对分数阶多智能体系统中存在时滞和非线性特性, 时滞往往会引起控制系统的性能下降甚至出现系统 不稳定等问题, 提出了一种含时滞非线性的分数阶多智能体系统自适应控制方法. 对于多智能体系统的控制协议, 设计了基于领导者和相邻智能体状态信息的自适应控制协议, 减小了过大常数控制增益带来的能源浪费. 对于一 致性, 利用图论基础、分数阶Halanay不等式稳定性定理、Kronecker积和Schur补引理, 获得了分数阶时滞非线性多 智能体系统的LMI一致性条件. 仿真结果验证了本文算法的正确性和有效性. 由于整数阶系统是分数阶系统的特殊 形式, 本文结论可以直接推广到整数阶多智能体系统中.     

SVPWM教学研究之重点解析

SVPWM是电力电子技术教学中的重要内容,本文对空间矢量调制的教学重点做了一个较为全面系统的梳理。重点阐述SVPWM的核心思想,特别是三相正弦交流电SVPWM的基本原理,严格证明了三相电压的空间矢量相等与线电压相等等价。同时,证明在SVPWM下,逆变器输出的最大圆为基本空间矢量构成的正六边形的内接圆、直流母线电压利用率提高了约15.4%。