检索结果-南通市图书馆

在线全文

学校读者我要写书评

暂无评论

变负载机械手轨迹跟踪控制器设计

控制理论与应用 2024年第4期41卷 738-744页

作者：赵兴强刘振朱全民青岛大学自动化学院山东青岛266071 青岛大学山东省工业控制技术重点实验室山东青岛266071 英国西英格兰大学工程设计与数学系英国布里斯托BS161QY

本文针对具有变负载的不确定刚性机械手系统,提出了一种依赖平均驻留时间的神经网络自适应切换控制策略.本控制方案将夹持不同负载的刚性机械手系统视为切换系统,即根据负载的不同将整个系统分为若干子系统,并基于平均驻留时间原则对每... 详细信息

本文针对具有变负载的不确定刚性机械手系统,提出了一种依赖平均驻留时间的神经网络自适应切换控制策略.本控制方案将夹持不同负载的刚性机械手系统视为切换系统,即根据负载的不同将整个系统分为若干子系统,并基于平均驻留时间原则对每个子系统分别设计控制器.在各子系统中,分别采用径向基函数(RBF)神经网络逼近系统结构参数,以避免控制器对系统精确模型的依赖.同时,基于神经网络设计鲁棒补偿项,以抑制集总扰动对系统的影响.然后,利用多Lyapunov函数方法证明了轨迹跟踪误差的一致最终有界性.最后,通过仿真验证,所提出的控制方案不仅可实现变负载机械手期望轨迹的高精度跟踪,而且可有效削弱输入力矩的抖振.

关键词：切换控制器机械手神经网络平均驻留时间自适应控制

同方期刊数据库博看期刊评论

在线全文

学校读者我要写书评

暂无评论

结构未知系统降维辨识方法——变量消去算法

控制与决策 2024年第1期39卷 171-179页

作者：陈晶程连元李俊红朱全民江南大学理学院江苏无锡214122 南通大学电气工程学院江苏南通226019 西英格兰大学工业设计和数学系英国布里斯托BS161QY

针对结构未知的系统提出一种新的降维辨识方法.借助核函数方法,利用一个高维Volterra模型逼近未知系统.由于Volterra模型未知参数维数较高,为避免高阶矩阵求逆和求特征值,提出变量消去算法,将高维系统的辨识问题转化为两个低维系统辨识... 详细信息

针对结构未知的系统提出一种新的降维辨识方法.借助核函数方法,利用一个高维Volterra模型逼近未知系统.由于Volterra模型未知参数维数较高,为避免高阶矩阵求逆和求特征值,提出变量消去算法,将高维系统的辨识问题转化为两个低维系统辨识问题.通过理论证明采用降维算法后降维系统信息矩阵条件数变小,参数收敛速度得到提高.进一步引入Aitken加速方法提高算法收敛速度,增强算法对步长的鲁棒特性.最后通过仿真例子验证所提出方法的有效性.

关键词：参数估计降维算法变量消去算法 Volterra模型结构未知系统矩阵条件数

Aμ-MAC:一种自适应的无线传感器网络MAC协议

在线全文

学校读者我要写书评

暂无评论

自动化学报 2010年第1期36卷 54-59页

作者：古连华程良伦 zhu quan-min 广东工业大学自动化学院广州510006 Intelligent Autonomous Systems Lab University of the West of England

结合基于竞争和调度机制的混合型方案是高效的无线传感器网络MAC协议的重要解决思路.μ-MAC是一种典型的混合型MAC协议,本文在深入研究μ-MAC的基础上,提出一种自适应的混合型协议Aμ-MAC.它针对动态流量的数据采集型应用,解决了μ-MAC... 详细信息

结合基于竞争和调度机制的混合型方案是高效的无线传感器网络MAC协议的重要解决思路.μ-MAC是一种典型的混合型MAC协议,本文在深入研究μ-MAC的基础上,提出一种自适应的混合型协议Aμ-MAC.它针对动态流量的数据采集型应用,解决了μ-MAC中动态拓扑适应性及时钟同步问题,并提供了良好的流量自适应性.仿真结果表明,Aμ-MAC增强了协议的扩展性和适应性,具有更好的网络生存能力,而保留了与μ-MAC相近的节能效率和时延性能.

关键词：无线传感器网络自适应混合型 MAC协议 μ-MAC Aμ—MAC

SAHRC：一种基于分簇的无线传感器网络路由控制算法

在线全文

学校读者我要写书评

暂无评论

电子与信息学报 2011年第8期33卷 2013-2017页

作者：张小波程良伦 zhu quan-min 广东工业大学自动化学院广州510006 英国西英格兰大学计算机工程与数学学院布里斯托尔BS16 1QY

设计特定应用场合的路由控制算法是无线传感器网络路由控制领域研究的热点之一。在深入研究经典网络路由算法(LEACH)的基础上,提出一种基于分簇的自适应混合型路由控制(SAHRC)算法。该算法针对大规模事件驱动型网络场景应用,采用网内节... 详细信息

设计特定应用场合的路由控制算法是无线传感器网络路由控制领域研究的热点之一。在深入研究经典网络路由算法(LEACH)的基础上,提出一种基于分簇的自适应混合型路由控制(SAHRC)算法。该算法针对大规模事件驱动型网络场景应用,采用网内节点启发机制解决了LEACH算法面对大规模网络缺乏自适应性,未考虑节点剩余能量,通信效率难以得到保障等问题。仿真结果表明,新的SAHRC算法比原有LEACH算法有更好的节能性和稳定性。

关键词：无线传感器网络路由控制算法自适应分簇自适应混合型路由控制(SAHRC)

Weak type(1, 1) of the Riesz transform on some direct product manifolds with exponential volume growth

在线全文

学校读者我要写书评

暂无评论

Science China Mathematics 2024年第7期67卷 1599-1622页

作者： Hong-quan Li Jie-Xiang zhu School of Mathematical Sciences Fudan UniversityShanghai200433China Center for Applied Mathematics Tianjin UniversityTianjin300072China

In this paper, we are concerned with the Riesz transform on the direct product manifold H^(n)× M,where H^(n) is the n-dimensional real hyperbolic space, and M is a connected complete non-compact Riemannian manifold satisfying the volume doubling property and generalized Gaussian or sub-Gaussian upper estimates for the heat kernel. We establish its weak type(1, 1) property. In addition, we obtain the weak type(1, 1) of the heat maximal operator in the same setting. Our arguments also work for a large class of direct product manifolds with exponential volume growth. Particularly, we provide a simpler proof of weak type(1, 1) boundedness of some operators considered in the work of Li et al.(2016).

关键词： Riesz transform heat kernel direct product real hyperbolic space

有理模型辨识的两类新方法—–混合迭代与柔性最小二乘法

在线全文

学校读者我要写书评

暂无评论

控制与决策 2022年第1期37卷 58-66页

作者：陈晶朱全民江南大学理学院江苏无锡214122 西英格兰大学工业设计和数学系英国布里斯托BS161QY

针对有理模型提出两类辨识方法.首先提出基于递阶辨识思想的混合辨识方法,将模型分解为分子和分母两个子模型,分别用最小二乘法辨识分子参数,用粒子群算法和智能多步长梯度迭代算法辨识分母参数.由于降低了模型维数,且信息向量与噪声不... 详细信息

针对有理模型提出两类辨识方法.首先提出基于递阶辨识思想的混合辨识方法,将模型分解为分子和分母两个子模型,分别用最小二乘法辨识分子参数,用粒子群算法和智能多步长梯度迭代算法辨识分母参数.由于降低了模型维数,且信息向量与噪声不相关,相对于传统的偏差补偿最小二乘算法,混合迭代法可以提高辨识精度并降低计算量.然后,为消除模型结构已知的假设,且充分利用最新数据更新系统参数,提出柔性递推最小二乘辨识方法,将有理模型转化为时变参数系统,进而辨识出时变系统的参数.仿真例子验证了所提出方法的有效性.

关键词：有理模型参数估计梯度迭代粒子群算法柔性递推最小二乘算法混合辨识算法

The electroweak monopole–antimonopole pair in the standard model

在线全文

学校读者我要写书评

暂无评论

Communications in Theoretical Physics 2024年第3期76卷 113-119页

作者： Dan zhu Khai-ming Wong Guo-quan Wong School of Physics Universiti Sains Malaysia11800 USMPenangMalaysia

We present the first numerical solution that corresponds to a pair of Cho–Maison monopoles and antimonopoles(MAPs) in the SU(2) × U(1) Weinberg–Salam(WS) *** monopoles are finitely separated,while each pole carries a magnetic charge ±4π/*** positive pole is situated in the upper hemisphere,whereas the negative pole is in the lower *** Cho–Maison MAP is investigated for a range of Weinberg angles,0.4675≤ tan θ_(W)≤10,and Higgs self-coupling,0 ≤ β ≤ *** magnetic dipole moment(μm) and pole separation(d_(z)) of the numerical solutions are calculated and *** total energy of the system,however,is infinite due to point singularities at the locations of monopoles.

关键词： magnetic monopole Cho-Maison monopole Weinberg-Salam model

Revealing the Structure-Property Difference of Natural Rubber Prepared by Different Methods: Protein and Gel Content are Key Factors

在线全文

学校读者我要写书评

暂无评论

Chinese Journal of Polymer Science 2024年第4期42卷 457-467页

作者： Shao-Qi Huang Jun-Qi Zhang Yong zhu Ling-min Kong Lu-Sheng Liao Fu-quan Zhang Zheng-Tian Xie Jin-Rong Wu State Key Laboratory of Polymer Materials Engineering College of Polymer Science and EngineeringSichuan UniversityChengdu610065China Hainan Provincial Key Laboratory of Natural Rubber Processing Agricultural Products Processing Research Institute of Chinese Academy of Tropical Agricultural SciencesZhanjiang524001China

Natural rubber (NR) is widely used in various fields including aerospace, military industry and transportation due to its superior elasticity and comprehensive mechanical properties. Nonetheless, the commercial NR prepared by different methods usually exhibits different mechanical properties, primarily due to variations in processing conditions during the conversion from latex to bulk rubber material. Consequently, this poses challenges in scientific research and industrial production of NR. In order to assess the properties of various commercially available NR and identify key structural and compositional components, this study systematically compares and analyzes four representative NR raw materials: air dried sheet (ADS), ribbed smoked sheets (RSS), constant viscidity NR (CV), and whole field latex rubber (WF). The investigation focuses on evaluating their static mechanical behavior, SIC behavior, wear resistance, and fatigue resistance. The findings indicate that protein and gel content exhibit a crucial influence on the NR properties. These constituents contribute to the formation of a high-crosslinking density region, generating a heterogeneous network structure within the rubber. This structure amplifies strains during deformation, leading to earlier and stronger strain-induced crystallization (SIC). Among the four commercial NR brands, RSS demonstrates superior overall mechanical and dynamic properties owing to its high protein and gel content. This study serves as a valuable reference for comprehending the differences in properties among various commercial NR, thereby offering guidance for the actual processing and selection of NR.

关键词： Natural rubber Processingmethod Protein Gel content Property

Neural network modeling and prediction of HfO2 thin film properties tuned by thermal annealing

在线全文

学校读者我要写书评

暂无评论

High Power Laser Science and Engineering 2024年第2期12卷 79-86页

作者： min Gao Chaoyi Yin Jianda Shao Meiping zhu School of Microelectronics Shanghai UniversityShanghaiChina Laboratory of Thin Film Optics Key Laboratory of Materials for High Power LaserShanghai Institute of Optics and Fine MechanicsChinese Academy of SciencesShanghaiChina Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of SciencesBeijingChina Hangzhou Institute for Advanced Study University of Chinese Academy of SciencesHangzhouChina

Plasma-enhanced atomic layer deposition (PEALD) is gaining interest in thin films for laser applications, and post-annealing treatments are often used to improve thin film properties. However, research to improve thin film properties is often based on an expensive and time-consuming trial-and-error process. In this study, PEALD-HfO2 thin film samples were deposited and treated under different annealing atmospheres and temperatures. The samples were characterized in terms of their refractive indices, layer thicknesses and O/Hf ratios. The collected data were split into training and validation sets and fed to multiple back-propagation neural networks with different hidden layers to determine the best way to construct the process–performance relationship. The results showed that the three-hidden-layer back-propagation neural network (THL-BPNN) achieved stable and accurate fitting. For the refractive index, layer thickness and O/Hf ratio, the THL-BPNN model achieved accuracy values of 0.99, 0.94 and 0.94, respectively, on the training set and 0.99, 0.91 and 0.90, respectively, on the validation set. The THL-BPNN model was further used to predict the laser-induced damage threshold of PEALD-HfO2 thin films and the properties of the PEALD-SiO2 thin films, both showing high accuracy. This study not only provides quantitative guidance for the improvement of thin film properties but also proposes a general model that can be applied to predict the properties of different types of laser thin films, saving experimental costs for process optimization.

关键词： laser-induced damage threshold laser thin film neural network plasma-enhanced atomic layer deposition