高斯曲率约束的MRG骨架提取优化算法

三维模型的骨架保持了模型的拓扑特性,并被广泛应用于模型相似性比较、计算机动画及压缩等领域.根据多分辨率Reeb图的原理,提出了一种基于离散高斯曲率约束的骨架提取优化算法.通过计算网格顶点的离散高斯曲牢判断曲面局部凸凹特性,以获取模型表面的双曲极值点作为约束点;并依据约束点及其邻域的μ函数值产生的分裂线进行区域细分,获得子连通区域、确定关节点、形成优化的骨架结构.实验结果表明,该算法有效地突出了模型的拓扑分支特征以及模型表面的细节,提高了骨架提取的精度和效率.     

基于事例推理技术在火炮设计中的应用

从基于事例推理技术理论出发,在Ｎａｖｉｎｃｈａｎｄｒａ、Ｂａｒｄａｓｚ、ＺｅｉｄＩ）等学者提出的设计事例表示方法基础上提出更为通用、有效的设计事例表示形式,即设计事例３级表示方法和它的组织结构,并结合火炮总体设计的具体特点,给出火炮总体设计事例表示形式定义、组织及推理策略,以及火炮设计专家系统模型。通过研究表明,采用事例形式的知识表示可以有效地利用火炮设计经验,提高火炮设计质量、缩短周期,有力地推     

Finite‐time attitude‐tracking control for rigid spacecraft with actuator failures and saturation constraints

In this article, the problem of finite‐time attitude‐tracking control for rigid spacecraft is addressed. Uncertainties caused by external disturbances, unknown inertial matrix, actuator failures, and saturation constraints are tackled simultaneously. First, a smooth function that is more qualified to approximate the standard saturation characteristics is presented to deal with the actuator saturation constraints. Second, a fast nonsingular terminal sliding mode (FNTSM) manifold is constructed as a foundation of controllers design. By incorporating the fuzzy logic system into FNTSM technique, a less demanding solution of coping with model uncertainties is provided because the requirement of a prior knowledge of unknown inertial parameters and external disturbances in many existing achievements is removed. To reduce the number of parameters to be estimated, the norm approximation approach is exploited. Subsequently, an antichattering attitude controller is presented such that all the tracking errors converge into arbitrary small domains around the origin in finite time. The result is further extended to obtain a fault‐tolerant controller against completely failed actuators. Finally, numerical simulation is conducted to verify the effectiveness of the proposed control scheme and comparison with relevant literature demonstrates its high performance. Furthermore, an experiment for the large satellite Hubble Space Telescope is carried out to validate the practical feasibility.     

The globally asymptotic stability analysis for a class of recurrent neural networks with delays

This paper considers the problem of global stability of neural networks with delays. By combining Lie algebra and the Lyapunov function with the integral inequality technique, we analyze the globally asymptotic stability of a class of recurrent neural networks with delays and give an estimate of the exponential stability. A few new sufficient conditions and criteria are proposed to ensure globally asymptotic stability of the equilibrium point of the neural networks. A few simulation examples are presented to demonstrate the effectiveness of the results and to improve feasibility.     

系统级故障诊断软件仿真系统的设计与实现

文章介绍了系统级故障诊断软件仿真系统的设计与实现,着重讨论了系统的功能设计、测试图生成、诊断算法,并给出了一个诊断实例。     

一种超小型无人机舵机控制系统的设计

舵机控制系统是飞行控制系统和舵机之间的接口,是超小型无人机系统中一个重要的单元;对舵机控制系统在信号隔离、多路PWM信号采集、多路PWM输出、控制模式切换以及通讯等方面存在的技术问题进行了研究,提出了采用数字隔离器进行信号隔离、PCA采集多路PWM信号、一路定时器输出多路PWM信号、软硬件冗余的控制模式切换电路以及SPI高速数据通讯等方法,设计了基于C8051F121单片机的高可靠性、高冗余性的舵机控制系统;经过多次试飞,证明了其实用性.     

基于双CPU结构的电能质量监测系统设计

电能质量的监测管理和电力污染的治理工作越来越重要。介绍以DSP TMS320F28335为数据处理核心和单片机C8051F340为调度控制核心的双CPU模式的电能质量监测系统的设计,实现对电网电能质量的实时监测和参数分析,为电能质量的监测管理和电力污染的治理提供可靠依据,与国内外同类产品相比,该系统具有较好的性价比。测试结果表明:该系统测量准确,符合国家关于电能质量的监测标准,可以广泛应用于各电力部门和大中型用电企业。     

项目管理技术在行业制造过程管理中的应用研究

适应行业制造管理的需求,提出了将项目管理技术应用到面向行业的产品制造过程管理中的模式。重点讨论了项目管理技术与行业制造过程管理结合的必然性、可行性和利用项目管理技术对产品制造过程进行管理的内容与手段。     

Cross-source Data Error Detection Approach Based on Federated Learning

With the emergence and accumulation of massive data, data governance has become an important manner to improve data quality and maximize data value. Specifically, data error detection is a crucial step to improve data quality, which has attracted wide attention from both industry and academia. At present, various detection methods tailored for a single data source have been proposed. However, in many real-world scenarios, data are not centrally stored or managed. Data from different sources but highly correlated can be employed to improve the accuracy of error detection. Unfortunately, due to privacy/security issues, cross-source data are often not allowed to be integrated centrally. To this end, this paper proposes FeLeDetect, a cross-source data error detection method based on federated learning, so as to improve the error detection accuracy by using cross-source data information on the premise of data privacy. First, a Graph-based Error Detection Model, namely GEDM, is presented to capture sufficient data features from each data source. On this basis, the paper then designs a federated co-training algorithm, namely FCTA, to collaboratively train GEDM by using different cross-source data without privacy leakage of data. Furthermore, the paper designs a series of optimization methods to reduce communication costs during federated learning and manual labeling efforts. Finally, extensive experiments on three real-world datasets demonstrate that (1) GEDM achieves an average improvement of 10.3% and 25.2% in terms of the $F1$ score in the local and centralized scenarios, respectively, outperforming all the five existing state-of-the-art methods for error detection; (2) the F1 score of the error detection by FeLeDetect is 23.2% on average higher than that by GEDM in the local scenario.     

Comparison of conventional and soft computing-based power control methods in mobile communications systems

 In this paper, we give a comparison between the conventional power control scheme and soft computing-based approaches in a mobile communications application. At the base station, the `bang–bang' control strategy and a neural network-based prediction control method are employed. In addition, full power command transmission mode, single-bit command transmission mode, and fuzzy logic-based power command enhancement unit are considered. Based on simulation experiments, we quantitatively evaluate the performance of various combinations of these control methods and command transmission modes. Conclusions on the optimal configuration are finally drawn.