用神经网络法编程加工轮毂上特殊曲面的探讨

根据轮毂上曲面的特殊性,提出了采用神经网络方法进行编程,实现数控加工,以取代传统的加工方法的设想。     

人工神经元网络在变压器分接头实时控制中的应用

本文提出了一种利用人工神经元网络实时控制变压器分接头的新方法。研究结果表明该方法具有快速准确等优点，适应电力系统高度自动化的要求，并为变压器分接头实时控制开辟了一条新途径。     

全球卫星导航星座网络协议体系与运行模式

针对全球卫星导航星座网络建设初期或论证阶段所涉及的网络体系结构、协议体系及相关组网等技术问题,开展了基于导航星座星间链路构建空间信息网络的技术研究,分别提出了由子网、接入网、骨干网等节点及其相互之间星间、星地无线链路构成的分层网络系统结构,设计了兼容遥控、遥测、测量与网络交互支持等业务的基于IP over CCSDS(基于空间数据系统咨询委员会标准的空间链路承载互联网协议业务)的协议体系,给出了全系统基本通信业务运行模式等。与传统高轨卫星通信系统相比,该星座网络具有高覆盖、低时延、随遇接入等优点,可实现星座导航性能与中低轨及地面用户通信性能的全方位提升,相关结果对我国全球卫星导航星座网络技术研究具有一定的参考意义。     

一种基于粗糙K-均值的椭球基函数神经网络

改进了一种椭球基函数神经网络,它与经典椭球单元神经网络的结构不同,而与径向基函数神经网络结构类似,即它有一个隐含层,并且隐层单元采用椭球基函数,区别于RBF网络的高斯函数。本文采用粗糙K-均值方法求取椭球函数的中心,并给出了该方法中确定初始阈值的步骤。这种改进方法不但使对输入空间的划分局部作用,而且划分区域封闭有界。因此,改进的神经网络具有较好的函数逼近能力和模式识别能力。仿真实验验证了该椭球基函数神经网络的正确性和有效性。     

HSDB网络稳定性的研究

高速数据总线（ＨｉｇｈＳｐｅｅｄＤａｔａＢｕｓ，ＨＳＤＢ）是新一代航空电子系统的数据总路线，ＨＳＤＢ网络实际上是基于线性令牌总线的一个专用计算机局域网，在网络上负载较重且时间非苛刻的消息到达是一般过程的条件十分别讨论了网络的局域和全域稳定性，并导出了保证整个网络稳定的令牌握有定时器最小值，同时给出了如何根据应用去调整得到的理论值。     

一般高速数据总线网络的吞吐量分析

分析了一般高速数据总线网络优先权方案的特性，建立了一般ＨＳＤＢ网络多优先权的平均吞吐量模型，导出了不同优先权的平均吞吐量解析表达式，并通过软件仿真验证了由得到的解析表达式。     

面向任务的空间信息网络体系结构可重组设计

针对空间信息网络体系结构设计和其使命任务的多重性、多维性的问题,提出了一种体系结构可重组设计方法。首先,介绍了空间信息网络及其体系结构的国内外发展现状,分析了研究空间信息网络体系结构可重组设计的需求。其次,结合空间信息网络的概念、结构和特征,建立了松耦合、兼容性、隔离性和可解构的可重组设计原则。同时,定义了管理中心（RMC）和资源中心（RRC）的内涵,分析了RMC和RRC的运行机制,从目标、拓扑、实体、数据、方案5个维度出发提出了一种形式化的可重组网络体系结构模型,并给出了可重组网络体系结构的实现算法流程。最后,以某反导作战活动为例进行了体系结构可组构设计的案例分析,基于STK平台进行可重组网络的仿真演示,着重展示和验证了可重组设计思想在实践中的具体应用,达到了预期目的。     

Rotating machinery fault diagnosis based on convolutional neural network and infrared thermal imaging

Rotating machinery is widely applied in industrial applications. Fault diagnosis of rotating machinery is vital in manufacturing system, which can prevent catastrophic failure and reduce financial losses. Recently, Deep Learning (DL)-based fault diagnosis method becomes a hot topic. Convolutional Neural Network (CNN) is an effective DL method to extract the features of raw data automatically. This paper develops a fault diagnosis method using CNN for InfRared Thermal (IRT) image. First, IRT technique is utilized to capture the IRT images of rotating machinery. Second, the CNN is applied to extract fault features from the IRT images. In the end, the obtained features are fed into the Softmax Regression (SR) classifier for fault pattern identification. The effectiveness of the proposed method is validated using two different experimental data. Results show that the proposed method has a superior performance in identification various faults on rotor and bearings comparing with other deep learning models and traditional vibration-based method.     

Analysis of the dynamic behavior of structures using the high-rate GNSS-PPP method combined with a wavelet-neural model: Numerical simulation and experimental tests

Recently, the high rate global navigation satellite system-precise point positioning (GNSS-PPP) technique has been used to detect the dynamic behavior of structures. This study aimed to increase the accuracy of the extraction oscillation properties of structural movements based on the high-rate (10?Hz) GNSS-PPP monitoring technique. A developmental model based on the combination of wavelet package transformation (WPT) de-noising and neural network prediction (NN) was proposed to improve the dynamic behavior of structures for GNSS-PPP method. A complicated numerical simulation involving highly noisy data and 13 experimental cases with different loads were utilized to confirm the efficiency of the proposed model design and the monitoring technique in detecting the dynamic behavior of structures. The results revealed that, when combined with the proposed model, GNSS-PPP method can be used to accurately detect the dynamic behavior of engineering structures as an alternative to relative GNSS method.     

A new bearing fault diagnosis method based on modified convolutional neural networks

Fault diagnosis is vital in manufacturing system. However, the first step of the traditional fault diagnosis method is to process the signal, extract the features and then put the features into a selected classifier for classification. The process of feature extraction depends on the experimenters’ experience, and the classification rate of the shallow diagnostic model does not achieve satisfactory results. In view of these problems, this paper proposes a method of converting raw signals into two-dimensional images. This method can extract the features of the converted two-dimensional images and eliminate the impact of expert’s experience on the feature extraction process. And it follows by proposing an intelligent diagnosis algorithm based on Convolution Neural Network (CNN), which can automatically accomplish the process of the feature extraction and fault diagnosis. The effect of this method is verified by bearing data. The influence of different sample sizes and different load conditions on the diagnostic capability of this method is analyzed. The results show that the proposed method is effective and can meet the timeliness requirements of fault diagnosis.