基于进化神经网络的组合导航系统滤波算法研究

组合导航系统中,卡尔曼滤波算法的估计特性依赖于精确的系统模型和准确的外观测数据.任何一个条件不满足将导致滤波精度下降甚至发散.为此,本文引入进化神经网络和自适应卡尔曼滤波技术.仿真结果证明,文中所提算法能够有效克服常规卡尔曼滤波的缺点,并保持较高的精度.     

Load balancing strategy and its lookup-table enhancement in deterministic space delay/disruption tolerant networks

Space networks, in which connectivity is deterministic and intermittent, can be modeled by delay/disruption tolerant networks. In space delay/disruption tolerant networks, a packet is usually transmitted from the source node to the destination node indirectly via a series of relay nodes. If anyone of the nodes in the path becomes congested, the packet will be dropped due to buffer overflow. One of the main reasons behind congestion is the unbalanced network traffic distribution. We propose a load balancing strategy which takes the congestion status of both the local node and relay nodes into account. The congestion status, together with the end-to-end delay, is used in the routing selection. A lookup-table enhancement is also proposed. The off-line computation and the on-line adjustment are combined together to make a more precise estimate of the end-to-end delay while at the same time reducing the onboard computation. Simulation results show that the proposed strategy helps to distribute network traffic more evenly and therefore reduces the packet drop ratio. In addition, the average delay is also decreased in most cases. The lookup-table enhancement provides a compromise between the need for better communication performance and the desire for less onboard computation.     

Virtual sensing method for monitoring vibration of continuously variable configuration structures using long short-term memory networks

Vibration monitoring by virtual sensing methods has been well developed for linear time-invariant structures with limited sensors. However, few methods are proposed for Time-Varying (TV) structures which are inevitable in aerospace engineering. The core of vibration monitoring for TV structures is to describe the TV structural dynamic characteristics with accuracy and efficiency. This paper propose a new method using the Long Short-Term Memory (LSTM) networks for Continuously Variable Configuration Structures (CVCSs), which is an important subclass of TV structures. The configuration parameters are used to represent the time-varying dynamic characteristics by the “freezing” method. The relationship between TV dynamic characteristics and vibration responses is established by LSTM, and can be generalized to estimate the responses with unknown TV processes benefiting from the time translation invariance of LSTM. A numerical example and a liquid-filled pipe experiment are used to test the performance of the proposed method. The results demonstrate that the proposed method can accurately estimate the unmeasured responses for CVCSs to reveal the actual characteristics in time-domain and modal-domain. Besides, the average one-step estimation time of responses is less than the sampling interval. Thus, the proposed method is promising to on-line estimate the important responses of TV structures.     

基于模拟退火算法预测储层参数

讨论了如何将地震资料、测井信息和先验地质知识有机地结合起来预测储层参数，以便揭示储层内部细节和纵横向变化特征。通过将模拟退火算法与人工神经网络相结合，能够解决非线性全局优化问题，勾画出地震信息和储层参数之间的复杂关系，为储层特征描述和储层横向预测提供提供直观、可靠的地层参数剖面一孔隙度、砂泥质百分含量、含水饱和度及渗透率等剖面。     

分布交互仿真的一个原型系统—DIFTS

介绍了分布交互仿真的概念及其背景，分析了它的特点及涉及的关键技术，然后，设计了分布交互仿真的一个原型系统－ＤＩＦＴＳ，分析了ＤＩＦＴＳ的功能模型，并对其进行了功能分解，给出了各模块的功能，最后，介绍了系统的硬件组成及支持工具。     

基于神经网络的多模态故障检测

研究了使用RBF网络进行多模态辨识的结构和其在多模态故障检测中的应用。在信号满足持续激励的条件下 ,状态估计误差渐进趋向于零 ,辨识器的参数估计能够达到满意的效果。使用多个神经网络辨识器 ,对一非线性系统的多模态故障检测进行了仿真研究     

STABILITY OF THE HIGH-ORDER BAM AND ITS PERFORMANCE ANALYSIS

由Ｔａｉ提出的高阶双向联想存储器（ＨＯＢＡＭ）推广了Ｋｏｓｋｏ的ＢＡＭ，它不仅拥有ＢＡＭ的优点，而且放宽了连续性假定以得到可靠的回忆，并大大改进了ＢＡＭ的存储容量和纠错性能。然而，Ｔａｉ对于ＨＯＢＡＭ的性能分析仅限于计算机模拟，并没有给出理论分析结果。本文填写了此方面空白，并给出ＨＯＢＡＭ稳定性和存储容量的理论证明，使得该系统能从理论上得以保证所有训练模式对成为其稳定点。     

Information space of sensor networks: Lagrangian,energy-momentum tensor,and applications

It is a challenge to investigate the interrelationship between the geometric structure and performance of sensor networks due to the increasingly complex and diverse architecture of them. This paper presents two new formulations for the information space of sensor networks, including Lagrangian and energy–momentum tensor, which are expected to integrate sensor networks target tracking and performance evaluation from a unified perspective. The proposed method presents two geometric objects to represent the dynamic state and manifold structure of the information space of sensor networks. Based on that, the authors conduct the property analysis and target tracking of sensor networks. To the best of our knowledge, it is the first time to investigate and analyze the information energy–momentum tensor of sensor networks and evaluate the performance of sensor networks in the context of target tracking. Simulations and examples confirm the competitive performance of the proposed method.     

基于粒子滤波的结构疲劳裂纹扩展动态贝叶斯推断方法

准确预测结构的疲劳裂纹扩展过程是开展飞机单机寿命监控与剩余寿命估算的基础。提出一种基于动态贝叶斯网络的结构疲劳裂纹扩展预测方法,结合疲劳裂纹扩展的先验知识与后验知识来准确地推断裂纹长度;研究粒子滤波算法中不同粒子数对动态贝叶斯网络推断精度的影响规律;通过对单孔板结构与耳片连接结构件在随机载荷谱下进行裂纹扩展研究。结果表明：动态贝叶斯网络方法可以对复杂结构的疲劳裂纹扩展进行准确预测,预测精度相对于传统方法提高50% 以上。     

基于改进Yolov3 算法的舰船目标检测识别系统

针对复杂战场环境下对海目标检测识别的需求,设计了一种基于改进Yolov3 算法的海面舰船目标实 时检测识别系统。使用微调分类网络、增加训练尺度、聚类目标边框维度、二级特征分类等方法对Yolov3 检 测识别网络模型进行了优化,在提高识别精度的同时有效降低了漏检率和虚警率。实验结果表明,优化后的网 络模型在自建的舰船图像数据库中将检测识别平均准确率提高到了79.3%,对真实海上航拍视频中舰船目标识 别的平均准确率达到了81% 以上。