职业院校学生异性交往问题与策略研究——以航海专业男生班为例

以航海专业男生班为实例,从人性角度,运用心理学方法,着重分析其缺少异性交往所出现的问题,探讨引导航海专业男生在异性交往中健康成长的方法与途径,给学校教育管理者在正确对待和引导学生异性交往,提高科学化、人性化教育管理水平方面提供一定的参考。     

基于卡尔曼滤波器及神经网络的发动机故障诊断

提出了一种基于卡尔曼滤波器及神经网络的航空燃气涡轮发动机气路故障诊断的方法.该方法用卡尔曼滤波器来估计发动机可测参数的变化量,再由神经网络来映射发动机性能参数的变化量,并据此进行发动机气路故障诊断.数字仿真表明,该方法是可行的,有效的.      

温度对磁屏蔽霍尔推力器磁场构型的影响研究

霍尔推力器磁路设计主要通过常温静态磁场仿真得到，并实测推力器非工作状态常温磁场进行复核。大功率霍尔推力器将面临更为严峻的热问题，推力器工作时磁路系统受高温影响，因此在常温下仿真得到的磁场位形会因温度升高而产生偏移，不能反映推力器真实工作时的磁场情况。为研究霍尔推力器工作时热量对磁路系统的影响，通过热磁耦合仿真对10kW磁屏蔽霍尔推力器的热态磁场分布进行研究，并对热态、常温仿真结果进行了对比，发现在阳极附近的径向磁感应强度Br的差异比放电室出口更大。常温设计的磁屏蔽构型在热态时偏离磁屏蔽，磁场和壁面最大不符合度达到13%，通过陶瓷出口型面修正后重新获得磁屏蔽效果，使最大不符合度降低到4.8%以下。合理热设计有助于降低热载荷，热仿真得到磁路系统最高温度低于500℃，低于0.78倍的居里温度Tc磁性急剧转变点，不会出现磁性能急剧下降，但热量对磁屏蔽霍尔推力器磁场构型的影响是应该考虑的。     

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.