基于AOK-TFR的轨道电路故障诊断方法

为弥补现有补偿电容检测方法在检测及时性和检测成本等方面的不足,基于传输线理论,提出了机车信号感应电压幅值包络(IVECS)仿真模型;在此基础上,分析了补偿电容故障对IVECS的影响规律,建立并验证了基于频率的IVECS等效回归模型;利用补偿电容故障位置前后IVECS的频率变化,提出了基于自适应最优核时-频分布的补偿电容故障诊断方法.该方法基于机车信号的实际数据,采用B样条离散二进小波对数据进行滤波降噪处理,并利用自适应最优核时-频分布提取处理结果的时-频信息,再通过检测时-频信息中频率的变化实现对故障电容的定位.经过对随机抽取的100段实际数据的测试,准确率约为92.58%,表明该方法能准确检测轨道电路发生故障的补偿电容位置.     

一种自适应正则化参数和模数的图像去卷积方法

自适应正则化方法是解决图像去卷积问题中平滑噪声和保持边缘矛盾的一种方法,传统的自适应正则化方法只考虑到图像的整体信息而忽略了图像内部不同区域的细节信息,本文提出的自适应正则化方法,利用Katsaggelos自适应正则化参数,自适应的改变正则化参数,并通过图像内部不同区域的信息自适应改变正则化模数矩阵．从而实现了平滑噪声和保持边缘的平衡,既保持了边缘又抑制了噪声,取得了更好的去卷积效果．     

舰船电力系统自适应保护技术研究

随着舰船电力系统的不断发展,系统配置、网络结构和运行模式等方面较传统舰船电力系统都发生了较大的改变,以离线整定为特征的三段式电流保护方法已不能满足要求.在全面分析传统继电保护特性的基础上,提出舰船电力系统自适应保护策略,利用网络结构和系统状态自动在线调节保护整定值,可以有效地提高保护的灵敏性和选择性.     

船舶汽轮机组优化设计研究

汽轮机组是船舶动力装置的重要组成部分,其重量和尺寸是影响船舶动力装置重量体积及布置的重要因素.建立包括汽轮机、冷凝器和齿轮减速器设计三部分的船舶汽轮机组数学模型,以汽轮机组重量最小为目标函数和在给定的约束条件下,采用不同的优化算法对机组进行了优化设计.与原方案相比,采用优化方案后汽轮机组重量明显减小,改进的自适应遗传算法有更好的优化性能,优化效果显著.     

Human driving data-based design of a vehicle adaptive cruise control algorithm

This paper presents a vehicle adaptive cruise control algorithm design with human factors considerations. Adaptive cruise control (ACC) systems should be acceptable to drivers. In order to be acceptable to drivers, the ACC systems need to be designed based on the analysis of human driver driving behaviour. Manual driving characteristics are investigated using real-world driving test data. The goal of the control algorithm is to achieve naturalistic behaviour of the controlled vehicle that would feel natural to the human driver in normal driving situations and to achieve safe vehicle behaviour in severe braking situations in which large decelerations are necessary. A non-dimensional warning index and inverse time-to-collision are used to evaluate driving situations. A confusion matrix method based on natural driving data sets was used to tune control parameters in the proposed ACC system. Using a simulation and a validated vehicle simulator, vehicle following characteristics of the controlled vehicle are compared with real-world manual driving radar sensor data. It is shown that the proposed control strategy can provide with natural following performance similar to human manual driving in both high speed driving and low speed stop-and-go situations and can prevent the vehicle-to-vehicle distance from dropping to an unsafe level in a variety of driving conditions.     

战术数据链技术的发展趋势

根据未来信息化战争对战术数据链网络的需求,从多网结构、自适应能力、频段、高阶、宽带调制、一体化、抗电磁干扰、软件无线电结构、应用扩展和安全保密等方面论述了战术数据链技术的发展趋势.     

High-speed train control based on multiple-model adaptive control with second-level adaptation

Speed uplift has become the leading trend for the development of current railway traffic. Ideally, under the high-speed transportation infrastructure, trains run at specified positions with designated speeds at appointed times. In view of the faster adaptation ability of multiple-model adaptive control with second-level adaptation (MMAC-SLA), we propose one type of MMAC-SLA for a class of nonlinear systems such as cascaded vehicles. By using an input decomposition technique, the corresponding stability proof is solved for the proposed MMAC-SLA, which synthesises the control signals from the weighted multiple models. The control strategy is utilised to challenge the position and speed tracking of high-speed trains with uncertain parameters. The simulation results demonstrate that the proposed MMAC-SLA can achieve small tracking errors with moderate in-train forces incurred under the control of flattening input signals with practical enforceability. This study also provides a new idea for the control of in-train forces by tracking the positions and speeds of cars while considering power constraints.     

基于单神经元自适应PID的矢量控制系统研究

转子磁场定向矢量控制中,转速PI调节器在控制对象参数变化时其鲁棒性能较差,而单神经元具有自学习、自适应能力,为了进一步改善矢量控制系统的性能,文章提出了用单神经元自适应PID控制器代替传统PI调节器。为了提高单神经元PID控制器的学习能力,将无监督的Hebb学习规则与有监督的Delta学习规则相结合,实现单神经元控制器的参数优化与在线自调。仿真结果表明,该系统不仅具有很好的静、动态性能,而且还具有很强的自适应性和鲁棒性。