双盘式磁力耦合器振动噪声分析与实验研究

为了在双盘式磁力耦合器设计阶段分析振动噪声特性,优化双盘式磁力耦合器设计,提出了一种利用模态叠加原理来分析其振动噪声的方法。双盘式磁力耦合器具有高转矩密度与高效率等优势,因此逐渐发展成为煤矿机械柔性传动装置。由于双盘式磁力耦合内部的转子磁场非正弦分布以及涡电流谐波等因素影响,双盘式磁力耦合器输出转矩中不可避免的存在波动。若根据计算得出的双盘式磁力耦合器的电磁振动噪声特点,在设计时选取振动噪声小的参数进行优化组合,可在实际中降低其电磁振动及噪声,有利于减少制造成本。本文结合双盘式磁力耦合器的结构特征,提出了一种模态叠加响应法计算电磁振动噪声,建立了其电磁径向力的解析模型,并在多物理场耦合作用下分析主要电磁径向力波在工作频率内的谐波响应,最后在额定功率为55 kW双盘式磁力耦合器实验台上进行试验验证。基于麦克斯韦张量法,建立了双盘式磁力耦合器的径向电磁力解析模型,并得出0阶与10阶电磁径向力波是产生振动噪声的最主要原因;利用多物理场耦合分析法进行谐波响应NVH特性分析,结果显示0阶力波的振动加速度与形变量均大于10阶力波的振动加速度与形变量,因此双盘式磁力耦合器的电磁振动主要来源于0阶力波;在额定功率为55 kW,最高转速为1 500 r/min的双盘式磁力耦合器实验台进行振动测试,试验结果显示在1 500 r/min时,试验得出的最大振动峰值及频率约为35 m/s^2和4 950 Hz,与有限元仿真结果的误差对应为6.3%和1.1%;而当变频电机的输入转速依次增大时,振动加速度的理论值、仿真值与试验值的曲线形态较为接近,误差较小;噪声估计值与实测值的最大误差仅为8.9%,基本验证了本文所提出模态叠加法的正确性。     

带延迟索赔和随机收入的离散风险模型的Gerber-Shiu分析（英）

破产理论是保险数学中的重要问题，它可以为保险公司决策者提供一个非常有用的早期风险预警手段．本文研究了一个带潜在延迟索赔和随机保费收入的复合二项风险模型．利用矩母函数的技巧，得到了 Gerber-Shiu 期望折罚函数的递推公式．特别地，还得到了贴现因子为 1 的特殊情形下的 Gerber-Shiu 期望折罚函数的解析表达式．最后还得到了实际应用中的一些重要的破产特征量，包括破产概率，破产时赤字的密度函数，破产前盈余与破产时赤字的联合密度函数，以及导致破产的索赔密度函数等．     

非高斯噪声激励下非线性漂移Fokker-Planck方程的非稳态解及其应用

非高斯噪声广泛存在于各种非线性系统，对非高斯噪声所驱动系统的非稳态演化行为进行研究可以更为深入的了解其内在的演化机理．本文对非高斯噪声和高斯白噪声共同驱动的非线性动力学系统的非稳态演化问题进行研究．首先应用格林函数的 $\Omega$ 展开理论在初始区域对非线性动力学系统进行线性化，然后结合本征值和本征矢理论推导出了该系统 Fokker-Planck 方程的近似非稳态解的表达式，最后以 Logistic 系统模型为例分析了非高斯噪声强度，关联时间及非高斯噪声偏离参数对非稳态解以及一阶矩的影响．研究结果表明，用 Logistic 模型描述产品产量增长时，其非稳态解可更好地反映产品产量在不稳定点附近的演化行为．     

基于LSTM-RF模型的城市道路交通流预测

交通流预测作为信号协调和出行时间预测等任务的基础,成为了交通领域的研究点。对于交通流预测问题,研究人员提出了多种方法,但这些方法大多只使用交通流数据的时域信息进行交通流预测,忽略了空间相关性对于预测目标路段流的影响,导致预测精度不理想。基于组合模型的思想提出了一种称为LSTM-RF的交通流预测模型。在交通流预测过程中,首先使用LSTM模型提取预测目标路段的时序特征,再将其预测值与采集的相邻上下游路段信息同时作为随机森林模型的输入特征,进行交通流时空相关性分析,获得最终的预测结果。并通过贵阳市车牌识别系统采集的城区132条路段的交通流数据进行实验验证。结果表明:该方法在预测精度上优于单一模型,并且预测误差相比单一模型有明显减少。     

Stochastic stabilization and destabilization of nonlinear and time‐varying hybrid systems by noise

The aim of this article is to design a suitable strength function g(t,x,r(t)) such that the Wiener noise g(t,x(t),r(t))dw(t) either stabilizes or destabilizes a given nonlinear and time‐varying hybrid system . To this end, the basic properties, including the existence and uniqueness of the local and global solutions and the nonzero property of solutions of the nonlinear and time‐varying hybrid stochastic systems, are first investigated as the theoretical basis of the article. Second, two theorems and the corresponding corollaries on the stability and instability of the hybrid stochastic systems are established. Third, the design method for the noise strength g(t,x,r(t)) is then proposed based on the established theorems. We also point out that the Markov jump r(t) may have a stabilizing (respectively, destabilizing) effect when we design the noise strength g(t,x,r(t)) so that the introduced noise g(t,x(t),r(t))dw(t) stabilizes (respectively, destabilizes) the corresponding hybrid system. Finally, we illustrate our method using two examples. Compared with the existing literature, our method is suitable for a wider class of nonlinear and time‐varying systems with weaker conditions than quasi‐linear systems.     

基于深度语义分割的多源遥感图像海面溢油监测

针对遥感图像海面溢油区域通常受到斑噪声以及强度不均等因素的影响,从而导致溢油区域监测效果较差的问题,本文引入了深度语义分割的方法,将深度卷积神经网络与全连接条件随机场相结合,形成端对端连接。以Resnet结构为基础,首先通过深度卷积神经网络对多源遥感图像粗分割并作为输入,然后经过改进的全连接条件随机场,利用高斯成对势和平均场近似定理,建立条件随机场形成递归神经网络作为输出。通过多源遥感图像对海面溢油区域进行监测,并利用可见光图像估计溢油区域面积。实验在所建立的多源遥感图像数据集上与其它先进模型进行对比,结果表明本文方法提高了溢油区域的分割精度以及精细细节程度,平均交并比为82.1%,监测效果具有明显地改善。     

Kernel-based fourth-order diffusion for image noise removal

The fourth-order partial differential equations have good performance on noise smoothing and edge preservation without creating blocky effects on smooth regions. However, for low signal-to-noise ratio images, the discrimination between edges and noise is a challenging problem. A novel kernel-based fourth-order diffusion is proposed in this paper. It introduces a kernelized gradient operator in the fourth-order diffusion process, which leads to more effective noise removal capability. Experiment results show that this method outperforms several previous anisotropic diffusion methods for noise removal and edge preservation.     

Development of low-noise drag-type vertical wind turbines

In this study, the aerodynamic noise characteristics of Savonius wind turbines were investigated using hybrid computational aero-acoustics techniques, and low-noise designs were proposed based on the understanding of the noise generation mechanism. First, the flow field around the turbine was analyzed in detail by solving three-dimensional unsteady incompressible Reynolds-averaged Navier–Stokes equations using computational fluid dynamics techniques. Then, the aerodynamic noise radiating from the wind turbine was predicted using the Ffowcs Williams and Hawkings equation with the obtained flow field information. Two distinct harmonic noise components—the blade passing frequency (BPF) and harmonics with a fundamental frequency that is much higher than the BPF—were identified in the predicted noise spectrum. The origin of the higher harmonic components was found to be related to vortex shedding from the rotating turbine. Based on this finding, the proposed low-noise design for Savonius wind turbines uses S-shaped blades. S-shaped blades were found to reduce the noise levels of Savonius wind turbines by up to 2.7 dB.     

Random-valued impulse noise removal using adaptive dual threshold median filter

Noise detection and its removal is very important in the image processing. Detection of noise is very crucial and significant in random valued impulse noise because it does not hamper the image pixels uniformly. This paper presents a novel and unique concept of adaptive dual threshold for the detection of random valued impulse noise along with simple median filter at noise removal stage. Simulation results shows that an efficient noise detection leads to a superior quality of de-noised image as compared to existing adaptive threshold based image de-noising techniques. Proposed threshold computation is based on averaging of pixel values of window which enhances the PSNR of our system as compared to existing median filter based image de-noising methods.