基于模糊增强信息的图像边缘检测改进算法

针对经典的Pal和King模糊增强算法存在的固有缺陷,提出改进的模糊增强算法;为增强图像的对比度而获取更多的图像边缘细节信息,提出基于模糊对比度的图像增强算法;结合全局模糊增强与局部模糊对比度增强两种算法思想,提出基于模糊增强信息的图像边缘检测改进算法,由于该算法在增强图像边缘细节信息的同时也增强了噪声,因此,采用中值平滑滤波方法消除噪声.实验证明,该算法取得了良好的边缘检测效果.     

利用SG平滑滤波优化GNSS-R潮位反演

利用全球导航卫星系统反射(GNSS-R)信号进行潮位反演时,需要对多路径频率进行估计.常规反演方法仅对主频率估计,因此存在数据利用率低、反演结果时间分辨率不足的问题.为解决该问题,本文利用Savitzky-Golay(SG)平滑滤波优化GNSS-R潮位反演.首先,利用Lomb-Scargle周期图(LSP)法提取信号功率的前4个频率f₁~f₄,并反演它们对应的潮位值;然后,利用SG平滑滤波方法提取最佳反演结果;最后,以法国BRST站和MAYG站30 d的数据验证算法的有效性.通过与LSP法和窗口LSP(WINLSP)法进行对比,结果表明:相比LSP法,滤波后BRST站和MAYG站的日均反演值数量分别提升34.3%和19.6%,反演值的最大时间间隔分别减少43.2%和29.4%,RMSE值变化不大;相比WINLSP法,滤波后BRST站和MAYG站的日均反演值数量分别提升24.2%和45.9%,反演值最大时间间隔分别减少25.4%和28.6%,RMSE值均减少了7 cm.总体而言,该方法能够在保证精度的前提下提高反演结果的数量,同时提高了数据的利用率和潮位反演的时间分辨率.     

Wind power smoothing using fuzzy logic pitch controller and energy capacitor system for improvement Micro-Grid performance in islanding mode

The need of reducing CO₂ emissions in electricity generation field for solving global warming problems has led to increase interest in Micro-Grid (MG) especially the one with renewable sources such as solar and wind generations. Wind speed fluctuations cause high fluctuations in output power of wind turbine which cause fluctuations in fr and voltages of the MG in the islanding mode and originate stability problems. In this study, a new fuzzy logic pitch controller and an energy storage ultra capacitor are proposed and developed to smooth the output power of wind turbine and enhance MG's performance in islanding mode. These two proposed controllers are compared with the conventional PI pitch controller, which is usually used to control wind generation system when the wind speed exceeds a rated value. Obtained results proved that our two proposed strategies are effective for the MG performance improvement during islanding mode. All models and controllers are developed using Matlab^® Simulink^® environment.     

数据平滑技术在自动机运动规律测试中的应用

基于自动机运动规律测试参数测试条件,试验得出的数据存在高频随机噪声,需要进行剔除.论述了对大量数据应用滑动平均平滑法、局部加权散点修正平滑法及鲁棒的局部加权散点修正平滑法处理的原理,选取不同的平滑点数进行仿真,同时与实测数据直接微分比较,将三者与更高精度的仪器所得的数据特征点值进行比对,得出较为合理的平滑消噪的方法.     

智能语音识别系统中噪声估计算法的研究和改进

智能语音识别技术的研究已有较长的时间,但由于语音信号本身所具有的多变性、瞬时性、连续性和动态性的特征,使得机器在不同的环境尤其是噪声环境中进行语音信号的识别仍具有一定的困难.为了提高带噪语音信号识别的准确率,本文研究了一种常用的噪声估计算法,即基于后验信噪比的时间递归平均算法.并在此算法的基础上提出了一种对平滑因子的改进算法,将语音活性检测算法与这两种算法在不同输入信噪比下进行模拟验证.通过运算结果的对比分析可以看出,改进后的算法相比于语音活性检测算法最高可以使输出分段SNR提高2.1 dB,相比于原时间递归平均算法最高可以使输出分段SNR提高0.5 dB,表明低输入SNR下改进后的算法可以有效提高语音信号的质量和可懂度.     

利用空间平滑的协方差秩最小化DOA估计方法

针对传统波达方向角估计算法在相干信号及非均匀噪声下估计精度差、分辨率低的问题,基于空间平滑方法,提出一种接收信号协方差矩阵秩最小化波达方向估计方法．在传统空间平滑方法的基础上,所提算法将接收信号协方差矩阵分别左右乘交换矩阵以得到空间后向平滑协方差矩阵;而后基于平滑矩阵的低秩性,将协方差矩阵重构为无噪声协方差矩阵;最后利用传统MUSIC算法实现波达方向估计．仿真结果表明,与传统MUSIC算法、基于矩阵补全理论的MUSIC算法和秩迹最小化算法相比,所提算法能较好地抑制非均匀噪声影响,且在相干条件下具有较好的波达方向估计性能．     

The smoothed finite element method (S-FEM): A framework for the design of numerical models for desired solutions

The smoothed finite element method (S-FEM) was originated by G R Liu by combining some meshfree techniques with the well-established standard finite element method (FEM). It has a family of models carefully designed with innovative types of smoothing domains. These models are found having a number of important and theoretically profound properties. This article first provides a concise and easy-to-follow presentation of key formulations used in the S-FEM. A number of important properties and unique features of S-FEM models are discussed in detail, including 1) theoretically proven softening effects; 2) upper-bound solutions; 3) accurate solutions and higher convergence rates; 4) insensitivity to mesh distortion; 5) Jacobian-free; 6) volumetric-locking-free; and most importantly 7) working well with triangular and tetrahedral meshes that can be automatically generated. The S-FEM is thus ideal for automation in computations and adaptive analyses, and hence has profound impact on AI-assisted modeling and simulation. Most importantly, one can now purposely design an S-FEM model to obtain solutions with special properties as wish, meaning that S-FEM offers a framework for design numerical models with desired properties. This novel concept of numerical model on-demand may drastically change the landscape of modeling and simulation. Future directions of research are also provided.     

Adaptive simulation of wave propagation problems including dislocation sources and random media

An adaptive Tikhonov regularization is integrated with an h-adaptive grid-based scheme for simulation of elastodynamic problems, involving seismic sources with discontinuous solutions and random media. The Tikhonov method is adapted by a newly-proposed detector based on the MINMOD limiters and the grids are adapted by the multiresolution analysis (MRA) via interpolation wavelets. Hence, both small and large magnitude physical waves are preserved by the adaptive estimations on non-uniform grids. Due to developing of non-dissipative spurious oscillations, numerical stability is guaranteed by the Tikhonov regularization acting as a post-processor on irregular grids. To preserve waves of small magnitudes, an adaptive regularization is utilized: using of smaller amount of smoothing for small magnitude waves. This adaptive smoothing guarantees also solution stability without over smoothing phenomenon in stochastic media. Proper distinguishing between noise and small physical waves are challenging due to existence of spurious oscillations in numerical simulations. This identification is performed in this study by the MINMOD limiter based algorithm. Finally, efficiency of the proposed concept is verified by: 1) three benchmarks of one-dimensional (1-D) wave propagation problems; 2) P-SV point sources and rupturing line-source including a bounded fault zone with stochastic material properties.     

基于数字地图的导弹可飞行面设计

数字地图预处理是导弹基于数字地图导航的关键技术,提出了一种考虑导弹最大飞行曲率限制的变步长数字地图预处理算法,该算法分区域采用曲率差控制平滑迭代步长,使平滑过程加快,提高迭代速度,平滑后的地形不仅能满足导弹的纵向机动能力的限制,而且能够较好地贴近原地形。最后对算法进行了仿真研究,仿真结果表明该算法可快速完成数字地图的平滑任务,能够为导弹提供理想的可飞行安全曲面。