稀疏测点条件下的结构法向速度重建

为了实现稀疏测点条件下结构表面法向速度的准确重建,利用声辐射模态包含结构表面几何形状信息的性质,以声辐射模态作为基函数提出了一种稀疏测点条件下的结构法向振速重建方法。首先对结构表面的声辐射模态进行计算,并建立结构表面法向振动速度与声辐射模态之间的关系;在此基础上,由实际布置情况形成测点位置处的振速与其声辐射模态值的关系,并通过最小二乘法求得展开系数;最后由展开系数重建出结构表面的全部法向振速。利用两端封闭的双层钢质圆柱壳体在消声水池中进行了试验验证,分别开启激振器和转子台进行激励,两个试验的结果均表明,当测点数目较少时,所测的结果不能准确地表示结构的实际振动情况,在波数域内就表现为与振动相关的波数成份的丢失;利用所提出的方法,可以较为准确地重建和恢复结构的表面法向速度及其波数成份,由此验证了所提出方法的有效性。      

基于IFD与DE-ELM的轴承故障诊断

针对局部均值分解（LMD）实现过程中存在的模态混淆现象和端点效应,影响识别精度的问题,提出了一种基于本征频率尺度分解（IFD）与差分进化极限学习机（DE-ELM）的方法。该方法将数字图像处理的频率分辨率概念与LMD结合起来;首先确定原始振动信号中的所有局部极值点的频率分辨率,将振动信号分为高频率分辨率区域和低频率分辨率区域;然后,构造本征频率尺度函数,将本征频率尺度函数添加到局部极值点低频率分辨率区域;最后,对添加本征频率尺度函数的原始振动信号进行LMD分解,在得到的乘积函数（PF）分量中剔除本征频率尺度函数,就可以得到突出原始信号振动特征的不同频率分辨率的PF分量,提取PF分量的特征参数构建特征向量作为DE-ELM的输入,进行故障类型识别。将该方法应用于轴承故障诊断,与LMD相比,故障识别精度提高了8.33%,表明了该方法的有效性与可行性。     

声场重构的声辐射模态阶数分析

利用声辐射模态法对椭球状声源辐射声场进行重构,分析了模态阶数对相对误差的影响,以及模态阶数与频率的关系;同时深入探讨了声源形状变化对模态阶数选取的影响。研究发现利用较少的模态阶数即可得到很好的重构效果,且声源形状的变化对重构所需模态阶数的影响较小,因此可将该方法用于任意形状声源的重构,体现了此方法在声场重构中的优越性。     

人体肱二头肌静态等长收缩振动模态研究*

本文研究肱二头肌静态收缩振动规律。利用多物理场有限元仿真软件COMSOL建立肱二头肌仿真模型,计算肱二头肌在静态时的本征频率及其模态,同时检测静态负荷弯举的肱二头肌肌声信号。模拟结果显示肱二头肌静态等长收缩振动模态主要有5种,且随振动频率的升高,振动模态趋于复杂。实验测量反映了肱二头肌静态收缩肌声振动能量集中在超低频区。仿真模拟结果与实验统计数据进行对比分析,发现肱二头肌在超低频区域振动的模态在y轴方向受力伸缩的同时,出现x轴和z轴方向的振动。振动主峰频率平均在12 Hz。     

利用高分子压电薄膜设计声辐射模态传感器

首先对声辐射模态进行改进,使声辐射模态形状在中、低频时与频率无关。然后在此基础上,以振动梁为例,提出通过设计特定形状的高分子电压薄膜作为传感器测量声辐射模态的伴随系数。实验结果表明这种声辐射模态传感器的设计是可行的。     

开孔对封闭空间共振特性的影响

研究了开孔对封闭空间声场的影响。通过将孔内振动空气等效为点源,用模态展开法建立了开孔封闭空间的声场模型,计算了开孔封闭空间高阶共振频率和在共振频率激励下的声压分布。结果显示:开孔等效于孔处声质量减小,一般使得开孔封闭空间的共振频率增加;但当孔位于某模态节点时,由于该阶模态与任一模态在开孔处未发生耦合,该模态共振频率不变;由于在开孔区域对应于激励频率的模态声压和其余各阶模态声压之和的相位相反,高阶共振频率激励下靠近小孔位置的声压减小。因此,开孔对封闭空间声场有影响,其影响程度与开孔位置和开孔尺寸有关。     

利用声辐射模态对声场进行重构

声辐射模态是定义在振动表面上的一组互相独立的基函数,描绘了振动表面的多极子辐射模式。本文利用声辐射模态对平板辐射的声场进行重构，并同时就频率和模态数对误差的影响进行了分析。针对复杂振动表面幅射的声场，重构时需正则化处理。数值计算结果和对应的理论值达到很好的吻合。     

微型扬声器摆动模态对其辐射声场的影响*

与传统电动式扬声器单元相比,微型扬声器单元由于缺少定位支片等部件,更容易受到摆动模态的影响。该文利用激光传感器采集微型扬声器单元振膜各点振动位移,通过理论计算,从辐射声压级、辐射指向性等角度探究矩形微型扬声器单元摆动模态对其辐射声场的影响。经过研究发现,摆动模态会造成微型扬声器单元频响凹陷、中低频存在指向性等现象,对其辐射声场产生明显的影响。     

环筋对水下平底圆柱壳的声振特性影响

本文建立了计算两端带平底板的有限长圆柱壳水中声辐射的FEM／BEM三维模型，探索了加筋的高度、宽度、数目对平底圆柱壳的辐射功率、辐射效率、法向声强、声场指向性的影响规律。计算方法是在有限元软件ANSYS中做加筋平底圆柱壳建模、模态分析基础上，将有关数据（网格、模态）导入边界元软件SYSNOISE中计算流体结构耦合状态下的辐射声场特性。结果表明：（1）随着环筋高度、宽度增大，激励点声压峰和法向声强峰在0-400Hz频率范围内数目减少且峰向高频方向移动，同时辐射声功率在减小（除个别模态峰值外），而辐射效率随筋高增大而增大。（2）环筋数目的增加使激励点辐射声压和法向声强峰数目明显减少，使辐射声功率明显低于无筋圆柱壳的辐射声功率，辐射效率随环筋数目增大而增大。（3）环筋宽度变化对声场指向性影响不大；圆柱壳声场指向性随环筋高度和数目增加出现较大变化，尤其是在研究的频段内的f=51Hz和f=301Hz上。这对于水下结构辐射噪声预报以及噪声抑制具有重要意义。     

基于硅基微电子机械系统仿生振膜的光纤麦克风

仿蝇耳声传感器是一种对声压梯度敏感的指向性微型麦克风.本文设计制备了桥连耦合双翼形硅基微电子机械系统仿蝇耳振膜,利用该振膜制作了光纤Fabry-Pérot干涉式麦克风,并对这种麦克风的特性进行了理论与实验研究.仿真结果指出:这种仿生振膜具有摇摆和弯曲两种振动模态,单位声压下摇摆模态的振幅依赖于入射声波的频率与传播方向,频率越接近摇摆模态本征值,振幅越大;摇摆模态振幅随传播方向在三维空间的变化呈纺锤形分布,纺锤的长轴平行于振膜的长轴,意味着传播方向平行于振膜长轴时麦克风灵敏度最高.实验测得的光纤仿生麦克风的摇摆模态本征频率略小于仿真值,其输出信号振幅随声源水平方位角的变化呈“8”字形分布,在0°—60°方位角范围二者呈线性关系,由此得出麦克风的方向灵敏度为39.98 mV/(°).     

Sensor number requirements for sound power prediction using two different mode decomposition methods

Normal velocity distribution on the vibrating surface can be used for sound radiation prediction, where the normal velocity distribution is commonly reconstructed by using the velocities measured at some individual locations on the surface. In this communication, by considering the precision of the mode amplitude estimation, the number requirements of velocity sensors are compared between using the vibration mode decomposition method (VMD) and the radiation mode decomposition method (RMD) for sound power prediction. For low damped baffled simply supported rectangular plates, it is shown that employing RMD requires fewer sensors below the first resonance frequency than employing VMD, while employing VMD at structural resonance frequencies requires fewer sensors than that employing RMD.     

A 3D shape retrieval method for orthogonal fringe projection based on a combination of variational image decomposition and variational mode decomposition

The orthogonal fringe projection technique has as wide as long practical application nowadays. In this paper, we propose a 3D shape retrieval method for orthogonal composite fringe projection based on a combination of variational image decomposition (VID) and variational mode decomposition (VMD). We propose a new image decomposition model to extract the orthogonal fringe. Then we introduce the VMD method to separate the horizontal and vertical fringe from the orthogonal fringe. Lastly, the 3D shape information is obtained by the differential 3D shape retrieval method (D3D). We test the proposed method on a simulated pattern and two actual objects with edges or abrupt changes in height, and compare with the recent, related and advanced differential 3D shape retrieval method (D3D) in terms of both quantitative evaluation and visual quality. The experimental results have demonstrated the validity of the proposed method.     

遗传算法优化变分模态分解提取舰船辐射噪声特征线谱方法

特征线谱提取是舰船目标识别的一个重要研究方向。针对传统的DEMON(Detection of Envelope Modulation On Noise)谱分析对噪声抑制能力弱的缺点，提出了一种基于遗传算法(Genetic Algorithm, GA)优化变分模态分解(Variational mode decomposition, VMD)的舰船辐射噪声特征线谱提取方法。首先利用遗传算法对VMD的两个关键参数进行优化，经过VMD分解得到各阶固有模态分量(Intrinsic mode function, IMF)。其次，通过对各阶分量的判别，保留信号主导分量，去除噪声主导分量，从而实现对噪声干扰的抑制效果。最后，对降噪后的重构信号进行频谱分析获得目标噪声特征线谱。仿真和实验数据处理结果均表明，相比DEMON谱分析法，基于GA-VMD的特征线谱提取方法具有更好的噪声抑制能力，对舰船辐射噪声的特征线谱提取效果更优。     

Nanoscale mass detection based on vibrating piezoelectric ultrathin films under thermo-electro-mechanical loads

The potential applications of piezoelectric nanofilms (PNFs) and double-piezoelectric-nanofilm (DPNF) systems as nanoelectromechanical mass sensors are examined. The PNFs carrying multiple nanoparticles at arbitrary locations are modeled as rectangular nonlocal plates with attached concentrated masses. Using the nonlocal elasticity theory and Hamilton’s principle, the differential equations of motion are derived for both PNF-based and DPNF-based nanosensors. The influences of small scale, initial stress and temperature change on the frequency shifts of the nanoelectromechanical sensors are taken into consideration. Explicit expressions are derived for the resonance frequencies of the nanosensors by employing the Galerkin method. The present results show that when the value of nonlocal parameter decreases, the frequency shifts of piezoelectric nanosensors increase. Further, the frequency shifts of DPNF-based mass sensors are always greater than those of PNF-based mass sensors. The present work would be helpful in the design of nanoelectromechanical mass sensors using PNFs.     

基于变分模态分解的语音情感识别方法*

针对传统语音情感特征参数在进行情感分类时性能不佳的问题,该文提出了一种基于变分模态分解的语音情感识别方法。情感语音信号首先由变分模态分解提取固有模态函数,然后对所选主导固有模态函数进行重新聚合,再提取梅尔倒谱系数和各固有模态函数的希尔伯特边际谱。为了验证该文提出的特征性能,选用两种语音数据库(EMODB、RAVDESS)进行实验,按该文方法提取特征后使用极限学习机进行语音情感分类识别。实验结果表明:相比基于经验模态分解和集合经验模态分解的语音情感特征,该文提出的特征有更好的识别性能,验证了该方法的实用性。     

Rolling Bearing Fault Diagnosis Based on VMD-MPE and PSO-SVM

The goal of the paper is to present a solution to improve the fault detection accuracy of rolling bearings. The method is based on variational mode decomposition (VMD), multiscale permutation entropy (MPE) and the particle swarm optimization-based support vector machine (PSO-SVM). Firstly, the original bearing vibration signal is decomposed into several intrinsic mode functions (IMF) by using the VMD method, and the feature energy ratio (FER) criterion is introduced to reconstruct the bearing vibration signal. Secondly, the multiscale permutation entropy of the reconstructed signal is calculated to construct multidimensional feature vectors. Finally, the constructed multidimensional feature vector is fed into the PSO-SVM classification model for automatic identification of different fault patterns of the rolling bearing. Two experimental cases are adopted to validate the effectiveness of the proposed method. Experimental results show that the proposed method can achieve a higher identification accuracy compared with some similar available methods (e.g., variational mode decomposition-based multiscale sample entropy (VMD-MSE), variational mode decomposition-based multiscale fuzzy entropy (VMD-MFE), empirical mode decomposition-based multiscale permutation entropy (EMD-MPE) and wavelet transform-based multiscale permutation entropy (WT-MPE)).     

Vibration analysis of defected and pristine triangular single-layer graphene nanosheets

This paper investigates the vibration behavior of pristine and defected triangular graphene sheets; which has recently attracted the attention of researchers and compare these two types in natural frequencies and sensitivity. Here, the molecular dynamics method has been employed to establish a virtual laboratory for this purpose. After measuring the different parameters obtained by the molecular dynamics approach, these data have been analyzed by using the frequency domain decomposition (FDD) method, and the dominant frequencies and mode shapes of the system have been extracted. By analyzing the vibration behaviors of pristine triangular graphene sheets in four cases (right angle of 45-90-45 configuration, right angle of 60-90-30 configuration, equilateral triangle and isosceles triangle), it has been demonstrated that the natural frequencies of these sheets are higher than the natural frequency of a square sheet, with the same number of atoms, by a minimum of 7.6% and maximum of 26.6%. Therefore, for increasing the resonance range of sensors based on 2D materials, non-rectangular structures, and especially the triangular structure, can be considered as viable candidates. Although the pristine and defective equilateral triangular sheets have the highest values of resonance, the sensitivity of defective (45,90,45) triangular sheet is more than other configurations and then, defective (45,90,45) sheet is the worst choice for sensor applications.     

URLLC occasional large time delay prediction based on unbalanced regression and LSTM

Occasional large time delay is one of the bottlenecks for 5G applied to the industrial field. Accurate measurement, analysis, and prediction of communication network latency are of great significance. Due to the strong randomness and small number of samples in ultra-reliable and low latency communication (URLLC), the prediction task of occasional large time delay is very challenging. This paper proposes an URLLC occasional large time delay prediction method based on unbalanced regression algorithms and long–short term memory (LSTM). We first decompose the original sequence by using the variational mode decomposition (VMD) to obtain relatively stable component sequences. The parameters of the VMD are automatically optimized by grasshopper optimization algorithm (GOA). In order to improve the prediction effect of URLLC occasional large time delay, we introduce unbalanced regression algorithms. Before the VMD decomposition of data samples, the SMOGN is introduced to balance the number of large-time delay samples and common-time delay samples. Next we calculate the weight of each sample based on the rarity of each sample point and our proposed LDSWeight. Finally, LSTM is used to complete cost-sensitive learning. The experimental results show that the URLLC occasional large time delay prediction method proposed in this paper has better prediction accuracy than other methods.     

Whispering-gallery mode lasing from polymer microsphere for humidity sensing

Microlasers based on high quality(Q) whispering-gallery mode(WGM) resonance are promising low threshold laser sources for bio-sensing and imaging applications. In this Letter, dye-doped polymer microspheres were fabricated by a controlling emulsion solvent evaporation method. WGM lasing with low threshold and high Q factors was realized in an individual microsphere under femtosecond laser pumping. The slight change of environmental relative humidity(RH) can be monitored by measuring the shift of the lasing modes at the exposure of water molecules, which demonstrates the sensitivity is as high as 6 pm/RH%. The results would offer an insight into employing microlasers as sensors.     

基于改进经验模态分解域内心动物理特征识别模式分量的心电信号重建

心电图(electrocardiogram,ECG)诊断心脏疾病的严格标准,要求有效地消除噪声并准确地重建ECG信号.经验模式分解(empirical mode decomposition,EMD)方法重建ECG信号中,模式混叠及重建采用模式分量的识别以经验为基础,导致重建ECG信号准确度降低,且方法不具有自适应和通用性.本文首先基于积分均值定理提出一种改进的EMD方法——积分均值模式分解(integral mean mode decomposition,IMMD)方法,经5000个高斯白噪声样本的蒙特卡罗法验证,IMMD方法比EMD具有更优多分辨率分析能力,能够有效地缓解模式混叠.其次,基于ECG信号内固有心动物理特征量识别重建ECG信号所采用的模式分量,具有现实物理意义,因此,方法具有自适应和通用性.经验证,提出方法重建47例ECG信号与原ECG信号的相关系数中:31例优于变分模式分解方法;33例优于Haar小波软阈值法;42例优于集总经验模式分解方法;45例优于EMD方法.相关系数均值为0.8904,方差为0.0071,表现稳定且最优.