Error Analysis in Frequency Domain for Linear Multipass Algorithms

Error analysis methods in frequency domain are developed in this paper for determining the characteristic root and transfer function errors when the linear multipass algorithms are used to solve linear differential equations. The relation between the local truncation error in time domain and the error in frequency domain is established, which is the basis for developing the error estimation methods. The error estimation methods for the digital simulation model constructed by using the Runge-Kutta algorithms and the linear multistep predictor-corrector algorithms are also given.

Multi-scale seismic full waveform inversion in the frequency-domain with a multi-grid method

Although full waveform inversion in the frequency domain can overcome the local minima problem in the time direction, such problem still exists in the space direction because of the media subsurface complexity. Based on the optimal steep descent methods, we present an algorithm which combines the preconditioned bi-conjugated gradient stable method and the multi-grid method to compute the wave propagation and the gradient space. The multiple scale prosperity of the waveform inversion and the multi-grid method can overcome the inverse problems local minima defect and accelerate convergence. The local inhomogeneous three-hole model simulated results and the Marmousi model certify the algorithm effectiveness.

Optimized dithering technique in frequency domain for high-quality three-dimensional depth data acquisition

On the basis of the objective functions,dithering optimization techniques can be divided into the intensity-based optimization technique and the phase-based optimization technique.However,both types of techniques are spatial-domain optimization techniques,while their measurement performances are essentially determined by the harmonic components in the frequency domain.In this paper,a novel genetic optimization technique in the frequency domain is proposed for highquality fringe generation.In addition,to handle the time-consuming difficulty of genetic algorithm(GA),we first optimize a binary patch,then join the optimal binary patches together according to periodicity and symmetry so as to generate a full-size pattern.It is verified that the proposed technique can significantly enhance the measured performance and ensure the robustness to various amounts of defocusing.

A Bregman adaptive sparse-spike deconvolution method in the frequency domain

To improve the anti-noise performance of the time-domain Bregman iterative algorithm,an adaptive frequency-domain Bregman sparse-spike deconvolution algorithm is proposed.By solving the Bregman algorithm in the frequency domain,the influence of Gaussian as well as outlier noise on the convergence of the algorithm is effectively avoided.In other words,the proposed algorithm avoids data noise effects by implementing the calculations in the frequency domain.Moreover,the computational efficiency is greatly improved compared with the conventional method.Generalized cross validation is introduced in the solving process to optimize the regularization parameter and thus the algorithm is equipped with strong self-adaptation.Different theoretical models are built and solved using the algorithms in both time and frequency domains.Finally,the proposed and the conventional methods are both used to process actual seismic data.The comparison of the results confirms the superiority of the proposed algorithm due to its noise resistance and self-adaptation capability.

Analysis of OSA Syndrome from PPG Signal Using CART-PSO Classifier with Time Domain and Frequency Domain Features

Obstructive Sleep Apnea(OSA)is a respiratory syndrome that occurs due to insufficient airflow through the respiratory or respiratory arrest while sleeping and sometimes due to the reduced oxygen saturation.The aim of this paper is to analyze the respiratory signal of a person to detect the Normal Breathing Activity and the Sleep Apnea(SA)activity.In the proposed method,the time domain and frequency domain features of respiration signal obtained from the PPG device are extracted.These features are applied to the Classification and Regression Tree(CART)-Particle Swarm Optimization(PSO)classifier which classifies the signal into normal breathing signal and sleep apnea signal.The proposed method is validated to measure the performance metrics like sensitivity,specificity,accuracy and F1 score by applying time domain and frequency domain features separately.Additionally,the performance of the CART-PSO(CPSO)classification algorithm is evaluated through comparing its measures with existing classification algorithms.Concurrently,the effect of the PSO algorithm in the classifier is validated by varying the parameters of PSO.

Watermarking Images in the Frequency Domain by Exploiting Self-Inverting Permutations

In this work we propose efficient codec algorithms for watermarking images that are intended for uploading on the web under intellectual property protection. Headed to this direction, we recently suggested a way in which an integer number w which being transformed into a self-inverting permutation, can be represented in a two dimensional (2D) object and thus, since images are 2D structures, we have proposed a watermarking algorithm that embeds marks on them using the 2D representation of w in the spatial domain. Based on the idea behind this technique, we now expand the usage of this concept by marking the image in the frequency domain. In particular, we propose a watermarking technique that also uses the 2D representation of self-inverting permutations and utilizes marking at specific areas thanks to partial modifications of the image’s Discrete Fourier Transform (DFT). Those modifications are made on the magnitude of specific frequency bands and they are the least possible additive information ensuring robustness and imperceptiveness. We have experimentally evaluated our algorithms using various images of different characteristics under JPEG compression. The experimental results show an improvement in comparison to the previously obtained results and they also depict the validity of our proposed codec algorithms.

Detection of Underwater Objects by Adaptive Threshold FCM Based on Frequency Domain and Time Domain

According to the characteristics of sonar image data with big data feature, In order to accurately detect underwater objects of sonar image, a novel adaptive threshold FCM (Fuzzy Clustering Algorithm, FCM) based on frequency domain and time domain is proposed. Based on the relationship between sonar image data and big data, Firstly, wavelet de-noising method is used to smooth noise. After de-noising, the sonar image is blocked and each sub-block region is processed by two-dimensional discrete Fourier transform, their maximum amplitude spectrum used as frequency domain character, then time domain of mean and standard deviation, frequency domain of maximum amplitude spectrum are taken for character to complete block k-means clustering, the initial clustering center is determined, after that made use of FCM on sonar image detection, based on clustered image, adaptive threshold is constructed by the distribution of sonar image sea-bottom reverberation region, and final detection results of sonar image are completed. The comparison different experiments demonstrate that the proposed algorithm get good detection precision and adaptability.

IMAGING ANALYSIS OF FREQUENCY MODULATION CONTINUOUS WAVE SYNTHETIC APERTURE RADAR IN NEAR RANGE MODE

Frequency-Modulation Continuous-Wave Synthetic Aperture Radar(FMCW SAR)has shown great potential in the applications of civil and military fields because of its easy deployment and low cost.However,most of these work and analysis are concentrated on airborne FMCW SAR,where the characteristics of the imaging geometry and signal are much similar to that of traditional pulsed-SAR.As a result,a series of test campaigns of automobile-based FMCW SAR were sponsored by Institute of Electronics,Chinese Academy of Sciences(IECAS)in the autumn of 2012.In this paper,we analyze the imaging issues of FMCW SAR in automobile mode(named as near range mode),where a vehicle is used as moving platform and a large looking angle is configured.The imaging geometry and signal properties are analyzed in detail.We emphasize the difference of the near range mode from the traditional airborne SAR mode.Based on the analysis,a focusing approach is proposed in the paper to handle the data focusing in the case.Simulation experiment and real data of automobile FMCW SAR are used to validate the analysis.

融合多特征信息与GWO-SVM的机械关键设备故障诊断

为了提高机械关键设备故障诊断的精度,建立机械关键设备故障诊断模型。文章提出一种融合机械关键设备故障信号多特征信息与灰狼优化算法(Grey Wolf Optimization Algorithm,GWO)改进支持向量机(Support Vector Machine,SVM)(GWO-SVM)的机械关键设备故障诊断模型。首先,提取机械关键设备故障信号的时域特征、频域特征和多尺度加权排列熵特征,分别对比不同特征的机械关键设备故障诊断结果。其次,为提高SVM模型性能,运用GWO算法对SVM模型的惩罚参数P和核函数参数g进行优化选择,提出一种融合多特征信息与GWO-SVM的机械设备故障诊断模型。与GA-SVM、PSO-SVM和SVM相比,基于GWO-SVM的机械设备故障诊断模型的诊断精度最高。这里算法可以有效提高机械关键设备故障诊断正确率,为机械关键设备故障诊断提供了新的方法。

基2-FFT输入分级截断算法在频域合成孔径超声成像中的研究

为提高超声频域成像算法的计算速度,提出一种应用于超声频域成像算法的基2-FFT输入分级截断算法。首先,借助于COMSOL多物理场仿真软件,建立钢件中含有孔缝缺陷的有限元模型进行声场仿真。仿真结果得到关于缺陷的回波信号,并通过PSM算法对频域内声场进行重建,得到成像区域的聚焦图像,和原始仿真信号的B扫图像相比效果更加直观且成像质量更好,验证了PSM算法的可行性。然后为了避免超声频域成像算法中二维傅里叶变换的冗余计算,进一步提出了支持任意非0值输入的基2-FFT输入分级截断算法。实验结果证明,基2-FFT输入分级截断算法比标准基2-FFT算法快27%,超声频域算法成像速度提高13%。

基于位移逆有理Krylov子空间算法的频率域可控源电磁快速正演

本文开发了一种双重复极点位移逆(SAI)有理Krylov子空间算法,实现了频率域可控源电磁法(CSEM)多频电磁场值的快速计算.在有理Krylov子空间算法中,极点选择是保障CSEM正演精度的关键,单重复极点有理Krylov子空间算法在频率域可控源电磁法应用最为广泛,但其缺点在于正演计算频段范围有限,增加极点个数能够获取更宽频段响应.为此,立足于有理Krylov子空间基本理论,推导了多重复极点位移逆算法Rayleigh商一阶秩修改公式,并利用该模型降阶算法开展了可控源电磁法正演模拟.另外,本文利用粒子群算法求解多极点收敛率函数,可以快速获取最优多极点,从而确保正演模拟精度.相比于单重复极点,多重复极点位移逆算法会增加极点计算时间,但能在更宽的频率范围内准确计算电磁场值.根据频率范围选定合适的极点后,该方法仅需要求解与极点数相同的多个线性方程组,通过场源项和系数矩阵求得有理Krylov子空间,再将正演算子投影到有理Krylov子空间中,显著降低正演算子的自由度,提高多频正演的计算效率.设计了均匀半空间和块状异常模型,并开展了算法测试,计算结果表明:在保证精度的情况下,相比于常规矢量有限元算法,单重复极点、双重复极点位移逆模型降阶算法的加速比超过10倍以上;双重复极点位移逆模型降阶算法总体计算精度要优于相应的单重复极点算法,且具有更宽的计算频带.

面向新型声波激励超低频天线的2FSK通信系统解调算法

针对声波激励超低频天线发射功率较小,低接收信噪比下传统二进制频移键控(2FSK)非相干解调中窄带滤波器设计难等问题,提出了一种基于Duffing混沌振子的2FSK弱信号解调方法。由于Duffing振子本身对相位敏感,首先设计了“粗同步+细同步”的相位同步方法,其中粗同步通过短时傅里叶变换完成,细同步则通过Duffing混沌振子实现;然后,利用混沌振子对同频周期信号敏感、对噪声信号免疫的特性,将信号并行通过2个Duffing振子,二者的内置信号频率与2个载频一致。在某一时刻必定有一个系统处于大尺度周期态,另一个处于混沌态,二者比较判决来实现信号解调。仿真结果表明,文中提出的基于Duffing混沌振子的解调算法比非相干解调和相干解调算法,分别有约5 dB和4 dB的性能提升。

融合TKEO和Goerztel算法的感应电机转子断条故障诊断

针对电机电流信号特征分析进行转子断条故障诊断时,在低负载工况或早期,转子断条故障存在故障特征微弱以及难以分离等问题,同时需借助速度传感器获取转速信息,增加了硬件成本,该文提出了融合Teager-Kaiser能量算子(TKEO)和Goertzel算法的感应电机转子断条故障诊断方法。首先,采用Teager-Kaiser能量算子对定子电流进行解调处理,将基频分量转化为直流分量并去除,实现边带故障特征分量到转子断条故障特征分量以及固有偏心谐波分量到转子速度谐波分量的转变,抑制基频频谱泄露对故障诊断和转速估计造成的干扰;其次,利用Goertzel算法对归一化解调信号进行频谱分析,完成转子断条故障特征分量和转子速度谐波分量的快速提取,进而实现不同负载和不同严重程度工况下转子断条的故障诊断。此外,与归一化频域能量算子进行比较,分析其计算效率和稳定性。实验结果表明,所提方法在不同负载和不同严重程度工况下均能够有效地识别转子断条故障特征分量,并且计算量少、可靠性高、实时性强以及硬件成本低。

永磁同步电动机无位置传感器全速域控制仿真实验设计

为了解决现有永磁同步电动机(PMSM)无位置传感器控制方法存在的位置估计精度低、各速域切换不平稳问题,提出了一种全速域复合控制策略。在低速域采用脉振高频电压注入法获取准确的转子位置。在中高速域采用基于快速超螺旋滑模(FSTSM)算法的模型参考自适应系统(MRAS)无传感器控制方法,通过增加反馈校正环节加快误差的收敛;采用FSTSM算法替代比例-积分(PI)环节,并引入双曲正切函数代替FSTSM算法中的符号函数。仿真实验结果验证了所提控制策略的可行性和有效性。

高分辨率地震勘探数据的噪声抑制算法研究

近年来,随着地震勘探技术的发展,高分辨率地震勘探数据成为地震勘探的重要数据源,这些数据提供了更细致、更清晰的地下结构信息,对于提高资源勘探的精度和效率具有重要意义。然而,在地震数据处理过程中,不可避免地存在噪声,严重影响了地震数据的分辨率和准确性。因此,为获得高分辨率地震数据,噪声抑制的重要性显而易见。基于此,详细讨论各类噪声的特性,探讨当前主要噪声抑制算法,比较这些算法的性能,对提高高分辨率地震勘探数据的质量,进一步揭示地下复杂结构信息,提高地震勘探和资源勘查准确性具有重要意义。

基于频率域峭度谱迭代阈值的多尺度形态学轴承故障诊断方法

在复杂恶劣的环境中,存在滚动轴承故障信息难以准确提取的问题。因此,引用多尺度数学形态学对滚动轴承故障诊断开展研究。由于小尺度形态学滤波能较好的保存信号的细节特征,大尺度形态学滤波能有效抑制噪声,因此为了能更好的兼顾噪声抑制和故障特征信息保存,采用迭代阈值的方法选取尺度范围,运用频率域峭度谱方法计算出阈值,然后通过迭代自适应获取最佳尺度区间。多尺度形态学信号重构加权方法引用加权多尺度形态梯度算法,该方法能保证小尺度具有较小的权重,大尺度具有大权重。通过仿真和实验表明:多尺度形态学有效检测出滚动轴承故障信号,深度挖掘滚动轴承故障特征信息。

车载变压器油纸绝缘受潮状态的评估方法研究

车载变压器内部油纸绝缘状况是影响动车运行的重要因素,采用FDS法测试了不同受潮程度车载变压器油纸绝缘试样,用修正的Havriliak-Negami介电弛豫模型分析模型参数随水分的变化,利用灰狼算法从模型参数中提取能够表征参量,对水分含量进行评估,对测试得到的水分含量为2.40%的车载变压器油纸绝缘样品进行评估。结果表明:提出的方法能够对车载变压器油纸绝缘的水分含量进行有效评估,利用该评估方法与实测值的误差在0.8%,为车载变压器油纸绝缘受潮状态评估提供了一种新的方法。

基于MFCC提取和DTW优化的连续音频识别算法设计

介绍了一种新型的利用梅尔频率倒谱系数(MFCC)提取和动态时间规整技术(DTW)优化的连续音频识别算法。首先对数学原理与算法步骤进行设计与规划,使用大规模音频数据库进行预处理,经过时域和频域分析提取相应的特征;然后利用双门限法把连续音频切分为不同的音频块,并对切分部分进行针对性识别,将其与时频域数据库的模板进行匹配比对,实现了较好的连续音频识别效果,在时域和频域识别上的准确性均能达到89%。该研究成果可应用于钢琴教学系统的开发,尤其是在辅助学习者正确弹出曲谱方面具有广阔的应用前景。

Research on Location Characteristics and Algorithms based on Frequency Domain for a 2D Underwater Active Electrolocation Positioning System

Weakly electric fish has an ability to generate a low-frequency electric field actively to locate the surrounding object in complete darkness by sensing the change of the electric field. This ability is called active electrolocation. In this paper, we designed a two-dimensional （2D） experimental platform of underwater active electrolocation system by simulating weakly electric fish. On the platform, location characteristics based on frequency domain were investigated. Results indicated that surface shape of 3D location characteristic curves for the 2D underwater active electrolocation positioning system was convex upwards or concave down which was influenced by the material of probed objects and the frequency of the electric field exci- tation signal. Experiments also confirmed that the amplitude of the electric field excitation signal and the size of the probed object will only influence the amplitude corresponding to 3D location characteristic curves. Based on above location charac- teristics, we present three location algorithms including Cross Location Algorithm （CLA）, Stochastic Location Algorithm （SLA） and Particle Swarm Optimization （PSO） location algorithm in frequency domain and achieved the task of the underwater positioning system. Our work may have reference value for underwater detection study.

A nonlinear model reference adaptive inverse control algorithm with pre-compensator

In this paper, the reduced-order modeling （ROM） technology and its corresponding linear theory are expanded from the linear dynamic system to the nonlinear one, and H∞ control theory is employed in the frequency domain to design some nonlinear system＇ s pre-compensator in some special way. The adaptive model inverse control （AMIC）theory coping with nonlinear system is improved as well. Such is the model reference adaptive inverse control with pre-compensator （PCMRAIC）. The aim of that algorithm is to construct a strategy of control as a whole. As a practical example of the application, the nunlerical simulation has been given on matlab software packages. The numerical result is given. The proposed strategy realizes the linearization control of nonlinear dynamic system. And it carries out a good performance to deal with the nonlinear system.