A novel feature extraction method for ship-radiated noise

To improve the feature extraction of ship-radiated noise in a complex ocean environment,a novel feature extraction method for ship-radiated noise based on complete ensemble empirical mode decomposition with adaptive selective noise(CEEMDASN) and refined composite multiscale fluctuation-based dispersion entropy(RCMFDE) is proposed.CEEMDASN is proposed in this paper which takes into account the high frequency intermittent components when decomposing the signal.In addition,RCMFDE is also proposed in this paper which refines the preprocessing process of the original signal based on composite multi-scale theory.Firstly,the original signal is decomposed into several intrinsic mode functions(IMFs)by CEEMDASN.Energy distribution ratio(EDR) and average energy distribution ratio(AEDR) of all IMF components are calculated.Then,the IMF with the minimum difference between EDR and AEDR(MEDR)is selected as characteristic IMF.The RCMFDE of characteristic IMF is estimated as the feature vectors of ship-radiated noise.Finally,these feature vectors are sent to self-organizing map(SOM) for classifying and identifying.The proposed method is applied to the feature extraction of ship-radiated noise.The result shows its effectiveness and universality.

Research on Feature Extraction of Remnant Particles of Aerospace Relays

The existence of remnant particles, which significantly reduce the reliability of relays, is a serious problem for aerospace relays. The traditional method for detecting remnant particles-particle impact noise detection （PIND）-can be used merely to detect the existence of the particle; it is not able to provide any information about the particles＇ material. However, information on the material of the particles is very helpful for analyzing the causes of remnants. By analyzing the output acoustic signals from a PIND tester, this paper proposes three feature extraction methods： unit energy average pulse durative time, shape parameter of signal power spectral density （PSD）, and pulse linear predictive coding coefficient sequence. These methods allow identified remnants to be classified into four categories based on their material. Furthermore, we prove the validity of this new method by processing P1ND signals from actual tests.

Micro-Doppler feature extraction of micro-rotor UAV under the background of low SNR

Micro-Doppler feature extraction of unmanned aerial vehicles(UAVs)is important for their identification and classification.Noise and the motion state of the UAV are the main factors that may affect feature extraction and estimation precision of the micro-motion parameters.The spectrum of UAV echoes is reconstructed to strengthen the micro-motion feature and reduce the influence of the noise on the condition of low signal to noise ratio(SNR).Then considering the rotor rate variance of UAV in the complex motion state,the cepstrum method is improved to extract the rotation rate of the UAV,and the blade length can be intensively estimated.The experiment results for the simulation data and measured data show that the reconstruction of the spectrum for the UAV echoes is helpful and the relative mean square root error of the rotating speed and blade length estimated by the proposed method can be improved.However,the computation complexity is higher and the heavier computation burden is required.

SIGNAL FEATURE EXTRACTION BASED UPON INDEPENDENT COMPONENT ANALYSIS AND WAVELET TRANSFORM

It is an important precondition for machine fault diagnosis that vibrationsignal can be extracted effectively. Based on the characteristic of noise interfused during thecourse of sampling vibration signal, independent component analysis (ICA) method is combined withwavelet to de-noise. Firstly, The sampled signal can be separated with ICA, then the function offrequency band chosen with multi-resolution wavelet transform can be used to judge whether thestochastic disturbance singular signal is interfused. By these ways, the vibration signals can beextracted effectively, which provides favorable condition for subsequent feature detection ofvibration signal and fault diagnosis.

Feature extraction of ship-radiated noise using higher-order spectrum

The features of the ship noises are analyzed by using the higher-order spectrum (HOS) after studying their distribution. The results show that the different ship noise has different ranges of the main frequency. The main frequencies of the first class ships are less than 120 Hz, while the second class ships drop in 130 Hz -- 320 Hz. The different relationship between w1 and w2 corresponds to different bispectrum graph. There are the same results in the trispectrum. The feature vector is consist of the wls which correspond to the maximum bispectrum B(wl, wl) and the maximum trispectrum B(wl, w1,wl) respectively, the al, w2 which correspond to the maximum bispectrum B(wl, w2).

A Novel De-noising Method Based on Discrete Cosine Transform and Its Application in the Fault Feature Extraction of Hydraulic Pump

Aiming at the existing problems of discrete cosine transform(DCT) de-noising method, we introduce the idea of wavelet neighboring coefficients(WNC) de-noising method, and propose the cosine neighboring coefficients(CNC) de-noising method. Based on DCT, a novel method for the fault feature extraction of hydraulic pump is analyzed. The vibration signal of pump is de-noised with CNC de-noising method, and the fault feature is extracted by performing Hilbert-Huang transform(HHT) to the output signal. The analysis results of the simulation signal and the actual one demonstrate that the proposed CNC de-noising method and the fault feature extraction method have more superior ability than the traditional ones.

An algorithm to remove noise from locomotive bearing vibration signal based on self-adaptive EEMD filter

An improved ensemble empirical mode decomposition(EEMD) algorithm is described in this work, in which the sifting and ensemble number are self-adaptive. In particular, the new algorithm can effectively avoid the mode mixing problem. The algorithm has been validated with a simulation signal and locomotive bearing vibration signal. The results show that the proposed self-adaptive EEMD algorithm has a better filtering performance compared with the conventional EEMD. The filter results further show that the feature of the signal can be distinguished clearly with the proposed algorithm, which implies that the fault characteristics of the locomotive bearing can be detected successfully.

Multi-Level Fusion in Ultrasound for Cancer Detection Based on Uniform LBP Features

Collective improvement in the acceptable or desirable accuracy level of breast cancer image-related pattern recognition using various schemes remains challenging.Despite the combination of multiple schemes to achieve superior ultrasound image pattern recognition by reducing the speckle noise,an enhanced technique is not achieved.The purpose of this study is to introduce a features-based fusion scheme based on enhancement uniform-Local Binary Pattern(LBP)and filtered noise reduction.To surmount the above limitations and achieve the aim of the study,a new descriptor that enhances the LBP features based on the new threshold has been proposed.This paper proposes a multi-level fusion scheme for the auto-classification of the static ultrasound images of breast cancer,which was attained in two stages.First,several images were generated from a single image using the pre-processing method.Themedian andWiener filterswere utilized to lessen the speckle noise and enhance the ultrasound image texture.This strategy allowed the extraction of a powerful feature by reducing the overlap between the benign and malignant image classes.Second,the fusion mechanism allowed the production of diverse features from different filtered images.The feasibility of using the LBP-based texture feature to categorize the ultrasound images was demonstrated.The effectiveness of the proposed scheme is tested on 250 ultrasound images comprising 100 and 150 benign and malignant images,respectively.The proposed method achieved very high accuracy(98%),sensitivity(98%),and specificity(99%).As a result,the fusion process that can help achieve a powerful decision based on different features produced from different filtered images improved the results of the new descriptor of LBP features in terms of accuracy,sensitivity,and specificity.

基于小波包分解与CEEMDAN能量熵的水电机组振动信号特征提取

针对水电机组振动信号非平稳、非线性及噪声问题,提出一种基于自适应噪声完备经验模态分解(CEEMDAN)与能量熵结合的特征提取方法,首先对采集的振动信号进行小波包降噪处理,然后对降噪后信号进行CEEMDAN分解,运用相关系数法筛选有效固有模态函数(IMF)并计算其能量熵,由此构建特征向量集,最后将其输入到海洋捕食者优化支持向量机算法(MPA-SVM)进行模式识别。基于模拟信号、实测信号验证所提特征提取方法的有效性,并与其他方法作对比。结果表明,基于小波包分解与CEEMDAN能量熵的特征提取方法能准确提取特征,有效区分机组不同状态,为工程领域提供了应用价值。

计算机图像智能识别下的割草机器人设计研究

为了提升割草机器人的工作效率、安全及自主性,基于堆叠降噪自动编码机设计了智能图像识别算法,用于实现割草机器人进行作业时自动化识别环境,以进一步提高工作效率。将割草机器人视觉传感器所采集的草地图像作为输入信号,通过叠加多层自动降噪编码机组成深度神经网络,可以深入挖掘草地图像所携带的信息,识别并提取图像特征。通过训练所建立网络,获得稳定输出,提高了割草机器人识别目标准确率。试验结果表明:本算法可进一步提高割草机器人识别准确率,从而提高工作效率。

面向辐射源识别的多尺度特征提取与特征选择网络

目前应用于辐射源识别的卷积神经网络对时序同相正交(in-phase and quadrature-phase,IQ)信号的处理有两种方式:一种方式是将其变换为图像,另一种方式是提取IQ时序数据的浅层特征。前一种方式会导致算法计算量大,而后一种方式会导致识别准确率低。针对上述问题,提出一种多尺度特征提取与特征选择网络。该网络以IQ信号为输入,经多尺度特征提取网络提取IQ信号的浅层特征和多尺度特征,采用特征选择网络降低多尺度特征的数据维度,通过自适应线性整流单元实现特征增强,使用单个全连接层对辐射源进行分类。在FIT/CorteXlab射频指纹识别数据集上,与ORACLE、CNN-DLRF和IQCNet对比实验表明,所提网络在一定程度上提高了识别准确率,降低了计算量。

CEEMDAN和盲源分离在轴承复合故障诊断中的应用

滚动轴承的复合故障信号中往往含有多个特征信息及背景噪声,为更高效实现故障信息的提取,提出一种基于具有自适应白噪声的完备集成经验模态分解(CEEMDAN)和盲源分离的滚动轴承复合故障特征提取方法。对实验所获取的故障数据进行CEEMDAN分解,得出一组固有模态函数(IMF),利用加权峭度因子选取其中有效IMF重构信号,再将重构的信号进行BSS分离。对分离出的信号做解调包络分析,从其解调谱中提取故障信号的特征频率。结果证明了此方法可以有效地分离轴承的内外圈故障,使故障特征更易被提取。

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环节,常采用传统的DEMON谱分析方法,处理过程中,一般对舰船噪声时域信号未予抑噪,低信噪比情况下,传统DEMON谱分析性能差。对此,提出一种采用遗传算法优化变分模态分解方法,用于分解舰船噪声原时域信号,获得抑制噪声后的舰船噪声重构信号,进而有效提取了舰船目标噪声幅度调制特征线谱。该方法首先采用遗传算法优化变分模态分解的两个关键输入参数(分解所取模态个数和惩罚因子),对变分模态分解得到的各阶固有模态分量加以判别,去除噪声主导分量,保留信号主导分量,使重构舰船噪声信号显著抑制了干扰噪声,然后对降噪后的重构信号进行频谱分析,获得目标噪声调制特征线谱。理论分析、仿真和实验数据处理结果表明,相比传统DEMON谱分析法,基于遗传算法优化变分模态分解的舰船噪声特征线谱提取方法具有更好的噪声抑制能力,所获取的舰船噪声幅度调制特征线谱信噪比明显高于传统DEMON方法,具有一定优势,前景良好。

Image Denoising Using Dual Convolutional Neural Network with Skip Connection

In recent years, deep convolutional neural networks have shown superior performance in image denoising. However, deep network structures often come with a large number of model parameters, leading to high training costs and long inference times, limiting their practical application in denoising tasks. This paper proposes a new dual convolutional denoising network with skip connections(DECDNet), which achieves an ideal balance between denoising effect and network complexity. The proposed DECDNet consists of a noise estimation network, a multi-scale feature extraction network, a dual convolutional neural network, and dual attention mechanisms. The noise estimation network is used to estimate the noise level map, and the multi-scale feature extraction network is combined to improve the model's flexibility in obtaining image features. The dual convolutional neural network branch design includes convolution and dilated convolution interactive connections, with the lower branch consisting of dilated convolution layers, and both branches using skip connections. Experiments show that compared with other models, the proposed DECDNet achieves superior PSNR and SSIM values at all compared noise levels, especially at higher noise levels, showing robustness to images with higher noise levels. It also demonstrates better visual effects, maintaining a balance between denoising and detail preservation.

基于状态划分和集成学习的轴承剩余使用寿命预测模型

针对滚动轴承剩余使用寿命(RUL)预测退化起始时间(DST)难以确定,以及单一寿命预测模型精度比较低的问题,提出了一种基于状态划分和集成学习模型的滚动轴承RUL预测方法。首先,提取了轴承振动信号的特征,利用滑动窗口不断更新3σ准则预警范围,结合连续触发机制自适应确定DST;然后,采用具有自适应噪声的完全集成经验模态分解(CEEMDAN)对退化阶段信号序列进行了自适应分解;最后,构建了集成学习模型,考虑分量的不同特性进行了多步滚动预测,融合预测结果得到了轴承RUL,采用滚动轴承XJTU-SY公开数据集进行了试验验证。研究结果表明:与基于长短时记忆神经网络(LSTM)、反向传播神经网络(BPNN)的预测方法相比,该方法预测结果的平均绝对误差分别降低了11.7%以及5.6%,相对均方根误差分别降低了12.2%以及10.7%,验证了该方法在轴承RUL预测中的有效性和优越性。

基于VMD与共振稀疏分解的舰船辐射噪声窄带特征提取

为了获取实测舰船辐射噪声信号中有效的目标信息、提高低信噪比条件下目标信号的可分性,文章提出了结合变分模态分解(Variational Mode Decomposition,VMD)和共振稀疏分解(Resonance-based Sparsity Signal Decomposition,RSSD)的舰船辐射噪声信号特征提取方法。基于舰船辐射噪声信号具有一定的周期性而外界干扰具有随机性的特点,首先利用VMD自相关分析的方法重构信号,主要剔除带外噪声分量;然后采用RSSD算法基于信号共振属性的不同,进一步滤除带内噪声和瞬态干扰,实现对信号中周期性振荡成分的提取;最后提取信号的波形结构特征用于目标的分类识别。仿真信号与实测信号分析表明,该方法可以较好地滤除带内外噪声,增强舰船辐射噪声信号固有的窄带特征。多类舰船目标的分类实验结果表明,该方法可以有效提高低信噪比信号的可分性,有利于提高目标识别的性能。

多视角手部肌肉疲劳动作智能识别方法仿真

干扰信号工频和谐波频率噪声会影响动作识别效果,为了提升手部肌肉的识别精度,提出基于肌电信号的多视角手部肌肉疲劳动作识别方法。利用肌电信号采集系统采集手部肌肉疲劳动作肌电信号,利用空域相关滤波算法优化肌电信号,消除干扰信号工频和谐波频率的生理噪声,提升动作识别精度。从时域和频域两个角度出发提取肌电信号特征,并输入支持向量机中,根据支持向量机的分类结果,实现多视角手部肌肉疲劳动作识别。实验结果表明,所提方法识别性能较好、识别精度较高,能够有效提升多视角下手部肌肉疲劳动作识别效果。

ALIF-NLM轴承微弱故障特征提取方法

针对强噪声背景下滚动轴承早期微弱故障特征难以提取的问题,结合自适应局部迭代滤波(Adaptive Local Iterative Filter,ALIF)和非局部均值(Non-Local Means,NLM)去噪方法的优势,提出了一种ALIF-NLM轴承微弱故障特征提取方法。首先,构建了加权峭度-能量比准则来筛选ALIF分解的本征模态函数(Intrinsic Mode Function,IMF)分量并重构信号。其次,结合峭度对冲击信号的敏感性同能量熵对信号能量分布均匀性和复杂程度的评价性能构建最小能量熵-峭度比指标,并以该指标为适应度函数,利用粒子群优化(Particle Swarm Optimization,PSO)算法实现了NLM方法中参数组合的自适应选取。最后,利用自适应NLM对重构信号进行故障特征提取。仿真和试验分析结果表明,该方法能有效提取出强噪声背景下的滚动轴承微弱故障特征信息。

混合高斯噪声条件下稀疏表示方法及其在冲击类故障特征提取中的应用

传统稀疏表示方法因其在冲击类信号特征提取中的独特优势而在故障诊断领域被广泛研究。然而,传统稀疏表示理论基于对干扰噪声的高斯分布假设,导致其难以适用于多种噪声分布混合的实际现场。针对上述问题,提出一种混合高斯噪声条件下的冲击类故障特征稀疏表示方法。基于传统稀疏表示理论的贝叶斯框架,借助混合高斯分布的万有逼近性质,建立了基于db4小波字典的混合高斯噪声稀疏分解模型,并推导了基于EM(Expectation-maximum,EM)和ADMM(Alternating direction method of multipliers,ADMM)的优化求解算法用于模型求解。仿真和实验结果表明,所提出的方法能够有效提取混合噪声干扰下的冲击类微弱故障特征信号。

基于深度聚类的通信辐射源个体识别方法

针对非合作通信条件下缺少标签数据的通信辐射源个体识别问题,提出了一种基于深度聚类的通信辐射源个体识别方法。利用自编码器网络强大的特征提取和数据重构能力对原始I/Q数据进行表征学习,提取个体识别的指纹特征,同时将表征学习过程和特征聚类过程进行联合优化,使表征学习和特征聚类契合度更高,更好地完成无标签条件下的通信辐射源个体识别。通过对5种ZigBee设备采集的信号进行实验,结果表明在信噪比高于0 dB时,可以达到85%以上的识别准确率,证明了本文方法的有效性和稳定性。