Adaptive investigation of the optical properties of polymer fibers from mixing noisy phase shifting microinterferograms using deep learning algorithms

In this article, an adaptive denoising method is suggested to accurate investigate the optical and structural features of polymeric fibers from noisy phase shifting microinterferograms. The mixed class of noise that may produce in the phase-shifting interferometric techniques is established. To our knowledge, this is an early study considered the mixing noises that may occur in microinterferograms. The suggested method utilized the convolution neural networks to detect the noise class and then denoising, it according to its class. Four convolution neural networks (Googlenet, VGG-19, Alexnet, and Alexnet–SVM) are refined to perform the automatic classification process for the noise class in the established data set. The network with the highest validation and testing accuracy of these networks is considered to apply the suggested method on realistic noisy microinterferograms for polymeric fibers, polypropylene and antimicrobial polyethylene terephthalate)/titanium dioxide, recoded using interference microscope. Also, the suggested method is applied on noisy microinterferograms include crazing and nanocomposite material. The demodulated phase maps and the three-dimensional birefringence profiles are calculated for tested fibers according to the suggested method. The obtained results are compared with the published data for these fibers and found to be in good agreements.     

一种基于全变分技术和Lp伪范数的椒盐噪声去除方法

针对传统各向同性全变分（Isotropy total variation，ITV）去噪算法容易导致图像边缘模糊、不易保持图像细节信息等问题，提出一种基于L _p 伪范数和各向同性全变分的图像去噪方法。该方法将L _p 伪范数代替ITV模型中的L ₁范数，利用交替方向乘子算法（Alternating direction method of multipliers, ADMM）将能量泛函拆解成若干个子问题，并将差分算子视为卷积算子；然后引入卷积定理和快速傅里叶变换(Fast Fourier transform, FFT)提高算法运算效率；最后通过Matlab进行仿真实验，运用图像质量的客观和主观评价方法进行评价分析。结果表明，本文方法能够较好地保留图像的边缘特性，有效提升去噪效果。     

数据驱动的机床等待过程节能方法研究

为了降低机床等待过程中的能耗，提出了一种实时数据驱动的机床等待时间预测与节能控制方法。首先，建立了射频识别驱动的生产进度评估方法，并以生产进度数据作为输入，构建了基于堆栈降噪自编码的机床等待时间预测模型；其次，依据预测的机床等待时间，提出了机床状态切换方法，以降低机床能耗；最后，通过一个电梯零部件制造车间的案例分析，表明该方法的预测误差仅为4.1%，同时将机床等待过程能耗降低了57%，实现了制造车间的节能减排。     

基于局部放电法的矿用高压电缆信号去噪算法研究

干扰噪声直接影响局部放电法有效检测矿用高压电缆故障。基于局部放电法，综合采用理论计算、仿真实验、现场试验的方法，对比分析了短时傅里叶变换和傅里叶分析去噪法的原理和优缺点，提出了一种矿用高压电缆的局部放电去噪算法——小波阈值去噪法，同时，选择了合理的阈值函数和去噪流程。基于此，采用白噪声和连续周期信号作为高压电缆的干扰噪声，进行了模拟仿真实验。结果表明，小波阈值去噪法可有效抑制白噪声，其中，Db2小波性能和去噪效果最好；同时，现场试验结果显示，去噪后信噪比得到了显著增加，验证了小波阈值去噪法的合理性和可靠性。     

Proposed network structures and combined adaptive algorithms for electrocardiogram signal denoising

An electrocardiogram (ECG) signal is a record of the electrical activities of heart muscle and is used clinically to diagnose heart diseases. An ECG signal should be presented as clear as possible to support accurate decisions made by doctors. This article proposes different combinations of combined adaptive algorithms to derive different noise-cancelling structures to remove (denoise) different kinds of noise from ECG signals. The algorithms are applied to the following types of noise: power line interference, baseline wander, electrode motion artifact, and muscle artifacts. Moreover, the results of the suggested models and algorithms are compared with those of conventional denoising tools such as the discrete wavelet transform, an adaptive filter, and a multilayer neural network (NN) to ensure the superiority of the proposed combined structures and algorithms. Furthermore, the hybrid concept is based on dual, triple, and quadruple combinations of well-known algorithms that derive adaptive filters, such as the least mean squares, normalized least mean squares and recursive least squares algorithms. The combinations are formulated based on partial update, variable step-size (VSS), and second iterative VSS algorithms, which are considered in different combinations. In addition, biased NN and unbiased linear neural network (ULNN) structures are considered. The performance of the different structures and related algorithms are evaluated by measuring the post-signal-to-noise ratio, mean square error, and percentage root mean square difference.     

A novel fusion paradigm for multi-channel image denoising

Multi-channel and single-channel image denoising are on two important development fronts. Integrating multi-channel and single-channel image denoisers for further improvement is a valuable research direction. A natural assumption is that using more useful information is helpful to the output results. In this paper, a novel multi-channel and single-channel fusion paradigm (MSF) is proposed. The proposed MSF works by fusing the estimates of a multi-channel image denoiser and a single-channel image denoiser. The performance of recent multi-channel image denoising methods involved in the proposed MSF can be further improved at low additional time-consuming cost. Specifically, the validity principle of the proposed MSF is that the fused single-channel image denoiser can produce auxiliary estimate for the involved multi-channel image denoiser in a designed underdetermined transform domain. Based on the underdetermined transformation, we create a corresponding orthogonal transformation for fusion and better restore the multi-channel images. The quantitative and visual comparison results demonstrate that the proposed MSF can be effectively applied to several state-of-the-art multi-channel image denoising methods.     

第二代小波降噪法在故障诊断中的应用

介绍了基于插值细分法的第二代小波变换的基本原理和变换过程,给出了小波降噪的阈值选取方法。用仿真信号进行验证,并将该算法应用于矿用通风机振动信号的降噪处理中,提高故障诊断的准确率。     

空空导弹导引头小波降噪参数优选仿真研究

在对空空导弹导引头含噪信号实施小波阈值降噪时,以提高信噪比为规则,优选了降噪参数,并进行了离线降噪处理和实时降噪处理两种模式的仿真实验。在离线降噪模式下,通过对降噪参数的选择优化,从强噪声背景中得到了与实际的视线角速度高度一致的有用信号。在实时降噪模式下,考虑到算法的复杂性要求对降噪参数的限制,有针对性地选取了计算比较简单的降噪参数,降噪后提高信噪比仍能达到5 dB以上。研究表明,利用小波降噪技术能够克服传统导引头降噪技术的固有缺陷,可以为导引头滤波器的设计与实现提供支持。     

基于梯度范数的暂态稳定评估模型的不平衡修正方法

为了解决电力系统中样本数量和质量不平衡造成的暂态稳定评估偏差问题,从评估模型的训练过程出发,通过预训练模型获得样本对模型参数修正的梯度范数,引入梯度范数均值比量化样本的不平衡程度,相较于先验信息,梯度范数均值比综合考虑了样本数量与样本质量的不平衡,并提出基于代价敏感法的不平衡修正方法,利用该方法改善模型的评估倾向性,以实现较好的修正效果。IEEE 39节点系统和华东电网系统的仿真结果验证了所提方法的有效性。     

自适应非局部3维全变分彩色图像去噪

目的 许多彩色图像去噪算法未充分利用图像局部和非局部的相似性信息,并且忽略了真实噪声在彩色图像不同区域内分布的差异,对不同图像块和不同颜色通道都进行同等处理,导致去噪图像中同时出现过平滑和欠平滑现象。针对这些问题,本文提出一种自适应非局部3维全变分去噪算法。方法 利用一个非局部3维全变分正则项获取彩色图像块内和块间的相似性信息,同时在优化模型的保真项内嵌入一个自适应权重矩阵,该权重矩阵可以根据每次迭代得到的中间去噪结果的剩余噪声来调整算法在每个图像块、每个颜色分量以及每次迭代中的去噪强度。结果 通过不同的高斯噪声添加方式得到两个彩色噪声图像数据集。将本文算法与其他6个基于全变分的算法进行比较,采用峰值信噪比(peak signal-to-noise ratio,PSNR)和结构相似性(structural similarity, SSIM)作为客观评价指标。相比于对比算法,本文算法在两个噪声图像数据集上的平均PSNR和SSIM分别提高了0.161.76 dB和0.12%6.13%,并获得了更好的图像视觉效果。结论 本文去噪算法不仅更好地兼顾了去噪与保边功能,而且提升了稳定性和鲁棒性,显示了在实际图像去噪中的应用潜力。