Working condition recognition of sucker rod pumping system based on 4-segment time-frequency signature matrix and deep learning

High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an efficient diagnosis method.However,the input of the DC as a two-dimensional image into the deep learning framework suffers from low feature utilization and high computational effort.Additionally,different SRPSs in an oil field have various system parameters,and the same SRPS generates different DCs at different moments.Thus,there is heterogeneity in field data,which can dramatically impair the diagnostic accuracy.To solve the above problems,a working condition recognition method based on 4-segment time-frequency signature matrix(4S-TFSM)and deep learning is presented in this paper.First,the 4-segment time-frequency signature(4S-TFS)method that can reduce the computing power requirements is proposed for feature extraction of DC data.Subsequently,the 4S-TFSM is constructed by relative normalization and matrix calculation to synthesize the features of multiple data and solve the problem of data heterogeneity.Finally,a convolutional neural network(CNN),one of the deep learning frameworks,is used to determine the functioning conditions based on the 4S-TFSM.Experiments on field data verify that the proposed diagnostic method based on 4S-TFSM and CNN(4S-TFSM-CNN)can significantly improve the accuracy of working condition recognition with lower computational cost.To the best of our knowledge,this is the first work to discuss the effect of data heterogeneity on the working condition recognition performance of SRPS.

Partition-Time Masking:一种唇语识别数据增强方法

提出了一种唇语识别数据增强方法Partition-Time Masking。该方法直接作用于输入数据,通过将输入划分为多个子序列再分别进行Mask操作最后再将各子序列按序拼接,使得模型能对部分帧缺失的输入具有更强的鲁棒性,从而增强泛化能力。实验前根据划分的子序列数目与掩码值来源不同而设计了5种增强策略,并与唇语识别研究中最重要的数据增强方法Time Masking进行了对比实验。实验在LRW数据集和LRW1000数据集上进行,实验结果表明Partition-Time Masking方法对模型性能提升的效果要优于Time Masking方法,其中子序列数目为3、掩码值选择各子序列平均帧时为最优策略,该策略使得目前最佳的唇语识别模型DC-TCN的性能从89.6%提高到90.0%。

The W transform and its improved methods for time-frequency analysis of seismic data

The conventional linear time-frequency analysis method cannot achieve high resolution and energy focusing in the time and frequency dimensions at the same time,especially in the low frequency region.In order to improve the resolution of the linear time-frequency analysis method in the low-frequency region,we have proposed a W transform method,in which the instantaneous frequency is introduced as a parameter into the linear transformation,and the analysis time window is constructed which matches the instantaneous frequency of the seismic data.In this paper,the W transform method is compared with the Wigner-Ville distribution(WVD),a typical nonlinear time-frequency analysis method.The WVD method that shows the energy distribution in the time-frequency domain clearly indicates the gravitational center of time and the gravitational center of frequency of a wavelet,while the time-frequency spectrum of the W transform also has a clear gravitational center of energy focusing,because the instantaneous frequency corresponding to any time position is introduced as the transformation parameter.Therefore,the W transform can be benchmarked directly by the WVD method.We summarize the development of the W transform and three improved methods in recent years,and elaborate on the evolution of the standard W transform,the chirp-modulated W transform,the fractional-order W transform,and the linear canonical W transform.Through three application examples of W transform in fluvial sand body identification and reservoir prediction,it is verified that W transform can improve the resolution and energy focusing of time-frequency spectra.

Rapid identification of time-frequency domain gravitational wave signals from binary black holes using deep learning

Recent developments in deep learning techniques have provided alternative and complementary approaches to the traditional matched-filtering methods for identifying gravitational wave(GW)signals.The rapid and accurate identification of GW signals is crucial to the advancement of GW physics and multi-messenger astronomy,particularly considering the upcoming fourth and fifth observing runs of LIGO-Virgo-KAGRA.In this study,we used the 2D U-Net algorithm to identify time-frequency domain GW signals from stellar-mass binary black hole(BBH)mergers.We simulated BBH mergers with component masses ranging from 7 to 50 M_(⊙)and accounted for the LIGO detector noise.We found that the GW events in the first and second observation runs could all be clearly and rapidly identified.For the third observing run,approximately 80% of the GW events could be identified.In contrast to traditional convolutional neural networks,the U-Net algorithm can output time-frequency domain signal images corresponding to probabilities,providing a more intuitive analysis.In conclusion,the U-Net algorithm can rapidly identify the time-frequency domain GW signals from BBH mergers.

Side-Channel Leakage Analysis of Inner Product Masking

The Inner Product Masking(IPM)scheme has been shown to provide higher theoretical security guarantees than the BooleanMasking(BM).This scheme aims to increase the algebraic complexity of the coding to achieve a higher level of security.Some previous work unfolds when certain(adversarial and implementation)conditions are met,and we seek to complement these investigations by understanding what happens when these conditions deviate from their expected behaviour.In this paper,we investigate the security characteristics of IPM under different conditions.In adversarial condition,the security properties of first-order IPMs obtained through parametric characterization are preserved in the face of univariate and bivariate attacks.In implementation condition,we construct two new polynomial leakage functions to observe the nonlinear leakage of the IPM and connect the security order amplification to the nonlinear function.We observe that the security of IPMis affected by the degree and the linear component in the leakage function.In addition,the comparison experiments from the coefficients,signal-to-noise ratio(SNR)and the public parameter show that the security properties of the IPM are highly implementation-dependent.

An Efficient and Secure Privacy-Preserving Federated Learning Framework Based on Multiplicative Double Privacy Masking

With the increasing awareness of privacy protection and the improvement of relevant laws,federal learning has gradually become a new choice for cross-agency and cross-device machine learning.In order to solve the problems of privacy leakage,high computational overhead and high traffic in some federated learning schemes,this paper proposes amultiplicative double privacymask algorithm which is convenient for homomorphic addition aggregation.The combination of homomorphic encryption and secret sharing ensures that the server cannot compromise user privacy from the private gradient uploaded by the participants.At the same time,the proposed TQRR(Top-Q-Random-R)gradient selection algorithm is used to filter the gradient of encryption and upload efficiently,which reduces the computing overhead of 51.78%and the traffic of 64.87%on the premise of ensuring the accuracy of themodel,whichmakes the framework of privacy protection federated learning lighter to adapt to more miniaturized federated learning terminals.

基于FFT和Masking的实时语音通话降噪算法

针对语音通话质量的提升问题,提出一种基于快速傅里叶变换(Fast Fourier Transform,FFT)和Masking技术的实时语音通话降噪算法。首先,提出一个实时语音通话降噪的基本框架,并研究了帧分割、窗函数处理及FFT的数学原理。其次,阐述了基于人耳听觉特性的Masking方法及其在频域中的应用。最后,通过逆快速傅里叶变换(Inverse Fast Fourier Transform,IFFT)将信号转换回时域,并进行实验分析。实验结果表明,该降噪算法可以有效改善语音的清晰度和整体感知质量。

High-resolution seismic inversion method based on joint data-driven in the time-frequency domain

Seismic inversion can be divided into time-domain inversion and frequency-domain inversion based on different transform domains.Time-domain inversion has stronger stability and noise resistance compared to frequencydomain inversion.Frequency domain inversion has stronger ability to identify small-scale bodies and higher inversion resolution.Therefore,the research on the joint inversion method in the time-frequency domain is of great significance for improving the inversion resolution,stability,and noise resistance.The introduction of prior information constraints can effectively reduce ambiguity in the inversion process.However,the existing modeldriven time-frequency joint inversion assumes a specific prior distribution of the reservoir.These methods do not consider the original features of the data and are difficult to describe the relationship between time-domain features and frequency-domain features.Therefore,this paper proposes a high-resolution seismic inversion method based on joint data-driven in the time-frequency domain.The method is based on the impedance and reflectivity samples from logging,using joint dictionary learning to obtain adaptive feature information of the reservoir,and using sparse coefficients to capture the intrinsic relationship between impedance and reflectivity.The optimization result of the inversion is achieved through the regularization term of the joint dictionary sparse representation.We have finally achieved an inversion method that combines constraints on time-domain features and frequency features.By testing the model data and field data,the method has higher resolution in the inversion results and good noise resistance.

Application of sparse time-frequency decomposition to seismic data

The Gabor and S transforms are frequently used in time-frequency decomposition methods. Constrained by the uncertainty principle, both transforms produce low-resolution time-frequency decomposition results in the time and frequency domains. To improve the resolution of the time-frequency decomposition results, we use the instantaneous frequency distribution function（IFDF） to express the seismic signal. When the instantaneous frequencies of the nonstationary signal satisfy the requirements of the uncertainty principle, the support of IFDF is just the support of the amplitude ridges in the signal obtained using the short-time Fourier transform. Based on this feature, we propose a new iteration algorithm to achieve the sparse time-frequency decomposition of the signal. The iteration algorithm uses the support of the amplitude ridges of the residual signal obtained with the short-time Fourier transform to update the time-frequency components of the signal. The summation of the updated time-frequency components in each iteration is the result of the sparse timefrequency decomposition. Numerical examples show that the proposed method improves the resolution of the time-frequency decomposition results and the accuracy of the analysis of the nonstationary signal. We also use the proposed method to attenuate the ground roll of field seismic data with good results.

TVAR Time-frequency Analysis for Non-stationary Vibration Signals of Spacecraft

Predicting the time-varying auto-spectral density of a spacecraft in high-altitude orbits requires an accurate model for the non-stationary random vibration signals with densely spaced modal frequency. The traditional time-varying algorithm limits prediction accuracy, thus affecting a number of operational decisions. To solve this problem, a time-varying auto regressive （TVAR） model based on the process neural network （PNN） and the empirical mode decomposition （EMD） is proposed. The time-varying system is tracked on-line by establishing a time-varying parameter model, and then the relevant parameter spectrum is obtained. Firstly, the EMD method is utilized to decompose the signal into several intrinsic mode functions （IMFs）. Then for each IMF, the PNN is established and the time-varying auto-spectral density is obtained. Finally, the time-frequency distribution of the signals can be reconstructed by linear superposition. The simulation and the analytical results from an example demonstrate that this approach possesses simplicity, effectiveness, and feasibility, as well as higher frequency resolution.

Intelligibility evaluation of enhanced whisper in joint time-frequency domain

Some factors influencing the intelligibility of the enhanced whisper in the joint time-frequency domain are evaluated. Specifically, both the spectrum density and different regions of the enhanced spectrum are analyzed. Experimental results show that for a spectrum of some density, the joint time-frequency gain-modification based speech enhancement algorithm achieves significant improvement in intelligibility. Additionally, the spectrum region where the estimated spectrum is smaller than the clean spectrum, is the most important region contributing to intelligibility improvement for the enhanced whisper. The spectrum region where the estimated spectrum is larger than twice the size of the clean spectrum is detrimental to speech intelligibility perception within the whisper context.

指数样本中多个异常值的Unmasking检验(英文)

指数样本中多个异常值的非一致性检验因受masking或swamping效应的影响而变得十分的困难和复杂，解决这一问题的关键在于K值的确定，传统的方法是无能为力的．本文基于变量选择的AIC准则的思想提出了异常值检验的一种新方法，它具有不预先指定k，计算简单且通过达到极大化MAIC就能达到确定k和消除检验中的masking或swamping的优点．还给出了易计算检验显著水平的统计量和公式．最后，通过实例的验证标明本文方法的有效性．

Effects of Gabor transform parameters on signa time-frequency resolution

In this paper, it is described that the time-frequency resolution of geophysical signals is affected by the time window function attenuation coefficient and sampling interval and how such effects are eliminated effectively. Improving the signal resolution is the key to signal time-frequency analysis processing and has wide use in geophysical data processing and extraction of attribute parameters. In this paper, authors research the effects of the attenuation coefficient choice of the Gabor transform window function and sampling interval on signal resolution. Unsuitable parameters not only decrease the signal resolution on the frequency spectrum but also miss the signals. It is essential to first give the optimum window and range of parameters through time-frequency analysis simulation using the Gabor transform. In the paper, the suggestions about the range and choice of the optimum sampling interval and processing methods of general seismic signals are given.

针对改进的Masking方法的差分功耗攻击

自1999年Kocher等人提出针对智能卡中DES的差分功耗攻击(DPA)以来,针对DPA的各种防御策略也被大量的提出,Masking就是其中一种简单、高效的方法。文献[2]中Akkar提出了一种改进的Masking方法,然而,本文基于实际的智能卡芯片对该方法成功进行了攻击试验。实验结果表明该方法并不能抵抗DPA的攻击。以此类推,文献[7]中提出的仅对密钥K进行掩码的方法亦不能抵抗DPA的攻击。

改进Mask RCNN的盾构隧道渗漏水检测方法

渗漏水是盾构隧道结构存在潜在损伤或缺陷的重要表征,快速、准确检测出渗漏水位置,对隧道安全运营和维护具有重要意义。现有的方法大多采用光学影像对隧道渗漏水进行检测,受隧道内空间和光线条件限制,难以获得高质量病害图片。因此,本文提出了一种基于激光点云数据与改进Mask RCNN相结合的渗漏水检测方法。首先对激光点云反射强度进行修正;然后生成灰度图像并建立渗漏水病害数据集;最后在Mask RCNN算法中引入空洞卷积和变形卷积,实现了隧道渗漏水病害的快速检测。利用某地铁采集的数据进行验证,结果表明,本文提出的改进Mask RCNN算法相较于原始算法和FCN算法检测精度均有明显提升,在盾构隧道渗漏水识别方面性能表现较好。

基于空间注意力机制的Mask R-CNN致密储层岩石薄片图像鉴定

针对陆相致密储层岩石薄片鉴定识别难、制片成本高、时间消耗长和人为主观强等难题,选取鄂尔多斯盆地临兴区块上古生界和松辽盆地三肇凹陷扶余油层为靶区,提出一种基于深度学习的致密油储层岩石薄片人工智能鉴定方法,引入图像预处理技术去除岩石薄片图像噪声并统一图像像素大小,构建空间几何增广机制,基于空间注意力机制改进Mask R-CNN算法,并将上述方法应用于实例靶区进行有效性验证。结果表明:图像预处理技术能够在保障图像特征的前提下,有效提高图像质量,减少噪声干扰;空间几何图像增广机制能够在在一定程度上增加可用样本的数量;基于空间注意力机制的Mask R-CNN算法可以同时完成复杂岩石薄片成分的分割与智能识别工作,分割精度在不同数据集情况下的平均精度为89.2%,整体识别准确率为93%,适用于致密油储层岩石薄片特征鉴定。

Mask在包装材料气体阻隔性能检测中的应用研究

目的研究和评价mask在材料气体阻隔性检测中的应用及其检测数据的重复性、准确性和数据稳定性。方法选用覆盖高阻隔、中阻隔、低阻隔等阻隔性能范围的5种样品,使用3种不同面积的mask和仪器测试腔原有面积对样品进行测试,并对测试结果重复性、稳定性和准确性进行分析评价。结果高阻隔材料PET硬片使用面积12.56 cm^(2)的mask测试时,可以得到较为稳定的检测结果,而在使用更小面积(1.77、5 cm^(2))的mask时,测试结果的相对标准偏差、相对极差和测试数据偏差都较差,不推荐使用。KOP/CPP在使用1.77 cm^(2)的mask测试时,测试结果相对标准偏差和测试数据偏差都略大于10%。PET/CPP在使用1.77 cm^(2)的mask测试时,其测试数据偏差略大于10%。BOPE/LDPE和TPU使用1.77 cm^(2)的mask测试可以得到良好的检测结果。结论Mask是解决试样材料特性、设备量程限制、试样尺寸等测试困难的优秀解决方案。对于中、低阻隔材料的透气性测试,使用mask可获得具有良好可信度和稳定性的测试数据。而在进行氧气透过率的测试时应尽量选择大的测试面积。小面积mask不适用于高阻隔材料的气体阻隔性测试。

改进Mask R-CNN的馆藏报纸图像内容分割

开展馆藏报纸图像内容分割研究,能提升文字识别准确率,对促进机器识别取代人工操作、提高图书馆数字化工作效率具有重要意义。文章根据报纸图像呈现的特征,提出一种基于改进MaskR-CNN的算法,实现报纸图像内容分割。首先,通过优化锚框比例和损失函数,对原始MaskR-CNN算法进行改进。其次,采用数据增强、调整训练参数开展样本训练。最后,通过实验的方式对改进后的MaskR-CNN算法训练模型和原始算法训练模型进行比较,并采用AP_bbox和AP_segm评价指标对实验结果进行评估,改进后的算法训练模型AP_bbox为0.935,AP_segm为0.943,均超过原始算法训练模型。实验结果表明,改进后的MaskR-CNN算法能够实现报纸图像内容有效检测与分割。

基于FPGA加速的Mask R-CNN稻瘟病高通量自适应识别模型研究

针对基于图像的稻瘟病现场检测技术依赖先验知识且受制于算力与田间网络状况,无法实现自适应实时检测的问题,提出一种可利用现场可编程门阵列(Field programmable gate array,FPGA)加速的Mask R-CNN(Mask region-based convolutional neural network)稻瘟病高通量自适应快速识别模型。首先将骨干网络改进为MobileNetV2,利用其倒残差模块降低计算量,提高模型并行处理能力;随后增加用于稻瘟病多尺度特征融合的特征金字塔网络模块,使模型具备多尺度自适应处理能力;最后由全卷积网络(Fully convolutional network,FCN)分支输出稻瘟病病斑的实例分割,同时使用交叉熵损失函数完成稻瘟病的定位与分类。稻瘟病实测数据集对模型的验证结果表明:当输入为全高清图像时,模型平均推理时间减少至85 ms,相较GPU服务器、同级别GPU边缘计算平台,速度分别提高86.2%、63.0%。在交并比为0.6时,准确率可达98.0%,病斑捕获能力平均提升21.2%。提出的Mask R-CNN自适应快速识别模型能够在田间恶劣网络状况下实现稻瘟病的快速现场检测,具有更好的抗噪能力和鲁棒性能,为水稻病害实时检测、察打一体提供了高效实时的片上系统方案。

基于改进Mask R-CNN的青菜杂质检测研究

绿叶蔬菜的智能包装加工是实现绿叶蔬菜智能化生产、降低生产成本的重要部分,对绿叶蔬菜在包装加工时的杂质检测是其重要前提。以青菜为研究对象,提出一种基于Mask R-CNN的青菜杂质检测模型。首先采集标注掺杂枯树叶、枯菜叶和碎纸片3种常见杂质的青菜图像1370多张,并通过数据增强的方法扩充建立含有2740张青菜杂质图像的数据集。为减少背景对杂质检测的影响,通过在Mask R-CNN模型中加入协调注意力机制,同时添加全连接层和Dropout层,增强模型特征提取能力,减少过拟合现象,并使用迁移学习方法对模型进行微调。结果表明改进后的Mask R-CNN算法对青菜杂质识别的平均精度均值为99.19%,检测速度为8.45 FPS,检测效果良好,可以满足青菜杂质的检测需求。