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.

Automatic modulation recognition of radiation source signals based on two-dimensional data matrix and improved residual neural network

Automatic modulation recognition(AMR)of radiation source signals is a research focus in the field of cognitive radio.However,the AMR of radiation source signals at low SNRs still faces a great challenge.Therefore,the AMR method of radiation source signals based on two-dimensional data matrix and improved residual neural network is proposed in this paper.First,the time series of the radiation source signals are reconstructed into two-dimensional data matrix,which greatly simplifies the signal preprocessing process.Second,the depthwise convolution and large-size convolutional kernels based residual neural network(DLRNet)is proposed to improve the feature extraction capability of the AMR model.Finally,the model performs feature extraction and classification on the two-dimensional data matrix to obtain the recognition vector that represents the signal modulation type.Theoretical analysis and simulation results show that the AMR method based on two-dimensional data matrix and improved residual network can significantly improve the accuracy of the AMR method.The recognition accuracy of the proposed method maintains a high level greater than 90% even at -14 dB SNR.

A reliability-oriented genetic algorithm-levenberg marquardt model for leak risk assessment based on time-frequency features

Since leaks in high-pressure pipelines transporting crude oil can cause severe economic losses,a reliable leak risk assessment can assist in developing an effective pipeline maintenance plan and avoiding unexpected incidents.The fast and accurate leak detection methods are essential for maintaining pipeline safety in pipeline reliability engineering.Current oil pipeline leakage signals are insufficient for feature extraction,while the training time for traditional leakage prediction models is too long.A new leak detection method is proposed based on time-frequency features and the Genetic Algorithm-Levenberg Marquardt(GA-LM)classification model for predicting the leakage status of oil pipelines.The signal that has been processed is transformed to the time and frequency domain,allowing full expression of the original signal.The traditional Back Propagation(BP)neural network is optimized by the Genetic Algorithm(GA)and Levenberg Marquardt(LM)algorithms.The results show that the recognition effect of a combined feature parameter is superior to that of a single feature parameter.The Accuracy,Precision,Recall,and F1score of the GA-LM model is 95%,93.5%,96.7%,and 95.1%,respectively,which proves that the GA-LM model has a good predictive effect and excellent stability for positive and negative samples.The proposed GA-LM model can obviously reduce training time and improve recognition efficiency.In addition,considering that a large number of samples are required for model training,a wavelet threshold method is proposed to generate sample data with higher reliability.The research results can provide an effective theoretical and technical reference for the leakage risk assessment of the actual oil pipelines.

Diverse Deep Matrix Factorization With Hypergraph Regularization for Multi-View Data Representation

Deep matrix factorization(DMF)has been demonstrated to be a powerful tool to take in the complex hierarchical information of multi-view data(MDR).However,existing multiview DMF methods mainly explore the consistency of multi-view data,while neglecting the diversity among different views as well as the high-order relationships of data,resulting in the loss of valuable complementary information.In this paper,we design a hypergraph regularized diverse deep matrix factorization(HDDMF)model for multi-view data representation,to jointly utilize multi-view diversity and a high-order manifold in a multilayer factorization framework.A novel diversity enhancement term is designed to exploit the structural complementarity between different views of data.Hypergraph regularization is utilized to preserve the high-order geometry structure of data in each view.An efficient iterative optimization algorithm is developed to solve the proposed model with theoretical convergence analysis.Experimental results on five real-world data sets demonstrate that the proposed method significantly outperforms stateof-the-art multi-view learning approaches.

基于Mask R-CNN的试管-支架系统Data Matrix码识别方法

在试管-支架自动化系统的输入图像中,Data Matrix(DM)码呈现为多个小目标,图像存在成像模糊、边缘干扰严重等问题,使得传统方法难以达到良好的识别效果。为此,提出一种基于深度学习的Data Matrix码识别方法DeepDMCode,以Mask R-CNN模型为基础,通过内容差异化数据合成和同步自动化标注,实现训练数据的增强,提升模型的学习能力。在模型分割结果的基础上,提出一种旋转校正方法,确保可用标准解码库实现DM码的解码。以分辨率为1600×1200、支架容量为96的数据实验表明,由于该方法在前期码定位阶段最大程度地还原码边界信息,准确度可达0.92(mIoU),完成单张图像中所有DM识别的平均速度为5.2 s,优于YOLO、SegNet、CenterNet等主流工业基准算法。

Damage Identification under Incomplete Mode Shape Data Using Optimization Technique Based on Generalized Flexibility Matrix

A generalized flexibility–based objective function utilized for structure damage identification is constructed for solving the constrained nonlinear least squares optimized problem. To begin with, the generalized flexibility matrix (GFM) proposed to solve the damage identification problem is recalled and a modal expansion method is introduced. Next, the objective function for iterative optimization process based on the GFM is formulated, and the Trust-Region algorithm is utilized to obtain the solution of the optimization problem for multiple damage cases. And then for computing the objective function gradient, the sensitivity analysis regarding design variables is derived. In addition, due to the spatial incompleteness, the influence of stiffness reduction and incomplete modal measurement data is discussed by means of two numerical examples with several damage cases. Finally, based on the computational results, it is evident that the presented approach provides good validity and reliability for the large and complicated engineering structures.

Optimal Estimation of High-Dimensional Covariance Matrices with Missing and Noisy Data

The estimation of covariance matrices is very important in many fields, such as statistics. In real applications, data are frequently influenced by high dimensions and noise. However, most relevant studies are based on complete data. This paper studies the optimal estimation of high-dimensional covariance matrices based on missing and noisy sample under the norm. First, the model with sub-Gaussian additive noise is presented. The generalized sample covariance is then modified to define a hard thresholding estimator , and the minimax upper bound is derived. After that, the minimax lower bound is derived, and it is concluded that the estimator presented in this article is rate-optimal. Finally, numerical simulation analysis is performed. The result shows that for missing samples with sub-Gaussian noise, if the true covariance matrix is sparse, the hard thresholding estimator outperforms the traditional estimate method.

Non-Linear Matrix Completion

Current methods for predicting missing values in datasets often rely on simplistic approaches such as taking median value of attributes, limiting their applicability. Real-world observations can be diverse, taking stock price as example, ranging from prices post-IPO to values before a company’s collapse, or instances where certain data points are missing due to stock suspension. In this paper, we propose a novel approach using Nonlinear Matrix Completion (NIMC) and Deep Matrix Completion (DIMC) to predict associations, and conduct experiment on financial data between dates and stocks. Our method leverages various types of stock observations to capture latent factors explaining the observed date-stock associations. Notably, our approach is nonlinear, making it suitable for datasets with nonlinear structures, such as the Russell 3000. Unlike traditional methods that may suffer from information loss, NIMC and DIMC maintain nearly complete information, especially in high-dimensional parameters. We compared our approach with state-of-the-art linear methods, including Inductive Matrix Completion, Nonlinear Inductive Matrix Completion, and Deep Inductive Matrix Completion. Our findings show that the nonlinear matrix completion method is particularly effective for handling nonlinear structured data, as exemplified by the Russell 3000. Additionally, we validate the information loss of the three methods across different dimensionalities.

Optimizing Memory Access Efficiency in CUDA Kernel via Data Layout Technique

Over the past decade, Graphics Processing Units (GPUs) have revolutionized high-performance computing, playing pivotal roles in advancing fields like IoT, autonomous vehicles, and exascale computing. Despite these advancements, efficiently programming GPUs remains a daunting challenge, often relying on trial-and-error optimization methods. This paper introduces an optimization technique for CUDA programs through a novel Data Layout strategy, aimed at restructuring memory data arrangement to significantly enhance data access locality. Focusing on the dynamic programming algorithm for chained matrix multiplication—a critical operation across various domains including artificial intelligence (AI), high-performance computing (HPC), and the Internet of Things (IoT)—this technique facilitates more localized access. We specifically illustrate the importance of efficient matrix multiplication in these areas, underscoring the technique’s broader applicability and its potential to address some of the most pressing computational challenges in GPU-accelerated applications. Our findings reveal a remarkable reduction in memory consumption and a substantial 50% decrease in execution time for CUDA programs utilizing this technique, thereby setting a new benchmark for optimization in GPU computing.

刀具柱面Data Matrix码几何畸变的仿真分析

分析了刀具柱面Data Matrix(DM)码几何畸变的原理与过程,利用透视投影的方法建立了柱面DM码的几何畸变模型并对其进行仿真.结果表明,该模型较好地模拟了DM码在刀具柱面的几何畸变状况.通过仿真分析,获得了在一定曲率柱面上标刻DM码的合适尺寸以及采集过程中相机的合适几何参数.

Data Matrix二维条形码解码器图像预处理研究

DM码是一种常见的二维条形码,图像预处理是DM码解码器自动识别过程中的重要步骤。提出一种实用的DM码识别图像预处理方法。它没有使用传统的边缘检测和直线检测手段,因此受背景噪声、几何失真的影响较小。此外,使用了校正铁路线坐标,并按区域取样生成码流,显著提高了DM码的识别速度和识别率。实验结果表明,该算法可以克服DM码识别过程中易受噪声干扰、光照不均和几何失真等影响的问题。

基于手机平台的Data Matrix 2维条码识别

传统的条码图像采集和识别是通过工业扫描枪。近年来,随着移动增值业务和3G技术的发展,2维条码在手机设备的应用中得到飞速发展。以Data Matrix为例,研究了基于嵌入式手机设备的2维条码识别技术。首先根据Data Matrix条码的特点,给出了一种基于链码跟踪和线段检测的快速Data Matrix检测算法。接着分析了条码信号经过点扩展函数卷积后的降质模型,并利用维纳滤波对条码信号进行反模糊滤波。最后,针对透视畸变的现象,设计了一种适合于嵌入式手机设备的快速反透视算法。实验结果表明,提出的识别算法具有优秀的性能,显著提高了条码的识别率,满足了实际使用的要求。

Data Matrix二维码图像处理与应用

以MeteorIIStandard图像采集卡为基础,以识别金属零件上的DataMatrix二维码为目的,对摄像头采集的图像进行处理。实现了该方法在工业流水线上的实时识别应用。

基于旋转采集Data Matrix码序列图像拼接方法

针对刀具柱面Data Matrix码图像拼接的特殊需求,已有算法拼接结果识读率低、实时性差的缺陷,提出了一种基于Data Matrix码图像特征的拼接方法.该方法基于缝隙内旋转采集的刀具柱面Data Matrix码序列图像,通过条码纵向位移校正与模块划分,完成基于模块信息的最小欧氏距离图像粗配准和归一化互相关图像精配准;为了保证条码结构正确,消除无重合区域对图像拼接的影响,根据匹配度先将序列图像融合成3部分并校正畸变;最后将这3部分按条码模块关系融合成一幅尺寸正确的图像,实现了条码定位、模块划分,以方便后续进行解码.实验结果表明,文中方法能够满足刀具柱面Data Matrix码序列图像的拼接需求,拼接结果解码正确率达到96%,拼接时间小于250ms,并且大大提高了解码速度.

基于Reed-Solomon算法的DataMatrix条码纠错码的研究

DataMatrix是一种矩阵二维条码,具有信息密度大、容量高、面积小等优点,同时,其译码时受噪声干扰也较大,因此,DataMatrix二维条码采用了ReedSolomon算法作为纠错码,可以有效地排除干扰进行纠错。首先介绍DataMatrix条码的特点,然后详细介绍了ReedSolomon算法的原理和伽罗华域的基本运算规则和构造规则,重点分析研究他在DataMatrix二维条码中的应用,构造了他的实现算法和其纠错编码的实现电路并通过实例进行了具体的说明,同时讨论了RS的译码步骤。

Data Matrix码识别技术研究

二维条码本身具有高容量、高密度、纠错能力强、安全强度高等特点,使得二维条码作为信息的载体在信息自动化领域发挥着越来越重要的作用。该文提出了一种基于手机应用平台的DM码识别方案,并对图像识别过程进行了优化。

基于Reed-Solomon码的Data Matrix条码纠错研究

为了研究Data Matrix条码的纠错能力,首先介绍了Data Matrix条码的特点和Reed-Solomon码的基本概念;接着研究了Reed-Solomon码在Data Matrix二维条码中的应用,重点分析了Data Matrix二维条码中Reed-Solomon编、解码的基本原理与步骤,并用C语言实现它的编、解码算法;最后对Reed-Solomon码的纠错能力进行了测试。实验结果表明,Data Matrix二维条码采用Reed-Solomon码作为纠错码,可以有效地排除干扰并进行纠错。

Data Matrix二维条码在奥运会门票系统中的应用研究

运用Data Matrix二维条码的特点重新设计奥运体育场馆电子门票系统,并构建了一个完整的奥运电子门票Data Matrix二维条码自动识别系统。对Data Matrix二维条码系统进行详细地规划,并通过实际条码生成的例子,证明其可行性和可实践性。本文突破了Data Matrix二维条码本身的适用领域的限制,有一定新意和实践价值。

一种Data Matrix条码的快速识别方法

为满足工业流水线对条码识别实时性的要求,提出了一种Data Matrix条码快速识别方法。采用形状参数、圆形性作为矩形特征,初步确定Data Matrix条码位置;采用距离为角度的函数对Data Matrix条码边界进行标记,将Data Matrix的2D条码边界转换为1D波形函数进行分析,进行一次求导获取条码边界的位置及角度;利用边界角度作为仿射变换的角度,将条码旋转到规格化位置,采用网格法进行数据提取并进行条码解码。对像素大小为640×480的400幅含Data Matrix条码图片在PC机上进行测试,单个条码识别平均时间为12.06 ms,识别率为99.22%,快速识别方法准确、迅速,达到工业现场实时性和可靠性要求。

金属表面Data Matrix条码高光区域的信息重构

为实现工业产品的可追溯性,直接将条码加工在零件表面的直接零件标识(Direct Part Marking,DPM)技术,在国内外受到了越来越多的关注。对于金属零件,由于其具有较高的反光性,由相机捕获的金属表面的条码图像常常产生局部高光现象,影响条码的正确读取。为此,针对金属表面激光标刻二维条码出现的局部高光现象,提出了基于五步重构模型的条码重构法,以重构高光区域的条码信息。对获得的条码图像进行倾斜校正,使"L"型实线边界位于图像左下角,对条码进行网格划分实现各个模块的定位。基于Modified Specular-Free(MSF)图像对高光区域进行检测。采用五步重构模型对条码的各个模块进行数值填充,对条码进行读取。实验表明,该算法能达到去除金属表面上条码局部高光的目的,并取得了较高的识读正确率。