抽样改进加权核大数据谱聚类算法

经典谱聚类算法将数据聚类转为图划分问题,在分析其Normalized Cut函数与传统加权核k-means等价基础上,设计了一种基于抽样改进加权核k-means算法的大规模数据集谱聚类算法,算法通过加权核k-means迭代优化避免Laplacian矩阵特征分解的大量资源占用,通过随机映射得到近似奇异值分解,并由近似奇异...     

改进Yolo V3算法在工件缺陷检测中的应用

为快速、准确地检测工业生产中工件表面产生的缺陷,提出了一种基于Yolo V3的工件表面缺陷检测方法.该方法以DarkNet卷积模型作为特征提取网络,通过引入数据增强方法防止产生过拟合现象,并针对工件表面缺陷形状单一、缺陷尺寸普遍偏小的特点改进了Yolo V3网络的特征融合方式,减少了冗余候选框的数量,提升了算法性能.以...     

Three-Dimensional Reconstruction of Welding Pool Surface by Binocular Vision

Current research of binocular vision systems mainly need to resolve the camera's intrinsic parameters before the reconstruction of three-dimensional(3D)objects.The classical Zhang'calibration is hardly to calculate all errors caused by perspective distortion and lens distortion.Also,the image-matching algorithm of the binocular vision system still needs to be improved to accelerate the reconstruction speed of welding pool surfaces.In this paper,a preset coordinate system was utilized for camera calibration instead of Zhang'calibration.The binocular vision system was modified to capture images of welding pool surfaces by suppressing the strong arc interference during gas metal arc welding.Combining and improving the algorithms of speeded up robust features,binary robust invariant scalable keypoints,and KAZE,the feature information of points(i.e.,RGB values,pixel coordinates)was extracted as the feature vector of the welding pool surface.Based on the characteristics of the welding images,a mismatch-elimination algorithm was developed to increase the accuracy of image-matching algorithms.The world coordinates of match-ing feature points were calculated to reconstruct the 3D shape of the welding pool surface.The effectiveness and accuracy of the reconstruction of welding pool surfaces were verified by experimental results.This research proposes the development of binocular vision algorithms that can reconstruct the surface of welding pools accurately to realize intelligent welding control systems in the future.     

Applications of digital signal processing methods in TOF calculation of ultrasonic gas flowmeter

Ultrasonic gas flowmeters (UGFM) are extensively used in petroleum, chemical, natural gas and other fields because of their distinctive measurement advantages. As the core technology of the UGFM, digital signal processing technology determines its measurement accuracy and stability. This paper systematically reviews the signal processing methods for measuring the TOF (time of flight) based on the principle of time difference method. According to the stability of the feature information extracted by different digital signal processing methods, the advantages and disadvantages of each method are analyzed. Aiming at the problems of serious energy attenuation, weak echo signal amplitude, and low signal-to-noise ratio (SNR) when ultrasonic signal propagate in the gas, different signal processing methods are evaluated and compared from the aspects of calculation accuracy, response speed, anti-interference ability and sensitivity to parameter changes. Finally, some improvement ideas and directions for further research are proposed according to the analysis results.     

压射冲头与压室间的摩擦特性

利用专用模拟装置，研究了载荷、温度及润滑剂等因素对压射冲头与压室间摩擦力的影响，并根据试验结果及摩擦学原理，对实际压射过程的摩擦特性进行了探讨     

往复压缩机相空间LDA模型在异常检测中的应用

针对往复压缩机异常检测不及时、漏报、误报的问题,提出一种多特征融合的相空间LDA(Latent Dirichlet Allocation)模型的异常检测方法。为了全面描述波形特征,提取往复压缩机正常运行数据和实时运行数据的特征集,对特征集进行预处理后,运用LDA方法计算正常状态和当前状态相空间分布模型,并用JS(Jensen Shannon divergence)距离计算两者差异度,若差异度超过设定值则认为发生故障。实验验证了该方法能有效实现往复压缩机异常检测,并能大幅提前往复压缩机典型故障异常检测报警时间点。     

The optimization of attribute selection in decision tree-based production control systems

This study develops a learning-based production control system (PCS) to support a manufacturing system to make on-line decisions that are robust in the face of various production requirements. Selecting essential system attributes (or features) based on various production requirements to construct PCS knowledge bases is a critical issue because of the existence of a large amount of shop floor information in a manufacturing system. However, a classical decision tree (DT) learning approach to construct dynamic dispatching knowledge bases does not consider the optimal subset of system attributes in the problem domain. To resolve this problem, this study develops a hybrid genetic algorithm/decision tree (GA/DT) approach for DT-based PCS. The hybrid GA/DT approach is used to simultaneously evolve an optimal subset of system attributes and determine learning parameters of the DT from a large set of candidate manufacturing system attributes according to various performance measures. For a given feature subset and learning parameters of a DT decoded by a GA, a DT was applied to evaluate the fitness in the GA process and to generate the PCS knowledge base. The results demonstrate that the proposed GA/DT-based PCS has, according to various performance criteria, a better long term system performance than those obtained with classical DT-based PCS and the heuristic individual dispatching rules, according to various performance criteria.     

Parametric process planning based on feature parameters of parts

This paper presents a model parametric process plan that is dependent on feature parameters of parts, and proposes a solution for the automated process planning of part families. Based on the parametric process plan templates for part families and the feature parameters of new parts, a prototype system is developed. First, parts are grouped into families considering their geometric or manufacturing similarities, and the parametric process plan template is pre-created for each family. Then, to plan the process of a new part, the system extracts feature parameters from its feature-based model and generates an a parametric process plan by searching the template library and solving related constraints. Finally, the system outputs the process plan sheets, if necessary. Although the system cannot creatively generate process plans for brand new parts, the system can meet two important requirements of the real industrial world to CAPP systems, that is good system performance and rapid response.     

Application of GMDH neural network technique to improve measuring precision of a simplified photon attenuation based two-phase flowmeter

Multiphase flowmeters have an important role to play in the industry and any attempts that lead to improvements in this field are of great interest. In the current study, group method of data handling (GMDH) technique was applied in order to increase measuring precision of a simple photon attenuation based two-phase flowmeter that has the ability to estimate the gas volumetric percentage in a two-phase flow without any dependency to flow regime pattern. The simple photon attenuation based system is comprised of a cobalt-60 radioisotope and only one 25.4 mm × 25.4 mm sodium iodide crystal detector. Four extracted features from recorded photon spectrum in sodium iodide crystal detector were used as the inputs of GMDH neural network. Equations related to the combination of the features and the error rate of each approximation is also reported in this paper. Applying the mentioned technique, the gas volumetric percentage in an oil-gas two phase flow was determined with the root mean square error of less than 2.71 without any dependency to the flow pattern. The obtained measuring precision in this study is at least 2.1 times better than reported in previous studies.     

面向反求工程的特征识别技术的研究与实现

论述了基于层析数据的面向反求工程的特征识别技术及实现方案。其特征识别过程分 2步进行 ,即层轮廓线特征识别和相邻层轮廓匹配。依据曲率及曲率的变化规律确定轮廓特征点和线段组成 ;引入轮廓编码技术、相邻轮廓变化域以及截面面积、形心、惯性矩等参数完成相邻轮廓的匹配。采用分段特征识别的方法构造特征体素 ,用CSG和B Rep混和建模方法构造三维CAD模型。