Bar counting method based on machine vision

本发明提供了一种基于机器视觉的棒材计数方法，包括采集棒材端面图像，图像预处理，计算棒材的相对位移，连通域分析，棒材匹配计数，确定分钢线位置以及判断初分钢结果是否正确等。图像预处理包括平滑、二值化及形态学操作；连通域分析首先将粘连棒材分割成单根棒材，然后再次用连通域分析单根棒材的信息。本发明方法基于机器视觉技术，实现了棒材的自动计数及分钢结果判断，有效解决了棒材的重叠与交叉问题，该方法速度快，准确率高，对生产线的改动小，易于维护，大大减少企业在生产过程中的人力成本

Recognition of Aluminum Casting Based on Texture Feature and SVM Classifier

随着全球经济的增长和铝铸件的广泛使用,全球铝铸件消费量逐年上升.由于应用场合不同,导致有各种各样的铝铸件,它们有不同的形状、结构、颜色、质地等.图像的纹理分类作为图像处理应用中的一个重要方面,本文通过分析铝铸件的特点,分别采用灰度共生矩阵、Gabor小波变换提取图像纹理特征,并加以融合对比,使用支持向量机SVM分类算法对特征进行分类.通过实验可知,使用Gabor小波变换对铝铸件分类的识别准确率和识别时间上效果都是最好的

Hierarchical fault monitoring method based on mixed characteristic evaluation and subspace decomposition

本发明涉及一种基于混合特性评价和子空间分解的层次故障监测方法，包括利用混合特性评价获取复杂工业过程的混合特性子空间，包括高斯线性子空间、高斯非线性子空间、非高斯线性子空间和非高斯非线性子空间，基于PCA‑ICA‑KPCA‑KICA的层次子空间分解方法建立故障监测模型，利用综合统计量和层次监测策略进行故障监测。本发明通过将Omnibus检验、加权非线性测量和基于PCA‑ICA‑KPCA‑KICA的层次子空间分解结合，考虑复杂工业过程的混合特性并存问题，克服现有故障监测方法依赖先验过程知识或未考虑高斯性、非高斯性、线性相关性与非线性相关性并存等局限，对监测异常工况和改善产品质量具有理论和实际意义

High precision size measurement system of industrial profiles section

This paper implements a non-contact measurement system based on machine vision for industrial profiles section, which uses double telecentric lens optical system as image acquisition device. The key procedures using double telecentric lens optical system to get cross section image of measured profile are described in details. Based on the profile characteristic of section itself, we applied smooth filtering and automatic threshold binarization algorithm in image preprocessing, and improve the detection accuracy through subpixel edge detection algorithm. Moreover, combined with high precision section edge images, we realized some practical functions such as geometric measurement, fitting, and contrasting with CAD drawings, etc. The proposed system with high speed, high accuracy and easy maintenance can satisfy the requirement of high precision for industrial profiles section size measurement

Industrial key application service terminal disaster recovery backup system and method based on cloud computing

本发明涉及基于云计算的工业关键应用服务终端化灾备系统及方法，将工业生产中关键生产环节的应用服务，进行在本地工业互联网环境下进行终端化灾备部署。在公有云计算服务可以稳定访问的情况下，本地终端部署的服务处于休眠状态，应用设备通过访问公有云中的计算服务获取生产信息支持。在由于网络中断或其它问题导致公有云计算服务无法访问时，本地终端部署的服务自动激活，为生产提供针对关键生产环节的临时服务，应用设备通过访问本地服务获取生产信息支持，保证生产过程的持续稳定运行。在公有云服务恢复后，应用设备重新访问公有云服务，并将本地终端服务运行过程中产生的数据同步到公有云中，保证生产过程中产生数据的完整存储

Multi-dimensional data visualisation method based on convex-corrected radviz

Radviz is one of the most commonly used multi-dimensional data visualisation methods. Because the projection points overlap a lot in Radviz, this paper puts forward a new Radviz optimisation method to correct the position of the projected data points. Firstly, the new method introduces the Prim algorithm to realise the optimal ordering of the dimension anchors on Radviz. Secondly, the convex hull mapping and the second Radviz mapping are used to correct the position of the projected data points. Finally, the data points are visualised. In addition, in order to verify the effectiveness of the algorithm, the Dunn index was used to do a quantitative evaluation of visualisation. By comparing multiple sets of data set experiments, the results show that the new method is beneficial to obtain the better visualisation effect of multi-dimensional data in Radviz projection.</p

Comprehensive monitoring of industrial processes using multivariable characteristics evaluation and subspace decomposition

Gaussianity, non-Gaussianity, linearity, and nonlinearity generally coexist within industrial process variables, and should be taken into account simultaneously for process modelling with monitoring. This paper presents a comprehensive monitoring method of industrial processes using multivariable characteristics evaluation and subspace decomposition. First, a multivariable characteristics evaluation method is presented to divide the process variables into the Gaussian linear, Gaussian nonlinear, non-Gaussian linear, and non-Gaussian nonlinear subspaces. Second, the PCA-ICA-KPCA-KICA-based multivariable subspace decomposition is proposed for process modelling. Furthermore, comprehensive monitoring is developed and final results are combined using comprehensive statistics. By multivariable characteristics evaluation and subspace decomposition, the proposed method could evaluate and seek the multivariable characteristics and enhance the performance of process monitoring. The effectiveness and feasibility of the proposed comprehensive monitoring method are demonstrated by a numerical system and the benchmark Tennessee Eastman (TE) process.</p