论文标题
铁路轨道超声诊断自动化问题的机器学习
Machine learning in problems of automation of ultrasound diagnostics of railway tracks
论文作者
论文摘要
本文介绍了用于实时自动解码铁路缺陷图的系统体系结构。该系统包括超声数据预处理模块，一组中性网络分类器，决策块。数据的预处理包括将测量信息的仿射转换为适合于神经网络操作的格式，以及在测量通道上的信息组合，具体取决于所定义的缺陷类型。分类器建立在卷积神经网络上。提出的解决方案可以在现代元素基础上有效实现，以执行并行计算，包括张量处理器和GPU。
The article presents the system architecture for automatic decoding of railway track defectograms in real time. The system includes an ultrasound data preprocessing module, a set of neutral network classifiers, a decision block. Preprocessing of data includes affine transformations of measurement information into a format suitable for the operation of a neural network, as well as a combination of information on measurement channels, depending on the type of defect being defined. The classifier is built on a convolutional neural network. The proposed solution can be effectively implemented on a modern elemental basis for performing parallel computing, including tensor processor and GPUs.
