铁路轨道超声诊断自动化问题中的机器学习
Machine learning in problems of automation of ultrasound diagnostics of railway tracks
摘要：
介绍了一种铁路轨道缺陷图实时自动解码系统的结构。该系统包括超声数据预处理模块、一组神经网络分类器、决策模块。数据的预处理包括将测量信息仿射变换为适合于神经网络操作的格式，以及测量通道上的信息的组合，这取决于所定义的缺陷类型。分类器建立在卷积神经网络上。所提出的解决方案可以有效地实现现代元素的基础上进行并行计算，包括张量处理器和GPU。
英文摘要：
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.
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