Abstract Decision support systems for nondestructive evaluation of defects are often trained on a personal computer environment, which may slow the assessment task. This work investigates the feasibility of embedding extreme learning machines in microcontrollers, in order to execute in situ training for an ultrasound nondestructive evaluation. Principal component analysis and autoencoders were evaluated as dimensionality reduction methods in a data set acquired from welded SAE 1020 carbon steel plates containing four types of defects. A binary-encoded random weight approach is proposed, whose results were tested over four different probability density functions used to generate the random weights. The obtained experimental results matched the accuracy of a previous work, run in computer environment for higher dimensionality data. In this work a dimensionality reduction of 75% and elapsed time for system training of less than 0.5 ms in microcontroller were achieved, indicating the suitability of such embedded networks for the studied case.

中文翻译：

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基于极限学习机的超声无损评价嵌入式决策支持系统

摘要 用于缺陷无损评估的决策支持系统通常在个人计算机环境中进行训练，这可能会减慢评估任务。这项工作调查了在微控制器中嵌入极限学习机的可行性，以便执行超声无损评估的原位训练。在从包含四种缺陷的焊接 SAE 1020 碳钢板获得的数据集中，主成分分析和自动编码器被评估为降维方法。提出了一种二进制编码的随机权重方法，其结果在用于生成随机权重的四种不同概率密度函数上进行了测试。获得的实验结果与先前工作的准确性相匹配，在计算机环境中运行以获得更高维的数据。