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Updated: Sep 16, 2025

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基于特征选择和参数优化,用于磁流泄漏测试中的铁路缺陷的智能分类方法.

Kailun Ji1, Ping Wang1, Yinliang Jia1

  • 1College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.

Sensors (Basel, Switzerland)
|July 12, 2025
PubMed
概括

这项研究通过使用优化的神经网络提高了铁路磁流泄漏 (MFL) 检测精度. 改进的框架显著提高了对各种铁路缺陷的分类性能,确保了更安全的基础设施.

关键词:
功能选择 功能选择不平衡的数据不平衡的数据.智能分类是一种智能分类.磁流泄漏 (MFL) 测试 磁流泄漏的测试模型优化 模型优化铁路缺陷检测检测 铁路缺陷检测 铁路缺陷检测

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