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一个可解释的传感器选择策略,用于阵列优化和性能提升

Haixia Mei1, Jingyi Peng1, Tao Wang2

  • 1Key Lab Intelligent Rehabil & Barrier free Disable (Ministry of Education), Changchun University, Changchun 130022, China.

ACS sensors
|August 26, 2025
PubMed
概括
此摘要是机器生成的。

使用SHMI-Select方法优化电子鼻子 (E-nose) 传感器阵列可以减少传感器数量和冗余性. 这提高了各种应用中的气体检测准确性和系统性能.

关键词:
电子鼻子美国阵列优化可解释性相互提供信息

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科学领域:

  • 传感器技术
  • 人工智能
  • 数据科学

背景情况:

  • 在电子鼻子 (E-nose) 系统中增加传感器集成可以改善气体检测,但也带来了诸如交叉敏感性和冗余性等挑战.
  • 数组优化对于提高多传感器系统性能和克服这些局限性至关重要.

研究的目的:

  • 为优化多传感器阵列提出一种可解释的传感器选择策略,SHMI-Select.
  • 为了降低硬件成本,计算复杂性和E-nose系统中的信息冗余性.
  • 确保系统适应性和稳定性用于各种气体检测任务.

主要方法:

  • 开发了SHMI-Select,该方法结合了Shapley值和传感器选择的相互信息.
  • 根据可解释性分析实施可解释的初级传感器选择.
  • 使用相互信息进行二次传感器识别,并采取增量方法进行最佳组合.
  • 对人类呼吸,葡萄酒质量和环境气体数据集进行验证.

主要成果:

  • 在所有数据集中,SHMI-Select显著减少了传感器冗余性.
  • 实现了显著的性能提升:减少了62.5%的传感器,呼吸数据的准确性提高了10%.
  • 显示了83.3%的传感器减少,葡萄酒分类的准确性提高了18%.
  • 显示了62.5%的传感器减少和2%的R2增加,用于环境气体检测.

结论:

  • SHMI-Select方法有效地优化了E-nose传感器阵列,降低了复杂性和成本.
  • 与现有算法相比, 提供了显著的精度和性能改进.
  • 对电子鼻子系统的工业化具有强大的应用价值和经济效益.