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相关概念视频

Olfaction01:25

Olfaction

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The sense of smell is achieved through the activities of the olfactory system. It starts when an airborne odorant enters the nasal cavity and reaches olfactory epithelium (OE). The OE is protected by a thin layer of mucus, which also serves the purpose of dissolving more complex compounds into simpler chemical odorants. The size of the OE and the density of sensory neurons varies among species; in humans, the OE is only about 9-10 cm2.
The olfactory receptors are embedded in the cilia of the...
44.0K

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Controlled Odor Mimic Permeation Systems for Olfactory Training and Field Testing
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在电子鼻子中使用强大的气味检测,使用转移学习驱动的气味制造器模型.

Wangze Ni1,2, Tao Wang3, Yu Wu4

  • 1National Key Laboratory of Advanced Micro and Nano Manufacture Technology, Shanghai Jiao Tong University, Shanghai 200240, China.

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概括

这项研究介绍了Scentformer,这是一种新的深度学习电子鼻子 (E-nose),可以准确检测55种自然气味. 它的转移学习能力允许高效地适应新气味,使用最小的数据.

关键词:
卷积神经网络是一种卷积神经网络.电子鼻子 电子鼻子多头注意力多头注意力气味检测 气味检测 气味检测 气味检测转移学习转移学习

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科学领域:

  • 人工智能的人工智能
  • 感官系统工程 感官系统工程
  • 计算化学计算化学

背景情况:

  • 电子鼻子 (E-noses) 模仿人类的嗅觉来检测气味.
  • 目前的E-noses在检测范围和通用性方面面临限制.
  • 开发先进的E-nose技术对于各种应用至关重要.

研究的目的:

  • 为了介绍一种基于深度学习的新型电子鼻子,Scentformer.
  • 为了克服现有的 E-nose 系统的局限性.
  • 为了提高气味检测的准确性和适应性.

主要方法:

  • 开发了一种使用Scentformer深度学习架构的新型E-nose系统.
  • 实施了一种自我适应的数据下方采样方法,以实现高效的处理.
  • 采用沙普利增量解释来解释模型的可解释性.
  • 利用转移学习来快速适应新的气味和气体.

主要成果:

  • 在55种天然气味的分类准确度达到了99.94%.
  • 使用转移学习 (准确率99.14%) 进行新的气味检测,以最小的数据 (1‰) 证明了强大的性能.
  • 通过Shapley添加剂解释提供了E-鼻子性能的定量解释.

结论:

  • 香味器在电子鼻子技术中提供了显著的进步.
  • 该系统具有高精度,广泛的检测范围和出色的通用性.
  • 转移学习显著减少了适应新的气味检测任务的数据需求.