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相关概念视频

Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

523
In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the...
523
Transformers in Distribution System01:27

Transformers in Distribution System

498
Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
Distribution substation transformers come in various ratings and typically use mineral oil for insulation and cooling. To prevent moisture and air from entering the oil, some transformers use an inert gas like nitrogen to fill the...
498

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TMU-Net:以变压器为基础的多模式框架,具有不确定性量化,用于检测驾驶员疲劳.

Yaxin Zhang1, Xuegang Xu1, Yuetao Du1

  • 1School of Electronic Information Engineering, Xi'an Technological University, Xi'an 710021, China.

Sensors (Basel, Switzerland)
|September 13, 2025
PubMed
概括

本研究介绍了TMU-Net,这是一个新的多式联网,使用电脑图 (EEG) 和电眼图 (EOG) 信号来准确检测驾驶员的疲劳. 该方法提高了跨主题测试的稳定性和稳定性.

关键词:
驾驶员疲劳检测 驾驶员疲劳检测电脑电图 (EEG) 是一种电脑电图.电眼电图 (EOG) 是指一个电眼电图.多式联络融合多式联络融合不确定性量化不确定性量化

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科学领域:

  • 神经科学是一个神经科学.
  • 生物医学工程 生物医学工程
  • 计算机科学 计算机科学

背景情况:

  • 驾驶疲劳是交通事故的主要原因之一.
  • 目前的自动化疲劳检测方法在准确性,稳定性和跨主题概括性方面面临挑战.
  • 多式联运数据融合有可能改善驾驶员疲劳估计.

研究的目的:

  • 为准确检测驾驶员疲劳提出一个新的多式联网注意力网络 (TMU-Net).
  • 整合电脑电图 (EEG) 和电眼电图 (EOG) 信号,以提高疲劳评估.
  • 提高疲劳检测系统的稳定性和实用性.

主要方法:

  • 开发了TMU-Net,采用单模特征提取 (因果卷积,ConvSparseAttention,变压器编码器) 和多模组合模块 (跨模态注意力,不确定性加权门).
  • 嵌入不确定性量化,以提高对噪声和个体变化的稳定性.
  • 在SEED-VIG数据集上使用23个受试者的跨主体测试验验证了绩效.

主要成果:

  • TMU-Net在跨主体疲劳检测中表现出卓越的性能稳定性.
  • 该网络有效地利用了来自EEG (全频带和五频带特征) 和EOG信号的互补信息.
  • 注意热图可视化证实了EEG-EOG信号融合策略的生理理理性.

结论:

  • TMU-Net通过有效地融合EEG和EOG信号来实现高精度的驾驶员疲劳检测.
  • 拟议的方法显示了强度和稳定性在跨主体疲劳检测方面的显著改进.
  • 这些发现凸显了先进疲劳监测系统的多式联运信号集成和注意力机制的潜力.