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相关概念视频

Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

501
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...
501

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相关实验视频

Updated: Jan 11, 2026

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基于变压器的深度学习模型用于预测fNIRS短通道信号.

Sabino Guglielmini1, Vittoria Banchieri1, Felix Scholkmann1,2,3

  • 1University Hospital Zurich, University of Zurich, Biomedical Optics Research Laboratory, Department of Neonatology, Zurich, Switzerland.

Neurophotonics
|November 17, 2025
PubMed
概括

这项研究引入了一种深度学习模型,用于预测功能近红外光谱 (fNIRS) 数据中的脑外信号. 这种虚拟方法为有效的血液动力学信号校正提供了一个硬件独立的替代方案.

关键词:
深度学习是一种深度学习.功能近红外光谱学近红外光谱学功能性近红外光谱学预处理生理学噪音是指生理学的噪音.短频道回归的方法信号无声化 信号无声化变压器编码器编码器

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Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels
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相关实验视频

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Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels
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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
  • 信号处理 信号处理

背景情况:

  • 功能近红外光谱学 (fNIRS) 提供了对大脑活动的非侵入性监测.
  • 脑外血动力学信号可能会污染fNIRS数据,需要使用短通道回归 (SCR) 等校正方法.
  • 用于SCR的物理短距离探测器可能受到硬件或实验设置的限制.

研究的目的:

  • 开发基于变压器的深度学习模型来预测从长距离fNIRS通道的短距离光密度 (OD) 信号.
  • 评估这些预测的虚拟信号在执行SCR和提高数据质量的有效性.

主要方法:

  • 一个变压器编码器模型被训练在静止状态的fNIRS数据与配对的短和长分离记录.
  • 该模型从长通道数据中预测了短通道OD信号.
  • 模型性能在三个独立的数据集上得到验证,包括静止状态和基于任务的范式.
  • 用MSE,NMSE和Pearson相关性等指标来评估信号相似性和无效的有效性.

主要成果:

  • 预测的短通道信号与地面真相测量有很高的对应性 (OD的中位数r=0.70).
  • 从模型中获得的虚拟回归器有效地拒绝了长通道fNIRS数据.
  • 该模型的性能在各种数据集中是稳定的,并且在排除低连贯性的道时得到了改进.
  • 在运动任务中,预测回归者保留了任务唤起的大脑活动,并减少了残余方差.

结论:

  • 基于变压器的深度学习模型可以从fNIRS数据中准确地重建脑外血液动力学信号.
  • 这为SCR提供了一个可行的虚拟替代物质短通道.
  • 该方法支持对fNIRS数据进行标准化,硬件独立的预处理.