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相关概念视频

Discrete Fourier Transform01:15

Discrete Fourier Transform

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The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
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Modeling with Differential Equations01:25

Modeling with Differential Equations

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Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...
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相关实验视频

Updated: Jan 13, 2026

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

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一种基于过的时域数据增强方法的神经网络模型,用于预测振动不适.

Zunming Wang1, Jianjiao Deng2, Yi Qiu3

  • 1College of Energy Engineering, Zhejiang University, Hangzhou, China.

Ergonomics
|October 29, 2025
PubMed
概括

研究人员开发了一种深度学习模型,以预测多功能车辆 (MPV) 第二排座位的振动不适. 该模型使用靠背加速准确预测不适,建立了座椅振动分析的框架.

关键词:
整个身体的振动.座椅振动 座椅振动是什么意思座位动态 座位动态主观评价是一个主观的评价.感觉不舒服的振动,不适的感觉.

相关实验视频

Last Updated: Jan 13, 2026

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

6.0K

科学领域:

  • 汽车工程 汽车工程
  • 人类因素工程 人类因素工程
  • 计算力学 计算力学 计算力学

背景情况:

  • 在车辆设计中,乘客舒适度至关重要,特别是在多功能车辆 (MPV) 中,第二排座位体验独特的振动动态.
  • 量化和预测振动引起的不适是具有挑战性的,因为复杂的振动传输路径和主观的人类感知.

研究的目的:

  • 为了调查MPV的第二排座位的振动引起的不适.
  • 开发和验证一个基于深度学习的预测模型来预测乘客的不适.
  • 建立一个框架来建模座椅振动和主观不适之间的关系.

主要方法:

  • 实验是在一个四个海报的测试装置上进行的,以记录靠背,座椅板和扶手的多方向加速.
  • 使用长期短期记忆 (LSTM) 神经网络来建模振动数据和不适度等级之间的非线性关系.
  • 使用包括过白噪声在内的数据增强技术来增强数据集和模型通用性.

主要成果:

  • 深度学习模型在使用三向靠背加速作为输入时,实现了对不适的高预测准确性.
  • 包括其他座椅组件 (座椅板,扶手) 的振动数据作为输入功能导致预测性能下降.
  • 该研究成功建立了一个预测框架,将客观振动测量与主观不适水平联系起来.

结论:

  • 靠背加速是预测MPV中第二排座椅振动不适的关键指标.
  • 开发的LSTM模型提供了一种有效的方法来量化振动引起的不适.
  • 未来的研究可以通过探索额外的振动特征和车辆操作条件来完善模型.