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通过基于Kriging的Sparse回归部分微分方程与反注射成型的基于Kriging的融合来增强脑电脑接口 节点位移效应的视图

Hanjui Chang1,2, Yue Sun1,2, Shuzhou Lu1,2

  • 1Department of Mechanical Engineering, College of Engineering, Shantou University, Shantou 515063, China.

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概括

这项研究通过使用Kriging和稀疏回归模型优化了脑计算机接口的注射成型. 新方法显著减少了电极位移,提高了EEG信号的准确性和设备的性能.

关键词:
在Kriging中使用Kriging.在PDE中,PDE是 PDE.大脑计算机接口 (BCI)在模具中的电子 (IME)节点的移位是节点的移位.

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科学领域:

  • 制造业 工程 制造工程
  • 生物医学工程 生物医学工程
  • 材料科学 材料科学 材料科学

背景情况:

  • 注塑成型是塑料制品的关键制造工艺.
  • 模具内电子 (IME) 将电子元件集成到成型部件中.
  • 大脑-计算机接口 (BCI) 需要精确集成组件,如信号传输的电极阵列.

研究的目的:

  • 为优化注塑成型参数嵌入电极线在BCI的薄膜.
  • 为了提高脑电图 (EEG) 信号传输的稳定性和准确性.
  • 为了降低成本并提高微电极阵列的集成性.

主要方法:

  • 使用Kriging预测模型与稀疏回归部分微分方程 (PDEs) 结合.
  • 专注于优化主要的注塑成型参数:保持压力,保持时间和化温度.
  • 评估了膜中节点的位移,以确保稳定性和可靠性.

主要成果:

  • 实现了最佳的注射参数:525 MPa的承压,50秒的承压时间和285°C的化温度.
  • 减少了犹他阵列 (UA) 的平均节点位移,从0.19mm降至0.89μm.
  • 在节点位移方面表现出95.32%的优化率,提高了信号传输的准确性.

结论:

  • 拟议的方法有效地优化了BCI中的IME应用的注塑成型.
  • 优化的参数确保了制造过程中电极线的稳定性和可靠性.
  • 这一进步提高了EEG信号的准确性和BCI系统的整体性能.