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Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre- and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
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使用自定义 CNN 架构在 LIBS 分析中提高预测稳定性和性能.

Pegah Dehbozorgi1, Ludovic Duponchel2, Vincent Motto-Ros3

  • 1Leibniz Institute of Photonics Technology, Member of Leibniz Health Technologies, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Albert-Einstein-Strasse 9, 07745, Jena, Germany; Institute of Physical Chemistry (IPC) and Abbe Centre of Photonics (ACP), Friedrich Schiller University Jena, Member of the Leibniz Centre for Photonics (LPI), Helmholtzweg4, 07743, Jena, Germany.

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概括

本研究比较了PLS和CNN两种方法,用于使用激光诱导分解光谱 (LIBS) 进行元素分析. 从模拟和真实LIBS数据中预测元素度时,CNNs表现出卓越的准确性和稳定性.

关键词:
经典回归是一种经典的回归.深度学习是一种深度学习.基本面分析 基本面分析在LIBS中,LIBS是指LIBS.这就是PLS PLS.

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科学领域:

  • 分析化学 分析化学
  • 频谱学是一种光谱学.
  • 机器学习 机器学习

背景情况:

  • 激光诱导分解光谱 (LIBS) 是一种强大的元素指纹技术,提供快速,同时的多元素分析.
  • 现有的LIBS分析往往忽略了等离子体温度和电子密度的变化,影响了准确性.
  • 弥合模拟和真实数据分析之间的差距对于稳健的LIBS应用程序至关重要.

研究的目的:

  • 开发和比较在LIBS光谱中的元素度的预测模型 (PLS和CNN).
  • 在LIBS数据分析中考虑等离子体温度和电子密度的变化.
  • 通过先进的机器学习提高元素度预测的准确性和稳定性.

主要方法:

  • 利用部分最小平方 (PLS) 和卷积神经网络 (CNN) 进行预测建模.
  • 在模拟的LIBS数据上训练并测试模型,使用根平均平方预测误差 (RMSEP) 评估性能.
  • 将预先训练的模型应用于真正的LIBS光谱和微调的CNN架构,用于元素特定的预测.

主要成果:

  • 在模拟数据上,CNN的中位数RMSEP值低于0.01,在模拟数据上表现优于PLS (0.01-0.05).
  • 真实LIBS光谱分析始终确定 (Al), (Si) 和铁 (Fe) 具有高预测度.
  • 修改后的CNN突出了规范化,样本权重和定制损失函数的影响,揭示了Ca,Mg,Zn,Ti和Ga的模式.

结论:

  • 与PLS相比,CNN在LIBS分析中为元素度提供了更高的稳定性和预测准确性.
  • 该研究成功地弥合了模拟和真实LIBS数据分析之间的差距,通过考虑关键等离子体参数.
  • 微调CNN提供了一条途径,在复杂的LIBS光谱中优先确定和检测特定元素.