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相关概念视频

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)01:20

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)

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When proton-coupled carbon-13 spectra are simplified by a broadband proton decoupling technique, structural information about the coupled protons is lost. Distortionless enhancement by polarization transfer (DEPT) is a technique that provides information on the number of hydrogens attached to each carbon in a molecule. While the DEPT experiment utilizes complex pulse sequences, the pulse delay and flip angle are specifically manipulated. The resulting signals have different phases depending on...
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相关实验视频

Updated: Jun 4, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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改进了基于自我培训的网络安全实体提取的远程标签撤销方法.

Ke Zhang1, Yunpeng Wang2, Ou Li1

  • 1Nuclear Power Institute of China, Chengdu, China.

PloS one
|December 17, 2024
PubMed
概括
此摘要是机器生成的。

本研究引入了一种新的自我训练方法,通过减少远程标记数据中的噪音,提高关键网络信息的提取精度来提高网络安全命名实体识别 (NER).

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科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 自然语言处理自然语言处理.

背景情况:

  • 命名实体识别 (NER) 对于提取网络安全信息至关重要.
  • 目前的方法需要大量的手工标签,因为缺乏特定的标签.
  • 这需要更有效的数据注释技术.

研究的目的:

  • 为网络安全实体提取开发一个改进的自我训练方法.
  • 为了应对噪音,远程标记数据的挑战.
  • 提高提取网络安全实体的准确性和效率.

主要方法:

  • 创建两个网络安全领域字典.
  • 开发一种结合逆最大匹配和POS标签的算法,用于远程标签生成.
  • 实施高可信度文本选择和受约束的自我训练算法,使用教师-学生模型.

主要成果:

  • 获得了高质量的网络安全远程标记数据.
  • 受约束的自我训练算法显著改善了NER模型的性能.
  • 实现了3.5%的F1得分改善供应商和3.35%的产品类别.

结论:

  • 拟议的方法有效地否认了远程标记的网络安全数据.
  • 这种方法提高了最先进的NER模型的性能.
  • 这有助于更准确,更有效地提取网络安全信息.