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相关概念视频

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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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An important concept in studying metabolism and energy is that of chemical equilibrium. Most chemical reactions are reversible. They can proceed in both directions, releasing energy into their environment in one direction, and absorbing it from the environment in the other direction. The same is true for the chemical reactions involved in cell metabolism, such as the breaking down and building up of proteins into and from individual amino acids, respectively. Reactants within a closed system...
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相关实验视频

Updated: Jan 6, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

980

优化软件工程英语翻译使用增强的灰狼优化与自我注意力和Bi-LSTM模型.

Fang Yuan1, Yao Liu2, Yongfeng Ju3

  • 1School of Foreign Languages, Huaiyin Normal University, Huai'an, China. iris@hytc.edu.cn.

Scientific reports
|October 10, 2025
PubMed
概括

这项研究介绍了自适应灰狼优化与自我注意和LSTM (AGWO-SALSTM) 模型用于增强机器翻译. 这种新的方法在专业领域显著提高了中英翻译的准确性和效率.

关键词:
注意力机制注意力机制这是一个双LSTM.灰狼优化 灰狼优化软件工程 软件工程 软件工程

相关实验视频

Last Updated: Jan 6, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

980

科学领域:

  • 自然语言处理自然语言处理.
  • 人工智能的人工智能
  • 计算语言学 计算语言学

背景情况:

  • 传统的神经机器翻译模型,如变压器和LSTM在动态超参数优化和域适应方面面临挑战.
  • 低于最佳的准确性和效率阻碍了现有模型在软件工程等专业领域的应用.
  • 有效地弥合语言差距需要先进的机器翻译解决方案.

研究的目的:

  • 提出一个增强的机器翻译模型,自适应灰狼优化与自我注意和LSTM (AGWO-SALSTM),以改进中英翻译.
  • 为了动态优化超参数和增强语境理解,以提高翻译准确性和效率.
  • 在专业领域解决传统模型的局限性.

主要方法:

  • 开发了AGWO-SALSTM模型,集成自适应灰狼优化 (AGWO) 进行超参数调整 (学习速率,注意力权重,网络配置).
  • 集成的自我注意力机制和双向长期短期记忆 (LSTM) 网络,以改善上下文理解和顺序数据处理.
  • 使用PARACRAWL,WMT,UM-Corpus和OPUS数据集对变压器,LSTM-Seq2Seq和MT5基线进行了AGWO-SALSTM模型的验证.

主要成果:

  • 在所有测试的数据集中,AGWO-SALSTM在基线模型中表现出优异的性能.
  • 实现了95.56%的平均翻译准确度,明显优于MT5 (90.94%).
  • 与变压器模型相比 (57次) 需要更少的代 (16-20次) 来实现融合,这表明效率提高.

结论:

  • AGWO-SALSTM 模型在机器翻译的准确性和效率方面提供了显著的进步,特别是在专门领域的中英翻译中.
  • 通过AGWO动态超参数优化和通过自我注意力和LSTM增强的上下文处理是模型性能改善的关键.
  • 拟议的模型有效地克服了传统神经机器翻译方法的局限性,为更强大,更有效的语言桥梁解决方案铺平了道路.