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相关概念视频

Improving Translational Accuracy02:07

Improving Translational Accuracy

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...
Improving Translational Accuracy02:07

Improving Translational Accuracy

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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相关实验视频

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A Piglet Model of Neonatal Hypoxic-Ischemic Encephalopathy
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Published on: May 16, 2015

通过基于转移学习的模型提高氨度预测:在养猪场的应用.

Sunhyoung Lee1, Rack-Woo Kim2, Hakjong Shin3

  • 1Department of Agriculture Engineering, College of Industrial Sciences, Kongju National University, 54 Daehak-ro, Yesan-eup, Yesan-gun 32439, Chungcheongnam-do, Republic of Korea.

Animals : an open access journal from MDPI
|February 27, 2026
PubMed
概括

人工智能使用转移学习准确地预测猪舍中的氨 (NH3),优于仅在本地数据上训练的模型. 这种方法可以在智能农业中实现高效的环境管理.

关键词:
在XGBoost中使用.氨是一种氨.人工智能的人工智能是人工智能.猪房子的猪房子转移学习转移学习

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Adaptation of Microelectrode Array Technology for the Study of Anesthesia-induced Neurotoxicity in the Intact Piglet Brain
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Published on: May 12, 2018

科学领域:

  • 农业工程 农业工程
  • 环境科学 环境科学
  • 人工智能的人工智能

背景情况:

  • 猪养殖的强化引发了人们对氨 (NH3) 排放的担忧.
  • 智能农业技术需要可靠的NH3监测,而不仅仅依赖昂贵的传感器.

研究的目的:

  • 开发一种基于人工智能的模型,用于预测商业猪舍中的NH3度.
  • 评估数据收集间隔和学习策略对预测准确性的影响.
  • 将独立模型与转移学习模型进行 NH3 预测的比较.

主要方法:

  • 为NH3度开发一个人工智能预测模型.
  • 在本地数据上训练的独立模型与转移学习模型的比较.
  • 使用随机森林和XGBoost算法对各种数据收集间隔 (10,20,30,60分钟) 的评估.

主要成果:

  • 转移学习模型在所有测试的数据收集间隔中始终优于独立模型.
  • 最好的随机森林和XGBoost模型实现了高精度,R2为0.969,RMSE~1.0 ppm,MAPE<5%.
  • 即使使用转移学习的稀疏数据,也可以实现准确的NH3预测.

结论:

  • 转移学习提供了一种可靠且数据效率高的方法,用于预测猪舍中的NH3度.
  • 这种人工智能驱动的方法支持猪业的可持续和改进的环境管理.
  • 这些发现有助于在智能农业环境中集成先进监测.