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Observational Learning01:12

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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
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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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通过半监督深度学习模型和新深度排序算法进行肉动物移动性评估.

Mustafa Jaihuni1, Hao Gan2, Tom Tabler1

  • 1Department of Animal Science, University of Tennessee, Knoxville, TN 37996, USA.

Animals : an open access journal from MDPI
|September 9, 2023
PubMed
概括

本研究引入了一种深度学习模型,用于跟踪肉的流动性,提高鸟类福利评估的准确性和效率. 这种新方法通过提供精确的,自动化的移动数据来加强肉福利监测.

关键词:
这是YOLOv5的.brojler 肉 肉 是一个移动性是一种流动性.新深层次的排序.半监督学习 半监督学习福利服务 福利服务

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科学领域:

  • 动物科学动物科学
  • 计算机科学 计算机科学
  • 农业工程 农业工程

背景情况:

  • 肉的流动性是关键的福利指标,但传统的评估是劳动密集型的,缺乏实时洞察力.
  • 需要自动化方法来有效和准确地监测肉福利.
  • 深度学习为先进的动物行为分析提供了潜力.

研究的目的:

  • 开发和验证一个半监督的深度学习模型,用于单个肉的移动性跟踪.
  • 通过自动化流动性分析,提高肉福利评估的准确性和效率.
  • 量化个体和群体水平的肉流动性,解决诸如闭塞和检测错误等挑战.

主要方法:

  • 使用YOLOv5 (你只看一次版本5) 的半监督深度学习 (DL) 方法.
  • YOLOv5模型最初是通过标记图像进行训练,然后通过半监督学习 (SSL) 使用伪标签进行微调.
  • 一个新型深度排序算法与训练有素的YOLOv5相结合,用于肉的检测,跟踪和移动性量化.

主要成果:

  • 半监督学习显著提高了YOLOv5模型的检测平均精度 (mAP),从81%提高到98%.
  • 组合模型在与手动测量相比,在估计个体肉每小时距离时达到约80%的验证准确性.
  • 该系统成功跟踪了28只肉牛,量化了每小时和每天的行程距离和速度,同时缓解了检测问题.

结论:

  • 拟议的半监督DL模型为自动化 brojler移动跟踪提供了一个准确和高效的工具.
  • 这项技术可以精确量化个体和群体水平的肉流动,这对于福利评估至关重要.
  • 该方法克服了动物跟踪的常见挑战,为精密畜牧业提供了强大的解决方案.