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相关概念视频

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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.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
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相关实验视频

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Eliciting and Analyzing Male Mouse Ultrasonic Vocalization USV Songs
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在Cherry Valley中基于深度学习的性别识别,通过声音分析.

Guofeng Han1,2, Yujing Liu1,2, Jiawen Cai1,2

  • 1Institute of Agricultural Facilities and Equipment, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China.

Animals : an open access journal from MDPI
|October 26, 2024
PubMed
概括

本研究引入了一种自动化方法,用于通过声音分析来识别子的性别. 卷积神经网络 (CNN) 实现了95%的准确性,为子生产中人工劳动提供了有效的替代方案.

关键词:
在BP神经网络中,神经网络卷积神经网络是一种卷积神经网络.深度神经网络是一个神经网络.性别识别性别识别提供了可靠的信息.

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科学领域:

  • 农业科学 农业科学
  • 生物声学是一种生物声学.
  • 机器学习 机器学习

背景情况:

  • 在养中,性别识别至关重要,但目前依赖于劳动密集型的手工方法.
  • 开发一个自动化,高效的系统来识别子的性别对该行业来说至关重要.

研究的目的:

  • 提出一种新的方法,根据它们的发声特征来区分雄性和雌性一天大.
  • 评估不同机器学习模型的有效性,用于自动化子性别识别.

主要方法:

  • 使用终点检测记录和提取一天大的子的有效声音数据.
  • 使用Mel-frequency cepstral系数 (MFCCs) 和它们的差异系数计算了36维特征向量.
  • 训练并评估了三个分类模型:反向传播神经网络 (BPNN),深度神经网络 (DNN) 和卷积神经网络 (CNN).

主要成果:

  • 对于BPNN,DNN和CNN的训练准确率分别为83.87%,83.94%和84.15%.
  • 预测准确率达到了93.33% (BPNN),91.67% (DNN) 和95.0% (CNN) 的水平.
  • 卷积神经网络 (CNN) 显示出最高的识别准确率为95.0%.

结论:

  • 一天大的子的基于声音的性别识别是一种非常准确的方法.
  • 拟议的自动化系统,特别是使用CNN,可以显著支持在子生产中有效的性别识别,减少人工劳动.