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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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一个综合的废物管理方法,使用GAN增强分类.

Yashashree Mahale1, Nida Khan1, Kunal Kulkarni1

  • 1Department of Artificial Intelligence and Machine Learning, Symbiosis International Deemed University, Symbiosis Institute of Technology, Pune, Maharashtra, India.

PeerJ. Computer science
|September 24, 2025
PubMed
概括

本研究使用生成对抗网络 (GAN) 来增强废物图像,提高对象分类的准确性. 将GAN与整体模型相结合,可以达到99.80%的准确性,从而实现更好的废物管理.

关键词:
计算机视觉 计算机视觉 计算机视觉数据增强数据增强组合学习学习 组合学习这是GANs (生成对抗网络).对象检测检测对象检测对象检测废物对象的分类 废物对象的分类

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科学领域:

  • 计算机视觉 计算机视觉
  • 机器学习 机器学习
  • 环境科学 环境科学

背景情况:

  • 数据增强对于机器学习模型性能至关重要,特别是在图像分类中.
  • 废物对象的分类是改善废物管理和可持续性的关键应用.

研究的目的:

  • 使用生成对抗网络 (GAN) 增强废物对象图像.
  • 通过集体学习来提高废物检测和分类准确性.
  • 为了研究基于GAN增强的有效性,对现实世界的应用进行整体模型.

主要方法:

  • 使用深度卷积GAN (DCGAN) 来实现现实的图像生成和可变性.
  • 使用DenseNet121,ConvNeXt和ResNet101进行对象检测和分类.
  • 集成的基于GAN的数据增强与整体分类模型.

主要成果:

  • 使用集体学习实现了高分类准确率99.80%.
  • 证明了GAN在生成多样化和现实的废物图像方面的有效性.
  • 验证了优化废物物体识别的新方法.

结论:

  • 基于GAN的数据增强与组合模型相结合,显著提高了废物对象分类的准确性.
  • 这种方法为通过先进的AI技术优化废物管理提供了宝贵的见解.
  • 未来的研究将探索先进的GAN架构和多模式数据,以进一步提高性能.