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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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基于深域适应的室内本地化算法转移学习

Jiahao Wang1, Yifu Fu1, Hainan Feng1

  • 1School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610000, China.

Sensors (Basel, Switzerland)
|December 9, 2023
PubMed
概括
此摘要是机器生成的。

这项研究引入了深度联合平均分布适应网络 (DJMDAN),以提高室内定位准确度. 这种新的方法增强了特征提取,以更好地适应域名,优于现有的算法.

关键词:
深域适应网络深域适应网络室内局部化 室内局部化转移学习转移学习

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科学领域:

  • 计算机科学 计算机科学
  • 信号处理 信号处理
  • 机器学习 机器学习

背景情况:

  • 室内定位系统由于不同的环境条件和信号漂移而面临准确度下降.
  • 现有的算法在训练和测试数据集之间存在不一致的数据分配问题.
  • 目前的转移学习方法由于浅薄的特征提取,提供有限的域调整.

研究的目的:

  • 在非理想条件下解决室内定位算法的准确性不稳定性.
  • 通过利用深度学习的特征提取能力来提高室内本地化领域的适应性.
  • 提出一种新的深度学习网络,用于强大的室内定位.

主要方法:

  • 开发了一个深度联合平均分布适应网络 (DJMDAN).
  • 该网络利用深度网络进行增强的特征提取.
  • DJMDAN 在不同的数据域中对齐全球和子域分布.

主要成果:

  • 拟议的DJMDAN方法在室内定位应用中表现出卓越的性能.
  • 这种方法通过学习更多可转移的特征,有效地减少了域差异.
  • 实验结果显示比较算法显著改进.

结论:

  • 基于深度学习的域调整为室内定位挑战提供了一个有希望的解决方案.
  • DJMDAN有效地减轻了因数据分布转移而导致的精度降低.
  • 拟议的方法提高了室内定位算法的可靠性和可行性.