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相关概念视频

IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the C=O, C=N, and C=C occur between 1600–1850 cm−1.
The...

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相关实验视频

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Spotting Cheetahs: Identifying Individuals by Their Footprints
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在WKNN指纹定位应用中的一个紧的蛇优化算法.

Weimin Zheng1, Senyuan Pang1, Ning Liu1

  • 1College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China.

Sensors (Basel, Switzerland)
|July 29, 2023
PubMed
概括

这项研究引入了紧蛇优化 (cSO) 以提高室内定位精度. 将cSO与加权的K-最近邻居 (WKNN) 和RSSI定位相结合,可以在具有挑战性的室内环境中显著减少本地化错误.

科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 信号处理 信号处理

背景情况:

  • 室内定位至关重要,但在狭窄的空间中具有挑战性.
  • 权重的K-最近邻居 (WKNN) 提高了定位的准确性.
  • 超启发式算法为复杂的优化问题提供了高效的解决方案.

研究的目的:

  • 为了提高室内定位准确度,使用元启发算法.
  • 引入和评估一个新的紧蛇优化 (cSO) 算法.
  • 在室内定位中验证启发式算法的有效性.

主要方法:

  • 开发了一个紧的蛇优化 (cSO) 算法,这是蛇优化 (SO) 的变体.
  • 评估cSO在28 CEC2013测试功能与其他智能计算算法相比.
  • 集成cSO与WKNN指纹和基于RSSI的室内定位.

主要成果:

  • 与现有的智能计算算法相比,cSO表现出卓越的性能.
  • 将cSO与WKNN和RSSI集成,大大减少了室内定位错误.
  • 在模拟中,cSO在提高本地化准确度方面被证明是有效的.
关键词:
在RSSI本地化.WKNNN 在线观看紧紧的 紧的 紧的蛇的优化是蛇的优化.

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结论:

  • 新的cSO算法为metaheuristic优化提供了一种高效的方法.
  • 将cSO与WKNN和RSSI定位相结合是准确的室内定位的可行策略.
  • 在资源有限的环境中,cSO有效地解决了计算和内存的限制.