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相关概念视频

Methods of Obtaining Topography01:25

Methods of Obtaining Topography

550
Topography involves measuring and mapping land elevations, natural features, and artificial structures to create accurate representations of the terrain. Topographic surveying relies on traditional and modern methods, each with distinct advantages and limitations.Traditional Surveying Methods:Transit stadia surveys and plane table surveys were widely used traditional surveying methods. These techniques relied on instruments like theodolites and stadia rods for measuring distances and angles,...
550
Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device01:30

Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device

454
Surveyors use Global Positioning System (GPS) technology to measure the precise location and elevation of points on Earth. In a recent survey, GPS receivers were used to determine the coordinates and elevations of two park monuments. The process involved careful mission planning, data collection, and correction to ensure accuracy. The survey began with mission planning to identify optimal satellite visibility and minimize Position Dilution of Precision (PDOP). A geodetic control point...
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相关实验视频

Updated: May 2, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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基于转移学习的地面目标识别方法

Qiuzhan Zhou1, Jikang Hu1, Huinan Wu1

  • 1College of Communication Engineering, Jilin University, Changchun 130012, China.

Sensors (Basel, Switzerland)
|January 25, 2025
PubMed
概括
此摘要是机器生成的。

本研究引入了一种使用卷积神经网络 (CNN) 和转移学习的新移动地面目标识别算法. 改进后的系统提高了识别行人和车辆的准确性和环境适应性.

关键词:
卷积神经网络是一种卷积神经网络.域名适应 域名适应目标识别 目标识别转移学习转移学习振动传感器 振动传感器

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科学领域:

  • 机器人和自动化机器人与自动化
  • 信号处理 信号处理
  • 机器学习 机器学习

背景情况:

  • 移动地面目标识别系统对于监测关键区域至关重要.
  • 基于振动传感器的系统提供了小尺寸和低功耗等优势.
  • 现有的算法与远距离目标,环境适应性和准确性作斗争.

研究的目的:

  • 开发一个改进的移动地面目标识别算法.
  • 提高适应新环境的能力,提高识别准确性.
  • 为了利用转移学习来实现可靠的目标识别.

主要方法:

  • 利用卷积神经网络 (CNN) 来从振动信号中提取特征.
  • 应用领域适应技术以提高环境稳定性.
  • 集成转移学习以提高算法的适应性.

主要成果:

  • 拟议的算法成功地从振动信号中识别了目标类型.
  • 在识别准确度方面取得了显著的改进.
  • 展示了有效地适应不同的环境条件.

结论:

  • 基于CNN和转移学习的新算法为移动地面目标识别提供了卓越的性能.
  • 该方法提高了适应性和准确性,克服了现有方法的局限性.
  • 这项工作为更先进的多模式融合和转移学习识别系统提供了基础.