基于GWO的毫米波系统和多层感知器的联合优化,用于考古应用
Julien Marot1, Flora Zidane2, Maha El-Abed2
1Centrale Mediterrannée, CNRS, Aix Marseille Université, Institut Fresnel, 13397 Marseille, France.
Sensors (Basel, Switzerland)
|May 11, 2024
概括
这项研究引入了一种使用太赫兹 (THz) 雷达进行陶器碎片分类的新方法,这对于了解古代农业至关重要. 开发的连续二进制三进制灰狼优化器 (CBTGWO) 显著降低了传感器数量并提高了精度.
科学领域:
- 考古学是一种科学.
- 雷达成像 雷达成像 雷达成像
- 机器学习 机器学习
背景情况:
- 太赫兹 (THz) 雷达是一种用于考古学应用的新型成像模式.
- 分类陶碎片对于追踪农业从肥沃的新月到欧洲的传播至关重要.
- 在雷达系统中优化传感器数量是成本效益和紧性的关键.
研究的目的:
- 共同设计THz雷达系统和用于陶器碎片分析的分类神经网络.
- 尽量减少传感器数量和相关成本,同时保持高分类准确度.
- 为了减少陶器碎片分类中的错误识别率.
主要方法:
- 开发一个新的连续二元三元灰狼优化器 (CBTGWO).
- 联合优化雷达系统设计和神经网络分类.
- 使用进化算法的传感器选择和放置策略.
主要成果:
- 在7频场景中,CBTGWO将37个传感器减少到1个,错误识别率为0%.
- 在单频场景中,CBTGWO在217个传感器中选择了2个,错误识别率为2%.
- 获取时间缩短到3.2秒,显著优于现有的GWO方法.
结论:
- CBTGWO有效地将基于雷达的THz考古分类的传感器要求降到最低.
- 这种方法为分析陶碎片提供了具有成本效益和效率的解决方案.
- 该方法具有很大的潜力,可以促进我们对历史农业传播的理解.
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