结合深度学习算法和a-star算法,提高追踪丢失马源的准确性
Truong Hoang Linh1, Nguyen Huynh Duy Khang1, Huynh Dinh Chuong2
1Faculty of Physics, Ho Chi Minh City University of Education, Ho Chi Minh City, Vietnam.
概括
一个新的A星CNN-RNN (ACR) 算法有效地定位丢失的马源. 这种深度学习方法显著减少了搜索步骤,并提高了准确性,即使有障碍,也优于传统方法.
科学领域:
- 核工程 核工程是指核工程.
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
背景情况:
- 定位丢失的马源对于核应用中的安全性和效率至关重要.
- 传统的搜索算法面临着复杂环境和障碍的挑战.
研究的目的:
- 开发和评估一个自动搜索算法,A-star CNN-RNN (ACR),用于定位丢失的马光源.
- 在各种模拟环境中,与梯度搜索 (GS) 相比,评估ACR的性能.
主要方法:
- 一种混合深度学习模型,将卷积神经网络 (CNN) 和循环神经网络 (RNN) 与A星算法相结合.
- 使用蒙特卡罗N粒子 (MCNP) 代码模拟辐射剂量率生成的输入数据.
- 基于平均步骤和预测失败率的性能评估,在有障碍和没有障碍的房间.
主要成果:
- 与GS相比,ACR在没有障碍的环境中显示了大约一半的平均跟踪步骤.
- ACR实现了高精度 (故障率<5%) 与障碍物,显著优于GS (44%的故障率与8米墙).
- 在没有重新训练的情况下,ACR在复杂的障碍场景 (平行墙,L形墙) 中保持了超过94%的预测准确性.
结论:
- A-star CNN-RNN (ACR) 算法是用于丢失的马源检测的强大而适应性的解决方案.
- 在具有挑战性的环境中,ACR比传统方法提供了更高的性能和精度.
- 作为核源定位的实用搜索算法,ACR显示出显著的潜力.
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