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Survival Tree01:19

Survival Tree

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
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A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
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可变数据结构和定制的深度学习替代品,以计算高效和可靠地描述埋藏的对象.

Reyhan Yurt1, Hamid Torpi2, Ahmet Kizilay2

  • 1Kırşehir Department of Electrical and Electronics Engineering, Kırşehir Ahi Evran University, 40100, Kırşehir, Turkey.

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|June 28, 2024
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概括
此摘要是机器生成的。

一个新的深度回归网络 (DRN) 使用地面透雷达 (GPR) 数据高效地描述埋藏的物体. 这种深度学习方法可将分析速度加快13倍,为各种地下场景提供准确的预测.

关键词:
人工智能的人工智能是人工智能.埋藏物体的特征描述埋藏物体的特征描述深度回归网络是一个深度回归网络.地面穿透雷达 (GPR) 是一种地面穿透雷达.代理模拟代理模拟时间频率谱图时间频率谱图

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科学领域:

  • 地质物理学 地质物理学
  • 人工智能的人工智能
  • 电磁学 电磁学 电磁学 电磁学

背景情况:

  • 使用地面透雷达 (GPR) 准确地描述埋藏的物体对于各种应用至关重要.
  • 传统方法通常涉及高计算成本和复杂的数据处理.
  • 深度学习为开发高效的替代模型提供了一个有希望的途径,用于GPR数据分析.

研究的目的:

  • 开发一种基于深度学习的替代模型模型方法,用于使用GPR来描述埋藏的物体.
  • 在各种地下介质中独立预测埋藏物体的特征参数 (半径,深度,侧面位置).
  • 分析不同GPR数据结构的计算成本和准确性之间的权衡.

主要方法:

  • 利用了GPR模型的3D全波电磁模拟.
  • 采用深度回归网络 (DRN) 来分析连续A扫描的时间频谱图 (TFS).
  • 将DRN的性能与使用B扫描图像 (2D数据) 和最先进的回归技术的传统网络模型进行了比较.
  • 使用噪音数据评估模型的稳定性,并用物理测量数据验证它.

主要成果:

  • 拟议的DRN模型与传统的B扫描图像模型相比,实现了大约13倍的加速.
  • 获得的平均绝对误差为3.6mm,对象表征的相对误差为4.7%.
  • 与最先进的回归技术相比,已证明具有竞争力的性能.
  • 显示适合处理噪音数据和物理测量数据.

结论:

  • 深度回归网络 (DRN) 为基于GPR的埋藏对象表征提供了一个计算效率高,准确的替代模型方法.
  • 使用DRN进行TFS分析,可以有效地预测不同地下条件下的物体参数.
  • 经过验证的方法显示了涉及物理GPR测量的现实应用的巨大潜力.