相关实验视频

Updated: Jul 13, 2025

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon
09:44

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Published on: October 16, 2018

10.2K

使用反向传播神经网络与主要组件分析重建缺失的时间变化的土地沉降数据.

Chih-Yu Liu1, Cheng-Yu Ku2, Jia-Fu Hsu3

  • 1Department of Civil Engineering, National Central University, Taoyuan, 320317, Taiwan.

Scientific reports
|October 13, 2023
PubMed
概括

这项研究使用一种新的算法考虑了八个因素来重建缺失的土地沉降数据. 反向传播神经网络准确地填补了数据空白,避免了复杂的水文地质模型.

更多相关视频

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.7K
Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar07:14

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

Published on: May 1, 2018

7.8K

相关概念视频

Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
85