An adaptive grid search algorithm for fitting spherical target of terrestrial LiDAR

Shi, Yintao; Zhao, Gang; Wang, Maowei; Yi, Xu

doi:10.1016/j.measurement.2022.111430

Cited by 11 publications

(2 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In machine learning prediction models, if the hyperparameters cannot be chosen and found correctly, underfitting or overfitting problems might be caused. Currently, two common optimization methods for hyperparameter sets are grid search [42] and random search [43]. Grid search determines the optimal value by finding all the points in the search range, while random search does not test all the values between the upper bound and the lower bound, but randomly selects sample points in the search range.…”

Section: Hyperparameter Tuning and Optimization Methodsmentioning

confidence: 99%

Experimental Analysis and Machine Learning of Ground Vibrations Caused by an Elevated High-Speed Railway Based on Random Forest and Bayesian Optimization

Cao,

Li,

Xiang

et al. 2023

Sustainability

View full text Add to dashboard Cite

With the aim of predicting the environmental vibrations induced by an elevated high-speed railway, a machine learning method was developed by combining a random forest algorithm and Bayesian optimization, using a dataset from on-site experiments. When it comes to achieving a rapid and effective prediction of environmental vibrations, there is little research on comparisons between and verifications of different algorithms, and none on the parameter tuning and optimization of machine learning algorithms. In this paper, a field experiment is firstly carried out to measure the ground vibrations caused by high-speed trains running on a bridge, and then the environmental vibration characteristics are analyzed in view of ground accelerations and weighted vibration levels. Subsequently, three machine learning algorithms using linear regression, support vector machine, and random forest are developed using an experimental database, and their prediction performance is discussed. Finally, two optimization models for the hyperparameter set of the random forest algorithm are further compared. The results show that the integrated random forest algorithm has a higher accuracy in predicting environmental vibrations than linear regression and the support vector machine; the Bayesian optimization has an excellent performance and a high efficiency in achieving efficient and in-depth optimization of parameters and can be combined with the RF machine learning algorithm to effectively predict the environmental vibrations induced by the high-speed railway.

show abstract

Section: Hyperparameter Tuning and Optimization Methodsmentioning

confidence: 99%

Experimental Analysis and Machine Learning of Ground Vibrations Caused by an Elevated High-Speed Railway Based on Random Forest and Bayesian Optimization

Cao,

Li,

Xiang

et al. 2023

Sustainability

View full text Add to dashboard Cite

show abstract

“…In terms of structured feature extraction from point clouds, Shi et al [19] proposed an iterative algorithm for the center extraction of spherical targets. Te method utilizes the centroid of the point cloud and the estimated radius of the spherical target as constraints and uses a grid search to determine the optimal solution of the parameters.…”

Section: Feature Point Extraction Technique Based On Point Cloudmentioning

confidence: 99%

Fast Extraction Algorithm of Planar Targets Based on Point Cloud Data for Monitoring the Synchronization of Bridge Jacking Displacements

Liang,

Zhang,

Zhang

et al. 2024

Structural Control and Health Monitoring

View full text Add to dashboard Cite

Transverse synchronization of vertical displacements of all jacking-up points is an important monitoring indicator to replace bearings in assembled multigirder bridges during the jacking phase. Currently, using target paper to identify the 3D coordinates of control points reduces the complexity of monitoring operations and improves the stability of data precision. However, the existing planar target locating methods have low accuracy, inefficiency, and subjectivity, which seriously hinders the construction process of bearing replacement. Accurately obtaining the center coordinates of multiple targets from a point cloud in a short monitoring period remains a challenge. This study proposes a high-precision automated algorithm to extract target center points in low-density point clouds to quickly calculate real target center points. First, we construct a standard point cloud model of the target papers for scanning, including color and geometric features. Then, we extract the measured point cloud of the typical jacking operation phase based on the reflection intensity and size information. Next, we map the intensity values of the measured point cloud into the color channel and register the measured point cloud with its standard point cloud model using the normal vector estimation and colored ICP algorithms. Finally, we extract the center point of the measured targets. Numerical experiments and engineering test results show that the proposed method converges quickly with high precision and good robustness, which saves 91.4% of the time compared with the traditional method. In general, this research can provide effective technical support for 3D laser scanning in monitoring the operation phase of bridge jacking.

show abstract

Optimal gait design for a soft quadruped robot via multi-fidelity Bayesian optimization

Tan,

Niu,

et al. 2025

Applied Soft Computing

View full text Add to dashboard Cite

An adaptive grid search algorithm for fitting spherical target of terrestrial LiDAR

Cited by 11 publications

References 38 publications

Experimental Analysis and Machine Learning of Ground Vibrations Caused by an Elevated High-Speed Railway Based on Random Forest and Bayesian Optimization

Experimental Analysis and Machine Learning of Ground Vibrations Caused by an Elevated High-Speed Railway Based on Random Forest and Bayesian Optimization

Fast Extraction Algorithm of Planar Targets Based on Point Cloud Data for Monitoring the Synchronization of Bridge Jacking Displacements

Optimal gait design for a soft quadruped robot via multi-fidelity Bayesian optimization

Contact Info

Product

Resources

About