id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_4dzt4w64yvf3vioq3v655rj5mi	Xinyu Liu	Non-destructive detection of highway hidden layer defects using a ground-penetrating radar and adaptive particle swarm support vector machine	2021	18	.pdf	application/pdf	5186	631	56	particle swarm support vector machine (SVM) method is proposed for detecting and optimization of the SVM algorithm, the grid search method and particle swarm Keywords 5 Ground penetrating radar (GPR), Image segmentation, Feature extraction, Support vector machine (SVM), Grid search method, Particle swarm optimization (PSO) Non-destructive detection of highway hidden layer defects using a ground-penetrating radar and adaptive particle swarm support vector machine. shallow hidden defect classifiers based on the support vector machine (SVM) algorithm. the SVM algorithm, this study optimizes and improves its parameter selection process. method and the particle swarm optimization (PSO) (Yang et al., 2018) algorithm, the Figure 6 Parameter optimization results of the grid search method. Figure 8 Test-set classification results after the grid search optimization of the SVM classifier parameters. The fitness curve of the improved adaptive mutation PSO algorithm for SVM-based approach, the accuracy of the SVM with parameters optimized using mutation PSO	./cache/work_4dzt4w64yvf3vioq3v655rj5mi.pdf	./txt/work_4dzt4w64yvf3vioq3v655rj5mi.txt