ArticleScientific reports2023
Identification of railway subgrade defects based on ground penetrating radar.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Topographic correction for ground penetrating radar for enhanced detection of dike subsurface anomalies.Scientific reports · 2025Article
- Study on intelligent recognition of urban road subgrade defect based on deep learning.Scientific reports · 2024Article
- Combined CNN and RNN Neural Networks for GPR Detection of Railway Subgrade Diseases.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
A recognition method is proposed to solve the problems in subgrade detection with ground penetrating radar, such as massive data, time-frequency and difference in experience. According to the sparsity of subgrade defects in radar images, the sparse representation of railway subgrade defects is studied from the aspects of the time domain, and time-frequency domain with compressive sensing theory. The features of the radar signal are extracted by sparse representation, thus the sampling data are reduced. Based on fuzzy C-means and generalized regression neural network, a rapid recognition of the railway subgrade defects is realized. Experimental results show that the redundancy of data is reduced, and the accuracy of identification is greatly increased.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.