Guided wave signal‐based sensing and classification for small geological structure
Abstract Sensing, Computing and Communication Integration (SC2) is widely believed as a new enabling technology. A non‐negative tensor sparse factorisation (NTSF) algorithm based on tensor analysis is proposed for sensing and classification of Small Geological Structure in coal mines. Utilising this...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Hindawi-IET
2023-07-01
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| Series: | IET Signal Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/sil2.12223 |
| _version_ | 1797425994394501120 |
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| author | Hongyu Sun Jiao Song Shanshan Zhou Qiang Liu Xiang Lu Mingming Qi |
| author_facet | Hongyu Sun Jiao Song Shanshan Zhou Qiang Liu Xiang Lu Mingming Qi |
| author_sort | Hongyu Sun |
| collection | DOAJ |
| description | Abstract Sensing, Computing and Communication Integration (SC2) is widely believed as a new enabling technology. A non‐negative tensor sparse factorisation (NTSF) algorithm based on tensor analysis is proposed for sensing and classification of Small Geological Structure in coal mines. Utilising this method, advanced detection of geological anomalies hidden in coal seams was achieved. The morphological properties of geological anomalies in coal seams and the propagation characteristics of guided waves were first thoroughly studied. A three‐dimensional (3D) medium geometry model was developed for a complicated coal seam with Goaf, collapse column, scouring zone, and tiny fault based on COMSOL Multiphysics. On this model, the third‐order tensors data was constructed. Then, the TUCKER‐based NTSF algorithm was employed for feature extraction and classification. To achieve multi‐dimensional feature, the two‐dimensional data in the form of a matrix is collected, and a multiplicative update method is introduced to update the algorithm iteratively. Finally, the Support Vector Machine (SVM) multi‐classifier with Gaussian radial basis kernel function is selected for classification of Small Geological Structure. The experimental results show that the classification accuracy based on the NTSF and SVM is as high as 97.33%, which demonstrates that the proposed algorithm is suitable for Sensing and Classification of Small Geological Structure in coal mines. |
| first_indexed | 2024-03-09T08:24:00Z |
| format | Article |
| id | doaj.art-d1d0935283714fd1887de4ee884add4a |
| institution | Directory Open Access Journal |
| issn | 1751-9675 1751-9683 |
| language | English |
| last_indexed | 2024-03-09T08:24:00Z |
| publishDate | 2023-07-01 |
| publisher | Hindawi-IET |
| record_format | Article |
| series | IET Signal Processing |
| spelling | doaj.art-d1d0935283714fd1887de4ee884add4a2023-12-02T21:35:12ZengHindawi-IETIET Signal Processing1751-96751751-96832023-07-01177n/an/a10.1049/sil2.12223Guided wave signal‐based sensing and classification for small geological structureHongyu Sun0Jiao Song1Shanshan Zhou2Qiang Liu3Xiang Lu4Mingming Qi5College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao ChinaCollege of Electronic and Information Engineering Shandong University of Science and Technology Qingdao ChinaJi'nan Special Equipment Inspection Research Institute Jinan ChinaCollege of Electronic and Information Engineering Shandong University of Science and Technology Qingdao ChinaCollege of Electronic and Information Engineering Shandong University of Science and Technology Qingdao ChinaSchool of Data Science and Artificial Intelligence Wenzhou University of Technology Wenzhou ChinaAbstract Sensing, Computing and Communication Integration (SC2) is widely believed as a new enabling technology. A non‐negative tensor sparse factorisation (NTSF) algorithm based on tensor analysis is proposed for sensing and classification of Small Geological Structure in coal mines. Utilising this method, advanced detection of geological anomalies hidden in coal seams was achieved. The morphological properties of geological anomalies in coal seams and the propagation characteristics of guided waves were first thoroughly studied. A three‐dimensional (3D) medium geometry model was developed for a complicated coal seam with Goaf, collapse column, scouring zone, and tiny fault based on COMSOL Multiphysics. On this model, the third‐order tensors data was constructed. Then, the TUCKER‐based NTSF algorithm was employed for feature extraction and classification. To achieve multi‐dimensional feature, the two‐dimensional data in the form of a matrix is collected, and a multiplicative update method is introduced to update the algorithm iteratively. Finally, the Support Vector Machine (SVM) multi‐classifier with Gaussian radial basis kernel function is selected for classification of Small Geological Structure. The experimental results show that the classification accuracy based on the NTSF and SVM is as high as 97.33%, which demonstrates that the proposed algorithm is suitable for Sensing and Classification of Small Geological Structure in coal mines.https://doi.org/10.1049/sil2.12223feature extractionobject detectionseismic wavessignal classification |
| spellingShingle | Hongyu Sun Jiao Song Shanshan Zhou Qiang Liu Xiang Lu Mingming Qi Guided wave signal‐based sensing and classification for small geological structure IET Signal Processing feature extraction object detection seismic waves signal classification |
| title | Guided wave signal‐based sensing and classification for small geological structure |
| title_full | Guided wave signal‐based sensing and classification for small geological structure |
| title_fullStr | Guided wave signal‐based sensing and classification for small geological structure |
| title_full_unstemmed | Guided wave signal‐based sensing and classification for small geological structure |
| title_short | Guided wave signal‐based sensing and classification for small geological structure |
| title_sort | guided wave signal based sensing and classification for small geological structure |
| topic | feature extraction object detection seismic waves signal classification |
| url | https://doi.org/10.1049/sil2.12223 |
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