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SiamWT-CRNet

2026-01Artigo científico Acesso embargado
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dc.contributor.authorLu, Faming
dc.contributor.authorLiu, Yi
dc.contributor.authorLin, Zedong
dc.contributor.authorHan, Xiangqi
dc.contributor.authorLiu, Cong
dc.contributor.institutionNOVA Information Management School (NOVA IMS)
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School
dc.contributor.pblElsevier Science B.V., Amsterdam.
dc.date.accessioned2025-12-04T21:16:17Z
dc.date.embargoedUntil2027-10-15
dc.date.issued2026-01
dc.descriptionLu, F., Liu, Y., Lin, Z., Han, X., & Liu, C. (2026). SiamWT-CRNet: A Siamese Wavelet Network with Cross-Domain Feature Fusion for Dynamic Coal-Rock Recognition in Top-Coal Caving Systems. Applied Soft Computing, 186, Part A, Article 113984. https://doi.org/10.1016/j.asoc.2025.113984
dc.description.abstractTo address the limitations of traditional deep learning methods due to strong data dependency and insufficient interpretability when recognizing “under-releasing" and “over-releasing" phenomena during top-coal caving in longwall mining, this study proposes an intelligent recognition framework, SiamWT-CRNet, based on joint time-frequency domain analysis of vibration signals. It leverages wavelet-domain Siamese network architecture combined with a cross-wavelet feature enhancement mechanism to achieve high-precision dynamic identification of the coal-rock interface. It introduces a cross-scale feature fusion strategy based on multi-family wavelet bases, constructing a physically interpretable enhanced feature space through heterogeneous wavelet decomposition. A lightweight Siamese wavelet convolution module, ECWT, is designed to integrate recursive wavelet decomposition with an improved attention mechanism, enabling focused extraction of critical frequency-band features while reducing parameter complexity. Furthermore, a cross-wavelet contrastive learning paradigm is adopted, where a dual-branch network is employed to mine the intrinsic differential features of coal and rock vibration signals. This is coupled with a hard-voting classifier to achieve efficient decision-making. Experimental results demonstrate that the proposed method significantly outperforms traditional models in terms of recognition robustness under strong noise interference. Moreover, the decision-making mechanism has been validated through frequency-domain interpretability analysis, aligning well with engineering expertise.en
dc.description.versionauthorsversion
dc.description.versionpublished
dc.format.extent12
dc.format.extent4310565
dc.identifier.doi10.1016/j.asoc.2025.113984
dc.identifier.issn1568-4946
dc.identifier.otherPURE: 132942878
dc.identifier.otherPURE UUID: a9fefebc-8523-4f9a-962a-94fec040bf7c
dc.identifier.otherScopus: 105018673141
dc.identifier.otherWOS: 001599133200003
dc.identifier.urihttp://hdl.handle.net/10362/191498
dc.identifier.urlhttps://www.scopus.com/pages/publications/105018673141
dc.identifier.urlhttps://www.webofscience.com/wos/woscc/full-record/WOS:001599133200003
dc.language.isoeng
dc.peerreviewedyes
dc.subjectFully mechanized mining
dc.subjectCoal and rock identification
dc.subjectVibration signals
dc.subjectWavelet transform
dc.subjectSiamese network
dc.subjectSoftware
dc.titleSiamWT-CRNeten
dc.title.subtitleA Siamese Wavelet Network with Cross-Domain Feature Fusion for Dynamic Coal-Rock Recognition in Top-Coal Caving Systemsen
dc.typejournal article
degois.publication.titleApplied Soft Computing
degois.publication.volume186, Part A
dspace.entity.typePublication
rcaap.rightsembargoedAccess

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NIMS: MagIC - Artigos em revista internacional com arbitragem científica (Peer-Review articles in international journals)