{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T17:41:59Z","timestamp":1754156519112,"version":"3.41.2"},"reference-count":48,"publisher":"Emerald","issue":"4","license":[{"start":{"date-parts":[[2023,6,15]],"date-time":"2023-06-15T00:00:00Z","timestamp":1686787200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJICC"],"published-print":{"date-parts":[[2023,10,24]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>The purpose of this study is to propose a new method for the end-to-end classification of steel surface defects.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>This study proposes an AM-AoN-SNN algorithm, which combines an attention mechanism (AM) with an All-optical Neuron-based spiking neural network (AoN-SNN). The AM enhances network learning and extracts defective features, while the AoN-SNN predicts both the labels of the defects and the final labels of the images. Compared to the conventional Leaky-Integrated and Fire SNN, the AoN-SNN has improved the activation of neurons.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>The experimental findings on Northeast University (NEU)-CLS demonstrate that the proposed neural network detection approach outperforms other methods. Furthermore, the network\u2019s effectiveness was tested, and the results indicate that the proposed method can achieve high detection accuracy and strong anti-interference capabilities while maintaining a basic structure.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>This study introduces a novel approach to classifying steel surface defects using a combination of a shallow AoN-SNN and a hybrid AM with different network architectures. The proposed method is the first study of SNN networks applied to this task.<\/jats:p><\/jats:sec>","DOI":"10.1108\/ijicc-02-2023-0034","type":"journal-article","created":{"date-parts":[[2023,6,14]],"date-time":"2023-06-14T02:19:10Z","timestamp":1686709150000},"page":"745-765","source":"Crossref","is-referenced-by-count":0,"title":["Steel surface defect classification approach using an All-optical Neuron-based SNN with attention mechanism"],"prefix":"10.1108","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6815-9454","authenticated-orcid":false,"given":"Liang","family":"Gong","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hang","family":"Dong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7770-2527","authenticated-orcid":false,"given":"Xin","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenghui","family":"Ge","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liangchao","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","published-online":{"date-parts":[[2023,6,15]]},"reference":[{"issue":"4","key":"key2023102015014370400_ref001","first-page":"2925","article-title":"Surface defects classification of hot-rolled steel strips using multi-directional shearlet features","volume":"44","year":"2018","journal-title":"Arabian Journal for Science and Engineering"},{"volume-title":"Neuronal Dynamics","year":"2009","key":"key2023102015014370400_ref002"},{"issue":"4","key":"key2023102015014370400_ref003","first-page":"221","article-title":"Strip defect classification based on improved genera-tive adversarial networks and MobileNetV3","volume":"58","year":"2021","journal-title":"Laser and Optoelectronics Progress"},{"issue":"10","key":"key2023102015014370400_ref004","first-page":"5939","article-title":"Temporal Coding in Spiking Neural Networks With Alpha Synaptic Function: Learning With Backpropagation","volume":"33","year":"2019"},{"issue":"3","key":"key2023102015014370400_ref005","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1108\/02634500810871339","article-title":"Model selection for direct marketing: performance criteria and validation methods","volume":"26","year":"2008","journal-title":"Marketing Intelligence and Planning"},{"key":"key2023102015014370400_ref006","first-page":"99","article-title":"Unsupervised learning of digit recognition using spike-timing-dependent plasticity","volume":"9","year":"2015","journal-title":"Frontiers in Computational Neuroscience"},{"year":"2015","key":"key2023102015014370400_ref007","article-title":"ShiDianNao: shifting vision processing closer to the sensor"},{"issue":"11","key":"key2023102015014370400_ref008","doi-asserted-by":"crossref","first-page":"3009","DOI":"10.1162\/neco_a_01125","article-title":"A simple model for low variability in neural spike trains","volume":"30","year":"2018","journal-title":"Neural Computation"},{"issue":"4","key":"key2023102015014370400_ref009","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1364\/OL.38.000419","article-title":"Pulse lead\/lag timing detection for adaptive feedback and control based on optical spike-timing-dependent plasticity","volume":"38","year":"2013","journal-title":"Optics Letters"},{"issue":"February","key":"key2023102015014370400_ref010","first-page":"101825.1","article-title":"A semi-supervised convolutional neural network-based method for steel surface defect recognition","volume":"61","year":"2020","journal-title":"Robotics and Computer Integrated Manufacturing"},{"key":"key2023102015014370400_ref011","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1016\/j.optlaseng.2019.05.005","article-title":"A deep-learning-based approach for fast and robust steel surface defects classification","volume":"121","year":"2019","journal-title":"Optics and Lasers in Engineering"},{"issue":"3","key":"key2023102015014370400_ref012","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1108\/IJICC-08-2021-0153","article-title":"The multilabel fault diagnosis model of bearing based on integrated convolutional neural network and gated recurrent unit","volume":"15","year":"2022","journal-title":"International Journal of Intelligent Computing and Cybernetics"},{"volume-title":"The Organization of Behavior a Neuropsychological Theory","year":"2013","key":"key2023102015014370400_ref013"},{"year":"2017","key":"key2023102015014370400_ref014","article-title":"Mobilenets: efficient convolutional neural networks for mobile vision applications"},{"issue":"1","key":"key2023102015014370400_ref015","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1007\/s11042-012-1248-0","article-title":"Classification of defects in steel strip surface based on multiclass support vector machine","volume":"69","year":"2014","journal-title":"Multimedia Tools and Applications"},{"first-page":"2261","article-title":"Densely Connected Convolutional Networks","year":"2016","key":"key2023102015014370400_ref016"},{"issue":"5","key":"key2023102015014370400_ref017","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1364\/JON.6.000434","article-title":"Two-wavelength switching with 1550 nm semiconductor laser amplifiers","volume":"6","year":"2007","journal-title":"Journal of Optical Networking"},{"issue":"24","key":"key2023102015014370400_ref018","doi-asserted-by":"crossref","first-page":"25170","DOI":"10.1364\/OE.18.025170","article-title":"Optical neuron using polarisation switching in a 1550nm-VCSEL","volume":"18","year":"2010","journal-title":"Optics Express"},{"issue":"17","key":"key2023102015014370400_ref019","doi-asserted-by":"crossref","first-page":"1373","DOI":"10.1080\/09500340.2020.1858200","article-title":"Advanced flexible wavelength routing based on Bragg trans-reflectance in 1550nm vcsel","volume":"67","year":"2020","journal-title":"Journal of Modern Optics"},{"issue":"3","key":"key2023102015014370400_ref020","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1108\/IJICC-11-2019-0119","article-title":"Computer-aided diabetic retinopathy diagnostic model using optimal thresholding merged with neural network","volume":"13","year":"2020","journal-title":"International Journal of Intelligent Computing and Cybernetics"},{"issue":"11","key":"key2023102015014370400_ref021","article-title":"Functional mechanisms underlie the emergence of a diverse range of plasticity phenomena","volume":"14","year":"2018","journal-title":"Plos Computational Biology"},{"key":"key2023102015014370400_ref022","doi-asserted-by":"publisher","first-page":"7005","DOI":"10.48550\/arXiv.1805.07866","article-title":"Hybrid macro\/micro level backpropagation for training deep spiking neural networks","year":"2018","journal-title":"Proceedings of the 32nd International Conference on Neural Information Processing Systems"},{"key":"key2023102015014370400_ref023","first-page":"56","article-title":"Stdp-based spiking deep neural networks for object recognition","volume":"99","year":"2016","journal-title":"Neural Networks the Official Journal of the International Neural Network Society"},{"issue":"6","key":"key2023102015014370400_ref024","first-page":"846","article-title":"Steel surface defect classification using deep residual neural network","volume":"10","year":"2020","journal-title":"Metals - Open Access Metallurgy Journal"},{"issue":"4","key":"key2023102015014370400_ref025","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1108\/IJICC-05-2021-0095","article-title":"Web-based remote sensing image retrieval using multiscale and multidirectional a-nalysis based on Contourlet and Haralick texture features","volume":"14","year":"2021","journal-title":"International Journal of Intelligent Computing and Cybernetics"},{"key":"key2023102015014370400_ref026","first-page":"10","article-title":"CASI-net: a novel and effect steel surface defect classification method based on coordinate attention and self-interaction mechanism","volume":"2022","year":"2022","journal-title":"Mathematics"},{"issue":"3","key":"key2023102015014370400_ref027","doi-asserted-by":"crossref","first-page":"951","DOI":"10.1007\/s11045-020-00720-5","article-title":"Improved contourlet transform construction and its application to surface defect recognition of metals","volume":"31","year":"2020","journal-title":"Multidimensional Systems and Signal Processing"},{"key":"key2023102015014370400_ref028","doi-asserted-by":"crossref","first-page":"23488","DOI":"10.1109\/ACCESS.2019.2898215","article-title":"Surface defect classification for hot-rolled steel strips by selectively dominant local binary patterns","volume":"7","year":"2019","journal-title":"IEEE Access"},{"issue":"9","key":"key2023102015014370400_ref029","doi-asserted-by":"publisher","first-page":"3525","DOI":"10.1108\/EC-11-2019-0529","article-title":"Composite fuzzy-wavelet-based active contour for medical image segmentation","volume":"37","year":"2020","journal-title":"Engineering Computations"},{"key":"key2023102015014370400_ref030","first-page":"2689","article-title":"Rate Equations for Modeling Dispersive Nonlinearity in Fabry-Perot Semiconductor Optical Amplifiers","volume-title":"Optics Express","year":"2003"},{"issue":"2","key":"key2023102015014370400_ref031","doi-asserted-by":"crossref","first-page":"468","DOI":"10.5937\/fme2002468P","article-title":"Surface roughness prediction of machined components using gray level co-occurrence matrix and bagging tree","volume":"48","year":"2020","journal-title":"FME Transactions"},{"issue":"22","key":"key2023102015014370400_ref032","article-title":"Delay and coupling-induced firing patterns in oscillatory neural loops","volume":"107","year":"2011","journal-title":"Physical Review Letters"},{"year":"2021","key":"key2023102015014370400_ref033","article-title":"Metal defect classification using deep learning"},{"issue":"7784","key":"key2023102015014370400_ref034","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1038\/s41586-019-1677-2","article-title":"Towards spike-based machine intelligence with neuromorphic computing","volume":"575","year":"2019","journal-title":"Nature"},{"year":"2014","key":"key2023102015014370400_ref035","article-title":"Very deep convolutional networks for large-scale image recognition"},{"first-page":"3262","volume-title":"Stochastic Dynamics of a Finite-Size Spiking Neural Network","year":"2007","key":"key2023102015014370400_ref036"},{"issue":"1","key":"key2023102015014370400_ref037","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1108\/IJICC-08-2016-0027","article-title":"A novel compound data classification method and its application in fault diagnosis of rolling bearings","volume":"10","year":"2017","journal-title":"International Journal of Intelligent Computing and Cybernetics"},{"key":"key2023102015014370400_ref038","doi-asserted-by":"publisher","first-page":"29","DOI":"10.3390\/machines11010029","article-title":"Detection of compound faults in ball bearings using multiscale-SinGAN, heat transfer search optimization, and extreme learning machine","volume":"11","year":"2023","journal-title":"Machines"},{"key":"key2023102015014370400_ref039","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1186\/s13640-015-0059-4","article-title":"Simplified spiking neural network architecture and stdp learning algorithm applied to image classification","volume":"2015","year":"2015","journal-title":"Eurasip Journal on Image and Video Processing"},{"key":"key2023102015014370400_ref040","first-page":"1","article-title":"Surface defects classification of hot-rolled steel strips using multi-directional shearlet features","volume":"44","year":"2018","journal-title":"Arabian Journal for Science and Engineering"},{"key":"key2023102015014370400_ref041","first-page":"1203","article-title":"Wavelet decomposition and phase encoding of temporal signals using spiking neurons","volume":"173","year":"2015","journal-title":"Neurocomputing"},{"key":"key2023102015014370400_ref042","doi-asserted-by":"crossref","first-page":"331","DOI":"10.3389\/fnins.2018.00331","article-title":"Spatio-temporal backpropagation for training high-performance spiking neural networks","volume":"12","year":"2018","journal-title":"Frontiers in Neuroscience"},{"key":"key2023102015014370400_ref043","first-page":"1","article-title":"Stdp-based unsupervised spike pattern learning in a photonic spiking neural network with vcsels and vcsoas","volume":"25","year":"2019","journal-title":"IEEE Journal of Selected Topics in Quantum Electronics"},{"issue":"12815","key":"key2023102015014370400_ref044","article-title":"A dendritic disinhibitory circuit mechanism for pathway-specific gating","volume":"7","year":"2016"},{"issue":"1","key":"key2023102015014370400_ref045","first-page":"1","article-title":"Effective and efficient computation with multiple-timescale spiking recurrent neural networks","year":"2020","journal-title":"International Conference on Neuromorphic Systems 2020"},{"issue":"05","key":"key2023102015014370400_ref046","first-page":"1","article-title":"Improving multi-layer spiking neural networks by incorporating brain-inspired rules","year":"2017","journal-title":"Science China Information Sciences"},{"volume-title":"Hebbian and Non-hebbian Plasticity Orchestrated to Form and Retrieve Memories in Spiking Networks","year":"2015","key":"key2023102015014370400_ref047"},{"issue":"2022","key":"key2023102015014370400_ref048","first-page":"1093","article-title":"Deconv-transformer (DecT): a histopathological image classification model for breast cancer based on color deconvolution and transformer architecture","volume":"608","year":"2022","journal-title":"Information Sciences"}],"container-title":["International Journal of Intelligent Computing and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/IJICC-02-2023-0034\/full\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/IJICC-02-2023-0034\/full\/html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T22:54:08Z","timestamp":1753397648000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.emerald.com\/ijicc\/article\/16\/4\/745-765\/133163"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,15]]},"references-count":48,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2023,6,15]]},"published-print":{"date-parts":[[2023,10,24]]}},"alternative-id":["10.1108\/IJICC-02-2023-0034"],"URL":"https:\/\/doi.org\/10.1108\/ijicc-02-2023-0034","relation":{},"ISSN":["1756-378X"],"issn-type":[{"type":"print","value":"1756-378X"}],"subject":[],"published":{"date-parts":[[2023,6,15]]}}}