{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,11]],"date-time":"2025-11-11T15:52:46Z","timestamp":1762876366902,"version":"build-2065373602"},"reference-count":25,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,4,25]],"date-time":"2022-04-25T00:00:00Z","timestamp":1650844800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China","award":["SQ2018YFC060172"],"award-info":[{"award-number":["SQ2018YFC060172"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>To improve the recognition accuracy of coal gangue images with the back propagation (BP) neural network, a coal gangue image recognition method based on BP neural network and ASGS-CWOA (ASGS-CWOA-BP) was proposed, which makes two key contributions. Firstly, a new feature extraction method for the unique features of coal and gangue images is proposed, known as \u201cEncircle\u2013City Feature\u201d. Additionally, a method that applied ASGS-CWOA to optimize the parameters of the BP neural network was introduced to address to the issue of its low accuracy in coal gangue image recognition, and a BP neural network with a simple structure and reduced computational consumption was designed. The experimental results showed that the proposed method outperformed the other six comparison methods, with recognition of 95.47% and 94.37% in the training set and the test set, respectively, showing good symmetry.<\/jats:p>","DOI":"10.3390\/sym14050880","type":"journal-article","created":{"date-parts":[[2022,4,26]],"date-time":"2022-04-26T02:14:39Z","timestamp":1650939279000},"page":"880","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["An Image Recognition Method for Coal Gangue Based on ASGS-CWOA and BP Neural Network"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0796-0261","authenticated-orcid":false,"given":"Dongxing","family":"Wang","sequence":"first","affiliation":[{"name":"R & D Department, Zhuhai Xinhe Technology Co., Ltd., Zhuhai 519600, China"},{"name":"School of Electrical Engineering, Zhejiang University, Hangzhou 310000, China"},{"name":"School of Mechanical Electronic & Information Engineering, China University of Mining & Technology-Beijing, Beijing 100083, China"}]},{"given":"Jingxiu","family":"Ni","sequence":"additional","affiliation":[{"name":"Comprehensive Experimental Teaching Demonstration Center of Engineering, Beijing Union University, Beijing 100101, China"}]},{"given":"Tingyu","family":"Du","sequence":"additional","affiliation":[{"name":"School of Mechanical Electronic & Information Engineering, China University of Mining & Technology-Beijing, Beijing 100083, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,25]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"Further on the sustainable mining of coal","volume":"43","author":"Qian","year":"2018","journal-title":"J. China Coal Soc."},{"key":"ref_2","first-page":"11","article-title":"Present situation and Prospect of coal gangue treatment technology","volume":"37","author":"Zhou","year":"2020","journal-title":"J. Min. Saf. Eng."},{"key":"ref_3","first-page":"57","article-title":"Coal-gangue image recognition in fully-mechanized caving face based on random forest","volume":"46","author":"Xue","year":"2020","journal-title":"Ind. Mine Autom."},{"key":"ref_4","first-page":"2207","article-title":"Machine vision recognition method and optimization for intelligent separation of coal and gangue","volume":"45","author":"Xu","year":"2020","journal-title":"J. China Coal Soc."},{"key":"ref_5","first-page":"966","article-title":"Expanded order co-occurrence matrix to differentiate between coal and gangue based on interval grayscale compression","volume":"8","author":"Yu","year":"2012","journal-title":"J. Image Graph."},{"key":"ref_6","first-page":"68","article-title":"A New Method for Image Recognition of Coal and Coal Gangue","volume":"17","author":"Yu","year":"2017","journal-title":"Mod. Comput."},{"key":"ref_7","first-page":"69","article-title":"Coal-gangue image classification method","volume":"46","author":"Rao","year":"2020","journal-title":"Ind. Mine Autom."},{"key":"ref_8","unstructured":"Wen, X. (2015). Intelligent Fault Diagnosis Technology: Matlab Application, Beijing University of Aeronautics and Astronautics Press."},{"key":"ref_9","first-page":"232","article-title":"Remote sensing image classification based on BP neural network model","volume":"15","author":"Zheng","year":"2005","journal-title":"Trans. Nonferrous Met. Soc. China"},{"key":"ref_10","unstructured":"Chen, Y.X., Liao, X.D., Wang, J.H., Tao, Z., and Sui, L.Y. (2018, January 25\u201327). Small Image Recognition Classification Based on PCA and GA-BP Neural Network. Proceedings of the 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC 2018), Xi\u2019an, China."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhou, W.C., Xie, G.S., and Liu, B. (2008, January 28\u201329). The application of mixed GA-BP algorithm on remote sensing image classification. Proceedings of the Conference: Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Classification of Remote Sensing Images, Guangzhou, China.","DOI":"10.1117\/12.813210"},{"key":"ref_12","first-page":"24","article-title":"Analysis of epileptic seizure detection method based on improved genetic algorithm optimization back propagation neural network","volume":"36","author":"Liu","year":"2019","journal-title":"Shengwu Yixue Gongchengxue Zazhi\/J. Biomed. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1016\/j.jvcir.2019.02.015","article-title":"Correlation identification in multimodal weibo via back propagation neural network with genetic algorithm","volume":"60","author":"Liu","year":"2019","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_14","unstructured":"Yu, J., Zhang, Z., Guo, P., Qin, H., and Zhang, J. (2008, January 25\u201327). Multispectral remote sensing image classification based on PSO-BP considering texture. Proceedings of the 7th World Congress on Intelligent Control and Automation (WCICA), Chongqing, China."},{"key":"ref_15","unstructured":"Chen, Y.X., Liao, X.D., Wang, J.H., Tao, Z., and Sui, L.Y. (2018, January 25\u201327). Small Image Recognition Classification Based on Random Dropout and PSO-BP. Proceedings of the 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, Xi\u2019an, China."},{"key":"ref_16","unstructured":"Yu, J., Li, Y., Zhang, Z.S., and Jiang, J.C. (2010, January 7\u20139). Research on supervised classification of fully polarimetric SAR image using BP neural network trained by PSO. Proceedings of the World Congress on Intelligent Control and Automation (WCICA), Jinan, China."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wei, B., Hu, L., Zhang, Y., and Zhang, Y. (2020, January 12\u201314). Parts Classification based on PSO-BP. Proceedings of the 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC 2020), Chongqing, China.","DOI":"10.1109\/ITNEC48623.2020.9084709"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1579","DOI":"10.2355\/isijinternational.ISIJINT-2020-451","article-title":"Surface defects classification of hot rolled strip based on improved convolutional neural network","volume":"61","author":"Wang","year":"2021","journal-title":"ISIJ Int."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"06011","DOI":"10.1051\/matecconf\/202133606011","article-title":"Research on recognition method of cloud precipitation particle shape based on bp neural network","volume":"336","author":"Dong","year":"2021","journal-title":"MATEC Web Conf."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Yang, C., Tu, X., and Chen, J. (2007, January 11\u201313). Algorithm of Marriage in Honey Bees Optimization Based on the Wolf Pack Search. Proceedings of the International Conference on Intelligent Pervasive Computing, Jeju, Korea.","DOI":"10.1109\/IPC.2007.104"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"9853","DOI":"10.1016\/j.ijleo.2016.07.056","article-title":"An oppositional wolf pack algorithm for Parameter identification of the chaotic systems","volume":"127","author":"Li","year":"2016","journal-title":"Opt. Int. J. Light Electron Opt."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1016\/j.neucom.2017.05.059","article-title":"Three-dimensional Unmanned Aerial Vehicle Path Planning Using Modified Wolf Pack Search Algorithm","volume":"266","author":"Chen","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_23","first-page":"35","article-title":"Solving Polynomial Equation Roots Based on Wolves Algorithm","volume":"15","author":"Yang","year":"2016","journal-title":"Sci. Technol. Vis."},{"key":"ref_24","first-page":"2629","article-title":"Wolf colony search algorithm based on leader strategy","volume":"30","author":"Zhou","year":"2013","journal-title":"Appl. Res. Comput."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"7986982","DOI":"10.1155\/2020\/7986982","article-title":"An Adaptive Shrinking Grid Search Chaos Wolf Optimization Algorithm with Adaptive Standard-Deviation Updating Amount","volume":"2020","author":"Wang","year":"2020","journal-title":"Comput. Intell. Neurosci."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/14\/5\/880\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:00:45Z","timestamp":1760137245000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/14\/5\/880"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,25]]},"references-count":25,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["sym14050880"],"URL":"https:\/\/doi.org\/10.3390\/sym14050880","relation":{},"ISSN":["2073-8994"],"issn-type":[{"type":"electronic","value":"2073-8994"}],"subject":[],"published":{"date-parts":[[2022,4,25]]}}}