{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T21:15:21Z","timestamp":1767993321769,"version":"3.49.0"},"reference-count":19,"publisher":"Engineering and Technology Publishing","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"published-print":{"date-parts":[[2016]]},"DOI":"10.12720\/jcm.11.9.813-818","type":"journal-article","created":{"date-parts":[[2016,12,2]],"date-time":"2016-12-02T04:03:41Z","timestamp":1480651421000},"source":"Crossref","is-referenced-by-count":8,"title":["A Novel Method for Wireless Communication Signal Modulation Recognition in Smart Grid"],"prefix":"10.12720","author":[{"name":"School of Electronic Information Engineering, Foshan University, Foshan, , China","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Faquan","family":"Yang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zan","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Simin","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Benjian","family":"Hao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peihan","family":"Qi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhongxian","family":"Pang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"4977","published-online":{"date-parts":[[2016]]},"reference":[{"key":"ref0","unstructured":"[1] L. Shichao, P. Xiaoping, and L. Abdulmotaleb, \"Modeling and distributed gain scheduling strategy for load frequency control in smart grids with communication topology changes,\" ISA Transactions, vol. 28, no. 8, pp. 313\u2013331, 2013."},{"key":"ref1","unstructured":"[2] V. C. Gungor, D. T. Kocak, S. Ergut, C. Buccella, C. Cecati, and G. P. Hancke, \"A survey on smart grid potential applications and communication requirements industrial informatics,\" IEEE Transactions on Communications, vol. 11, no. 8, pp. 289\u2013305, 2013."},{"key":"ref2","doi-asserted-by":"crossref","unstructured":"[3] B. Chai and Z. Yang, \"Impacts of unreliable communication and modified regret matching based anti-jamming approach in smart microgrid,\" Ad Hoc Networks, vol. 12, no. 2, pp. 453\u2013461, 2014.","DOI":"10.1109\/ISGT.2014.6816472"},{"key":"ref3","doi-asserted-by":"crossref","unstructured":"[4] E. S. Ataollah, \"A novel method for automatic modulation recognition,\" Applied Soft Computing, vol. 12, no. 2, pp. 453\u2013461, 2012.","DOI":"10.1016\/j.asoc.2011.08.025"},{"key":"ref4","unstructured":"[5] Z. D. Yin and Z. L.Wu, \"MLP neural network based adaptive UWB modulation scheme recognition algorithm,\" Journal of Chongqing University of Posts and Telecommunications (Natural Science), vol. 33, no. 2, pp. 156-159, 2012."},{"key":"ref5","unstructured":"[6] X. J. Wei and H. Xie, \"Digital modulation recognition algorithm based on support vector machine,\" Electronic Design Engineering, vol. 35, no. 6, pp. 89-91, 2011."},{"key":"ref6","doi-asserted-by":"crossref","unstructured":"[7] E. Avci, D. Hanba, and A. VarolL, \"An expert discrete wavelet adaptive network based fuzzy inference system for digital modulation recognition,\" Expert Systems with Applications, vol. 30, no. 3, pp. 582\u2013589, 2013.","DOI":"10.1016\/j.eswa.2006.06.001"},{"key":"ref7","unstructured":"[8] F. Q. Yang, Z. Li, et al, \"Method of modulation recognition of mixed modulation signal,\" Acta Scientiarum Naturalium Universitatis Sunyatseni, vol. 53, no. 1, pp. 42-46, 2014."},{"key":"ref8","unstructured":"[9] K. A. Mehdi, Z. H. Ali, and B. Mehdi, \"Automatic digital modulation recognition in presence of noise using SVM and PSO,\" Engineering Applications of Artificial Intelligence, vol. 23, no. 12, pp. 357\u2013370, 2014."},{"key":"ref9","unstructured":"[10] E. Kathleen and P. Shrideed, \"On the performance of high dimensional data clustering and classification algorithms,\" IEEE Communications Letters, vol. 12, no. 5, pp. 801\u2013813, 2010."},{"key":"ref10","unstructured":"[11] F. Q. Yang, Z. Li, et al, \"A new specific combination method of wireless communication modulation recognition based on clustering and neural network,\" Acta Scientiarum Naturalium Universitatis Sunyatseni, vol. 54, no. 2, pp. 25-30, 2015."},{"key":"ref11","doi-asserted-by":"crossref","unstructured":"[12] P. H. Li, H. X. Zhang, X. Y. Wang, et al, \"Modulation recognition of communication signals based on high order cumulants and support vector machine,\" The Journal of China Universities of Posts and Telecommunications, vol. 19, no. 9, pp. 61-65, 2012.","DOI":"10.1016\/S1005-8885(11)60468-8"},{"key":"ref12","doi-asserted-by":"crossref","unstructured":"[13] P. Kevin, U. Noronha, et al, \"Automated classification of glaucoma stages using higher order cumulant features,\" Biomedical Signal Processing and Control, vol. 10, no. 10, pp. 174\u2013183, 2014.","DOI":"10.1016\/j.bspc.2013.11.006"},{"key":"ref13","unstructured":"[14] Y. Zhou and A. F. Zhu, \"Sample data selection method for neural network classifiers,\" Journal of Huazhong University of Science and Technology (Nature Science Edition), vol. 40, no. 6, pp. 23-26, 2012."},{"key":"ref14","doi-asserted-by":"crossref","unstructured":"[15] J. Antari, S. Chabaa, R. Iqdour, et al, \"Identification of quadratic systems using higher order cumulants and neural networks,\" Applied Soft Computing, vol. 11, no. 1, pp. 1\u201310, 2011.","DOI":"10.1016\/j.asoc.2010.03.007"},{"key":"ref15","unstructured":"[16] L. M. Ai and C. Guo, \"Tire tread pattern recognition based on composite feature extraction and hierarchical support vector machine,\" Computer Engineering and Applications, vol. 48, no. 6, pp. 14-17, 2015."},{"key":"ref16","doi-asserted-by":"crossref","unstructured":"[17] C. Y. Yang, J. J. Wang, et al, \"Confirming robustness of fuzzy support vector machine Via \u03be\u2013\u03b1 bound,\" Neurocomputing, vol. 16, no. 25, pp. 256-266, 2015.","DOI":"10.1016\/j.neucom.2015.03.046"},{"key":"ref17","doi-asserted-by":"crossref","unstructured":"[18] X. J. Peng, D. Xu, L. Y. Kong, et al, \"L1-Norm loss based twin support vector machine for data recognition,\" Information Sciences, vol. 340, no. 1, pp. 86\u2013103, 2016.","DOI":"10.1016\/j.ins.2016.01.023"},{"key":"ref18","unstructured":"[19] H. Zhang and C. Yi, \"Digital modulation mode recognition based on multiclassification of support vector machine,\" Journal of Chongqing University, vol. 34, no. 12, pp. 78-81, 2012."}],"container-title":["Journal of Communications"],"original-title":[],"link":[{"URL":"http:\/\/www.jocm.us\/uploadfile\/2016\/0928\/20160928053029492.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,9,16]],"date-time":"2019-09-16T01:55:04Z","timestamp":1568598904000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.jocm.us\/index.php?m=content&c=index&a=show&catid=166&id=1025"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016]]},"references-count":19,"URL":"https:\/\/doi.org\/10.12720\/jcm.11.9.813-818","relation":{},"ISSN":["2374-4367"],"issn-type":[{"value":"2374-4367","type":"print"}],"subject":[],"published":{"date-parts":[[2016]]}}}