{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T00:12:43Z","timestamp":1778285563161,"version":"3.51.4"},"reference-count":47,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Engineering Applications of Artificial Intelligence"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.engappai.2026.114742","type":"journal-article","created":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T21:46:30Z","timestamp":1775598390000},"page":"114742","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"P1","title":["Class-aware contrastive learning for radio signal generalized category discovery"],"prefix":"10.1016","volume":"176","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-8412-1400","authenticated-orcid":false,"given":"Jie","family":"Chen","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3089-4346","authenticated-orcid":false,"given":"Shilian","family":"Zheng","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3121-0364","authenticated-orcid":false,"given":"Luxin","family":"Zhang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0880-9798","authenticated-orcid":false,"given":"Keqiang","family":"Yue","sequence":"additional","affiliation":[]},{"given":"Zhijin","family":"Zhao","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"9","key":"10.1016\/j.engappai.2026.114742_b1","doi-asserted-by":"crossref","first-page":"16196","DOI":"10.1109\/JIOT.2024.3350927","article-title":"Achieving efficient feature representation for modulation signal: A cooperative contrast learning approach","volume":"11","author":"Bai","year":"2024","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.engappai.2026.114742_b2","doi-asserted-by":"crossref","unstructured":"Cao, X., Zheng, X., Wang, G., Yu, W., Shen, Y., Li, K., Lu, Y., Tian, Y., 2024. Solving the catastrophic forgetting problem in generalized category discovery. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 16880\u201316889.","DOI":"10.1109\/CVPR52733.2024.01597"},{"key":"10.1016\/j.engappai.2026.114742_b3","doi-asserted-by":"crossref","unstructured":"Caron, M., Touvron, H., Misra, I., J\u00e9gou, H., Mairal, J., Bojanowski, P., Joulin, A., 2021. Emerging Properties in Self-Supervised Vision Transformers. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. ICCV, pp. 9650\u20139660.","DOI":"10.1109\/ICCV48922.2021.00951"},{"key":"10.1016\/j.engappai.2026.114742_b4","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijepes.2025.110982","article-title":"Resilient memory sampled-data controller for synchronization of semi-Markovian jump competitive neural networks with mixed delays","volume":"171","author":"Chandrasekar","year":"2025","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"10.1016\/j.engappai.2026.114742_b5","series-title":"Proceedings of the 37th International Conference on Machine Learning","first-page":"1597","article-title":"A simple framework for contrastive learning of visual representations","volume":"vol. 119","author":"Chen","year":"2020"},{"issue":"11","key":"10.1016\/j.engappai.2026.114742_b6","first-page":"8065","article-title":"Adversarial reciprocal points learning for open set recognition","volume":"44","author":"Chen","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.engappai.2026.114742_b7","article-title":"Sinkhorn distances: Lightspeed computation of optimal transport","volume":"26","author":"Cuturi","year":"2013","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"2","key":"10.1016\/j.engappai.2026.114742_b8","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1049\/iet-com:20050176","article-title":"Survey of automatic modulation classification techniques: classical approaches and new trends","volume":"1","author":"Dobre","year":"2007","journal-title":"IET Commun."},{"key":"10.1016\/j.engappai.2026.114742_b9","article-title":"Deep learning-stochastic ensemble for RUL prediction and predictive maintenance with dynamic mission abort policies","volume":"259","author":"Faizanbasha","year":"2025","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"10.1016\/j.engappai.2026.114742_b10","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.jmsy.2024.12.002","article-title":"Optimizing burn-in and predictive maintenance for enhanced reliability in manufacturing systems: A two-unit series system approach","volume":"78","author":"Faizanbasha","year":"2025","journal-title":"J. Manuf. Syst."},{"key":"10.1016\/j.engappai.2026.114742_b11","doi-asserted-by":"crossref","unstructured":"Fini, E., Sangineto, E., Lathuili\u00e8re, S., Zhong, Z., Nabi, M., Ricci, E., 2021. A Unified Objective for Novel Class Discovery. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. ICCV, pp. 9284\u20139292.","DOI":"10.1109\/ICCV48922.2021.00915"},{"key":"10.1016\/j.engappai.2026.114742_b12","first-page":"21271","article-title":"Bootstrap your own latent - a new approach to self-supervised learning","volume":"vol. 33","author":"Grill","year":"2020"},{"key":"10.1016\/j.engappai.2026.114742_b13","doi-asserted-by":"crossref","unstructured":"Gu, P., Zhang, C., Xu, R., He, X., 2023. Class-relation Knowledge Distillation for Novel Class Discovery. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. ICCV, pp. 16474\u201316483.","DOI":"10.1109\/ICCV51070.2023.01510"},{"issue":"12","key":"10.1016\/j.engappai.2026.114742_b14","doi-asserted-by":"crossref","first-page":"9052","DOI":"10.1109\/TPAMI.2024.3415112","article-title":"A survey on self-supervised learning: Algorithms, applications, and future trends","volume":"46","author":"Gui","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"12","key":"10.1016\/j.engappai.2026.114742_b15","doi-asserted-by":"crossref","first-page":"5884","DOI":"10.1109\/TWC.2009.12.080883","article-title":"On the likelihood-based approach to modulation classification","volume":"8","author":"Hameed","year":"2009","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"10.1016\/j.engappai.2026.114742_b16","unstructured":"Han, K., Rebuffi, S.-A., Ehrhardt, S., Vedaldi, A., Zisserman, A., 2020. Automatically Discovering and Learning New Visual Categories with Ranking Statistics. In: International Conference on Learning Representations. ICLR."},{"key":"10.1016\/j.engappai.2026.114742_b17","doi-asserted-by":"crossref","unstructured":"Han, K., Vedaldi, A., Zisserman, A., 2019. Learning to Discover Novel Visual Categories via Deep Transfer Clustering. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. ICCV, pp. 8400\u20138408.","DOI":"10.1109\/ICCV.2019.00849"},{"key":"10.1016\/j.engappai.2026.114742_b18","doi-asserted-by":"crossref","unstructured":"Hazza, A., Shoaib, M., Alshebeili, S.A., Fahad, A., 2013. An overview of feature-based methods for digital modulation classification. In: 2013 1st International Conference on Communications, Signal Processing, and their Applications. ICCSPA, pp. 1\u20136.","DOI":"10.1109\/ICCSPA.2013.6487244"},{"key":"10.1016\/j.engappai.2026.114742_b19","doi-asserted-by":"crossref","unstructured":"He, K., Chen, X., Xie, S., Li, Y., Doll\u00e1r, P., Girshick, R., 2022. Masked Autoencoders Are Scalable Vision Learners. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. CVPR, pp. 16000\u201316009.","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"10.1016\/j.engappai.2026.114742_b20","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., Girshick, R., 2020. Momentum Contrast for Unsupervised Visual Representation Learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. CVPR, pp. 9729\u20139738.","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"10.1016\/j.engappai.2026.114742_b21","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J., 2016. Deep Residual Learning for Image Recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. CVPR, pp. 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"10.1016\/j.engappai.2026.114742_b22","first-page":"18661","article-title":"Supervised contrastive learning","volume":"vol. 33","author":"Khosla","year":"2020"},{"issue":"1\u20132","key":"10.1016\/j.engappai.2026.114742_b23","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1002\/nav.3800020109","article-title":"The hungarian method for the assignment problem","volume":"2","author":"Kuhn","year":"1955","journal-title":"Nav. Res. Logist. Q."},{"key":"10.1016\/j.engappai.2026.114742_b24","doi-asserted-by":"crossref","unstructured":"Li, W., Fan, Z., Huo, J., Gao, Y., 2023. Modeling inter-class and intra-class constraints in novel class discovery. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 3449\u20133458.","DOI":"10.1109\/CVPR52729.2023.00336"},{"key":"10.1016\/j.engappai.2026.114742_b25","doi-asserted-by":"crossref","unstructured":"Li, Y., Yuan, L., Zhou, F., Wu, Q., Al-Dhahir, N., Wong, K.-K., 2024. KGAMC: A Novel Knowledge Graph Driven Automatic Modulation Classification Scheme. In: ICC 2024 - IEEE International Conference on Communications. pp. 4857\u20134862.","DOI":"10.1109\/ICC51166.2024.10622216"},{"issue":"7","key":"10.1016\/j.engappai.2026.114742_b26","doi-asserted-by":"crossref","first-page":"6022","DOI":"10.1109\/TPAMI.2025.3557502","article-title":"ProtoGCD: Unified and unbiased prototype learning for generalized category discovery","volume":"47","author":"Ma","year":"2025","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.engappai.2026.114742_b27","doi-asserted-by":"crossref","unstructured":"O\u2019Shea, T.J., Corgan, J., Clancy, T.C., 2016. Convolutional radio modulation recognition networks. In: Proc. 17th Int. Conf. Eng. Appl. Neural Netw.. EANN, pp. 213\u2013226.","DOI":"10.1007\/978-3-319-44188-7_16"},{"issue":"12","key":"10.1016\/j.engappai.2026.114742_b28","doi-asserted-by":"crossref","first-page":"7020","DOI":"10.1109\/TNNLS.2021.3085433","article-title":"A survey of modulation classification using deep learning: Signal representation and data preprocessing","volume":"33","author":"Peng","year":"2022","journal-title":"IEEE Trans. Neural Networks Learn. Syst."},{"key":"10.1016\/j.engappai.2026.114742_b29","doi-asserted-by":"crossref","unstructured":"Pu, N., Zhong, Z., Sebe, N., 2023. Dynamic Conceptional Contrastive Learning for Generalized Category Discovery. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. CVPR, pp. 7579\u20137588.","DOI":"10.1109\/CVPR52729.2023.00732"},{"key":"10.1016\/j.engappai.2026.114742_b30","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2025.111420","article-title":"Spatial-temporal multi-sensor information fusion network with prior knowledge embedding for equipment remaining useful life prediction","author":"Qin","year":"2025","journal-title":"Reliab. Eng. Syst. Saf."},{"issue":"1","key":"10.1016\/j.engappai.2026.114742_b31","doi-asserted-by":"crossref","first-page":"775","DOI":"10.1109\/TWC.2023.3281896","article-title":"Deepsig: A hybrid heterogeneous deep learning framework for radio signal classification","volume":"23","author":"Qiu","year":"2023","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"10.1016\/j.engappai.2026.114742_b32","first-page":"1","article-title":"Robust dissipative sliding mode control synchronization of memristive inertial competitive neural networks with time-varying delay","author":"Subhashri","year":"2025","journal-title":"Eur. Phys. J. Spec. Top."},{"issue":"11","key":"10.1016\/j.engappai.2026.114742_b33","article-title":"Visualizing data using t-SNE","volume":"9","author":"Van der Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"issue":"2","key":"10.1016\/j.engappai.2026.114742_b34","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1007\/s10994-019-05855-6","article-title":"A survey on semi-supervised learning","volume":"109","author":"Van Engelen","year":"2020","journal-title":"Mach. Learn."},{"key":"10.1016\/j.engappai.2026.114742_b35","doi-asserted-by":"crossref","unstructured":"Vaze, S., Han, K., Vedaldi, A., Zisserman, A., 2022. Generalized category discovery. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 7492\u20137501.","DOI":"10.1109\/CVPR52688.2022.00734"},{"key":"10.1016\/j.engappai.2026.114742_b36","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"16590","article-title":"Parametric classification for generalized category discovery: A baseline study","author":"Wen","year":"2023"},{"issue":"10","key":"10.1016\/j.engappai.2026.114742_b37","doi-asserted-by":"crossref","first-page":"14304","DOI":"10.1109\/TWC.2024.3412234","article-title":"MCLHN: Toward automatic modulation classification via masked contrastive learning with hard negatives","volume":"23","author":"Xiao","year":"2024","journal-title":"IEEE Trans. Wirel. Commun."},{"issue":"10","key":"10.1016\/j.engappai.2026.114742_b38","doi-asserted-by":"crossref","first-page":"1629","DOI":"10.1109\/LWC.2020.2999453","article-title":"A spatiotemporal multi-channel learning framework for automatic modulation recognition","volume":"9","author":"Xu","year":"2020","journal-title":"IEEE Wirel. Commun. Lett."},{"issue":"2","key":"10.1016\/j.engappai.2026.114742_b39","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1007\/s40745-015-0040-1","article-title":"A comprehensive survey of clustering algorithms","volume":"2","author":"Xu","year":"2015","journal-title":"Ann. Data Sci."},{"key":"10.1016\/j.engappai.2026.114742_b40","doi-asserted-by":"crossref","unstructured":"Yang, M., Zhu, Y., Yu, J., Wu, A., Deng, C., 2022. Divide and Conquer: Compositional Experts for Generalized Novel Class Discovery. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. CVPR, pp. 14268\u201314277.","DOI":"10.1109\/CVPR52688.2022.01387"},{"issue":"1","key":"10.1016\/j.engappai.2026.114742_b41","doi-asserted-by":"crossref","first-page":"520","DOI":"10.1109\/TII.2020.3041159","article-title":"Intelligent-driven green resource allocation for industrial internet of things in 5G heterogeneous networks","volume":"18","author":"Yu","year":"2022","journal-title":"IEEE Trans. Ind. Informatics"},{"key":"10.1016\/j.engappai.2026.114742_b42","series-title":"Proceedings of the 38th International Conference on Machine Learning","first-page":"12310","article-title":"Barlow twins: Self-supervised learning via redundancy reduction","volume":"vol. 139","author":"Zbontar","year":"2021"},{"key":"10.1016\/j.engappai.2026.114742_b43","doi-asserted-by":"crossref","unstructured":"Zhang, S., Khan, S., Shen, Z., Naseer, M., Chen, G., Khan, F.S., 2023. PromptCAL: Contrastive Affinity Learning via Auxiliary Prompts for Generalized Novel Category Discovery. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. CVPR, pp. 3479\u20133488.","DOI":"10.1109\/CVPR52729.2023.00339"},{"key":"10.1016\/j.engappai.2026.114742_b44","doi-asserted-by":"crossref","DOI":"10.1016\/j.dsp.2022.103650","article-title":"Deep learning based automatic modulation recognition: Models, datasets, and challenges","volume":"129","author":"Zhang","year":"2022","journal-title":"Digit. Signal Process."},{"issue":"3","key":"10.1016\/j.engappai.2026.114742_b45","doi-asserted-by":"crossref","first-page":"564","DOI":"10.1109\/TCCN.2023.3243899","article-title":"Toward next-generation signal intelligence: A hybrid knowledge and data-driven deep learning framework for radio signal classification","volume":"9","author":"Zheng","year":"2023","journal-title":"IEEE Trans. Cogn. Commun. Netw."},{"key":"10.1016\/j.engappai.2026.114742_b46","doi-asserted-by":"crossref","unstructured":"Zhong, Z., Fini, E., Roy, S., Luo, Z., Ricci, E., Sebe, N., 2021. Neighborhood Contrastive Learning for Novel Class Discovery. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. CVPR, pp. 10867\u201310875.","DOI":"10.1109\/CVPR46437.2021.01072"},{"issue":"3","key":"10.1016\/j.engappai.2026.114742_b47","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3689036","article-title":"A comprehensive survey on deep clustering: Taxonomy, challenges, and future directions","volume":"57","author":"Zhou","year":"2024","journal-title":"ACM Comput. Surv."}],"container-title":["Engineering Applications of Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626010249?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626010249?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T23:28:17Z","timestamp":1778282897000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0952197626010249"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":47,"alternative-id":["S0952197626010249"],"URL":"https:\/\/doi.org\/10.1016\/j.engappai.2026.114742","relation":{},"ISSN":["0952-1976"],"issn-type":[{"value":"0952-1976","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Class-aware contrastive learning for radio signal generalized category discovery","name":"articletitle","label":"Article Title"},{"value":"Engineering Applications of Artificial Intelligence","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.engappai.2026.114742","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"114742"}}