{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,24]],"date-time":"2025-10-24T11:52:20Z","timestamp":1761306740887,"version":"build-2065373602"},"publisher-location":"Singapore","reference-count":11,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819520947"},{"type":"electronic","value":"9789819520954"}],"license":[{"start":{"date-parts":[[2025,10,25]],"date-time":"2025-10-25T00:00:00Z","timestamp":1761350400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,10,25]],"date-time":"2025-10-25T00:00:00Z","timestamp":1761350400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-981-95-2095-4_22","type":"book-chapter","created":{"date-parts":[[2025,10,24]],"date-time":"2025-10-24T11:47:34Z","timestamp":1761306454000},"page":"267-278","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Research on Pose Control Dataset Augmentation Method Based on Generative Adversarial Networks"],"prefix":"10.1007","author":[{"given":"Zhe","family":"Sun","sequence":"first","affiliation":[]},{"given":"Han","family":"Xiao","sequence":"additional","affiliation":[]},{"given":"Peng","family":"Sun","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,10,25]]},"reference":[{"key":"22_CR1","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1016\/j.jmsy.2023.01.007","volume":"67","author":"H Zhou","year":"2023","unstructured":"Zhou, H., Yang, G., Wang, B., et al.: An attention-based deep learning approach for inertial motion recognition and estimation in human-robot collaboration. J. Manuf. Syst. 67, 97\u2013110 (2023)","journal-title":"J. Manuf. Syst."},{"key":"22_CR2","doi-asserted-by":"crossref","unstructured":"Li, C., Zheng, P., Yin, Y., et al.: An AR-assisted deep reinforcement learning-based approach towards mutual-cognitive safe human-robot interaction. Robot. Comput.-Integr. Manuf. 80, 102471 (2023)","DOI":"10.1016\/j.rcim.2022.102471"},{"issue":"1","key":"22_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3603618","volume":"56","author":"C Zheng","year":"2023","unstructured":"Zheng, C., Wu, W., Chen, C., et al.: Deep learning-based human pose estimation: a survey. ACM Comput. Surv. 56(1), 1\u201337 (2023)","journal-title":"ACM Comput. Surv."},{"issue":"1","key":"22_CR4","doi-asserted-by":"publisher","first-page":"19186","DOI":"10.1038\/s41598-022-23692-x","volume":"12","author":"R Gulakala","year":"2022","unstructured":"Gulakala, R., Markert, B., Stoffel, M.: Generative adversarial network based data augmentation for CNN based detection of Covid-19. Sci. Rep. 12(1), 19186 (2022)","journal-title":"Sci. Rep."},{"issue":"13","key":"22_CR5","doi-asserted-by":"publisher","first-page":"19120","DOI":"10.1002\/er.7013","volume":"45","author":"F Naaz","year":"2021","unstructured":"Naaz, F., Herle, A., Channegowda, J., et al.: A generative adversarial network-based synthetic data augmentation technique for battery condition evaluation. Int. J. Energy Res. 45(13), 19120\u201319135 (2021)","journal-title":"Int. J. Energy Res."},{"key":"22_CR6","unstructured":"Cao, J., Mo, L., Zhang, Y., et al.: Multi-marginal wasserstein GAN. Adv. Neural inf. Process. Syst. 32 (2019)"},{"key":"22_CR7","doi-asserted-by":"crossref","unstructured":"Li, J., Niu, K., Liao, L., et al.: A generative steganography method based on WGAN-GP. In: proceedings of the International Conference on Artificial Intelligence and Security. Springer (2020)","DOI":"10.1007\/978-981-15-8083-3_34"},{"key":"22_CR8","doi-asserted-by":"crossref","unstructured":"Xu, Q., Huang, G., Yuan, Y., et al.: An empirical study on evaluation metrics of generative adversarial networks. arXiv e-prints (2018)","DOI":"10.1109\/BigData.2018.8622525"},{"key":"22_CR9","doi-asserted-by":"crossref","unstructured":"Gretton, A., Borgwardt, K., Rasch, M.J., et al.: A kernel method for the two-sample problem. arXiv e-prints, arXiv:0805.2368 (2008)","DOI":"10.7551\/mitpress\/7503.003.0069"},{"issue":"1","key":"22_CR10","first-page":"75","volume":"10","author":"MA Ali","year":"2022","unstructured":"Ali, M.A., Hussain, A.J., Sadiq, A.T.: Human body posture recognition approaches: a review. ARO-The Sci. J. Koya Univ. 10(1), 75\u201384 (2022)","journal-title":"ARO-The Sci. J. Koya Univ."},{"key":"22_CR11","unstructured":"Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., et al.: Generative adversarial nets. Adv. Neural inf. Process. Syst. 27 (2014)"}],"container-title":["Lecture Notes in Computer Science","Intelligent Robotics and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-2095-4_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,24]],"date-time":"2025-10-24T11:47:39Z","timestamp":1761306459000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-2095-4_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,25]]},"ISBN":["9789819520947","9789819520954"],"references-count":11,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-2095-4_22","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,10,25]]},"assertion":[{"value":"25 October 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIRA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Robotics and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Okayama","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 August 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 August 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icira2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.icira2025.com\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}