{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T03:23:59Z","timestamp":1742959439391,"version":"3.40.3"},"publisher-location":"Cham","reference-count":12,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030890919"},{"type":"electronic","value":"9783030890926"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-89092-6_31","type":"book-chapter","created":{"date-parts":[[2021,10,19]],"date-time":"2021-10-19T10:03:32Z","timestamp":1634637812000},"page":"338-344","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["An Online Intelligent Kinematic Calibration Method for Quadruped Robots Based on Machine Vision and Deep Learning"],"prefix":"10.1007","author":[{"given":"Handing","family":"Xu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenguo","family":"Nie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinyu","family":"Cui","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"ShiKeat","family":"Lee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qizhi","family":"Meng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fugui","family":"Xie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin-Jun","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,10,20]]},"reference":[{"key":"31_CR1","doi-asserted-by":"crossref","unstructured":"Smisek, J., Jancosek, M., Pajdla, T.: 3D with Kinect . In: 2011 IEEE International Conference on Computer Vision Workshops, pp. 3\u201325 (2013)","DOI":"10.1007\/978-1-4471-4640-7_1"},{"key":"31_CR2","doi-asserted-by":"publisher","unstructured":"Horaud, R., Dornaika, F.: Hand-eye calibration. Int. J. Robot. Res. 14(3), 195\u2013210 (1995). https:\/\/doi.org\/10.1177\/027836499501400301","DOI":"10.1177\/027836499501400301"},{"key":"31_CR3","doi-asserted-by":"crossref","unstructured":"Sreenivas, T., Subhash, K.: Inverse kinematics in robotics using neural networks. Inf. Sci. 116, 147\u2013164 (1999)","DOI":"10.1016\/S0020-0255(98)10098-1"},{"key":"31_CR4","doi-asserted-by":"crossref","unstructured":"Xu, Q., et al.: SuperMeshing: a new deep learning architecture for increasing the mesh density of physical fields in metal forming numerical simulation . J. Appl. Mech. 89, 1\u201311 (2002)","DOI":"10.1115\/1.4052195"},{"key":"31_CR5","doi-asserted-by":"crossref","unstructured":"Jiang, H., Nie, Z., Yeo, R., Farimani, A.B., Kara, L.B.: StressGAN: a generative deep learning model for two-dimensional stress distribution prediction . J. Appl. Mech. 88(5), 051005 (2021)","DOI":"10.1115\/1.4049805"},{"key":"31_CR6","doi-asserted-by":"crossref","unstructured":"Nie, Z., Lin, T., Jiang, H., Kara, L.B.: Topologygan: topology optimization using generative adversarial networks based on physical fields over the initial domain . J. Mech. Des. 143(3), 031715(2021)","DOI":"10.1115\/1.4049533"},{"key":"31_CR7","unstructured":"Pfaff, T., Fortunato, M., Sanchez-Gonzalez, A., et al.: Learning mesh-based simulation with graph networks. arXiv preprint arXiv:201003409 (2020)"},{"issue":"4","key":"31_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3450626.3459822","volume":"37","author":"Y Xie","year":"2018","unstructured":"Xie, Y., Franz, E., Chu, M., et al.: tempogan: a temporally coherent, volumetric GAM for super-resolution fluid flow. ACM Trans. Graph. (TOG) 37(4), 1\u201315 (2018)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"31_CR9","doi-asserted-by":"crossref","unstructured":"Klaus, H., Strobl, G.H.: Optimal hand-eye calibration. In: 2006 IEEE\/RSJ International Conference on Intelligent Robots and Systems (2006)","DOI":"10.1109\/IROS.2006.282250"},{"key":"31_CR10","unstructured":"N\u00f8RG\u00e5rd, P.M., Ravn, O., Poulsen, N.K., et al.: Neural Networks for Modelling and Control of Dynamic Systems-A Practitioner\u2019s Handbook, Springer, London (2000)"},{"key":"31_CR11","doi-asserted-by":"crossref","unstructured":"Bayramoglu, E., Andersen, N.A., Ravn, O., et al.: Pre-trained neural networks used for non-linear state estimation. In: Proceedings of the 2011 10th International Conference on Machine Learning and Applications and Workshops. IEEE (2011)","DOI":"10.1109\/ICMLA.2011.118"},{"issue":"2","key":"31_CR12","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1016\/j.advengsoft.2009.06.006","volume":"41","author":"AT Hasan","year":"2010","unstructured":"Hasan, A.T., Ismail, N., Hamouda, A.M.S., et al.: Artificial neural network-based kinematics Jacobian solution for serial manipulator passing through singular configurations. Adv. Eng. Softw. 41(2), 359\u201367 (2010)","journal-title":"Adv. Eng. Softw."}],"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\/978-3-030-89092-6_31","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,10,19]],"date-time":"2021-10-19T10:21:17Z","timestamp":1634638877000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-89092-6_31"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030890919","9783030890926"],"references-count":12,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-89092-6_31","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"20 October 2021","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":"Yantai","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 October 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 October 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icira2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.icira2021.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}