{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:23:55Z","timestamp":1760239435860,"version":"build-2065373602"},"reference-count":39,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2020,11,9]],"date-time":"2020-11-09T00:00:00Z","timestamp":1604880000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The correction of wavefront aberration plays a vital role in active optics. The traditional correction algorithms based on the deformation of the mirror cannot effectively deal with disturbances in the real system. In this study, a new algorithm called deep learning correction algorithm (DLCA) is proposed to compensate for wavefront aberrations and improve the correction capability. The DLCA consists of an actor network and a strategy unit. The actor network is utilized to establish the mapping of active optics systems with disturbances and provide a search basis for the strategy unit, which can increase the search speed; The strategy unit is used to optimize the correction force, which can improve the accuracy of the DLCA. Notably, a heuristic search algorithm is applied to reduce the search time in the strategy unit. The simulation results show that the DLCA can effectively improve correction capability and has good adaptability. Compared with the least square algorithm (LSA), the algorithm we proposed has better performance, indicating that the DLCA is more accurate and can be used in active optics. Moreover, the proposed approach can provide a new idea for further research of active optics.<\/jats:p>","DOI":"10.3390\/s20216403","type":"journal-article","created":{"date-parts":[[2020,11,9]],"date-time":"2020-11-09T19:08:29Z","timestamp":1604948909000},"page":"6403","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Deep Learning Correction Algorithm for The Active Optics System"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0050-5135","authenticated-orcid":false,"given":"Wenxiang","family":"Li","sequence":"first","affiliation":[{"name":"Nanjing Astronomical Instruments Research Center, University of Science and Technology of China, Hefei 230026, China"}]},{"given":"Chao","family":"Kang","sequence":"additional","affiliation":[{"name":"Nanjing Astronomical Instruments Research Center, University of Science and Technology of China, Hefei 230026, China"}]},{"given":"Hengrui","family":"Guan","sequence":"additional","affiliation":[{"name":"Nanjing Astronomical Instruments Research Center, University of Science and Technology of China, Hefei 230026, China"}]},{"given":"Shen","family":"Huang","sequence":"additional","affiliation":[{"name":"CAS Nanjing Astronomical Instruments Co., Ltd., Nanjing 210042, China"}]},{"given":"Jinbiao","family":"Zhao","sequence":"additional","affiliation":[{"name":"CAS Nanjing Astronomical Instruments Co., Ltd., Nanjing 210042, China"}]},{"given":"Xiaojun","family":"Zhou","sequence":"additional","affiliation":[{"name":"Nanjing Astronomical Instruments Research Center, University of Science and Technology of China, Hefei 230026, China"},{"name":"CAS Nanjing Astronomical Instruments Co., Ltd., Nanjing 210042, China"}]},{"given":"Jinpeng","family":"Li","sequence":"additional","affiliation":[{"name":"CAS Nanjing Astronomical Instruments Co., Ltd., Nanjing 210042, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1359","DOI":"10.1364\/JOSAA.29.001359","article-title":"Removing static aberrations from the active optics system of a wide-field telescope","volume":"29","author":"Schipani","year":"2012","journal-title":"J. Opt. Soc. Am. A"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1573","DOI":"10.1364\/AO.55.001573","article-title":"Active optics system of the VLT Survey Telescope","volume":"55","author":"Schipani","year":"2016","journal-title":"Appl. Opt."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"A119","DOI":"10.1051\/0004-6361\/201219505","article-title":"The VISTA Science Archive","volume":"548","author":"Cross","year":"2012","journal-title":"Astron. Astrophys."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Thomas, S.J., Xin, B., Tsai, T.-W., Contaxis, C., Claver, C., Lotz, P., and Neill, D. (2017, January 25\u201330). The LSST real-time active optics system. Proceedings of the 5th Adaptive Optics for Extremely Large Telescopes (AO4ELT 2017), Tenerife, Spain.","DOI":"10.26698\/AO4ELT5.0137"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Wang, H., Liang, M., Yao, D., Zuo, Y., Zheng, X., and Yang, J. (2020). Study on the application of the free-vibration modes of an annular mirror in the active optics system. J. Astron. Telesc. Instrum. Syst., 6.","DOI":"10.1117\/1.JATIS.6.1.019002"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"107914","DOI":"10.1016\/j.measurement.2020.107914","article-title":"Axial force measurement of the bolt\/nut assemblies based on the bending mode shape frequency of the protruding thread part using ultrasonic modal analysis","volume":"162","author":"Hosoya","year":"2020","journal-title":"J. Int. Meas. Confed."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Dolkens, D., Van Marrewijk, G., and Kuiper, H. (2018, January 9\u201312). Active correction system of a deployable telescope for earth observation. Proceedings of the International Conference on Space Optics (ICSO 2018), Chania, Greece.","DOI":"10.1117\/12.2535929"},{"key":"ref_8","first-page":"2238","article-title":"Deformation of thin primary mirror fitted with its vibration mode","volume":"40","author":"Chen","year":"2011","journal-title":"Infrared Laser Eng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2551","DOI":"10.3788\/OPE.20172510.2551","article-title":"Active correction of 1.23 m SiC mirror using bending mode","volume":"25","author":"Zhu","year":"2017","journal-title":"Opt. Precis. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Han, Y., Fan, B., Li, C., and Liu, H. (2016, January 26\u201329). Analysis of surface error correction capability of 1.2m active support system. Proceedings of the 8th International Symposium on Advanced Optical Manufacturing and Testing Technology (AOMATT2016), Suzhou, China.","DOI":"10.1117\/12.2243755"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1364\/JOT.86.000341","article-title":"Active correction experiment on a 12 m thin primary mirror","volume":"86","author":"Dai","year":"2019","journal-title":"J. Opt. Technol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"5740","DOI":"10.1364\/AO.58.005740","article-title":"Development of space active optics for a whiffletree supported mirror","volume":"58","author":"Zhou","year":"2019","journal-title":"Appl. Opt."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Schwaer, C., Sinn, A., and Schitter, G. (2019, January 4\u20136). Mechatronic approach towards lightweight mirrors with active optics for telescope systems. Proceedings of the 8th IFAC Symposium on Mechatronic Systems (MECHATRONICS), Vienna, Austria.","DOI":"10.1016\/j.ifacol.2019.11.641"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ashraf, I., Hur, S., Park, S., and Park, Y. (2019). DeepLocate: Smartphone Based Indoor Localization with a Deep Neural Network Ensemble Classifier. Sensors, 20.","DOI":"10.3390\/s20010133"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12859-019-3321-4","article-title":"Biomedical named entity recognition using deep neural networks with contextual information","volume":"20","author":"Cho","year":"2019","journal-title":"BMC Bioinform."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Guo, H., Xu, Y., Li, Q., Du, S., He, D., Wang, Q., and Huang, Y. (2019). Improved Machine Learning Approach for Wavefront Sensing. Sensors, 19.","DOI":"10.3390\/s19163533"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1117\/1.OE.58.6.065103","article-title":"Accelerating optics design optimizations with deep learning","volume":"58","author":"Hegde","year":"2019","journal-title":"Opt. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"G\u00f3mez, S.L.S., Gonz\u00e1lez-Guti\u00e9rrez, C., Riesgo, F.G., Lasheras, F.S., Lasheras, F.S., and Rodr\u00edguez, J.D.S. (2019). Convolutional Neural Networks Approach for Solar Reconstruction in SCAO Configurations. Sensors, 19.","DOI":"10.3390\/s19102233"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1998","DOI":"10.1364\/AO.58.001998","article-title":"Deep learning control model for adaptive optics systems","volume":"58","author":"Xu","year":"2019","journal-title":"Appl. Opt."},{"key":"ref_20","unstructured":"Xu, Y., Zhao, Q., Li, J., and Jiao, C. (2020, November 09). Simulation Analysis of Large-Aperture Standard Planar Mirror Based on Active Correction. (In Chinese)."},{"key":"ref_21","first-page":"166","article-title":"Correction experiment of 620 m thin mirror active optics telescope","volume":"43","author":"Li","year":"2014","journal-title":"Infrared Laser Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1462","DOI":"10.1107\/S1600577519007938","article-title":"Kirkpatrick\u2013Baez active optics system at FERMI: System performance analysis","volume":"26","author":"Raimondi","year":"2019","journal-title":"J. Synchrotron Radiat."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Spiga, D., Barbera, M., Basso, S., Civitani, M., Collura, A., Dell\u2019Agostino, S., Lo Cicero, U., Lullo, G., Pelliciari, C., and Riva, M. (2014, January 17\u201321). Active shape correction of a thin glass\/plastic X-ray mirror. Proceedings of the SPIE Optical Engineering + Applications: Adaptive X-ray Optics III, San Diego, CA, USA.","DOI":"10.1117\/12.2063349"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"Lecun","year":"2015","journal-title":"Nature"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Duchanoy, C.A., Moreno-Armend\u00e1riz, M.A., Moreno-Torres, J.C., and Cruz-Villar, C.A. (2019). A Deep Neural Network Based Model for a Kind of Magnetorheological Dampers. Sensors, 19.","DOI":"10.3390\/s19061333"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1187","DOI":"10.1177\/1077546319890188","article-title":"A new control method based on fuzzy controller, time delay estimation, deep learning, and non-dominated sorting genetic algorithm-III for powertrain mount system","volume":"26","author":"Guo","year":"2019","journal-title":"J. Vib. Control."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Yoon, K., Kim, D.Y., Yoon, Y.-C., and Jeon, M. (2019). Data Association for Multi-Object Tracking via Deep Neural Networks. Sensors, 19.","DOI":"10.3390\/s19030559"},{"key":"ref_28","unstructured":"Kingma, D.P., and Ba, J.L. (2015, January 7\u20139). Adam: A method for stochastic optimization. Proceedings of the 3rd International Conference on Learning Representations (ICLR 2015), San Diego, CA, USA."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Lopes, F.F., Ferreira, J.C., and Fernandes, M.A.C. (2019). Parallel Implementation on FPGA of Support Vector Machines Using Stochastic Gradient Descent. Electronics, 8.","DOI":"10.3390\/electronics8060631"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Guo, X., Zhang, J., Tie, L., and Luo, M. (2019). HS-SA-Based Precise Modeling of the Aircraft Fuel Center of Gravity Using Sensors Data. Sensors, 19.","DOI":"10.3390\/s19112457"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"109813","DOI":"10.1016\/j.engstruct.2019.109813","article-title":"Cable force optimization of a curved cable-stayed bridge with combined simulated annealing method and cubic B-Spline interpolation curves","volume":"201","author":"Guo","year":"2019","journal-title":"Eng. Struct."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2438","DOI":"10.1109\/TSMC.2018.2847448","article-title":"Target Disassembly Sequencing and Scheme Evaluation for CNC Machine Tools Using Improved Multiobjective Ant Colony Algorithm and Fuzzy Integral","volume":"49","author":"Feng","year":"2018","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Sun, G.-C., Xiang, J., Xing, M., Yang, J., and Guo, L. (2018). A Channel Phase Error Correction Method Based on Joint Quality Function of GF-3 SAR Dual-Channel Images. Sensors, 18.","DOI":"10.3390\/s18093131"},{"key":"ref_34","first-page":"1299","article-title":"Application of Improved Genetic Algorithm in Function Optimization","volume":"35","author":"Yan","year":"2019","journal-title":"J. Inf. Sci. Eng."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Ningombam, D.D., and Shin, S. (2019). Optimal Resource Management and Binary Power Control in Network-Assisted D2D Communications for Higher Frequency Reuse Factor. Sensors, 19.","DOI":"10.3390\/s19020251"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"7287","DOI":"10.1007\/s00500-018-3376-6","article-title":"A framework based on evolutionary algorithm for strategy optimization in robot soccer","volume":"23","author":"Larik","year":"2018","journal-title":"Soft Comput."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1007\/s12293-019-00280-7","article-title":"A multi-level knee point based multi-objective evolutionary algorithm for AUC maximization","volume":"11","author":"Qiu","year":"2019","journal-title":"Memetic Comput."},{"key":"ref_38","first-page":"1","article-title":"Maximum Likelihood Estimation for Three-Parameter Weibull Distribution Using Evolutionary Strategy","volume":"2019","author":"Yang","year":"2019","journal-title":"Math. Probl. Eng."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Yadav, S., and Shukla, S. (2016, January 27\u201328). Analysis of k-Fold Cross-Validation over Hold-Out Validation on Colossal Datasets for Quality Classification. Proceedings of the 2016 IEEE 6th International conference on advanced computing (IACC), Bhimavaram, India.","DOI":"10.1109\/IACC.2016.25"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/21\/6403\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:31:20Z","timestamp":1760178680000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/21\/6403"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,9]]},"references-count":39,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2020,11]]}},"alternative-id":["s20216403"],"URL":"https:\/\/doi.org\/10.3390\/s20216403","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2020,11,9]]}}}