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| The Adjustment Analysis Method of the Active Surface Antenna Based on Convolutional Neural Network | |
Ban, You1,2; Shi, Shang1; Wang, Na2 ; Xu, Qian2; Feng, Shufei3
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| 2024-06-01 | |
| Source Publication | RESEARCH IN ASTRONOMY AND ASTROPHYSICS
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| ISSN | 1674-4527 |
| Volume | 24Issue:6Pages:065024 |
| Contribution Rank | 2 |
| Abstract | Active surface technique is one of the key technologies to ensure the reflector accuracy of the millimeter/submillimeter wave large reflector antenna. The antenna is complex, large-scale, and high-precision equipment, and its active surfaces are affected by various factors that are difficult to comprehensively deal with. In this paper, based on the advantage of the deep learning method that can be improved through data learning, we propose the active adjustment value analysis method of large reflector antenna based on deep learning. This method constructs a neural network model for antenna active adjustment analysis in view of the fact that a large reflector antenna consists of multiple panels spliced together. Based on the constraint that a single actuator has to support multiple panels (usually 4), an autonomously learned neural network emphasis layer module is designed to enhance the adaptability of the active adjustment neural network model. The classical 8-meter antenna is used as a case study, the actuators have a mean adjustment error of 0.00252 mm, and the corresponding antenna surface error is 0.00523 mm. This active adjustment result shows the effectiveness of the method in this paper. |
| Keyword | techniques: radar astronomy telescopes methods: analytical methods: numerical |
| DOI | 10.1088/1674-4527/ad4963 |
| Indexed By | SCI |
| Language | 英语 |
| WOS Keyword | MAIN REFLECTOR ; ERROR |
| Funding Project | National Key R&D Program of China[2021YFC220350] ; National Natural Science Foundation of China[12303094] ; National Natural Science Foundation of China[52165053] ; Natural Science Foundation of Xinjiang Uygur Autonomous Region[2022D01C683] ; China Postdoctoral Science Foundation[2023T160549] ; China Postdoctoral Science Foundation[2021M702751] ; Guangdong Basic and Applied Basic Research Foundation[2020A1515111043] ; Guangdong Basic and Applied Basic Research Foundation[2023A1515010703] |
| WOS Research Area | Astronomy & Astrophysics |
| WOS Subject | Astronomy & Astrophysics |
| WOS ID | WOS:001249983000001 |
| Publisher | NATL ASTRONOMICAL OBSERVATORIES, CHIN ACAD SCIENCES |
| Funding Organization | National Key R&D Program of China ; National Natural Science Foundation of China ; Natural Science Foundation of Xinjiang Uygur Autonomous Region ; China Postdoctoral Science Foundation ; Guangdong Basic and Applied Basic Research Foundation |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.xao.ac.cn/handle/45760611-7/6807 |
| Collection | 射电天文研究室_脉冲星研究团组 射电天文研究室_天线技术实验室 |
| Corresponding Author | Ban, You; Wang, Na |
| Affiliation | 1.Xinjiang Univ, Sch Mech Engn, Urumqi 830017, Peoples R China 2.Chinese Acad Sci, Xinjiang Astron Observ, Urumqi 830011, Peoples R China 3.Dongguan Univ Technol, Sch Mech Engn, Dongguan 523808, Peoples R China |
| First Author Affilication | Xinjiang Astronomical Observatory, Chinese Academy of Sciences |
| Corresponding Author Affilication | Xinjiang Astronomical Observatory, Chinese Academy of Sciences |
| Recommended Citation GB/T 7714 | Ban, You,Shi, Shang,Wang, Na,et al. The Adjustment Analysis Method of the Active Surface Antenna Based on Convolutional Neural Network[J]. RESEARCH IN ASTRONOMY AND ASTROPHYSICS,2024,24(6):065024. |
| APA | Ban, You,Shi, Shang,Wang, Na,Xu, Qian,&Feng, Shufei.(2024).The Adjustment Analysis Method of the Active Surface Antenna Based on Convolutional Neural Network.RESEARCH IN ASTRONOMY AND ASTROPHYSICS,24(6),065024. |
| MLA | Ban, You,et al."The Adjustment Analysis Method of the Active Surface Antenna Based on Convolutional Neural Network".RESEARCH IN ASTRONOMY AND ASTROPHYSICS 24.6(2024):065024. |
| Files in This Item: | ||||||
| File Name/Size | DocType | Version | Access | License | ||
| Ban-2024-The Adjustm（2573KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Application Full Text | |
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