{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T04:32:20Z","timestamp":1782275540477,"version":"3.54.5"},"reference-count":41,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T00:00:00Z","timestamp":1729814400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Nature Science Foundation of China (NSFC)","award":["12363010"],"award-info":[{"award-number":["12363010"]}]},{"name":"National Nature Science Foundation of China (NSFC)","award":["42274055"],"award-info":[{"award-number":["42274055"]}]},{"name":"National Nature Science Foundation of China (NSFC)","award":["ZK[2023] 039"],"award-info":[{"award-number":["ZK[2023] 039"]}]},{"name":"National Nature Science Foundation of China (NSFC)","award":["[2023] 352"],"award-info":[{"award-number":["[2023] 352"]}]},{"name":"National Nature Science Foundation of China (NSFC)","award":["QKHFQ[2023]003"],"award-info":[{"award-number":["QKHFQ[2023]003"]}]},{"name":"Guizhou Provincial Basic Research Program (Natural Science)","award":["12363010"],"award-info":[{"award-number":["12363010"]}]},{"name":"Guizhou Provincial Basic Research Program (Natural Science)","award":["42274055"],"award-info":[{"award-number":["42274055"]}]},{"name":"Guizhou Provincial Basic Research Program (Natural Science)","award":["ZK[2023] 039"],"award-info":[{"award-number":["ZK[2023] 039"]}]},{"name":"Guizhou Provincial Basic Research Program (Natural Science)","award":["[2023] 352"],"award-info":[{"award-number":["[2023] 352"]}]},{"name":"Guizhou Provincial Basic Research Program (Natural Science)","award":["QKHFQ[2023]003"],"award-info":[{"award-number":["QKHFQ[2023]003"]}]},{"name":"Key Technology R&amp;D Program","award":["12363010"],"award-info":[{"award-number":["12363010"]}]},{"name":"Key Technology R&amp;D Program","award":["42274055"],"award-info":[{"award-number":["42274055"]}]},{"name":"Key Technology R&amp;D Program","award":["ZK[2023] 039"],"award-info":[{"award-number":["ZK[2023] 039"]}]},{"name":"Key Technology R&amp;D Program","award":["[2023] 352"],"award-info":[{"award-number":["[2023] 352"]}]},{"name":"Key Technology R&amp;D Program","award":["QKHFQ[2023]003"],"award-info":[{"award-number":["QKHFQ[2023]003"]}]},{"name":"Guizhou Provincial Science and Technology Projects","award":["12363010"],"award-info":[{"award-number":["12363010"]}]},{"name":"Guizhou Provincial Science and Technology Projects","award":["42274055"],"award-info":[{"award-number":["42274055"]}]},{"name":"Guizhou Provincial Science and Technology Projects","award":["ZK[2023] 039"],"award-info":[{"award-number":["ZK[2023] 039"]}]},{"name":"Guizhou Provincial Science and Technology Projects","award":["[2023] 352"],"award-info":[{"award-number":["[2023] 352"]}]},{"name":"Guizhou Provincial Science and Technology Projects","award":["QKHFQ[2023]003"],"award-info":[{"award-number":["QKHFQ[2023]003"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The Five-hundred-meter Aperture Spherical Telescope (FAST), as the world\u2019s most sensitive single-dish radio telescope, necessitates highly accurate positioning of its feed cabin to utilize its full observational potential. Traditional positioning methods that rely on GNSS and IMU, integrated with TS devices, but the GNSS and TS devices are vulnerable to other signal and environmental disruptions, which can significantly diminish position accuracy and even cause observation to stop. To address these challenges, this study introduces a novel time-series prediction model that integrates Long Short-Term Memory (LSTM) networks with a Self-Attention mechanism. This model can hold the precision of feed cabin positioning when the measure devices fail. Experimental results show that our LSTM-Self-Attention model achieves a Mean Absolute Error (MAE) of less than 10 mm and a Root Mean Square Error (RMSE) of approximately 12 mm, with the errors across different axes following a near-normal distribution. This performance meets the FAST measurement precision requirement of 15 mm, a standard derived from engineering practices where measurement accuracy is set at one-third of the control accuracy, which is around 48 mm (according to the accuracy form the official threshold analysis on the focus cabin of FAST). This result not only compensates for the shortcomings of traditional methods in consistently solving feed cabin positioning, but also demonstrates the model\u2019s ability to handle complex time-series data under specific conditions, such as sensor failures, thus providing a reliable tool for the stable operation of highly sensitive astronomical observations.<\/jats:p>","DOI":"10.3390\/s24216857","type":"journal-article","created":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T08:42:13Z","timestamp":1729845733000},"page":"6857","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Study of Five-Hundred-Meter Aperture Spherical Telescope Feed Cabin Time-Series Prediction Studies Based on Long Short-Term Memory\u2013Self-Attention"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-9763-4909","authenticated-orcid":false,"given":"Shuai","family":"Peng","sequence":"first","affiliation":[{"name":"State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7969-7271","authenticated-orcid":false,"given":"Minghui","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-3009-8690","authenticated-orcid":false,"given":"Benning","family":"Song","sequence":"additional","affiliation":[{"name":"National Astronomical Observatories, Chinese Academy of Sciences (CAS), Beijing 10010l, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongjun","family":"Yu","sequence":"additional","affiliation":[{"name":"National Astronomical Observatories, Chinese Academy of Sciences (CAS), Beijing 10010l, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-0462-9515","authenticated-orcid":false,"given":"Yabo","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Traffic and Transportation Engineering, Changsha University of Science and Technology, Changsha 410114, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingliang","family":"Yang","sequence":"additional","affiliation":[{"name":"National Astronomical Observatories, Chinese Academy of Sciences (CAS), Beijing 10010l, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-1650-4771","authenticated-orcid":false,"given":"Yu","family":"Feng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-9967-601X","authenticated-orcid":false,"given":"Kaibin","family":"Yu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaxue","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,25]]},"reference":[{"key":"ref_1","unstructured":"Petovello, M.G. (2003). Real-Time Integration of a Tactical-Grade IMU and GPS for High-Accuracy Positioning and Navigation. [Ph.D. Thesis, University of Calgary]."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Schipperijn, J., Kerr, J., Duncan, S., Madsen, T., Klinker, C.D., and Troelsen, J. (2014). Dynamic Accuracy of GPS Receivers for Use in Health Research: A Novel Method to Assess GPS Accuracy in Real-World Settings. Front. Public Health, 2.","DOI":"10.3389\/fpubh.2014.00021"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"22","DOI":"10.4236\/pos.2014.51003","article-title":"Satellite Clock Error and Orbital Solution Error Estimation for Precise Navigation Applications","volume":"5","author":"Bidikar","year":"2014","journal-title":"Positioning"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2470","DOI":"10.1016\/j.asr.2021.05.025","article-title":"Prediction models of GNSS satellite clock errors: Evaluation and application in PPP","volume":"68","author":"Ge","year":"2021","journal-title":"Adv. Space Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1007\/s13349-017-0228-5","article-title":"Geotechnical monitoring using total stations and laser scanners: Critical aspects and solutions","volume":"7","author":"Lienhart","year":"2017","journal-title":"J. Civ. Struct. Health Monit."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"36","DOI":"10.3901\/JME.2017.17.036","article-title":"Accuracy Analysis on Focus Cabin of FAST","volume":"53","author":"Yao","year":"2017","journal-title":"J. Mech. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"959502","DOI":"10.1007\/s11433-018-9376-1","article-title":"Commissioning progress of the FAST","volume":"62","author":"Jiang","year":"2019","journal-title":"Sci. China Phys. Mech. Astron."},{"key":"ref_8","first-page":"1587253","article-title":"Research on Extended Kalman Filter and Particle Filter Combinational Algorithm in UWB and Foot-Mounted IMU Fusion Positioning","volume":"1","author":"Li","year":"2018","journal-title":"Mob. Inf. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3361","DOI":"10.1016\/j.optcom.2011.02.085","article-title":"A new method to improve power efficiencies of optical systems with Cassegrain-telescope receivers","volume":"284","author":"Wu","year":"2011","journal-title":"Opt. Commun."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Chung, S., Lim, J., Noh, K.J., Kim, G., and Jeong, H. (2019). Sensor Data Acquisition and Multimodal Sensor Fusion for Human Activity Recognition Using Deep Learning. Sensors, 19.","DOI":"10.3390\/s19071716"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Fayyad, J., Jaradat, M.A., Gruyer, D., and Najjaran, H. (2020). Deep Learning Sensor Fusion for Autonomous Vehicle Perception and Localization: A Review. Sensors, 20.","DOI":"10.3390\/s20154220"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2648","DOI":"10.1109\/TIM.2019.2928346","article-title":"An Enhanced Intelligent Diagnosis Method Based on Multi-Sensor Image Fusion via Improved Deep Learning Network","volume":"69","author":"Wang","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1109\/MAES.2020.3049030","article-title":"Machine Learning\/Artificial Intelligence for Sensor Data Fusion\u2013Opportunities and Challenges","volume":"36","author":"Blasch","year":"2021","journal-title":"IEEE Aerosp. Electron. Syst. Mag."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.inffus.2021.11.006","article-title":"Multi-sensor information fusion based on machine learning for real applications in human activity recognition: State-of-the-art and research challenges","volume":"80","author":"Qiu","year":"2022","journal-title":"Inf. Fusion"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1341","DOI":"10.1007\/s10291-017-0617-6","article-title":"Real-time precise point positioning augmented with high-resolution numerical weather prediction model","volume":"21","author":"Wilgan","year":"2017","journal-title":"GPS Solut."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1631\/FITEE.2410000","article-title":"Recent advances in artificial intelligence generated content","volume":"25","author":"Zhang","year":"2024","journal-title":"Front. Inf. Technol. Electron. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.ins.2023.01.095","article-title":"A time series attention mechanism based model for tourism demand forecasting","volume":"628","author":"Dong","year":"2023","journal-title":"Inf. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Yin, H., Jin, D., Gu, Y.H., Park, C.J., Han, S.K., and Yoo, S.J. (2020). STL-ATTLSTM: Vegetable Price Forecasting Using STL and Attention Mechanism-Based LSTM. Agriculture, 10.","DOI":"10.3390\/agriculture10120612"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"108296","DOI":"10.1016\/j.compchemeng.2023.108296","article-title":"Attention-mechanism based DiPLS-LSTM and its application in industrial process time series big data prediction","volume":"176","author":"Wang","year":"2023","journal-title":"Comput. Chem. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"052037","DOI":"10.1088\/1757-899X\/569\/5\/052037","article-title":"AT-LSTM: An Attention-based LSTM Model for Financial Time Series Prediction","volume":"569","author":"Zhang","year":"2019","journal-title":"IOP Conf. Ser. Mater. Sci. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Huang, L., Mao, F., Zhang, K., and Li, Z. (2022). Spatial-Temporal Convolutional Transformer Network for Multivariate Time Series Forecasting. Sensors, 22.","DOI":"10.3390\/s22030841"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"8737","DOI":"10.1007\/s00521-021-06871-1","article-title":"Self-attention-based time-variant neural networks for multi-step time series forecasting","volume":"34","author":"Gao","year":"2022","journal-title":"Neural Comput. Appl."},{"key":"ref_23","first-page":"37","article-title":"A Comprehensive Survey of Recent Transformers in Image, Video and Diffusion Models","volume":"80","author":"Le","year":"2024","journal-title":"CMC-Comput. Mater. Contin."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"011022","DOI":"10.1117\/1.JATIS.8.1.011022","article-title":"Precise timescale, frequency, and time-transfer technology for the Square Kilometer Array","volume":"8","author":"Hendre","year":"2022","journal-title":"J. Astron. Telesc. Instrum. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"8440","DOI":"10.1109\/TVT.2018.2848294","article-title":"Deep Learning for an Effective Nonorthogonal Multiple Access Scheme","volume":"67","author":"Gui","year":"2018","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"108752","DOI":"10.1016\/j.ymssp.2021.108752","article-title":"Vibration-based anomaly detection using LSTM\/SVM approaches","volume":"169","author":"Vos","year":"2022","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.ins.2022.11.136","article-title":"Residual long short-term memory network with multi-source and multi-frequency information fusion: An application to China\u2019s stock market","volume":"622","author":"Li","year":"2023","journal-title":"Inf. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"651","DOI":"10.31577\/cai_2023_3_651","article-title":"Classification of Sentiment Using Optimized Hybrid Deep Learning Model","volume":"42","author":"Touate","year":"2023","journal-title":"Comput. Inform."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"110314","DOI":"10.1016\/j.asoc.2023.110314","article-title":"Policy gradient empowered LSTM with dynamic skips for irregular time series data","volume":"142","author":"Weerakody","year":"2023","journal-title":"Appl. Soft Comput."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"6689204","DOI":"10.1155\/2021\/6689204","article-title":"Hybrid LSTM Self-Attention Mechanism Model for Forecasting the Reform of Scientific Research in Morocco","volume":"1","author":"Fahim","year":"2021","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zhou, F., Chen, Y., and Liu, J. (2023). Application of a New Hybrid Deep Learning Model That Considers Temporal and Feature Dependencies in Rainfall-Runoff Simulation. Remote Sens., 15.","DOI":"10.3390\/rs15051395"},{"key":"ref_32","unstructured":"Bahdanau, D., Cho, K., and Bengio, Y. (2016). Neural Machine Translation by Jointly Learning to Align and Translate. arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"132306","DOI":"10.1016\/j.physd.2019.132306","article-title":"Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network","volume":"404","author":"Sherstinsky","year":"2020","journal-title":"Phys. D Nonlinear Phenom."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-term Memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Pulver, A., and Lyu, S. (2017, January 14\u201319). LSTM with working memory. Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, USA.","DOI":"10.1109\/IJCNN.2017.7965940"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1049\/iet-its.2016.0208","article-title":"LSTM network: A deep learning approach for short-term traffic forecast","volume":"11","author":"Zhao","year":"2017","journal-title":"IET Intell. Transp. Syst."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1007\/s10346-018-01127-x","article-title":"Time series analysis and long short-term memory neural network to predict landslide displacement","volume":"16","author":"Yang","year":"2019","journal-title":"Landslides"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"106181","DOI":"10.1016\/j.asoc.2020.106181","article-title":"Financial time series forecasting with deep learning: A systematic literature review: 2005\u20132019","volume":"90","author":"Sezer","year":"2020","journal-title":"Appl. Soft Comput."},{"key":"ref_39","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017, January 4\u20139). Attention is All you Need. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"48322","DOI":"10.1109\/ACCESS.2023.3276628","article-title":"Time Series Prediction Based on LSTM-Attention-LSTM Model","volume":"11","author":"Wen","year":"2023","journal-title":"IEEE Access"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"104785","DOI":"10.1016\/j.knosys.2019.05.028","article-title":"EA-LSTM: Evolutionary attention-based LSTM for time series prediction","volume":"181","author":"Li","year":"2019","journal-title":"Knowl.-Based Syst."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/21\/6857\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:20:31Z","timestamp":1760113231000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/21\/6857"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,25]]},"references-count":41,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2024,11]]}},"alternative-id":["s24216857"],"URL":"https:\/\/doi.org\/10.3390\/s24216857","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,25]]}}}