{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T07:10:34Z","timestamp":1770966634726,"version":"3.50.1"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2023,4,11]],"date-time":"2023-04-11T00:00:00Z","timestamp":1681171200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,4,11]],"date-time":"2023-04-11T00:00:00Z","timestamp":1681171200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62103440"],"award-info":[{"award-number":["62103440"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National key Basic Research Development Program","award":["2015CB755800"],"award-info":[{"award-number":["2015CB755800"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2023,10]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Flight risk evaluation based on data-driven approach is an essential topic of aviation safety management. Existing risk analysis methods ignore the coupling and time-variant characteristics of flight parameters, and cannot accurately establish the mapping relationship between flight state and loss-of-control risk. To deal with the problem, a flight state deep clustering network (FSDCN) model was proposed to mine latent loss-of-control risk information implicating in raw flight parameters. FSDCN integrates the feature extraction and clustering into an end-to-end deep hybrid network to extract latent risk features from multivariate time-series flight parameters and cluster them. In the FSDCN model, a sequential multi-attention encoder\u2013decoder network is designed to extract embedded risk features, and the feature clustering layer is designed to iteratively refine clustering effects and feature extraction. Besides, a loss-of-control classifier is added to optimize the risk feature vector expression and ensure sufficient dividing feature for facilitate clustering. The multi-task joint learning strategy is adopted to improve the clustering performance of the model further. According to extracted risk features and similarity metrics, the optimal clusters number of flight states is set as 5. Comparative experiments show that FSDCN significantly performs better than other clustering models with performance percentage error below <jats:inline-formula><jats:alternatives><jats:tex-math>$$6\\%$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mrow>\n                    <mml:mn>6<\/mml:mn>\n                    <mml:mo>%<\/mml:mo>\n                  <\/mml:mrow>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula>. Through statistical analysis of clustering results, the risk level is quantified for each cluster. Three high-difficulty maneuver cases are presented to demonstrate FSDCN for flight risk evaluation. The flight parameter sequences of the maneuver cases are input into the well-trained FSDCN to obtain the risk prediction results. The spatiotemporal distribution characteristics of the risk-quantized results are consistent with flight parameters over-limit situations, which demonstrates the effectiveness of FSDCN on clustering flight states. The experimental results on flight maneuver cases show that FSDCN can find potential loss-of-control risk features according to multivariate time-series flight data and provide support for in-flight risk warnings.<\/jats:p>","DOI":"10.1007\/s40747-023-01053-z","type":"journal-article","created":{"date-parts":[[2023,4,11]],"date-time":"2023-04-11T05:03:20Z","timestamp":1681189400000},"page":"5893-5906","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Flight risk evaluation based on flight state deep clustering network"],"prefix":"10.1007","volume":"9","author":[{"given":"Guozhi","family":"Wang","sequence":"first","affiliation":[]},{"given":"Haojun","family":"Xu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5786-4403","authenticated-orcid":false,"given":"Binbin","family":"Pei","sequence":"additional","affiliation":[]},{"given":"Haoyu","family":"Cheng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,4,11]]},"reference":[{"issue":"10","key":"1053_CR1","first-page":"313","volume":"9","author":"S Li","year":"2015","unstructured":"Li S, Fan L (2015) Research on risk early-warning model in airport flight area based on information entropy attribute reduction and BP neural network. Int J Secur Appl 9(10):313\u2013322","journal-title":"Int J Secur Appl"},{"issue":"9","key":"1053_CR2","first-page":"599","volume":"12","author":"S Balachandran","year":"2015","unstructured":"Balachandran S, Atkins EM (2015) Flight safety assessment and management for takeoff using deterministic Moore machines. J Aerosp Inf Syst 12(9):599\u2013615","journal-title":"J Aerosp Inf Syst"},{"issue":"8","key":"1053_CR3","doi-asserted-by":"publisher","first-page":"2146","DOI":"10.1016\/j.cja.2020.03.025","volume":"33","author":"Y Wei","year":"2020","unstructured":"Wei Y, Xu H, Xue Y et al (2020) Quantitative assessment and visualization of flight risk induced by coupled multi-factor under icing conditions. Chin J Aeronaut 33(8):2146\u20132161","journal-title":"Chin J Aeronaut"},{"issue":"3","key":"1053_CR4","doi-asserted-by":"publisher","first-page":"399","DOI":"10.2514\/1.C035564","volume":"57","author":"K Yasue","year":"2020","unstructured":"Yasue K (2020) Extraction of monophasic data from flight test data via cluster analysis. J Aircr 57(3):399\u2013407","journal-title":"J Aircr"},{"key":"1053_CR5","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-07812-0","volume-title":"Real world data mining applications","author":"M Abou-Nasr","year":"2015","unstructured":"Abou-Nasr M, Lessmann S, Stahlbock R et al (2015) Real world data mining applications. Springer, Cham"},{"key":"1053_CR6","doi-asserted-by":"crossref","unstructured":"Sharifi F, Mohammed E, Crump T et al (2019) A cluster-based machine learning model for large healthcare data analysis. In: Proceedings of the 5th international joint conference on big data innovations and applications, pp 92\u2013106","DOI":"10.1007\/978-3-030-27355-2_7"},{"issue":"4","key":"1053_CR7","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1186\/s12859-022-04667-1","volume":"23","author":"H Hadipour","year":"2022","unstructured":"Hadipour H, Liu C, Davis R et al (2022) Deep clustering of small molecules at large-scale via variational autoencoder embedding and K-means. BMC Bioinform 23(4):132\u2013153","journal-title":"BMC Bioinform"},{"key":"1053_CR8","doi-asserted-by":"crossref","unstructured":"Yan Y, He M, Song L (2021) Evaluation of regional industrial cluster innovation capability based on particle swarm clustering algorithm and multi-objective optimization. Complex Intell Syst","DOI":"10.1007\/s40747-021-00521-8"},{"issue":"9","key":"1053_CR9","first-page":"587","volume":"12","author":"L Li","year":"2015","unstructured":"Li L, Das S, John Hansman R et al (2015) Analysis of flight data using clustering techniques for detecting abnormal operations. J Aerosp Inf Syst 12(9):587\u2013598","journal-title":"J Aerosp Inf Syst"},{"issue":"1","key":"1053_CR10","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1109\/TITS.2017.2691000","volume":"19","author":"M-H Nguyen","year":"2018","unstructured":"Nguyen M-H, Alam S (2018) Airspace collision risk hot-spot identification using clustering models. IEEE Trans Intell Transp Syst 19(1):48\u201357","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"1053_CR11","doi-asserted-by":"crossref","unstructured":"Lishuai L, Gariel M, Hansman RJ et al (2011) Anomaly detection in onboard-recorded flight data using cluster analysis. In: Proceedings of IEEE\/AIAA 30th digital avionics systems conference, pp 1\u201311","DOI":"10.1109\/DASC.2011.6096068"},{"issue":"7","key":"1053_CR12","doi-asserted-by":"publisher","first-page":"1106","DOI":"10.1049\/iet-its.2018.5379","volume":"13","author":"W Rao","year":"2019","unstructured":"Rao W, Xia J, Liu W et al (2019) Interval data-based k-means clustering method for traffic state identification at urban intersections. IET Intell Transp Syst 13(7):1106\u2013115","journal-title":"IET Intell Transp Syst"},{"key":"1053_CR13","unstructured":"Yang W, Li X, Deng Y (2022) A clustering based method to complete frame of discernment. Chin J Aeronaut"},{"key":"1053_CR14","doi-asserted-by":"crossref","unstructured":"Sheridan K, Puranik TG, Mangortey E et al (2020) An application of DBSCAN clustering for flight anomaly detection during the approach phase. In: Proceedings of AIAA scitech forum, pp 1851\u20131871","DOI":"10.2514\/6.2020-1851"},{"key":"1053_CR15","unstructured":"Jiang Q, Liu Y, Ding Z et al (2022) Behavior pattern mining based on spatiotemporal trajectory multidimensional information fusion. Chin J Aeronaut"},{"key":"1053_CR16","doi-asserted-by":"crossref","unstructured":"Zhou W, Wang L, Han X et al (2022) A novel density deviation multi-peaks automatic clustering algorithm. Complex Intell Syst","DOI":"10.1007\/s40747-022-00798-3"},{"issue":"1","key":"1053_CR17","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1007\/s40747-020-00191-y","volume":"7","author":"L Chen","year":"2021","unstructured":"Chen L, Guo Q, Liu Z et al (2021) Enhanced synchronization-inspired clustering for high-dimensional data. Complex Intell Syst 7(1):203\u2013223","journal-title":"Complex Intell Syst"},{"issue":"6","key":"1053_CR18","doi-asserted-by":"publisher","first-page":"2369","DOI":"10.1109\/TKDE.2019.2954317","volume":"33","author":"H Liu","year":"2021","unstructured":"Liu H, Li J, Wu Y et al (2021) Clustering with outlier removal. IEEE Trans Knowl Data Eng 33(6):2369\u20132379","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1053_CR19","doi-asserted-by":"crossref","unstructured":"Gao X, Cheng Z, Huo W (2020) Anomaly location method for QAR data based on principal component analysis hierarchical clustering. In: Proceedings of the 1th international conference on materials science and engineering, pp 12085\u201312095","DOI":"10.1088\/1757-899X\/790\/1\/012085"},{"key":"1053_CR20","doi-asserted-by":"crossref","unstructured":"Aslaner HE, Unal C, Iyigun C (2016) Applying data mining techniques to detect abnormal flight characteristics. In: Proceedings of the international conference on society for optical engineering, pp 1\u201318","DOI":"10.1117\/12.2224061"},{"key":"1053_CR21","doi-asserted-by":"crossref","unstructured":"Zhao W, He F, Li L et al (2018) An adaptive online learning model for flight data cluster analysis. In: Proceedings of IEEE\/AIAA 37th digital avionics systems conference, pp 1\u20137","DOI":"10.1109\/DASC.2018.8569600"},{"key":"1053_CR22","doi-asserted-by":"crossref","unstructured":"Si\u0142ka J, Wieczorek M, Wozniak M (2022) Recurrent neural network model for high-speed train vibration prediction from time series. Neural Comput Appl 34:13305\u201313318","DOI":"10.1007\/s00521-022-06949-4"},{"issue":"70","key":"1053_CR23","first-page":"1","volume":"11","author":"M Mittal","year":"2022","unstructured":"Mittal M, Kobielnik M, Gupta S et al (2022) An efficient quality of services based wireless sensor network for anomaly detection using soft computing approaches. JoCCASA 11(70):1\u201321","journal-title":"JoCCASA"},{"key":"1053_CR24","doi-asserted-by":"publisher","first-page":"110203","DOI":"10.1016\/j.chaos.2020.110203","volume":"140","author":"M Wieczorek","year":"2020","unstructured":"Wieczorek M, Si\u0142ka J, Wozniak M (2020) Neural network powered COVID-19 spread forecasting model. Chaos Soliton Fract 140:110203\u2013110218","journal-title":"Chaos Soliton Fract"},{"issue":"6","key":"1053_CR25","doi-asserted-by":"publisher","first-page":"329","DOI":"10.3390\/aerospace9060329","volume":"9","author":"K Qin","year":"2022","unstructured":"Qin K, Wang Q, Lu B et al (2022) Flight anomaly detection via a deep hybrid model. Aerospace 9(6):329","journal-title":"Aerospace"},{"key":"1053_CR26","unstructured":"Xie J, Girshick R, Farhadi A (2016) Unsupervised deep embedding for clustering analysis. In: Proceedings of the 33th international conference on machine learning, pp 1\u201310"},{"key":"1053_CR27","doi-asserted-by":"crossref","unstructured":"Zhong H, Wu J, Chen C et al (2021) Graph contrastive clustering. In: Proceedings of IEEE international conference on computer vision and pattern recognition, pp 9204\u20139213","DOI":"10.1109\/ICCV48922.2021.00909"},{"key":"1053_CR28","doi-asserted-by":"crossref","unstructured":"Huang J, Gong S, Zhu X (2020) Deep semantic clustering by partition confidence maximisation. In: Proceedings of IEEE international conference on computer vision and pattern recognition, pp 8846\u20138855","DOI":"10.1109\/CVPR42600.2020.00887"},{"key":"1053_CR29","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1016\/j.neucom.2018.10.016","volume":"325","author":"Y Ren","year":"2019","unstructured":"Ren Y, Hu K, Dai X et al (2019) Semi-supervised deep embedded clustering. Neurocomputing 325:121\u2013130","journal-title":"Neurocomputing"},{"key":"1053_CR30","doi-asserted-by":"crossref","unstructured":"Ienco D, Interdonato R (2020) Deep multivariate time series embedding clustering via attentive-gated autoencoder. In: Proceedings of the 24th international conference on advances in knowledge discovery and data mining, pp 318\u2013329","DOI":"10.1007\/978-3-030-47426-3_25"},{"key":"1053_CR31","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1016\/j.neucom.2020.12.094","volume":"433","author":"B Diallo","year":"2021","unstructured":"Diallo B, Hu J, Li T et al (2021) Deep embedding clustering based on contractive autoencoder. Neurocomputing 433:96\u2013107","journal-title":"Neurocomputing"},{"issue":"1","key":"1053_CR32","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1049\/itr2.12011","volume":"15","author":"H Li","year":"2021","unstructured":"Li H, Bai Q, Zhao Y et al (2021) TSDCN: traffic safety state deep clustering network for real-time traffic crash-prediction. IET Intell Transp Syst 15(1):132\u2013146","journal-title":"IET Intell Transp Syst"},{"issue":"3","key":"1053_CR33","doi-asserted-by":"publisher","first-page":"1367","DOI":"10.1007\/s40747-021-00274-4","volume":"7","author":"H Xia","year":"2021","unstructured":"Xia H, Luo Y, Liu Y (2021) Attention neural collaboration filtering based on GRU for recommender systems. Complex Intell Syst 7(3):1367\u20131379","journal-title":"Complex Intell Syst"},{"issue":"2","key":"1053_CR34","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1109\/TPAMI.2018.2858826","volume":"42","author":"T Lin","year":"2021","unstructured":"Lin T, Goyal P, Girshick R et al (2021) Focal loss for dense object detection. IEEE Trans Pattern Anal Mach Intell 42(2):318\u2013327","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1053_CR35","doi-asserted-by":"crossref","unstructured":"Xia H, Huang K, Liu Y (2022) Unexpected interest recommender system with graph neural network. Complex Intell Syst","DOI":"10.1007\/s40747-022-00849-9"},{"issue":"5","key":"1053_CR36","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1049\/iet-its.2014.0288","volume":"10","author":"J Sun","year":"2016","unstructured":"Sun J, Sun JA (2016) Real-time crash prediction on urban expressways: identification of key variables and a hybrid support vector machine model. IET Intell Transp Syst 10(5):331\u2013337","journal-title":"IET Intell Transp Syst"},{"issue":"1","key":"1053_CR37","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1007\/s11277-018-5466-2","volume":"103","author":"J Xiang","year":"2018","unstructured":"Xiang J, Chen Z (2018) Traffic state estimation of signalized intersections based on stacked denoising auto-encoder model. Wirel Pers Commun 103(1):625\u2013638","journal-title":"Wirel Pers Commun"},{"key":"1053_CR38","volume-title":"Smart metro station systems: data science and engineering","author":"H Liu","year":"2022","unstructured":"Liu H, Chen C, Li Y (2022) Smart metro station systems: data science and engineering. Elsevier, Amsterdam"}],"container-title":["Complex &amp; Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-023-01053-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-023-01053-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-023-01053-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,22]],"date-time":"2023-09-22T17:27:32Z","timestamp":1695403652000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-023-01053-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,11]]},"references-count":38,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["1053"],"URL":"https:\/\/doi.org\/10.1007\/s40747-023-01053-z","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"value":"2199-4536","type":"print"},{"value":"2198-6053","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,11]]},"assertion":[{"value":"12 November 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 March 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 April 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}