{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T18:53:43Z","timestamp":1782154423691,"version":"3.54.5"},"reference-count":76,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Advanced Engineering Informatics"],"published-print":{"date-parts":[[2026,11]]},"DOI":"10.1016\/j.aei.2026.104913","type":"journal-article","created":{"date-parts":[[2026,6,20]],"date-time":"2026-06-20T06:45:01Z","timestamp":1781937901000},"page":"104913","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PA","title":["A hybrid physics-aware and self-supervised generative framework for heterogeneous DFOS traffic monitoring"],"prefix":"10.1016","volume":"76","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-0252-4154","authenticated-orcid":false,"given":"Yufan","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-5384-1710","authenticated-orcid":false,"given":"Jiangchuan","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3154-1232","authenticated-orcid":false,"given":"Xianyong","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2115-3802","authenticated-orcid":false,"given":"Zejiao","family":"Dong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4098-8522","authenticated-orcid":false,"given":"Yunfei","family":"Yin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-1370-0208","authenticated-orcid":false,"given":"Guowei","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-5602-6220","authenticated-orcid":false,"given":"Qilin","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-8898-4748","authenticated-orcid":false,"given":"Abaho G.","family":"Gershome","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"19","key":"10.1016\/j.aei.2026.104913_b1","doi-asserted-by":"crossref","first-page":"7550","DOI":"10.3390\/s22197550","article-title":"Distributed acoustic sensing for monitoring linear infrastructures: Current status and trends","volume":"22","author":"Zhu","year":"2022","journal-title":"Sensors"},{"issue":"9","key":"10.1016\/j.aei.2026.104913_b2","doi-asserted-by":"crossref","first-page":"228","DOI":"10.3390\/infrastructures10090228","article-title":"Distributed acoustic sensing for road traffic monitoring: Principles, signal processing, and emerging applications","volume":"10","author":"Deng","year":"2025","journal-title":"Infrastructures"},{"key":"10.1016\/j.aei.2026.104913_b3","doi-asserted-by":"crossref","DOI":"10.1016\/j.jappgeo.2024.105616","article-title":"Enhancing traffic monitoring with noise-robust distributed acoustic sensing and deep learning","volume":"233","author":"Wang","year":"2025","journal-title":"J. Appl. Geophys."},{"issue":"6","key":"10.1016\/j.aei.2026.104913_b4","doi-asserted-by":"crossref","first-page":"7678","DOI":"10.1109\/TITS.2025.3556234","article-title":"Precision traffic monitoring: Leveraging distributed acoustic sensing and deep neural networks","volume":"26","author":"Khacef","year":"2025","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.aei.2026.104913_b5","doi-asserted-by":"crossref","DOI":"10.1016\/j.jappgeo.2026.106117","article-title":"Triple-duty distributed acoustic sensing in urban environments: Concurrent subsurface imaging, pipeline diagnostics, and traffic surveillance","volume":"246","author":"Song","year":"2026","journal-title":"J. Appl. Geophys."},{"key":"10.1016\/j.aei.2026.104913_b6","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2024.102424","article-title":"Localizing and tracking of in-pipe inspection robots based on distributed optical fiber sensing","volume":"60","author":"Zhu","year":"2024","journal-title":"Adv. Eng. Informatics"},{"key":"10.1016\/j.aei.2026.104913_b7","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.112259","article-title":"Multidimensional information fusion and broad learning system-based condition recognition for energy pipeline safety","volume":"300","author":"Zhu","year":"2024","journal-title":"Knowl.-Based Syst."},{"issue":"4","key":"10.1016\/j.aei.2026.104913_b8","doi-asserted-by":"crossref","first-page":"2477","DOI":"10.1785\/0220240298","article-title":"Intelligent traffic monitoring with distributed acoustic sensing","volume":"96","author":"Xie","year":"2025","journal-title":"Seismol. Res. Lett."},{"issue":"16","key":"10.1016\/j.aei.2026.104913_b9","doi-asserted-by":"crossref","first-page":"6060","DOI":"10.3390\/s22166060","article-title":"Research progress in distributed acoustic sensing techniques","volume":"22","author":"Shang","year":"2022","journal-title":"Sensors"},{"key":"10.1016\/j.aei.2026.104913_b10","series-title":"Artificial Intelligence and Statistics","first-page":"1095","article-title":"Heterogeneous domain adaptation for multiple classes","author":"Zhou","year":"2014"},{"key":"10.1016\/j.aei.2026.104913_b11","article-title":"Intelligent vehicle automatic identification and classification with distributed acoustic sensing","author":"Zhang","year":"2025","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.aei.2026.104913_b12","doi-asserted-by":"crossref","DOI":"10.1016\/j.conbuildmat.2025.142179","article-title":"Hybrid physics-data-driven model for temperature field prediction of asphalt pavement based on physics-informed neural network","volume":"489","author":"Quan","year":"2025","journal-title":"Constr. Build. Mater."},{"issue":"20","key":"10.1016\/j.aei.2026.104913_b13","doi-asserted-by":"crossref","first-page":"3276","DOI":"10.3390\/ani13203276","article-title":"Unsupervised domain adaptation for mitigating sensor variability and interspecies heterogeneity in animal activity recognition","volume":"13","author":"Ahn","year":"2023","journal-title":"Animals"},{"key":"10.1016\/j.aei.2026.104913_b14","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2025.117668","article-title":"Urban traffic monitoring through distributed acoustic sensing: Trial analysis of a potent monitoring tool","author":"Fakhruzi","year":"2025","journal-title":"Measurement"},{"key":"10.1016\/j.aei.2026.104913_b15","article-title":"Generative adversarial nets","volume":"27","author":"Goodfellow","year":"2014","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"5","key":"10.1016\/j.aei.2026.104913_b16","doi-asserted-by":"crossref","first-page":"3551","DOI":"10.1007\/s00521-022-06888-0","article-title":"A comprehensive review on GANs for time-series signals","volume":"34","author":"Zhang","year":"2022","journal-title":"Neural Comput. Appl."},{"issue":"9","key":"10.1016\/j.aei.2026.104913_b17","doi-asserted-by":"crossref","first-page":"1017","DOI":"10.3390\/rs11091017","article-title":"Topology-aware road network extraction via multi-supervised generative adversarial networks","volume":"11","author":"Zhang","year":"2019","journal-title":"Remote. Sens."},{"key":"10.1016\/j.aei.2026.104913_b18","article-title":"An ensemble Wasserstein generative adversarial network method for road extraction from high resolution remote sensing images in rural areas","volume":"8","author":"Yang","year":"2020","journal-title":"IEEE Access"},{"issue":"1","key":"10.1016\/j.aei.2026.104913_b19","doi-asserted-by":"crossref","first-page":"25321","DOI":"10.1038\/s41598-025-10979-y","article-title":"Map geographic information road extraction method based on generative adversarial network and U-Net","volume":"15","author":"Liu","year":"2025","journal-title":"Sci. Rep."},{"key":"10.1016\/j.aei.2026.104913_b20","first-page":"1","article-title":"Vehicle trajectory extraction from weak and discontinuous quasi-static DAS signals using a multiscale context pix2pix","volume":"74","author":"Peng","year":"2025","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.aei.2026.104913_b21","doi-asserted-by":"crossref","first-page":"35946","DOI":"10.52202\/068431-2605","article-title":"Masked autoencoders as spatiotemporal learners","volume":"35","author":"Feichtenhofer","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.aei.2026.104913_b22","series-title":"Visionts: Visual masked autoencoders are free-lunch zero-shot time series forecasters","author":"Chen","year":"2024"},{"key":"10.1016\/j.aei.2026.104913_b23","doi-asserted-by":"crossref","unstructured":"M. Singh, Q. Duval, K.V. Alwala, H. Fan, V. Aggarwal, A. Adcock, A. Joulin, P. Doll\u00e1r, C. Feichtenhofer, R. Girshick, et al., The effectiveness of mae pre-pretraining for billion-scale pretraining, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2023, pp. 5484\u20135494.","DOI":"10.1109\/ICCV51070.2023.00505"},{"key":"10.1016\/j.aei.2026.104913_b24","doi-asserted-by":"crossref","unstructured":"F. Wang, H. Wang, D. Wang, Z. Guo, Z. Zhong, L. Lan, W. Yang, J. Zhang, Harnessing Massive Satellite Imagery with Efficient Masked Image Modeling, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, ICCV, 2025, pp. 6935\u20136947.","DOI":"10.1109\/ICCV51701.2025.00652"},{"key":"10.1016\/j.aei.2026.104913_b25","doi-asserted-by":"crossref","unstructured":"M.U. Saleem, E. Pinyoanuntapong, M.J. Patel, H. Xue, A. Helmy, S. Das, P. Wang, MaskHand: Generative Masked Modeling for Robust Hand Mesh Reconstruction in the Wild, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, ICCV, 2025, pp. 8372\u20138383.","DOI":"10.1109\/ICCV51701.2025.00784"},{"key":"10.1016\/j.aei.2026.104913_b26","doi-asserted-by":"crossref","unstructured":"T. Yao, Y. Li, Y. Pan, Z. Qiu, T. Mei, Denoising Token Prediction in Masked Autoregressive Models, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, ICCV, 2025, pp. 18024\u201318033.","DOI":"10.1109\/ICCV51701.2025.01675"},{"key":"10.1016\/j.aei.2026.104913_b27","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.112936","article-title":"Irrelevant patch-masked autoencoders for enhancing vision transformers under limited data","volume":"310","author":"Ren","year":"2025","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.aei.2026.104913_b28","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.112370","article-title":"A robust operators\u2019 cognitive workload recognition method based on denoising masked autoencoder","volume":"301","author":"Yu","year":"2024","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.aei.2026.104913_b29","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.113090","article-title":"JAMC: A jigsaw-based autoencoder with masked contrastive learning for cardiovascular disease diagnosis","volume":"311","author":"Ge","year":"2025","journal-title":"Knowl.-Based Syst."},{"issue":"3","key":"10.1016\/j.aei.2026.104913_b30","doi-asserted-by":"crossref","first-page":"4187","DOI":"10.1109\/TII.2023.3316180","article-title":"Generative adversarial and self-supervised dehazing network","volume":"20","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Ind. Informatics"},{"key":"10.1016\/j.aei.2026.104913_b31","article-title":"IHDCP: Single image dehazing using inverted haze density correction prior","author":"Liu","year":"2026","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.aei.2026.104913_b32","article-title":"VNDHR: Variational single nighttime image dehazing for enhancing visibility in intelligent transportation systems via hybrid regularization","author":"Liu","year":"2025","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.aei.2026.104913_b33","series-title":"Edge computing in distributed acoustic sensing: An application in traffic monitoring","author":"Truong","year":"2024"},{"key":"10.1016\/j.aei.2026.104913_b34","doi-asserted-by":"crossref","DOI":"10.1016\/j.yofte.2025.104228","article-title":"Traffic flow and speed monitoring based on optical fiber distributed acoustic sensor","volume":"93","author":"Wang","year":"2025","journal-title":"Opt. Fiber Technol."},{"key":"10.1016\/j.aei.2026.104913_b35","series-title":"Training a distributed acoustic sensing traffic monitoring network with video inputs","author":"Cohen","year":"2024"},{"key":"10.1016\/j.aei.2026.104913_b36","series-title":"2021 55th Asilomar Conference on Signals, Systems, and Computers","first-page":"1104","article-title":"Next-generation traffic monitoring with distributed acoustic sensing arrays and optimum array processing","author":"van den Ende","year":"2021"},{"issue":"3","key":"10.1016\/j.aei.2026.104913_b37","doi-asserted-by":"crossref","first-page":"2947","DOI":"10.1109\/TITS.2022.3223084","article-title":"Deep deconvolution for traffic analysis with distributed acoustic sensing data","volume":"24","author":"van den Ende","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"2","key":"10.1016\/j.aei.2026.104913_b38","doi-asserted-by":"crossref","first-page":"1913","DOI":"10.1109\/TITS.2023.3322355","article-title":"Spatial deep deconvolution u-net for traffic analyses with distributed acoustic sensing","volume":"25","author":"Yuan","year":"2023","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.aei.2026.104913_b39","doi-asserted-by":"crossref","first-page":"31293","DOI":"10.1109\/ACCESS.2023.3260780","article-title":"Distributed acoustic sensor systems for vehicle detection and classification","volume":"11","author":"Chiang","year":"2023","journal-title":"IEEE Access"},{"issue":"5","key":"10.1016\/j.aei.2026.104913_b40","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1109\/IOTM.001.2300287","article-title":"A distributed acoustic sensor system for intelligent transportation using deep learning","volume":"7","author":"Chiang","year":"2024","journal-title":"IEEE Internet Things Mag."},{"issue":"9","key":"10.1016\/j.aei.2026.104913_b41","doi-asserted-by":"crossref","first-page":"3454","DOI":"10.1109\/JLT.2024.3358490","article-title":"Distributed acoustic sensing-based weight measurement method","volume":"42","author":"Ding","year":"2024","journal-title":"J. Lightwave Technol."},{"issue":"10","key":"10.1016\/j.aei.2026.104913_b42","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11431-025-3030-7","article-title":"Real-time campus resilience monitoring with distributed acoustic sensing: A case study","volume":"68","author":"Zhu","year":"2025","journal-title":"Sci. China Technol. Sci."},{"issue":"2","key":"10.1016\/j.aei.2026.104913_b43","first-page":"4","article-title":"Deep learning based threat classification for fiber optic distributed acoustic sensing using SNR dependent data generation","volume":"1","author":"Uzundurukan","year":"2020","journal-title":"J. Sci. Technol. Eng. Res."},{"issue":"11","key":"10.1016\/j.aei.2026.104913_b44","doi-asserted-by":"crossref","first-page":"19381","DOI":"10.1109\/TITS.2025.3592117","article-title":"A semi-supervised diffusion-based paradigm for vehicle-track system health monitoring with distributed acoustic sensing","volume":"26","author":"Han","year":"2025","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.aei.2026.104913_b45","doi-asserted-by":"crossref","first-page":"54046","DOI":"10.52202\/075280-2352","article-title":"Toward understanding generative data augmentation","volume":"36","author":"Zheng","year":"2023","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.aei.2026.104913_b46","article-title":"Time-series generative adversarial networks","volume":"32","author":"Yoon","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.aei.2026.104913_b47","series-title":"C-RNN-GAN: Continuous recurrent neural networks with adversarial training","author":"Mogren","year":"2016"},{"issue":"1","key":"10.1016\/j.aei.2026.104913_b48","doi-asserted-by":"crossref","first-page":"757","DOI":"10.1109\/TETCI.2024.3406719","article-title":"Least information spectral gan with time-series data augmentation for industrial iot","volume":"9","author":"Seon","year":"2024","journal-title":"IEEE Trans. Emerg. Top. Comput. Intell."},{"issue":"2","key":"10.1016\/j.aei.2026.104913_b49","doi-asserted-by":"crossref","first-page":"493","DOI":"10.3390\/s25020493","article-title":"Time series data augmentation for energy consumption data based on improved TimeGAN","volume":"25","author":"Tang","year":"2025","journal-title":"Sensors"},{"key":"10.1016\/j.aei.2026.104913_b50","series-title":"Tts-cgan: A transformer time-series conditional gan for biosignal data augmentation","author":"Li","year":"2022"},{"issue":"2","key":"10.1016\/j.aei.2026.104913_b51","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3583593","article-title":"Ts-gan: Time-series gan for sensor-based health data augmentation","volume":"4","author":"Yang","year":"2023","journal-title":"ACM Trans. Comput. Healthcare"},{"key":"10.1016\/j.aei.2026.104913_b52","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2023.110920","article-title":"HAT-GAE: Self-supervised graph autoencoders with hierarchical adaptive masking and trainable corruption","volume":"279","author":"Sun","year":"2023","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.aei.2026.104913_b53","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.111559","article-title":"An edge-aware graph autoencoder trained on scale-imbalanced data for traveling salesman problems","volume":"291","author":"Liu","year":"2024","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.aei.2026.104913_b54","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.115081","article-title":"A multi-view graph neural network with subgraph variational autoencoder for class-imbalanced node classification","volume":"334","author":"Du","year":"2026","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.aei.2026.104913_b55","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2023.110313","article-title":"DistVAE: Distributed variational autoencoder for sequential recommendation","volume":"264","author":"Li","year":"2023","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.aei.2026.104913_b56","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.111922","article-title":"Enhancing equipment safeguarding in IIoT: A self-supervised fault diagnosis paradigm based on asymmetric graph autoencoder","volume":"296","author":"Chen","year":"2024","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.aei.2026.104913_b57","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.112445","article-title":"Semi-supervised noise-resilient anomaly detection with feature autoencoder","volume":"304","author":"Zhu","year":"2024","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.aei.2026.104913_b58","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2025.104139","article-title":"Optimal sensor placement for heat transfer digital twin with denoising autoencoder and discrete optimization","volume":"70","author":"Yeo","year":"2026","journal-title":"Adv. Eng. Informatics"},{"key":"10.1016\/j.aei.2026.104913_b59","article-title":"Defective wafer detection using a denoising autoencoder for semiconductor manufacturing processes","volume":"46","author":"Fan","year":"2020","journal-title":"Adv. Eng. Informatics"},{"key":"10.1016\/j.aei.2026.104913_b60","series-title":"2022 IEEE 34th International Conference on Tools with Artificial Intelligence","first-page":"982","article-title":"Mtsmae: Masked autoencoders for multivariate time-series forecasting","author":"Tang","year":"2022"},{"key":"10.1016\/j.aei.2026.104913_b61","series-title":"Ti-mae: Self-supervised masked time series autoencoders","author":"Li","year":"2023"},{"key":"10.1016\/j.aei.2026.104913_b62","series-title":"2024 IEEE 40th International Conference on Data Engineering","first-page":"1228","article-title":"Temporal-frequency masked autoencoders for time series anomaly detection","author":"Fang","year":"2024"},{"issue":"5","key":"10.1016\/j.aei.2026.104913_b63","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1007\/s11265-025-01968-5","article-title":"MAET: A generalizable masked autoencoding framework for anomaly detection in time-series data","volume":"97","author":"Wang","year":"2025","journal-title":"J. Signal Process. Syst."},{"key":"10.1016\/j.aei.2026.104913_b64","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.112114","article-title":"Multiscale-attention masked autoencoder for missing data imputation of wind turbines","volume":"299","author":"Fan","year":"2024","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.aei.2026.104913_b65","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2024.102938","article-title":"A self-supervised masked spatial distribution learning method for predicting machinery remaining useful life with missing data reconstruction","volume":"64","author":"Niu","year":"2025","journal-title":"Adv. Eng. Informatics"},{"key":"10.1016\/j.aei.2026.104913_b66","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2025.104242","article-title":"Remaining useful life prediction based on self-attention mechanism -sequential variational autoencoder: From a semi-supervised perspective","volume":"71","author":"Zhang","year":"2026","journal-title":"Adv. Eng. Informatics"},{"key":"10.1016\/j.aei.2026.104913_b67","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2026.104450","article-title":"TSAE: A teacher-student transformer autoencoder for restoration of fNIRS signals with channel contribution weights analysis","volume":"72","author":"Yang","year":"2026","journal-title":"Adv. Eng. Informatics"},{"key":"10.1016\/j.aei.2026.104913_b68","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2025.104243","article-title":"Fusion-driven EEG reconstruction and cognitive workload recognition using conditional diffusion and graph-based learning","volume":"71","author":"Shafi","year":"2026","journal-title":"Adv. Eng. Informatics"},{"key":"10.1016\/j.aei.2026.104913_b69","first-page":"1","article-title":"Mask-guided model for seismic data denoising","volume":"19","author":"Fang","year":"2022","journal-title":"IEEE Geosci. Remote. Sens. Lett."},{"key":"10.1016\/j.aei.2026.104913_b70","first-page":"1","article-title":"Self-supervised pretraining vision transformer with masked autoencoders for building subsurface model","volume":"61","author":"Li","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.aei.2026.104913_b71","doi-asserted-by":"crossref","DOI":"10.1016\/j.compgeo.2024.106194","article-title":"3D seismic mask auto encoder: Seismic inversion using transformer-based reconstruction representation learning","volume":"169","author":"Dou","year":"2024","journal-title":"Comput. Geotech."},{"key":"10.1016\/j.aei.2026.104913_b72","series-title":"DAGM German Conference on Pattern Recognition","first-page":"317","article-title":"Senpa-mae: Sensor parameter aware masked autoencoder for multi-satellite self-supervised pretraining","author":"Prexl","year":"2024"},{"issue":"2","key":"10.1016\/j.aei.2026.104913_b73","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3729485","article-title":"Openmae: efficient masked autoencoder for vibration sensing with open-domain data enrichment","volume":"9","author":"Hu","year":"2025","journal-title":"Proc. the ACM Interact. Mob. Wearable Ubiquitous Technol."},{"key":"10.1016\/j.aei.2026.104913_b74","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2022.110218","article-title":"Latent feature learning via autoencoder training for automatic classification configuration recommendation","volume":"261","author":"Deng","year":"2023","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.aei.2026.104913_b75","article-title":"Enhanced cross-domain projection based heterogeneous domain adaptation method for cross-domain fault diagnosis","author":"Chen","year":"2025","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.aei.2026.104913_b76","article-title":"Vibration visualization: High-quality vehicle detection with DVS data using deep learning network","author":"Chen","year":"2025","journal-title":"IEEE Trans. Instrum. Meas."}],"container-title":["Advanced Engineering Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1474034626006051?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1474034626006051?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T18:43:28Z","timestamp":1782153808000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1474034626006051"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,11]]},"references-count":76,"alternative-id":["S1474034626006051"],"URL":"https:\/\/doi.org\/10.1016\/j.aei.2026.104913","relation":{},"ISSN":["1474-0346"],"issn-type":[{"value":"1474-0346","type":"print"}],"subject":[],"published":{"date-parts":[[2026,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A hybrid physics-aware and self-supervised generative framework for heterogeneous DFOS traffic monitoring","name":"articletitle","label":"Article Title"},{"value":"Advanced Engineering Informatics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.aei.2026.104913","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"104913"}}