{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T06:24:12Z","timestamp":1773469452620,"version":"3.50.1"},"reference-count":56,"publisher":"World Scientific Pub Co Pte Ltd","issue":"11","funder":[{"DOI":"10.13039\/501100005632","name":"Polish National Center for Research and Development","doi-asserted-by":"crossref","award":["POIR.01.01.01-00-0300\/19."],"award-info":[{"award-number":["POIR.01.01.01-00-0300\/19."]}],"id":[{"id":"10.13039\/501100005632","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Neur. Syst."],"published-print":{"date-parts":[[2022,11]]},"abstract":"<jats:p>We propose here a novel neural architecture dedicated to the prediction of time series. It can be considered as an adaptation of the idea of (GQN) to the data which is of a sequence nature. The new approach, dubbed here as the (RGQN), allows for efficient prediction of time series. The predictor information (i.e. the independent variable) is one or more of the other time series which are in some relationship with the predicted sequence. Each time series is accompanied by additional meta-information reflecting its selected properties. This meta-information, together with the standard dynamic component, is provided simultaneously in (RNN). During the inference phase, meta-information becomes a query reflecting the expected properties of the predicted time series. The proposed idea is illustrated with use cases of strong practical relevance. In particular, we discuss the example of an industrial pipeline that transports liquid media. The trained RGQN model is applied to predict pressure signals, assuming that the training was carried out during routine operational conditions. The subsequent comparison of the prediction with the actual data gathered under extraordinary circumstances, e.g. during the leakage, leads to a specific residual distribution of the prediction. This information can be applied directly within the data-driven Leak Detection and Location framework. The RGQN approach can be applied not only to pressure time series but also in many other use cases where the quantity of sequence nature is accompanied by a meta-descriptor.<\/jats:p>","DOI":"10.1142\/s0129065722500563","type":"journal-article","created":{"date-parts":[[2022,8,31]],"date-time":"2022-08-31T08:57:46Z","timestamp":1661936266000},"source":"Crossref","is-referenced-by-count":3,"title":["Predicting a Time-Dependent Quantity Using Recursive Generative Query Network"],"prefix":"10.1142","volume":"32","author":[{"given":"Grzegorz","family":"Miebs","sequence":"first","affiliation":[{"name":"PSI Poland, Advanced Analytics Team, Towarowa 37, 61-896 Pozna\u0144, Poland"},{"name":"Institute of Computing Science, Pozna\u0144 University of Technology, Piotrowo 2, 60-965 Pozna\u0144, Poland"}]},{"given":"Micha\u0142","family":"W\u00f3jcik","sequence":"additional","affiliation":[{"name":"PSI Poland, Advanced Analytics Team, Towarowa 37, 61-896 Pozna\u0144, Poland"},{"name":"Institute of Computing Science, Pozna\u0144 University of Technology, Piotrowo 2, 60-965 Pozna\u0144, Poland"}]},{"given":"Adam","family":"Karaszewski","sequence":"additional","affiliation":[{"name":"PSI Poland, Advanced Analytics Team, Towarowa 37, 61-896 Pozna\u0144, Poland"}]},{"given":"Ma\u0142gorzata","family":"Mochol-Grzelak","sequence":"additional","affiliation":[{"name":"PSI Poland, Advanced Analytics Team, Towarowa 37, 61-896 Pozna\u0144, Poland"}]},{"given":"Paulina","family":"Wawdysz","sequence":"additional","affiliation":[{"name":"PSI Poland, Advanced Analytics Team, Towarowa 37, 61-896 Pozna\u0144, Poland"}]},{"given":"Rafa\u0142 A.","family":"Bachorz","sequence":"additional","affiliation":[{"name":"PSI Poland, Advanced Analytics Team, Towarowa 37, 61-896 Pozna\u0144, Poland"},{"name":"Institute of Medical Biology, Polish Academy of Sciences, Lodowa 103, 93-232 \u0141\u00f3d\u017a, Poland"}]}],"member":"219","published-online":{"date-parts":[[2022,10,29]]},"reference":[{"issue":"2","key":"S0129065722500563BIB001","first-page":"653","volume":"16","author":"Abadi A.","year":"2014","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"S0129065722500563BIB002","doi-asserted-by":"crossref","first-page":"557","DOI":"10.1016\/j.protcy.2013.12.228","volume":"11","author":"Radzuan N. F. M.","year":"2013","journal-title":"Proc. Technol."},{"key":"S0129065722500563BIB003","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1016\/j.energy.2004.05.026","volume":"30","author":"Shamshad A.","year":"2005","journal-title":"Energy"},{"key":"S0129065722500563BIB004","first-page":"282","volume":"5","author":"Li L.","year":"2020","journal-title":"Infect. Dis. Model."},{"key":"S0129065722500563BIB005","doi-asserted-by":"crossref","first-page":"2301","DOI":"10.1007\/s11063-020-10195-x","volume":"51","author":"Miebs G.","year":"2020","journal-title":"Neural Process. Lett."},{"key":"S0129065722500563BIB006","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065719500205"},{"key":"S0129065722500563BIB007","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/S0304-3800(00)00262-3","volume":"126","author":"Balzter H.","year":"2000","journal-title":"Ecol. Model."},{"key":"S0129065722500563BIB008","first-page":"652","volume-title":"2009 Asia-Pacific Power and Energy Engineering Conf.","author":"Li Y.-Z.","year":"2009"},{"key":"S0129065722500563BIB009","doi-asserted-by":"publisher","DOI":"10.1016\/j.clineuro.2020.106446"},{"key":"S0129065722500563BIB010","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-020-01639-x"},{"key":"S0129065722500563BIB011","doi-asserted-by":"publisher","DOI":"10.1016\/j.engstruct.2018.10.065"},{"key":"S0129065722500563BIB012","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2017.05.029"},{"key":"S0129065722500563BIB013","doi-asserted-by":"publisher","DOI":"10.1111\/exsy.12354"},{"key":"S0129065722500563BIB014","doi-asserted-by":"publisher","DOI":"10.1016\/j.engstruct.2019.02.031"},{"key":"S0129065722500563BIB015","doi-asserted-by":"publisher","DOI":"10.1111\/mice.12575"},{"key":"S0129065722500563BIB016","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1061\/(ASCE)0733-947X(2002)128:3(232)","volume":"128","author":"Karim A.","year":"2002","journal-title":"J. Transp. Eng."},{"key":"S0129065722500563BIB017","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1061\/(ASCE)0733-947X(2003)129:1(57)","volume":"129","author":"Karim A.","year":"2003","journal-title":"J. Transp. Eng."},{"key":"S0129065722500563BIB018","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.solener.2015.02.032","volume":"115","author":"Bright J.","year":"2015","journal-title":"Sol. Energy"},{"key":"S0129065722500563BIB019","doi-asserted-by":"crossref","first-page":"63915","DOI":"10.1109\/ACCESS.2021.3075063","volume":"9","author":"Li S.","year":"2021","journal-title":"IEEE Access"},{"key":"S0129065722500563BIB020","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2009.05.044"},{"key":"S0129065722500563BIB021","doi-asserted-by":"crossref","first-page":"1204","DOI":"10.1109\/IJCNN.2007.4371129","volume-title":"2007 Int. Joint Conf. Neural Networks","author":"Ruta D.","year":"2007"},{"key":"S0129065722500563BIB022","doi-asserted-by":"crossref","first-page":"2150057","DOI":"10.1142\/S012906572150057X","volume":"31","author":"Xue Y.","year":"2021","journal-title":"Int. J. Neural Syst."},{"key":"S0129065722500563BIB023","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065720500598"},{"key":"S0129065722500563BIB024","doi-asserted-by":"crossref","first-page":"2150042","DOI":"10.1142\/S0129065721500428","volume":"32","author":"Jodas D. S.","year":"2022","journal-title":"Int. J. Neural Syst."},{"key":"S0129065722500563BIB025","doi-asserted-by":"crossref","first-page":"2150054","DOI":"10.1142\/S0129065721500544","volume":"32","author":"Kov\u00e1cs P.","year":"2022","journal-title":"Int. J. Neural Syst."},{"key":"S0129065722500563BIB026","doi-asserted-by":"crossref","first-page":"2150041","DOI":"10.1142\/S0129065721500416","volume":"32","author":"Olamat A.","year":"2022","journal-title":"Int. J. Neural Syst."},{"key":"S0129065722500563BIB027","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065720500550"},{"key":"S0129065722500563BIB028","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065720500549"},{"key":"S0129065722500563BIB029","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065721300011"},{"key":"S0129065722500563BIB030","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/A:1012074215150","volume":"31","author":"Frank R. J.","year":"2001","journal-title":"J. Intell. Robot. Syst."},{"key":"S0129065722500563BIB031","first-page":"2672","volume-title":"Proc. 27th Int. Conf. Neural Information Processing Systems \u2014 Volume 2, NIPS\u201914","author":"Goodfellow I. J.","year":"2014"},{"key":"S0129065722500563BIB032","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.09.013"},{"key":"S0129065722500563BIB033","first-page":"734","volume-title":"2018 IEEE 15th Int. Symp. Biomedical Imaging (ISBI 2018)","author":"Han C.","year":"2018"},{"key":"S0129065722500563BIB036","first-page":"214","volume-title":"Proc 34th Int. Conf. Machine Learning","volume":"70","author":"Arjovsky M.","year":"2017"},{"key":"S0129065722500563BIB037","doi-asserted-by":"crossref","first-page":"5967","DOI":"10.1109\/CVPR.2017.632","volume-title":"2017 IEEE Conf. Computer Vision and Pattern Recognition (CVPR)","author":"Isola P.","year":"2017"},{"key":"S0129065722500563BIB039","first-page":"5508","volume-title":"Advances in Neural Information Processing Systems","volume":"32","author":"Yoon J.","year":"2019"},{"key":"S0129065722500563BIB040","doi-asserted-by":"crossref","first-page":"1204","DOI":"10.1126\/science.aar6170","volume":"360","author":"Eslami S. M. A.","year":"2018","journal-title":"Science"},{"issue":"11","key":"S0129065722500563BIB041","doi-asserted-by":"crossref","first-page":"2548","DOI":"10.3390\/s19112548","volume":"19","author":"Adegboye M. A.","year":"2019","journal-title":"Sensors"},{"key":"S0129065722500563BIB042","first-page":"139","volume-title":"Offshore Europe","author":"Turner N.","year":"1991"},{"key":"S0129065722500563BIB043","first-page":"41","volume":"44","author":"Hovey D.","year":"1999","journal-title":"Pipes Pipelines Int."},{"key":"S0129065722500563BIB044","first-page":"1","volume-title":"2017 14th Int. Conf. Electrical Engineering, Computing Science and Automatic Control (CCE)","author":"Delgado M. R.","year":"2017"},{"key":"S0129065722500563BIB045","doi-asserted-by":"publisher","DOI":"10.1016\/j.psep.2018.07.023"},{"key":"S0129065722500563BIB046","doi-asserted-by":"crossref","first-page":"3769","DOI":"10.1109\/ICSMC.2009.5346676","volume-title":"2009 IEEE Int. Conf. Systems, Man and Cybernetics","author":"Li H.","year":"2009"},{"key":"S0129065722500563BIB047","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1016\/j.engstruct.2016.01.040","volume":"113","author":"Ostapkowicz P.","year":"2016","journal-title":"Eng. Struct."},{"issue":"1","key":"S0129065722500563BIB048","doi-asserted-by":"crossref","first-page":"189","DOI":"10.3390\/s120100189","volume":"12","author":"Wan J.","year":"2011","journal-title":"Sensors"},{"key":"S0129065722500563BIB049","first-page":"1","volume-title":"Proc. Pipeline Technology 2006 Conf","author":"Geiger G."},{"key":"S0129065722500563BIB050","doi-asserted-by":"publisher","DOI":"10.1016\/j.jlp.2012.05.010"},{"key":"S0129065722500563BIB051","volume-title":"SPE Nigeria Annual Int. Conf. and Exhibition","author":"Akinsete O."},{"key":"S0129065722500563BIB052","doi-asserted-by":"crossref","first-page":"3282","DOI":"10.2166\/ws.2021.101","volume":"21","author":"Hu Z.","year":"2021","journal-title":"Water Supply"},{"key":"S0129065722500563BIB053","first-page":"1","volume-title":"2020 IEEE Electric Power and Energy Conference (EPEC)","author":"Amini I.","year":"2020"},{"key":"S0129065722500563BIB054","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1016\/j.compchemeng.2017.09.022","volume":"108","author":"Arifin B.","year":"2018","journal-title":"Comput. Chem. Eng."},{"key":"S0129065722500563BIB055","first-page":"1","volume-title":"2021 10th Int. Conf. Modern Circuits and Systems Technologies (MOCAST)","author":"Angelopoulos K.","year":"2021"},{"key":"S0129065722500563BIB056","doi-asserted-by":"crossref","first-page":"107290","DOI":"10.1016\/j.compchemeng.2021.107290","volume":"149","author":"Zheng J.","year":"2021","journal-title":"Comput. Chem. Eng."},{"key":"S0129065722500563BIB057","doi-asserted-by":"publisher","DOI":"10.1016\/j.psep.2019.01.010"},{"key":"S0129065722500563BIB060","doi-asserted-by":"publisher","DOI":"10.1109\/MCSE.2007.55"},{"key":"S0129065722500563BIB062","first-page":"2825","volume":"12","author":"Pedregosa F.","year":"2011","journal-title":"J. Mach. Learn. Res."}],"container-title":["International Journal of Neural Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0129065722500563","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,16]],"date-time":"2023-02-16T18:19:20Z","timestamp":1676571560000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/10.1142\/S0129065722500563"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,29]]},"references-count":56,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2022,11]]}},"alternative-id":["10.1142\/S0129065722500563"],"URL":"https:\/\/doi.org\/10.1142\/s0129065722500563","relation":{},"ISSN":["0129-0657","1793-6462"],"issn-type":[{"value":"0129-0657","type":"print"},{"value":"1793-6462","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,29]]},"article-number":"2250056"}}