FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4293766734> ?p ?o ?g. }

• W4293766734 abstract "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." @default.
• W4293766734 created "2022-08-31" @default.
• W4293766734 creator A5022251442 @default.
• W4293766734 creator A5025025567 @default.
• W4293766734 creator A5048464909 @default.
• W4293766734 creator A5059618025 @default.
• W4293766734 creator A5062469834 @default.
• W4293766734 creator A5064456725 @default.
• W4293766734 date "2022-10-29" @default.
• W4293766734 modified "2023-10-14" @default.
• W4293766734 title "Predicting a Time-Dependent Quantity Using Recursive Generative Query Network" @default.
• W4293766734 cites W1946238955 @default.
• W4293766734 cites W1968398927 @default.
• W4293766734 cites W1991151011 @default.
• W4293766734 cites W1993260141 @default.
• W4293766734 cites W2011301426 @default.
• W4293766734 cites W2015028539 @default.
• W4293766734 cites W2028612364 @default.
• W4293766734 cites W2041253116 @default.
• W4293766734 cites W2079309933 @default.
• W4293766734 cites W2101227080 @default.
• W4293766734 cites W2123513648 @default.
• W4293766734 cites W2147350962 @default.
• W4293766734 cites W2170362173 @default.
• W4293766734 cites W2261040853 @default.
• W4293766734 cites W2617614397 @default.
• W4293766734 cites W2757751999 @default.
• W4293766734 cites W2794022343 @default.
• W4293766734 cites W2808492412 @default.
• W4293766734 cites W2886910949 @default.
• W4293766734 cites W2898456550 @default.
• W4293766734 cites W2899818400 @default.
• W4293766734 cites W2913408913 @default.
• W4293766734 cites W2916071927 @default.
• W4293766734 cites W2944758627 @default.
• W4293766734 cites W2954043074 @default.
• W4293766734 cites W2963073614 @default.
• W4293766734 cites W3003648978 @default.
• W4293766734 cites W3031087087 @default.
• W4293766734 cites W3037485096 @default.
• W4293766734 cites W3037916286 @default.
• W4293766734 cites W3042758249 @default.
• W4293766734 cites W3080798910 @default.
• W4293766734 cites W3113483010 @default.
• W4293766734 cites W3137262131 @default.
• W4293766734 cites W3137749107 @default.
• W4293766734 cites W3143873535 @default.
• W4293766734 cites W3158666754 @default.
• W4293766734 cites W3202342022 @default.
• W4293766734 cites W4206276614 @default.
• W4293766734 cites W4226157171 @default.
• W4293766734 cites W4226178922 @default.
• W4293766734 doi "https://doi.org/10.1142/s0129065722500563" @default.
• W4293766734 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36309813" @default.
• W4293766734 hasPublicationYear "2022" @default.
• W4293766734 type Work @default.
• W4293766734 citedByCount "0" @default.
• W4293766734 crossrefType "journal-article" @default.
• W4293766734 hasAuthorship W4293766734A5022251442 @default.
• W4293766734 hasAuthorship W4293766734A5025025567 @default.
• W4293766734 hasAuthorship W4293766734A5048464909 @default.
• W4293766734 hasAuthorship W4293766734A5059618025 @default.
• W4293766734 hasAuthorship W4293766734A5062469834 @default.
• W4293766734 hasAuthorship W4293766734A5064456725 @default.
• W4293766734 hasConcept C11413529 @default.
• W4293766734 hasConcept C119857082 @default.
• W4293766734 hasConcept C124101348 @default.
• W4293766734 hasConcept C143724316 @default.
• W4293766734 hasConcept C151406439 @default.
• W4293766734 hasConcept C151730666 @default.
• W4293766734 hasConcept C154945302 @default.
• W4293766734 hasConcept C155512373 @default.
• W4293766734 hasConcept C199360897 @default.
• W4293766734 hasConcept C2776214188 @default.
• W4293766734 hasConcept C2778112365 @default.
• W4293766734 hasConcept C41008148 @default.
• W4293766734 hasConcept C43521106 @default.
• W4293766734 hasConcept C50644808 @default.
• W4293766734 hasConcept C54355233 @default.
• W4293766734 hasConcept C86803240 @default.
• W4293766734 hasConceptScore W4293766734C11413529 @default.
• W4293766734 hasConceptScore W4293766734C119857082 @default.
• W4293766734 hasConceptScore W4293766734C124101348 @default.
• W4293766734 hasConceptScore W4293766734C143724316 @default.
• W4293766734 hasConceptScore W4293766734C151406439 @default.
• W4293766734 hasConceptScore W4293766734C151730666 @default.
• W4293766734 hasConceptScore W4293766734C154945302 @default.
• W4293766734 hasConceptScore W4293766734C155512373 @default.
• W4293766734 hasConceptScore W4293766734C199360897 @default.
• W4293766734 hasConceptScore W4293766734C2776214188 @default.
• W4293766734 hasConceptScore W4293766734C2778112365 @default.
• W4293766734 hasConceptScore W4293766734C41008148 @default.
• W4293766734 hasConceptScore W4293766734C43521106 @default.
• W4293766734 hasConceptScore W4293766734C50644808 @default.
• W4293766734 hasConceptScore W4293766734C54355233 @default.
• W4293766734 hasConceptScore W4293766734C86803240 @default.
• W4293766734 hasFunder F4320335039 @default.
• W4293766734 hasIssue "11" @default.
• W4293766734 hasLocation W42937667341 @default.
• W4293766734 hasLocation W42937667342 @default.

• next