Advanced hybrid LSTM-transformer architecture for real-time multi-task prediction in engineering systems

In the field of engineering systems—particularly in underground drilling and green stormwater management—real-time predictions are vital for enhancing operational performance, ensuring safety, and increasing efficiency. Addressing this niche, our study introduces a novel LSTM-transformer hybrid arch...

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Vydané v:	Scientific reports Ročník 14; číslo 1; s. 4890 - 24
Hlavní autori:	Cao, Kangjie, Zhang, Ting, Huang, Jueqiao
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London Nature Publishing Group UK 28.02.2024 Nature Publishing Group Nature Portfolio
Predmet:	639/166 639/705/1042 639/705/1046 639/705/117 Adaptability Computer applications Distillation Drilling Engineering Engineering systems Green stormwater management Humanities and Social Sciences LSTM-transformer hybrid architecture Multi-task learning multidisciplinary Prediction models Real-time predictions Science Science (multidisciplinary) Stormwater Stormwater management Underground drilling Real-time predictions Knowledge distillation LSTM-transformer hybrid architecture Underground drilling Predictive accuracy Multi-task learning Online learning Engineering systems Green stormwater management
ISSN:	2045-2322, 2045-2322
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Shrnutí:	In the field of engineering systems—particularly in underground drilling and green stormwater management—real-time predictions are vital for enhancing operational performance, ensuring safety, and increasing efficiency. Addressing this niche, our study introduces a novel LSTM-transformer hybrid architecture, uniquely specialized for multi-task real-time predictions. Building on advancements in attention mechanisms and sequence modeling, our model integrates the core strengths of LSTM and Transformer architectures, offering a superior alternative to traditional predictive models. Further enriched with online learning, our architecture dynamically adapts to variable operational conditions and continuously incorporates new field data. Utilizing knowledge distillation techniques, we efficiently transfer insights from larger, pretrained networks, thereby achieving high predictive accuracy without sacrificing computational resources. Rigorous experiments on sector-specific engineering datasets validate the robustness and effectiveness of our approach. Notably, our model exhibits clear advantages over existing methods in terms of predictive accuracy, real-time adaptability, and computational efficiency. This work contributes a pioneering predictive framework for targeted engineering applications, offering actionable insights into.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	2045-2322 2045-2322
DOI:	10.1038/s41598-024-55483-x