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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Bibliographic Details
Published in:Scientific reports Vol. 14; no. 1; pp. 4890 - 24
Main Authors: Cao, Kangjie, Zhang, Ting, Huang, Jueqiao
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 28.02.2024
Nature Publishing Group
Nature Portfolio
Subjects:
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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
Online Access:Get full text
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Summary: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.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-55483-x