Real-Time Prediction of Pre-ignition and Super-Knock in Internal
                    Combustion Engines

Manzoor, Waqas; Rawashdeh, Samir A.; Mohammadi, Alireza

doi:10.4271/03-16-03-0021

Cited by 4 publications

(1 citation statement)

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“…As data sources grow more diverse, models need to focus on multiple aspects simultaneously. Multi-head attention, where multiple attention patterns are learned concurrently, and hierarchical attention, which learns attention at different granularities, have been explored to address such complexities 24 .…”

Section: Multi-head and Hierarchical Attentionmentioning

confidence: 99%

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

Cao,

Zhang,

Huang

2024

Sci Rep

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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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Section: Multi-head and Hierarchical Attentionmentioning

confidence: 99%

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

Cao,

Zhang,

Huang

2024

Sci Rep

View full text Add to dashboard Cite

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Koopman Operator-Based Data-Driven Identification of Tethered Subsatellite Deployment Dynamics

2023

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Vehicular Applications of Koopman Operator Theory—A Survey

2023

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Koopman operator theory has proven to be a promising approach to nonlinear system identification and global linearization. For nearly a century, there had been no efficient means of calculating the Koopman operator for applied engineering purposes. The introduction of a recent computationally efficient method in the context of fluid dynamics, which is based on the system dynamics decomposition to a set of normal modes in descending order, has overcome this long-lasting computational obstacle. The purely data-driven nature of Koopman operators holds the promise of capturing unknown and complex dynamics for reduced-order model generation and system identification, through which the rich machinery of linear control techniques can be utilized. Given the ongoing development of this research area and the many existing open problems in the fields of smart mobility and vehicle engineering, a survey of techniques and open challenges of applying Koopman operator theory to this vibrant area is warranted. This review focuses on the various solutions of the Koopman operator which have emerged in recent years, particularly those focusing on mobility applications, ranging from characterization and component-level control operations to vehicle performance and fleet management. Moreover, this comprehensive review of over 100 research papers highlights the breadth of ways Koopman operator theory has been applied to various vehicular applications with a detailed categorization of the applied Koopman operator-based algorithm type. Furthermore, this review paper discusses theoretical aspects of Koopman operator theory that have been largely neglected by the smart mobility and vehicle engineering community and yet have large potential for contributing to solving open problems in these areas.

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Real-Time Prediction of Pre-ignition and Super-Knock in Internal Combustion Engines

Cited by 4 publications

References 0 publications

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

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

Koopman Operator-Based Data-Driven Identification of Tethered Subsatellite Deployment Dynamics

Vehicular Applications of Koopman Operator Theory—A Survey

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