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

AbstractIn 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.

DMSeqNet-mBART: A State-of-the-Art Adaptive-DropMessage Enhanced mBART Architecture for Superior Chinese Short News Text Summarization

Mandarin Chinese, a widely spoken language globally, has abundant, regularly updated short news texts online. Generating precise summaries of these texts is vital for effective information transmission and comprehension. This article introduces DMSeqNet-mBART, an enhanced mBART-based model, as a state-of-the-art solution for Chinese short news text summarization. This model incorporates Adaptive-DropMessage technology, a novel approach that intelligently discards or retains information based on the attention mechanism’s output. This paper demonstrates that DMSeqNet-mBART excels across several benchmarks, including BERTScore, BLEU, and ROUGE metrics, surpassing other advanced models like GPT-4, T5, and MLC. The paper outlines the Adaptive-DropMessage mechanism, enhanced dynamic convolutional layers, gated residual connections, custom feed-forward networks with batch normalization, and improvements to self-attention and cross-attention. Results from comparative experiments on six recognized Chinese short news text summary datasets indicate that the model’s performance in terms of fluency, completeness, robustness, and accuracy significantly outperforms leading industry models. The DMSeqNet-mBART’s success is attributed to its unique combination of architectural and methodological enhancements, suggesting its suitability for various complex text data processing tasks. The model provides novel insights and methods for processing similar complex text data.

Optimization of Python sorting algorithm for CIFAR-10 image classification dataset

Optimization of Pose Recognition Algorithms in Smart Wearable Devices

Optimization Study of KNN Classification Algorithm on Large-Scale Datasets: Real-Time Optimization Strategy Based on Balanced KD Tree and Multi-threaded Parallel Computing