FNReq-Net: A hybrid computational framework for functional and non-functional requirements classification

Saleem, Summra; Asim, Muhammad Nabeel; Elst, Ludger van; Dengel, Andreas

doi:10.1016/j.jksuci.2023.101665

Cited by 4 publications

(1 citation statement)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The new capsule network structure has a better classification effect and performance than CNNs and can provide better accuracy and convergence when dealing with complex data. Meanwhile, the attention mechanism can focus on the vital information in the data by assigning different weights to each part of the information input to improve the model performance [14]. Therefore, integrating the attention mechanism with the capsule network not only addresses the limitations of traditional CNN but also enables improved focus on crucial information within the target.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis for Power Batteries Based on a Stacked Sparse Autoencoder and a Convolutional Block Attention Capsule Network

Zhou,

Zhang,

Wang

2024

Processes

View full text Add to dashboard Cite

The power battery constitutes the fundamental component of new energy vehicles. Rapid and accurate fault diagnosis of power batteries can effectively improve the safety and power performance of the vehicle. In response to the issues of limited generalization ability and suboptimal diagnostic accuracy observed in traditional power battery fault diagnosis models, this study proposes a fault diagnosis method utilizing a Convolutional Block Attention Capsule Network (CBAM-CapsNet) based on a stacked sparse autoencoder (SSAE). The reconstructed dataset is initially input into the SSAE model. Layer-by-layer greedy learning using unsupervised learning is employed, combining unsupervised learning methods with parameter updating and local fine-tuning to enhance visualization capabilities. The CBAM is then integrated into the CapsNet, which not only mitigates the effect of noise on the SSAE but also improves the model’s ability to characterize power cell features, completing the fault diagnosis process. The experimental comparison results show that the proposed method can diagnose power battery failure modes with an accuracy of 96.86%, and various evaluation indexes are superior to CNN, CapsNet, CBAM-CapsNet, and other neural networks at accurately identifying fault types with higher diagnostic accuracy and robustness.

show abstract

Section: Introductionmentioning

confidence: 99%