RLANet: A Kepler Optimization Algorithm-Optimized Framework for Fluorescence Spectra Analysis with Applications in Oil Spill Detection
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| Titel: | RLANet: A Kepler Optimization Algorithm-Optimized Framework for Fluorescence Spectra Analysis with Applications in Oil Spill Detection |
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| Autoren: | Shubo Zhang, Yafei Yuan, Jing Li |
| Quelle: | Processes ; Volume 13 ; Issue 4 ; Pages: 934 |
| Verlagsinformationen: | Multidisciplinary Digital Publishing Institute |
| Publikationsjahr: | 2025 |
| Bestand: | MDPI Open Access Publishing |
| Schlagwörter: | ResNet-LSTM-Attention, Kepler Optimization Algorithm (KOA), global average pooling, mixture classification, resource-constrained environments |
| Geographisches Schlagwort: | agris |
| Beschreibung: | This paper presents a novel deep learning model, RLANet, based on the ResNet-LSTM-Multihead Attention module, designed for processing and classifying one-dimensional spectral data. The model incorporates ResNet, LSTM, and attention mechanisms, omitting the traditional fully connected layer to significantly reduce the parameter count while maintaining global spectral feature extraction. This design enables RLANet to be lightweight and computationally efficient, making it suitable for real-time applications, especially in resource-constrained environments. Furthermore, this study introduces the Kepler Optimization Algorithm (KOA) for hyperparameter tuning in deep learning, demonstrating its superiority over the traditional Bayesian optimization (BO) in achieving optimal hyperparameter configurations for complex models. Experimental results indicate that the RLANet model successfully achieves accurate identification of three types of engine oil products and their mixtures, with classification accuracy approaching one. Compared to conventional deep learning models, it features a significantly reduced parameter count of only 0.09 M, enabling the deployment of compact devices for rapid on-site classification of oil spill types. Furthermore, relative to traditional machine learning models, RLANet demonstrates a lower sensitivity to preprocessing methods, with the standard deviation of classification accuracy maintained within approximately 0.001, thereby underscoring its excellent end-to-end analytical capabilities. Moreover, even under a strong noise interference at a signal-to-noise ratio of 15 dB, its classification performance declines by only 19% relative to the baseline, attesting to its robust resilience. These results highlight the model’s potential for practical deployment in end-to-end online spectral analysis, particularly in resource-constrained hardware environments. |
| Publikationsart: | text |
| Dateibeschreibung: | application/pdf |
| Sprache: | English |
| Relation: | Energy Systems; https://dx.doi.org/10.3390/pr13040934 |
| DOI: | 10.3390/pr13040934 |
| Verfügbarkeit: | https://doi.org/10.3390/pr13040934 |
| Rights: | https://creativecommons.org/licenses/by/4.0/ |
| Dokumentencode: | edsbas.B7B7ACA0 |
| Datenbank: | BASE |
Nájsť tento článok vo Web of Science | Abstract: | This paper presents a novel deep learning model, RLANet, based on the ResNet-LSTM-Multihead Attention module, designed for processing and classifying one-dimensional spectral data. The model incorporates ResNet, LSTM, and attention mechanisms, omitting the traditional fully connected layer to significantly reduce the parameter count while maintaining global spectral feature extraction. This design enables RLANet to be lightweight and computationally efficient, making it suitable for real-time applications, especially in resource-constrained environments. Furthermore, this study introduces the Kepler Optimization Algorithm (KOA) for hyperparameter tuning in deep learning, demonstrating its superiority over the traditional Bayesian optimization (BO) in achieving optimal hyperparameter configurations for complex models. Experimental results indicate that the RLANet model successfully achieves accurate identification of three types of engine oil products and their mixtures, with classification accuracy approaching one. Compared to conventional deep learning models, it features a significantly reduced parameter count of only 0.09 M, enabling the deployment of compact devices for rapid on-site classification of oil spill types. Furthermore, relative to traditional machine learning models, RLANet demonstrates a lower sensitivity to preprocessing methods, with the standard deviation of classification accuracy maintained within approximately 0.001, thereby underscoring its excellent end-to-end analytical capabilities. Moreover, even under a strong noise interference at a signal-to-noise ratio of 15 dB, its classification performance declines by only 19% relative to the baseline, attesting to its robust resilience. These results highlight the model’s potential for practical deployment in end-to-end online spectral analysis, particularly in resource-constrained hardware environments. |
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| DOI: | 10.3390/pr13040934 |