Aerodynamic surrogate model based on deep long short-term memory network: An application on high-lift device control

Zhang, Yi; Wang, Shuyue; Sun, Gang; Mao, Jinfeng

doi:10.1177/09544100211027023

Cited by 5 publications

(2 citation statements)

References 42 publications

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“…At its core, RMSprop aims to dynamically adjust each parameter's learning rate based on its gradients' historical magnitudes [34]. This method of RMSprop solves the common drawback of a static learning rate, which, if set too high, can cause unstable training with erratic oscillations, and, if too low, results in an agonizingly slow convergence [35,36].…”

Section: Rmsprop and Adjusted Learning Rate Methodsmentioning

confidence: 99%

From Plants to Pixels: The Role of Artificial Intelligence in Identifying Sericea Lespedeza in Field-Based Studies

Siddique,

Cook,

Holt

et al. 2024

Agronomy

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The increasing use of convolutional neural networks (CNNs) has brought about a significant transformation in numerous fields, such as image categorization and identification. In the development of a CNN model to classify images of sericea lespedeza [SL; Lespedeza cuneata (Dum-Cours) G. Don] from weed images, four architectures were explored: CNN model variant 1, CNN model variant 2, the Visual Geometry Group (VGG16) model, and ResNet50. CNN model variant 1 (batch normalization with adjusted dropout method) demonstrated 100% validation accuracy, while variant 2 (RMSprop optimization with adjusted learning rate) achieved 90.78% validation accuracy. Pre-trained models, like VGG16 and ResNet50, were also analyzed. In contrast, ResNet50’s steady learning pattern indicated the potential for better generalization. A detailed evaluation of these models revealed that variant 1 achieved a perfect score in precision, recall, and F1 score, indicating superior optimization and feature utilization. Variant 2 presented a balanced performance, with metrics between 86% and 93%. VGG16 mirrored the behavior of variant 2, both maintaining around 90% accuracy. In contrast, ResNet50’s results revealed a conservative approach for class 0 predictions. Overall, variant 1 stood out in performance, while both variant 2 and VGG16 showed balanced results. The reliability of CNN model variant 1 was highlighted by the significant accuracy percentages, suggesting potential for practical implementation in agriculture. In addition to the above, a smartphone application for the identification of SL in a field-based trial showed promising results with an accuracy of 98–99%. The conclusion from the above is that a CNN model with batch normalization has the potential to play a crucial role in the future in redefining and optimizing the management of undesirable vegetation.

show abstract

Section: Rmsprop and Adjusted Learning Rate Methodsmentioning

confidence: 99%

From Plants to Pixels: The Role of Artificial Intelligence in Identifying Sericea Lespedeza in Field-Based Studies

Siddique,

Cook,

Holt

et al. 2024

Agronomy

View full text Add to dashboard Cite

show abstract

“…At its core, RMSprop aims to dynamically adjust each parameter's learning rate based on its gradients' historical magnitudes [34] (Yu et al, 2020). This method of RMSprop solves the common drawback of a static learning rate, which, if set too high, can cause unstable training with erratic oscillations, and, if too low, results in an agonizingly slow convergence [35,36].…”

Section: Rmsprop and Adjusted Learning Rate Methodsmentioning

confidence: 99%

From Plants to Pixels: The Role of Artificial Intelligence in Identifying Sericea Lespedeza

Siddique,

Cook,

Holt

et al. 2024

Preprint

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The increasing use of Convolutional Neural Networks (CNN) has brought about a significant transformation in numerous fields, such as image categorization and identification. In the development of a CNN model for classifying images of sericea lespedeza (SL; Lespedeza cuneata) from weed images, four architectures were explored: CNN-Model Variant 1, CNN-Model Variant 2, VGG16, and ResNet50. The CNN-Model Variant 1 demonstrated 100 % validation accuracy, while Variant 2 achieved 90.78% validation accuracy. Pre-trained models, like VGG16 and ResNet50, were also analyzed. In contrast, ResNet50's steady learning pattern indicated potential for better generalization. A detailed evaluation of these models revealed that Variant 1 achieved a perfect score in precision, recall, and F1-score, indicating superior optimization and feature utilization. Variant 2 presented a balanced performance, with metrics between 86% and 93%. The VGG16 mirrored the behavior of Variant 2, both maintaining around 90% accuracy, but ResNet50's results revealed a conservative approach for class 0 predictions. Overall, Variant 1 stood out in performance, while both Variant 2 and VGG16 showed balanced results. The reliability of CNN model Variant 1 is highlighted by the significant accuracy percentages, which demonstrate its potential for practical implementation in agriculture. Smartphone application for the identification of SL in a field-based trial has shown promising results with an accuracy of 98%-99%. Using a CNN model with batch normalization has the potential to play a crucial role in redefining and optimizing the management of undesirable vegetation in the future.

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A comprehensive review of digital twin — part 1: modeling and twinning enabling technologies

Thelen

Fink

et al. 2022

Struct Multidisc Optim

116

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Aerodynamic surrogate model based on deep long short-term memory network: An application on high-lift device control

Cited by 5 publications

References 42 publications

From Plants to Pixels: The Role of Artificial Intelligence in Identifying Sericea Lespedeza in Field-Based Studies

From Plants to Pixels: The Role of Artificial Intelligence in Identifying Sericea Lespedeza in Field-Based Studies

From Plants to Pixels: The Role of Artificial Intelligence in Identifying Sericea Lespedeza

A comprehensive review of digital twin — part 1: modeling and twinning enabling technologies

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