A novel generative approach for modal frequency probabilistic prediction under varying environmental condition using incomplete information

Mu, He‐Qing; Shen, Ji-Hui; Zhao, Zitong; Liu, Han-Teng; Yuen, Ka‐Veng

doi:10.1016/j.engstruct.2021.113571

Cited by 8 publications

(2 citation statements)

References 45 publications

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“…Where y i is the true label for which class the sample belongs to, p i is a prediction probability [22] belonging to class i. Dropout operation has been added.…”

Section: Modelmentioning

confidence: 99%

Identification of Coffee Leaf Pests and Diseases based on Transfer Learning and Knowledge Distillation

Wang,

Ren,

2023

FCIS

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The yield of coffee has a significant effect on the development of the economy. It is important to monitor the health status of coffee plants. Leaves can represent the growth of crops. Analysis leaf image is an effective method to monitor crop growth status. With the advancement of artificial intelligence technology, neural networks with strong learning ability have been proposed. They have high accuracy in identifying leaf pests and diseases. However, the structure of these networks is complex and the speed of computing is slow. They are not conducive to real-time analysis. For simple networks, it is difficult to achieve high recognition accuracy directly. To solve this problem, a lightweight model is designed for leaf image analysis. Leaf images are learned by VGG network with pre-trained weights on ImageNet. Use the VGG network as a teacher network. Then design a lightweight student network. Train student network with knowledge distillation method. A lightweight model with high recognition accuracy can be obtained. This research explored the effect of the method on the coffee leaf data set. Experiment proved that the accuracy of the proposed method is 96.73%. The accuracy was 4.29% higher than directly training. Meantime, the calculation speed of the model is quick. The proposed method is of great practical significance for identifying coffee leaf pests and diseases.

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“…Where y i is the true label for which class the sample belongs to, p i is a prediction probability [22] belonging to class i. Dropout operation has been added.…”

Section: Modelmentioning

confidence: 99%

Identification of Coffee Leaf Pests and Diseases based on Transfer Learning and Knowledge Distillation

Wang,

Ren,

2023

FCIS

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show abstract

“…The most suitable/plausible model class of measurement error is the one possessing maximum posterior probability. Recently, Bayesian model class selection has been studied and developed for structural health monitoring [ 25 , 26 , 27 , 28 , 29 ], structural damage detection [ 30 , 31 , 32 , 33 ], and risk assessment [ 24 , 34 , 35 ]. In particular, Simoen et al [ 23 ] solely considered spatial correlation for measurement error and performed Bayesian model class selection to select the most suitable/plausible model class of spatially correlated measurement error in model updating.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Structural Health Monitoring Data with Correlated Measurement Error by Bayesian System Identification: Theory and Application

Liang²,

Shen³

et al. 2022

Sensors

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Measurement error is non-negligible and crucial in SHM data analysis. In many applications of SHM, measurement errors are statistically correlated in space and/or in time for data from sensor networks. Existing works solely consider spatial correlation for measurement error. When both spatial and temporal correlation are considered simultaneously, the existing works collapse, as they do not possess a suitable form describing spatially and temporally correlated measurement error. In order to tackle this burden, this paper generalizes the form of correlated measurement error from spatial correlation only or temporal correlation only to spatial-temporal correlation. A new form of spatial-temporal correlation and the corresponding likelihood function are proposed, and multiple candidate model classes for the measurement error are constructed, including no correlation, spatial correlation, temporal correlation, and the proposed spatial-temporal correlation. Bayesian system identification is conducted to achieve not only the posterior probability density function (PDF) for the model parameters, but also the posterior probability of each candidate model class for selecting the most suitable/plausible model class for the measurement error. Examples are presented with applications to model updating and modal frequency prediction under varying environmental conditions, ensuring the necessity of considering correlated measurement error and the capability of the proposed Bayesian system identification in the uncertainty quantification at the parameter and model levels.

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Prediction of long-term dynamic responses of a heritage masonry building under thermal effects by automated kernel-based regression modeling

Sarmadi,

Behkamal,

Entezami

2024

Artificial Intelligence Applications for Sustainable Construction

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A novel generative approach for modal frequency probabilistic prediction under varying environmental condition using incomplete information

Cited by 8 publications

References 45 publications

Identification of Coffee Leaf Pests and Diseases based on Transfer Learning and Knowledge Distillation

Identification of Coffee Leaf Pests and Diseases based on Transfer Learning and Knowledge Distillation

Analysis of Structural Health Monitoring Data with Correlated Measurement Error by Bayesian System Identification: Theory and Application

Prediction of long-term dynamic responses of a heritage masonry building under thermal effects by automated kernel-based regression modeling

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