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Deep order-wavelet convolutional variational autoencoder for fault identification of rolling bearing under fluctuating speed conditions
Deep order-wavelet convolutional variational autoencoder for fault identification of rolling bearing under fluctuating speed conditions
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Deep order-wavelet convolutional variational autoencoder for fault identification of rolling bearing under fluctuating speed conditions

Yan, Xiaoan ; She, Daoming ; Xu, Yadong

Expert systems with applications, 2023-04, Vol.216, p.119479, Article 119479 [Periódico revisado por pares]

Elsevier Ltd

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A data-driven methodology for bridge indirect health monitoring using unsupervised computer vision
A data-driven methodology for bridge indirect health monitoring using unsupervised computer vision
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A data-driven methodology for bridge indirect health monitoring using unsupervised computer vision

Hurtado, A. Calderon ; Alamdari, M. Makki ; Atroshchenko, E. ; Chang, K.C. ; Kim, C.W.

Mechanical systems and signal processing, 2024-03, Vol.210, p.111109, Article 111109 [Periódico revisado por pares]

Elsevier Ltd

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Attention mechanism-guided residual convolution variational autoencoder for bearing fault diagnosis under noisy environments
Attention mechanism-guided residual convolution variational autoencoder for bearing fault diagnosis under noisy environments
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Attention mechanism-guided residual convolution variational autoencoder for bearing fault diagnosis under noisy environments

Yan, Xiaoan ; Lu, Yanyu ; Liu, Ying ; Jia, Minping

Measurement science & technology, 2023-12, Vol.34 (12), p.125046 [Periódico revisado por pares]

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Characterization of discrete fracture networks with deep-learning based hydrogeophysical inversion
Characterization of discrete fracture networks with deep-learning based hydrogeophysical inversion
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Characterization of discrete fracture networks with deep-learning based hydrogeophysical inversion

Deng, Yaping ; Kang, Xueyuan ; Ma, Haichun ; Qian, Jiazhong ; Ma, Lei ; Luo, Qiankun

Journal of hydrology (Amsterdam), 2024-03, Vol.631, p.130819, Article 130819 [Periódico revisado por pares]

Elsevier B.V

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5
CVAE-DF: A hybrid deep learning framework for fertilization status detection of pre-incubation duck eggs based on VIS/NIR spectroscopy
CVAE-DF: A hybrid deep learning framework for fertilization status detection of pre-incubation duck eggs based on VIS/NIR spectroscopy
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CVAE-DF: A hybrid deep learning framework for fertilization status detection of pre-incubation duck eggs based on VIS/NIR spectroscopy

Wang, Dongqiao ; Wang, Qiaohua ; Chen, Zhuoting ; Guo, Juncai ; Li, Shijun

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy, 2024-11, Vol.320, p.124569, Article 124569 [Periódico revisado por pares]

Elsevier B.V

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6
Evolving Deep Convolutional Variational Autoencoders for Image Classification
Evolving Deep Convolutional Variational Autoencoders for Image Classification
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Evolving Deep Convolutional Variational Autoencoders for Image Classification

Chen, Xiangru ; Sun, Yanan ; Zhang, Mengjie ; Peng, Dezhong

IEEE transactions on evolutionary computation, 2021-10, Vol.25 (5), p.815-829 [Periódico revisado por pares]

IEEE

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7
A Zero-Shot Fault Detection Method for UAV Sensors Based on a Novel CVAE-GAN Model
A Zero-Shot Fault Detection Method for UAV Sensors Based on a Novel CVAE-GAN Model
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A Zero-Shot Fault Detection Method for UAV Sensors Based on a Novel CVAE-GAN Model

Li, Chuanjiang ; Luo, Kai ; Yang, Lei ; Li, Shaobo ; Wang, Haoyu ; Zhang, Xiangjie ; Liao, Zihao

IEEE sensors journal, 2024-07, Vol.24 (14), p.23239-23254 [Periódico revisado por pares]

IEEE

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8
Fault Classification in High-Dimensional Complex Processes Using Semi-Supervised Deep Convolutional Generative Models
Fault Classification in High-Dimensional Complex Processes Using Semi-Supervised Deep Convolutional Generative Models
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Fault Classification in High-Dimensional Complex Processes Using Semi-Supervised Deep Convolutional Generative Models

Ko, Taeyoung ; Kim, Heeyoung

IEEE transactions on industrial informatics, 2020-04, Vol.16 (4), p.2868-2877

Piscataway: IEEE

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Non-Invasive Detection of Rotor Inter-Turn Short Circuit in Large Hydrogenerators by Using Stray Flux Measurement Combined With Convolutional Variational Autoencoder Analysis (CVAE)
Non-Invasive Detection of Rotor Inter-Turn Short Circuit in Large Hydrogenerators by Using Stray Flux Measurement Combined With Convolutional Variational Autoencoder Analysis (CVAE)
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Non-Invasive Detection of Rotor Inter-Turn Short Circuit in Large Hydrogenerators by Using Stray Flux Measurement Combined With Convolutional Variational Autoencoder Analysis (CVAE)

Bechara, Helene ; Zemouri, Ryad ; Kedjar, Bachir ; Merkhouf, Arezki ; Al-Haddad, Kamal ; Tahan, Antoine

IEEE transactions on industry applications, 2024-01, Vol.60 (1), p.1-10 [Periódico revisado por pares]

New York: IEEE

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Smart manufacturing under limited and heterogeneous data: a sim-to-real transfer learning with convolutional variational autoencoder in thermoforming
Smart manufacturing under limited and heterogeneous data: a sim-to-real transfer learning with convolutional variational autoencoder in thermoforming
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Smart manufacturing under limited and heterogeneous data: a sim-to-real transfer learning with convolutional variational autoencoder in thermoforming

Ramezankhani, Milad ; Harandi, Mehrtash ; Seethaler, Rudolf ; Milani, Abbas S.

International journal of computer integrated manufacturing, 2024-02, Vol.37 (1-2), p.18-36 [Periódico revisado por pares]

Taylor & Francis

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