Výsledky vyhľadávania - deep convolutional variational autoencoder (dcvae)

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  1. 1

    Inverse design of reflective metasurface antennas using deep learning from small‐scale statistically random pico‐cells Autor You, Xi Chong, Lin, Feng Han

    ISSN: 0895-2477, 1098-2760
    Vydavateľské údaje: New York Wiley Subscription Services, Inc 01.02.2024
    “…A small‐scale data‐selection method is proposed for deep‐learning‐based inverse design of reflective metasurface antennas (MAs…”
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    Journal Article
  2. 2

    An efficient epileptic seizure detection based on tunable Q-wavelet transform and DCVAE-stacked Bi-LSTM model using electroencephalogram Autor Sivasaravanababu, S., Prabhu, V., Parthasarathy, V., Mahendran, Rakesh Kumar

    ISSN: 1951-6355, 1951-6401
    Vydavateľské údaje: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2022
    “…The recording and measurement of the electric events of the brain utilizing an electroencephalogram (EEG) has turned out to be a prominent equipment among the…”
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  3. 3

    Automatic Classification of 2D Partial Discharge from Generator On-Line Measurement Autor Hudon, Claude, Levesque, Melanie, Kokoko, Olivier, Amyot, Normand, Zemouri, Ryad

    ISSN: 2576-6791
    Vydavateľské údaje: IEEE 07.06.2021
    “…Quantification of on-line Partial Discharge (PD) measurements is a challenge in the industry for several reasons, amongst them: instrumental characteristics,…”
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  4. 4

    Wood-species identification based on terahertz spectral data augmentation and pseudo-label guided deep clustering Autor Wang, Yuan, Wang, Zhi-Gang, He, Yi-Hao, Avramidis, Stavros

    ISSN: 1748-0272, 1748-0280, 1748-0280
    Vydavateľské údaje: Abingdon Taylor & Francis 02.09.2024
    Vydané v Wood material science and engineering (02.09.2024)
    “… (deep conditional variational autoencoder)-PLCAE (pseudo-label convolutional autoencoders…”
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  5. 5

    DVMTICANET: A Synthetic Multisource Interactive Adaptation Framework for Intelligent Fault Diagnosis Under Large Cross-Domain Discrepancies Autor Jiang, Huiming, Zhu, Haifeng, Yuan, Jing, Zhao, Qian, Li, Yanbin, Chen, Jin

    ISSN: 1530-437X, 1558-1748
    Vydavateľské údaje: New York IEEE 01.05.2025
    Vydané v IEEE sensors journal (01.05.2025)
    “…Multisource transfer fault diagnosis is currently a hot topic in the field of intelligent fault diagnosis (IFD). However, in practical scenarios, large…”
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