Výsledky vyhledávání - "variational autoencoder generative adversarial network"

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  1. 1

    Dual-Encoder VAE-GAN With Spatiotemporal Features for Emotional EEG Data Augmentation Autor Tian, Chenxi, Ma, Yuliang, Cammon, Jared, Fang, Feng, Zhang, Yingchun, Meng, Ming

    ISSN: 1534-4320, 1558-0210, 1558-0210
    Vydáno: United States IEEE 2023
    “…The current data scarcity problem in EEG-based emotion recognition tasks leads to difficulty in building high-precision models using existing deep learning…”
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    Journal Article
  2. 2

    Credit Card Fraud Detection Based on Improved Variational Autoencoder Generative Adversarial Network Autor Ding, Yuanming, Kang, Wei, Feng, Jianxin, Peng, Bo, Yang, Anna

    ISSN: 2169-3536, 2169-3536
    Vydáno: Piscataway IEEE 2023
    Vydáno v IEEE access (2023)
    “…The rapid spread of mobile banking and e-commerce has coincided with a dramatic increase in fraudulent online payments in recent years. Although machine…”
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  3. 3

    MCVAE-GAN: A Novel Diagnosis Method for Solving Harmonic Reducer Fault Signal Scarcity and Sample Diversity Problem Autor Zhao, Zida, Sun, Shilong, Wang, Dong, Xu, Wenfu

    ISSN: 0018-9456, 1557-9662
    Vydáno: New York IEEE 2024
    “…Harmonic reducers are vital for robotics and aerospace due to their ability to deliver high torque and reduction, but they face challenges in fault diagnosis…”
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  4. 4

    基于VAE-GAN数据增强算法的小样本滚动轴承故障分类方法 Autor 张钊光, 蒋庆磊, 詹瑜滨, 侯修群, 郑英, 崔运佳

    ISSN: 1000-6931
    Vydáno: 核动力运行研究所,湖北武汉 430200 20.07.2023
    Vydáno v 原子能科学技术 (20.07.2023)
    “…TP277%TH17; 近些年,数据增强算法被广泛应用于小样本故障分类中.然而,传统的数据增强模型在训练中经常出现梯度爆炸、梯度消失等问题,这在一定程度上限制了其在滚动轴承故障…”
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  5. 5

    Data augmentation and machine learning techniques for control strategy development in bio-polymerization process Autor Wei, Sizhou, Chen, Zhiyuan, Arumugasamy, Senthil Kumar, Chew, Irene Mei Leng

    ISSN: 2666-4984, 2096-9643, 2666-4984
    Vydáno: Elsevier B.V 01.07.2022
    “…Machine learning has been increasingly used in biochemistry. However, in organic chemistry and other experiment-based fields, data collected from real…”
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  6. 6

    Transfer Learning with Attributes for Improving the Landslide Spatial Prediction Performance in Sample-Scarce Area Based on Variational Autoencoder Generative Adversarial Network Autor Lin, Mansheng, Teng, Shuai, Chen, Gongfa, Bassir, David

    ISSN: 2073-445X, 2073-445X
    Vydáno: Basel MDPI AG 21.02.2023
    Vydáno v Land (Basel) (21.02.2023)
    “…Owing to the complexity of obtaining the landslide inventory data, it is a challenge to establish a landslide spatial prediction model with limited labeled…”
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  7. 7

    Improved Prediction of Aquatic Beetle Diversity in a Stagnant Pool by a One-Dimensional Convolutional Neural Network Using Variational Autoencoder Generative Adversarial Network-Generated Data Autor Hu, Miao, Jiang, Shujiao, Jia, Fenglong, Yang, Xiaomei, Li, Zhiqiang

    ISSN: 2076-3417, 2076-3417
    Vydáno: Basel MDPI AG 01.08.2023
    Vydáno v Applied sciences (01.08.2023)
    “…Building a reasonable model for predicting biodiversity using limited data is challenging. Expanding limited experimental data using a variational autoencoder…”
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  8. 8

    An Improved Variational Autoencoder Generative Adversarial Network with Convolutional Neural Network for Fraud Financial Transaction Detection Autor Gudivaka, Basava Ramanjaneyulu, Almusawi, Muntather, Priyanka, M S, Dhanda, Madhava Rao, Thanjaivadivel, M

    Vydáno: IEEE 17.05.2024
    “…In recent years, the rapid development of ecommerce technologies has made it possible for people to conveniently shop from stores worldwide without leaving…”
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  9. 9

    A conditional variational autoencoding generative adversarial networks with self-modulation for rolling bearing fault diagnosis Autor Liu, Yunpeng, Jiang, Hongkai, Wang, Yanfeng, Wu, Zhenghong, Liu, Shaowei

    ISSN: 0263-2241, 1873-412X
    Vydáno: London Elsevier Ltd 31.03.2022
    “…•Translating and scaling is performed on the input to alleviate overfitting.•Self-Modulation module is adopted in generator to achieve Nash…”
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  10. 10

    A Novel Cross-Domain Data Augmentation and Bearing Fault Diagnosis Method Based on an Enhanced Generative Model Autor Sun, Shilong, Ding, Hao, Huang, Haodong, Zhao, Zida, Wang, Dong, Xu, Wenfu

    ISSN: 0018-9456, 1557-9662
    Vydáno: New York IEEE 2024
    “…In actual industrial production, differences in product ion conditions lead to variations in the collected data distribution. This gives rise to a particular…”
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