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    Wyświetlanie 1-5 z 5
    Tytuł:
    Underwater image enhancement via efficient generative adversarial network
    Autorzy:
    Qian, Xin
    Ge, Peng
    Powiązania:
    https://bibliotekanauki.pl/articles/2033886.pdf
    Data publikacji:
    2021
    Wydawca:
    Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
    Tematy:
    underwater
    image dehazing
    generative adversarial network
    GAN
    Opis:
    Underwater image enhancement has been receiving much attention due to its significance in facilitating various marine explorations. Inspired by the generative adversarial network (GAN) and residual network (ResNet) in many vision tasks, we propose a simplified designed ResNet model based on GAN called efficient GAN (EGAN) for underwater image enhancement. In particular, for the generator of EGAN we design a new pair of convolutional kernel size for the residual block in the ResNet. Secondly, we abandon batch normalization (BN) after every convolution layer for faster training and less artifacts. Finally, a smooth loss function is introduced for halo-effect alleviation. Extensive qualitative and quantitative experiments show that our methods accomplish considerable improvements compared to the state-of-the-art methods.
    Źródło:
    Optica Applicata; 2021, 51, 4; 483-497
    0078-5466
    1899-7015
    Pojawia się w:
    Optica Applicata
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Mask face inpainting based on improved generative adversarial network
    Autorzy:
    Liu, Qingyu
    Juanatas, Roben A.
    Powiązania:
    https://bibliotekanauki.pl/articles/30148248.pdf
    Data publikacji:
    2023
    Wydawca:
    Polskie Towarzystwo Promocji Wiedzy
    Tematy:
    face inpainting
    generative adversarial network
    residual network
    attention mechanism
    Opis:
    Face recognition technology has been widely used in all aspects of people's lives. However, the accuracy of face recognition is greatly reduced due to the obscuring of objects, such as masks and sunglasses. Wearing masks in public has been a crucial approach to preventing illness, especially since the Covid-19 outbreak. This poses challenges to applications such as face recognition. Therefore, the removal of masks via image inpainting has become a hot topic in the field of computer vision. Deep learning-based image inpainting techniques have taken observable results, but the restored images still have problems such as blurring and inconsistency. To address such problems, this paper proposes an improved inpainting model based on generative adversarial network: the model adds attention mechanisms to the sampling module based on pix2pix network; the residual module is improved by adding convolutional branches. The improved inpainting model can not only effectively restore faces obscured by face masks, but also realize the inpainting of randomly obscured images of human faces. To further validate the generality of the inpainting model, tests are conducted on the datasets of CelebA, Paris Street and Place2, and the experimental results show that both SSIM and PSNR have improved significantly.
    Źródło:
    Applied Computer Science; 2023, 19, 2; 25-42
    1895-3735
    2353-6977
    Pojawia się w:
    Applied Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Remaining useful life prediction with insufficient degradation data based on deep learning approach
    Autorzy:
    Lyu, Yi
    Jiang, Yijie
    Zhang, Qichen
    Chen, Ci
    Powiązania:
    https://bibliotekanauki.pl/articles/2038109.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
    Tematy:
    deep learning
    remaining useful life
    degradation data
    data amplification
    cycle-consistent generative adversarial network
    Opis:
    Remaining useful life (RUL) prediction plays a crucial role in decision-making in conditionbased maintenance for preventing catastrophic field failure. For degradation-failed products, the data of performance deterioration process are the key for lifetime estimation. Deep learning has been proved to have excellent performance in RUL prediction given that the degradation data are sufficiently large. However, in some applications, the degradation data are insufficient, under which how to improve the prediction accuracy is yet a challenging problem. To tackle such a challenge, we propose a novel deep learning-based RUL prediction framework by amplifying the degradation dataset. Specifically, we leverage the cycle-consistent generative adversarial network to generate the synthetic data, based on which the original degradation dataset is amplified so that the data characteristics hidden in the sample space could be captured. Moreover, the sliding time window strategy and deep bidirectional long short-term memory network are employed to complete the RUL prediction framework. We show the effectiveness of the proposed method by running it on the turbine engine data set from the National Aeronautics and Space Administration. The comparative experiments show that our method outperforms a case without the use of the synthetically generated data.
    Źródło:
    Eksploatacja i Niezawodność; 2021, 23, 4; 745-756
    1507-2711
    Pojawia się w:
    Eksploatacja i Niezawodność
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Applications of generative models with a latent observation subspace in vibrodiagnostics
    Autorzy:
    Puchalski, Andrzej
    Komorska, Iwona
    Powiązania:
    https://bibliotekanauki.pl/articles/27313835.pdf
    Data publikacji:
    2023
    Wydawca:
    Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN
    Tematy:
    vibration signal
    deep neural network
    generative adversarial network
    GAN model
    synthetic subspace
    sygnał wibracyjny
    głęboka sieć neuronowa
    GAN
    wibrodiagnostyka
    Opis:
    The vibration signal is one of the most essential diagnostic signals, the analysis of which allows for determining the dynamic state of the monitored machine set. In the era of cyber-physical industrial systems, making diagnostic decisions involves the study of large databases from previous registers and data downloaded from machines in real-time. However, the recorded signals mainly concern the operational status of the monitored object. Insufficient training data regarding failure states hinders the operation of classification algorithms. Progress in machine learning has created a new avenue for the advancement of diagnostic methods based on models. These methods now have the capability to produce signals through random sampling from a hidden space or generate fresh instances of input data from noise. The article suggests the use of a Generative Adversarial Network (GAN) model as a tool to create synthetic measurement observations for vibration monitoring. The effectiveness of the synthetic data generation algorithm was verified on the example of the vibration signal recorded during tests of the drive system of a motor vehicle.
    Źródło:
    Diagnostyka; 2023, 24, 4; art. no. 2023413
    1641-6414
    2449-5220
    Pojawia się w:
    Diagnostyka
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Developing generative adversarial nets to extend training sets and optimize diiscrete actions
    Autorzy:
    Zhang, R. L.
    Furusho, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/116509.pdf
    Data publikacji:
    2019
    Wydawca:
    Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
    Tematy:
    Maritime Education and Training (MET)
    Generative Adversarial Network (GAN)
    discrete actions
    MET System in Japan
    Lifeboat
    Monte Carlo Tree Search (MCTS)
    learning methods
    unmanned ship navigation
    Opis:
    This study proposes the use of generative adversarial networks (GANs) to solve two crucial problems in the unmanned ship navigation: insufficient training data for neural networks and convergence of optimal actions under discrete conditions. To achieve smart collision avoidance of unmanned ships in various sea environments, first, this study proposes a collision avoidance decision model based on a deep reinforcement learning method. Then, it utilizes GANs to generate enough realistic image training sets to train the decision model. According to generative network learning, the conditional probability distribution of ship maneuvers is learnt (action units). Subsequently, the decision system can select a reasonable action to avoid the obstacles due to the discrete responses of the generated model to different actions and achieve the effect of intelligent collision avoidance. The experimental results showed that the generated target ship image set can be used as the training set of decision neural networks. Further, a theoretical reference to optimize the optimal convergence of discrete actions is provided.
    Źródło:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2019, 13, 4; 875-880
    2083-6473
    2083-6481
    Pojawia się w:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
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