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  • Open Access


    APST-Flow: A Reversible Network-Based Artistic Painting Style Transfer Method

    Meng Wang*, Yixuan Shao, Haipeng Liu

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5229-5254, 2023, DOI:10.32604/cmc.2023.036631

    Abstract In recent years, deep generative models have been successfully applied to perform artistic painting style transfer (APST). The difficulties might lie in the loss of reconstructing spatial details and the inefficiency of model convergence caused by the irreversible en-decoder methodology of the existing models. Aiming to this, this paper proposes a Flow-based architecture with both the en-decoder sharing a reversible network configuration. The proposed APST-Flow can efficiently reduce model uncertainty via a compact analysis-synthesis methodology, thereby the generalization performance and the convergence stability are improved. For the generator, a Flow-based network using Wavelet additive coupling (WAC) layers is implemented to… More >

  • Open Access


    Physics-Informed AI Surrogates for Day-Ahead Wind Power Probabilistic Forecasting with Incomplete Data for Smart Grid in Smart Cities

    Zeyu Wu1, Bo Sun1,2, Qiang Feng2,*, Zili Wang1, Junlin Pan1

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 527-554, 2023, DOI:10.32604/cmes.2023.027124

    Abstract Due to the high inherent uncertainty of renewable energy, probabilistic day-ahead wind power forecasting is crucial for modeling and controlling the uncertainty of renewable energy smart grids in smart cities. However, the accuracy and reliability of high-resolution day-ahead wind power forecasting are constrained by unreliable local weather prediction and incomplete power generation data. This article proposes a physics-informed artificial intelligence (AI) surrogates method to augment the incomplete dataset and quantify its uncertainty to improve wind power forecasting performance. The incomplete dataset, built with numerical weather prediction data, historical wind power generation, and weather factors data, is augmented based on generative… More > Graphic Abstract

    Physics-Informed AI Surrogates for Day-Ahead Wind Power Probabilistic Forecasting with Incomplete Data for Smart Grid in Smart Cities

  • Open Access


    A Survey of GAN Based Image Synthesis

    Jiahe Ni*

    Journal of Information Hiding and Privacy Protection, Vol.4, No.2, pp. 79-88, 2022, DOI:10.32604/jihpp.2022.039751

    Abstract Image generation is a hot topic in the academic recently, and has been applied to AI drawing, which can bring Vivid AI paintings without labor costs. In image generation, we represent the image as a random vector, assuming that the images of the natural scene obey an unknown distribution, we hope to estimate its distribution through some observation samples. Especially, with the development of GAN (Generative Adversarial Network), The generator and discriminator improve the model capability through adversarial, the quality of the generated image is also increasing. The image quality generated by the existing GAN based image generation model is… More >

  • Open Access


    Fault Diagnosis of Power Transformer Based on Improved ACGAN Under Imbalanced Data

    Tusongjiang. Kari1, Lin Du1, Aisikaer. Rouzi2, Xiaojing Ma1,*, Zhichao Liu1, Bo Li1

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4573-4592, 2023, DOI:10.32604/cmc.2023.037954

    Abstract The imbalance of dissolved gas analysis (DGA) data will lead to over-fitting, weak generalization and poor recognition performance for fault diagnosis models based on deep learning. To handle this problem, a novel transformer fault diagnosis method based on improved auxiliary classifier generative adversarial network (ACGAN) under imbalanced data is proposed in this paper, which meets both the requirements of balancing DGA data and supplying accurate diagnosis results. The generator combines one-dimensional convolutional neural networks (1D-CNN) and long short-term memories (LSTM), which can deeply extract the features from DGA samples and be greatly beneficial to ACGAN’s data balancing and fault diagnosis.… More >

  • Open Access


    Variant Wasserstein Generative Adversarial Network Applied on Low Dose CT Image Denoising

    Anoud A. Mahmoud1,*, Hanaa A. Sayed2,3, Sara S. Mohamed1

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4535-4552, 2023, DOI:10.32604/cmc.2023.037087

    Abstract Computed Tomography (CT) images have been extensively employed in disease diagnosis and treatment, causing a huge concern over the dose of radiation to which patients are exposed. Increasing the radiation dose to get a better image may lead to the development of genetic disorders and cancer in the patients; on the other hand, decreasing it by using a Low-Dose CT (LDCT) image may cause more noise and increased artifacts, which can compromise the diagnosis. So, image reconstruction from LDCT image data is necessary to improve radiologists’ judgment and confidence. This study proposed three novel models for denoising LDCT images based… More >

  • Open Access


    Multi-Generator Discriminator Network Using Texture-Edge Information

    Kyeongseok Jang1, Seongsoo Cho2, Kwang Chul Son3,*

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3537-3551, 2023, DOI:10.32604/cmc.2023.030557

    Abstract In the proposed paper, a parallel structure type Generative Adversarial Network (GAN) using edge and texture information is proposed. In the existing GAN-based model, many learning iterations had to be given to obtaining an output that was somewhat close to the original data, and noise and distortion occurred in the output image even when learning was performed. To solve this problem, the proposed model consists of two generators and three discriminators to propose a network in the form of a parallel structure. In the network, each edge information and texture information were received as inputs, learning was performed, and each… More >

  • Open Access


    MayGAN: Mayfly Optimization with Generative Adversarial Network-Based Deep Learning Method to Classify Leukemia Form Blood Smear Images

    Neenavath Veeraiah1,*, Youseef Alotaibi2, Ahmad F. Subahi3

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 2039-2058, 2023, DOI:10.32604/csse.2023.036985

    Abstract Leukemia, often called blood cancer, is a disease that primarily affects white blood cells (WBCs), which harms a person’s tissues and plasma. This condition may be fatal when if it is not diagnosed and recognized at an early stage. The physical technique and lab procedures for Leukaemia identification are considered time-consuming. It is crucial to use a quick and unexpected way to identify different forms of Leukaemia. Timely screening of the morphologies of immature cells is essential for reducing the severity of the disease and reducing the number of people who require treatment. Various deep-learning (DL) model-based segmentation and categorization… More >

  • Open Access


    A Novel Motor Fault Diagnosis Method Based on Generative Adversarial Learning with Distribution Fusion of Discrete Working Conditions

    Qixin Lan, Binqiang Chen*, Bin Yao

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.2, pp. 2017-2037, 2023, DOI:10.32604/cmes.2023.025307

    Abstract Many kinds of electrical equipment are used in civil and building engineering. The motor is one of the main power components of this electrical equipment, which can provide stable power output. During the long-term use of motors, various motor faults may occur, which affects the normal use of electrical equipment and even causes accidents. It is significant to apply fault diagnosis for the motors at the construction site. Aiming at the problem that signal data of faulty motor lack diversity, this research designs a multi-layer perceptron Wasserstein generative adversarial network, which is used to enhance training data through distribution fusion.… More >

  • Open Access


    GraphCWGAN-GP: A Novel Data Augmenting Approach for Imbalanced Encrypted Traffic Classification

    Jiangtao Zhai1,*, Peng Lin1, Yongfu Cui1, Lilong Xu1, Ming Liu2

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.2, pp. 2069-2092, 2023, DOI:10.32604/cmes.2023.023764

    Abstract Encrypted traffic classification has become a hot issue in network security research. The class imbalance problem of traffic samples often causes the deterioration of Machine Learning based classifier performance. Although the Generative Adversarial Network (GAN) method can generate new samples by learning the feature distribution of the original samples, it is confronted with the problems of unstable training and mode collapse. To this end, a novel data augmenting approach called GraphCWGAN-GP is proposed in this paper. The traffic data is first converted into grayscale images as the input for the proposed model. Then, the minority class data is augmented with… More >

  • Open Access


    Auxiliary Classifier of Generative Adversarial Network for Lung Cancer Diagnosis

    P. S. Ramapraba1,*, P. Epsiba2, K. Umapathy3, E. Sivanantham4

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2177-2189, 2023, DOI:10.32604/iasc.2023.032040

    Abstract The classification of lung nodules is a challenging problem as the visual analysis of the nodules and non-nodules revealed homogenous textural patterns. In this work, an Auxiliary Classifier (AC)-Generative Adversarial Network (GAN) based Lung Cancer Classification (LCC) system is developed. The proposed AC-GAN-LCC system consists of three modules; preprocessing, Lungs Region Detection (LRD), and AC-GAN classification. A Wiener filter is employed in the preprocessing module to remove the Gaussian noise. In the LRD module, only the lung regions (left and right lungs) are detected using iterative thresholding and morphological operations. In order to extract the lung region only, flood filling… More >

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