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  • Open Access

    ARTICLE

    Data-Driven Structural Topology Optimization Method Using Conditional Wasserstein Generative Adversarial Networks with Gradient Penalty

    Qingrong Zeng, Xiaochen Liu, Xuefeng Zhu*, Xiangkui Zhang, Ping Hu

    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2065-2085, 2024, DOI:10.32604/cmes.2024.052620 - 31 October 2024

    Abstract Traditional topology optimization methods often suffer from the “dimension curse” problem, wherein the computation time increases exponentially with the degrees of freedom in the background grid. Overcoming this challenge, we introduce a real-time topology optimization approach leveraging Conditional Generative Adversarial Networks with Gradient Penalty (CGAN-GP). This innovative method allows for nearly instantaneous prediction of optimized structures. Given a specific boundary condition, the network can produce a unique optimized structure in a one-to-one manner. The process begins by establishing a dataset using simulation data generated through the Solid Isotropic Material with Penalization (SIMP) method. Subsequently, we More >

  • Open Access

    ARTICLE

    Robot Vision over CosGANs to Enhance Performance with Source-Free Domain Adaptation Using Advanced Loss Function

    Laviza Falak Naz1, Rohail Qamar2,*, Raheela Asif1, Muhammad Imran2, Saad Ahmed3

    Intelligent Automation & Soft Computing, Vol.39, No.5, pp. 855-887, 2024, DOI:10.32604/iasc.2024.055074 - 31 October 2024

    Abstract Domain shift is when the data used in training does not match the ones it will be applied to later on under similar conditions. Domain shift will reduce accuracy in results. To prevent this, domain adaptation is done, which adapts the pre-trained model to the target domain. In real scenarios, the availability of labels for target data is rare thus resulting in unsupervised domain adaptation. Herein, we propose an innovative approach where source-free domain adaptation models and Generative Adversarial Networks (GANs) are integrated to improve the performance of computer vision or robotic vision-based systems in… More >

  • Open Access

    ARTICLE

    TGAIN: Geospatial Data Recovery Algorithm Based on GAIN-LSTM

    Lechan Yang1,*, Li Li2, Shouming Ma3

    CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1471-1489, 2024, DOI:10.32604/cmc.2024.056379 - 15 October 2024

    Abstract Accurate geospatial data are essential for geographic information systems (GIS), environmental monitoring, and urban planning. The deep integration of the open Internet and geographic information technology has led to increasing challenges in the integrity and security of spatial data. In this paper, we consider abnormal spatial data as missing data and focus on abnormal spatial data recovery. Existing geospatial data recovery methods require complete datasets for training, resulting in time-consuming data recovery and lack of generalization. To address these issues, we propose a GAIN-LSTM-based geospatial data recovery method (TGAIN), which consists of two main works:… More >

  • Open Access

    ARTICLE

    Stroke Electroencephalogram Data Synthesizing through Progressive Efficient Self-Attention Generative Adversarial Network

    Suzhe Wang*, Xueying Zhang, Fenglian Li, Zelin Wu

    CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1177-1196, 2024, DOI:10.32604/cmc.2024.056016 - 15 October 2024

    Abstract Early and timely diagnosis of stroke is critical for effective treatment, and the electroencephalogram (EEG) offers a low-cost, non-invasive solution. However, the shortage of high-quality patient EEG data often hampers the accuracy of diagnostic classification methods based on deep learning. To address this issue, our study designed a deep data amplification model named Progressive Conditional Generative Adversarial Network with Efficient Approximating Self Attention (PCGAN-EASA), which incrementally improves the quality of generated EEG features. This network can yield full-scale, fine-grained EEG features from the low-scale, coarse ones. Specially, to overcome the limitations of traditional generative models… More >

  • Open Access

    ARTICLE

    Network Traffic Synthesis and Simulation Framework for Cybersecurity Exercise Systems

    Dong-Wook Kim1, Gun-Yoon Sin2, Kwangsoo Kim3, Jaesik Kang3, Sun-Young Im3, Myung-Mook Han1,*

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3637-3653, 2024, DOI:10.32604/cmc.2024.054108 - 12 September 2024

    Abstract In the rapidly evolving field of cybersecurity, the challenge of providing realistic exercise scenarios that accurately mimic real-world threats has become increasingly critical. Traditional methods often fall short in capturing the dynamic and complex nature of modern cyber threats. To address this gap, we propose a comprehensive framework designed to create authentic network environments tailored for cybersecurity exercise systems. Our framework leverages advanced simulation techniques to generate scenarios that mirror actual network conditions faced by professionals in the field. The cornerstone of our approach is the use of a conditional tabular generative adversarial network (CTGAN),… More >

  • Open Access

    ARTICLE

    EDU-GAN: Edge Enhancement Generative Adversarial Networks with Dual-Domain Discriminators for Inscription Images Denoising

    Yunjing Liu1,, Erhu Zhang1,2,,*, Jingjing Wang3, Guangfeng Lin2, Jinghong Duan4

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1633-1653, 2024, DOI:10.32604/cmc.2024.052611 - 18 July 2024

    Abstract Recovering high-quality inscription images from unknown and complex inscription noisy images is a challenging research issue. Different from natural images, character images pay more attention to stroke information. However, existing models mainly consider pixel-level information while ignoring structural information of the character, such as its edge and glyph, resulting in reconstructed images with mottled local structure and character damage. To solve these problems, we propose a novel generative adversarial network (GAN) framework based on an edge-guided generator and a discriminator constructed by a dual-domain U-Net framework, i.e., EDU-GAN. Unlike existing frameworks, the generator introduces the… More >

  • Open Access

    ARTICLE

    An Enhanced GAN for Image Generation

    Chunwei Tian1,2,3,4, Haoyang Gao2,3, Pengwei Wang2, Bob Zhang1,*

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 105-118, 2024, DOI:10.32604/cmc.2024.052097 - 18 July 2024

    Abstract Generative adversarial networks (GANs) with gaming abilities have been widely applied in image generation. However, gamistic generators and discriminators may reduce the robustness of the obtained GANs in image generation under varying scenes. Enhancing the relation of hierarchical information in a generation network and enlarging differences of different network architectures can facilitate more structural information to improve the generation effect for image generation. In this paper, we propose an enhanced GAN via improving a generator for image generation (EIGGAN). EIGGAN applies a spatial attention to a generator to extract salient information to enhance the truthfulness… More >

  • Open Access

    ARTICLE

    YOLO-Based Damage Detection with StyleGAN3 Data Augmentation for Parcel Information-Recognition System

    Seolhee Kim1, Sang-Duck Lee2,*

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 195-215, 2024, DOI:10.32604/cmc.2024.052070 - 18 July 2024

    Abstract Damage to parcels reduces customer satisfaction with delivery services and increases return-logistics costs. This can be prevented by detecting and addressing the damage before the parcels reach the customer. Consequently, various studies have been conducted on deep learning techniques related to the detection of parcel damage. This study proposes a deep learning-based damage detection method for various types of parcels. The method is intended to be part of a parcel information-recognition system that identifies the volume and shipping information of parcels, and determines whether they are damaged; this method is intended for use in the… More >

  • Open Access

    ARTICLE

    Cloud-Edge Collaborative Federated GAN Based Data Processing for IoT-Empowered Multi-Flow Integrated Energy Aggregation Dispatch

    Zhan Shi*

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 973-994, 2024, DOI:10.32604/cmc.2024.051530 - 18 July 2024

    Abstract The convergence of Internet of Things (IoT), 5G, and cloud collaboration offers tailored solutions to the rigorous demands of multi-flow integrated energy aggregation dispatch data processing. While generative adversarial networks (GANs) are instrumental in resource scheduling, their application in this domain is impeded by challenges such as convergence speed, inferior optimality searching capability, and the inability to learn from failed decision making feedbacks. Therefore, a cloud-edge collaborative federated GAN-based communication and computing resource scheduling algorithm with long-term constraint violation sensitiveness is proposed to address these challenges. The proposed algorithm facilitates real-time, energy-efficient data processing by More >

  • Open Access

    ARTICLE

    GAN-DIRNet: A Novel Deformable Image Registration Approach for Multimodal Histological Images

    Haiyue Li1, Jing Xie2, Jing Ke3, Ye Yuan1, Xiaoyong Pan1, Hongyi Xin4, Hongbin Shen1,*

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 487-506, 2024, DOI:10.32604/cmc.2024.049640 - 18 July 2024

    Abstract Multi-modal histological image registration tasks pose significant challenges due to tissue staining operations causing partial loss and folding of tissue. Convolutional neural network (CNN) and generative adversarial network (GAN) are pivotal in medical image registration. However, existing methods often struggle with severe interference and deformation, as seen in histological images of conditions like Cushing’s disease. We argue that the failure of current approaches lies in underutilizing the feature extraction capability of the discriminator in GAN. In this study, we propose a novel multi-modal registration approach GAN-DIRNet based on GAN for deformable histological image registration. To… More >

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