Haiyue Li¹, Jing Xie², Jing Ke³, Ye Yuan¹, Xiaoyong Pan¹, Hongyi Xin⁴, Hongbin Shen^1,*

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 >

Shuai Li¹, Xiaodong Zhao^1,2,*, Jinghu Zhou¹, Xiyue Wang¹

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2587-2611, 2024, DOI:10.32604/cmes.2024.052203 - 08 July 2024

Abstract Accurate quantification of the uncertainty in the mechanical characteristics of dielectric solids is crucial for advancing their application in high-precision technological domains, necessitating the development of robust computational methods. This paper introduces a Conditional Generation Adversarial Network Isogeometric Analysis (CGAN-IGA) to assess the uncertainty of dielectric solids’ mechanical characteristics. IGA is utilized for the precise computation of electric potentials in dielectric, piezoelectric, and flexoelectric materials, leveraging its advantage of integrating seamlessly with Computer-Aided Design (CAD) models to maintain exact geometrical fidelity. The CGAN method is highly efficient in generating models for piezoelectric and flexoelectric materials, More >

Cheng Yang^1,2, Miaojia Lou^2,*, Xiaoyu Chen^1,2, Zixuan Ren¹

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4107-4126, 2024, DOI:10.32604/cmc.2024.049183 - 20 June 2024

Abstract Chinese shadow puppetry has been recognized as a world intangible cultural heritage. However, it faces substantial challenges in its preservation and advancement due to the intricate and labor-intensive nature of crafting shadow puppets. To ensure the inheritance and development of this cultural heritage, it is imperative to enable traditional art to flourish in the digital era. This paper presents an Interactive Collaborative Creation System for shadow puppets, designed to facilitate the creation of high-quality shadow puppet images with greater ease. The system comprises four key functions: Image contour extraction, intelligent reference recommendation, generation network, and… More >

Mahatir Ahmed Tusher¹, Saket Choudary Kongara¹, Sagar Dhanraj Pande², SeongKi Kim^3,*, Salil Bharany^4,*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4063-4080, 2024, DOI:10.32604/cmc.2024.049007 - 20 June 2024

Abstract The challenging task of handwriting style synthesis requires capturing the individuality and diversity of human handwriting. The majority of currently available methods use either a generative adversarial network (GAN) or a recurrent neural network (RNN) to generate new handwriting styles. This is why these techniques frequently fall short of producing diverse and realistic text pictures, particularly for terms that are not commonly used. To resolve that, this research proposes a novel deep learning model that consists of a style encoder and a text generator to synthesize different handwriting styles. This network excels in generating conditional… More >

Xueya Wang¹, Yiming Zhang^2,3,*, Qi Liu⁴, Huanran Wang¹

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2907-2922, 2024, DOI:10.32604/cmc.2024.050736 - 15 May 2024

Abstract Concrete subjected to fire loads is susceptible to explosive spalling, which can lead to the exposure of reinforcing steel bars to the fire, substantially jeopardizing the structural safety and stability. The spalling of fire-loaded concrete is closely related to the evolution of pore pressure and temperature. Conventional analytical methods involve the resolution of complex, strongly coupled multifield equations, necessitating significant computational efforts. To rapidly and accurately obtain the distributions of pore-pressure and temperature, the Pix2Pix model is adopted in this work, which is celebrated for its capabilities in image generation. The open-source dataset used herein… More >

Jingyi Mao, Yuchen Zhou, Yifan Wang, Junyu Li, Ziqing Liu, Fanliang Bu^*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 837-855, 2024, DOI:10.32604/cmc.2024.048703 - 25 April 2024

Abstract Voice portrait technology has explored and established the relationship between speakers’ voices and their facial features, aiming to generate corresponding facial characteristics by providing the voice of an unknown speaker. Due to its powerful advantages in image generation, Generative Adversarial Networks (GANs) have now been widely applied across various fields. The existing Voice2Face methods for voice portraits are primarily based on GANs trained on voice-face paired datasets. However, voice portrait models solely constructed on GANs face limitations in image generation quality and struggle to maintain facial similarity. Additionally, the training process is relatively unstable, thereby… More >

Chengkan Xu^1,2,4, Xiaofei Wang³, Yixuan Li², Guannan Wang^2,*, He Zhang^2,4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 957-974, 2024, DOI:10.32604/cmes.2024.047327 - 16 April 2024

Abstract Structural damage in heterogeneous materials typically originates from microstructures where stress concentration occurs. Therefore, evaluating the magnitude and location of localized stress distributions within microstructures under external loading is crucial. Repeating unit cells (RUCs) are commonly used to represent microstructural details and homogenize the effective response of composites. This work develops a machine learning-based micromechanics tool to accurately predict the stress distributions of extracted RUCs. The locally exact homogenization theory efficiently generates the microstructural stresses of RUCs with a wide range of parameters, including volume fraction, fiber/matrix property ratio, fiber shapes, and loading direction. Subsequently, More > Graphic Abstract

Yuanle Chen¹, Haobo Wang¹, Chunyu Liu¹, Linyi Wang², Jiaxin Liu¹, Wei Wu^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 2985-3009, 2024, DOI:10.32604/cmes.2023.046837 - 11 March 2024

Abstract Recently, there have been significant advancements in the study of semantic communication in single-modal scenarios. However, the ability to process information in multi-modal environments remains limited. Inspired by the research and applications of natural language processing across different modalities, our goal is to accurately extract frame-level semantic information from videos and ultimately transmit high-quality videos. Specifically, we propose a deep learning-based Multi-Modal Mutual Enhancement Video Semantic Communication system, called M3E-VSC. Built upon a Vector Quantized Generative Adversarial Network (VQGAN), our system aims to leverage mutual enhancement among different modalities by using text as the main More >

Kun Fang, Julong Pan^*, Lingyi Li, Ruihan Xiang

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 493-514, 2024, DOI:10.32604/cmc.2023.045008 - 30 January 2024

Abstract With the widespread use of Internet of Things (IoT) technology in daily life and the considerable safety risks of falls for elderly individuals, research on IoT-based fall detection systems has gained much attention. This paper proposes an IoT-based spatiotemporal data processing framework based on a depthwise separable convolution generative adversarial network using skip-connection (Skip-DSCGAN) for fall detection. The method uses spatiotemporal data from accelerometers and gyroscopes in inertial sensors as input data. A semisupervised learning approach is adopted to train the model using only activities of daily living (ADL) data, which can avoid data imbalance… More >

Yue Mei^1,2,3, Jianwei Deng^1,2, Dongmei Zhao^1,2, Changjiang Xiao^1,2, Tianhang Wang⁴, Li Dong⁵, Xuefeng Zhu^1,6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 911-935, 2024, DOI:10.32604/cmes.2023.043810 - 30 December 2023

Abstract Elastography is a non-invasive medical imaging technique to map the spatial variation of elastic properties of soft tissues. The quality of reconstruction results in elastography is highly sensitive to the noise induced by imaging measurements and processing. To address this issue, we propose a deep learning (DL) model based on conditional Generative Adversarial Networks (cGANs) to improve the quality of nonhomogeneous shear modulus reconstruction. To train this model, we generated a synthetic displacement field with finite element simulation under known nonhomogeneous shear modulus distribution. Both the simulated and experimental displacement fields are used to validate More >