Haolun Zhang¹, Mengna Yang¹, Jie Wei², Yufeng Nie^2,*

Digital Engineering and Digital Twin, Vol.2, pp. 49-77, 2024, DOI:10.32604/dedt.2023.044180

Abstract This paper mainly considers the formulation and theoretical analysis of the reduced-order numerical method constructed by proper orthogonal decomposition (POD) for nonlocal diffusion problems with a finite range of nonlocal interactions. We first set up the classical finite element discretization for nonlocal diffusion equations and briefly explain the difference between nonlocal and partial differential equations (PDEs). Nonlocal models have to handle double integrals when using finite element methods (FEMs), which causes the generation of algebraic systems to be more challenging and time-consuming, and discrete systems have less sparsity than those for PDEs. So we establish a reduced-order model (ROM) for… More >

Manar Ahmed Hamza^1,*, Hala J. Alshahrani², Najm Alotaibi³, Mohamed K. Nour⁴, Mahmoud Othman⁵, Gouse Pasha Mohammed¹, Mohammed Rizwanullah¹, Mohamed I. Eldesouki⁶

Computer Systems Science and Engineering, Vol.48, No.1, pp. 97-114, 2024, DOI:10.32604/csse.2023.034520

Abstract Computational linguistics is the scientific and engineering discipline related to comprehending written and spoken language from a computational perspective and building artefacts that effectively process and produce language, either in bulk or in a dialogue setting. This paper develops a Chaotic Bird Swarm Optimization with deep ensemble learning based Arabic poem classification and dictarization (CBSOEDL-APCD) technique. The presented CBSOEDL-APCD technique involves the classification and dictarization of Arabic text into Arabic poetries and prose. Primarily, the CBSOEDL-APCD technique carries out data pre-processing to convert it into a useful format. Besides, the ensemble deep learning (EDL) model comprising deep belief network (DBN),… More >

Hwa-Young Jang¹, Sang-Jun Beon², Sung-Hoon Kim¹, In-Kyung Song¹, Won-Jung Shin^1,*

Congenital Heart Disease, Vol.18, No.6, pp. 639-647, 2023, DOI:10.32604/chd.2023.030065

Abstract Objectives: We investigated whether the selective cerebral perfusion (SCP) technique causes differences in changes in cerebral perfusion between both hemispheres in young infants, using cerebral oxygen saturation (ScO₂) as an index. Further, we determined the association between the discrepancy in ScO₂ and cerebral perfusion pressure during SCP. Methods: The difference in ScO₂ between the left and right cerebral hemispheres (ΔScO_{2 Rt-Lt}) was calculated during clamping of the innominate artery (IA) and during SCP. Results: In 25 infants (aged 2 to 78 days), the left and right ScO₂ were well maintained (median 63.2% and 60.9% during IA clamping, respectively; 64.0% and… More > Graphic Abstract

Karim Ragui¹, Mengshuai Chen^1,2, Lin Chen^1,2,3,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.27, No.3, pp. 1-2, 2023, DOI:10.32604/icces.2023.010378

Abstract The ambiguous dynamics associated with heat and mass transfer of invading carbon dioxide in sub-critical and supercritical states, as well as the response of pore-scale resident fluids, play a key role in understanding CO₂ capture and storage (CCUS) and the corresponding phase equilibrium mechanisms. To this end, this paper reveals the transport mechanisms of invading supercritical carbon dioxide (sCO₂) in polluted micromodels using a variant of Lattice-Boltzmann Color Fluid model and descriptive experimental data. The breakthrough time is evaluated by characterizing the displacement velocity, the capillary to pressuredifference ratio, and the transient heat and mass diffusion at a series of… More >

Hong Qian^1,2, Haixin Wang^1,*, Guangji Wang³, Qingyun Yan⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 679-698, 2024, DOI:10.32604/cmes.2023.043377

Abstract This study presents a proposed method for assessing the condition and predicting the future status of condensers operating in seawater over an extended period. The aim is to address the problems of scaling and corrosion, which lead to increased loss of cold resources. The method involves utilising a set of multivariate feature parameters associated with the condenser as input for evaluation and trend prediction. This methodology offers a precise means of determining the optimal timing for condenser cleaning, with the ultimate goal of improving its overall performance. The proposed approach involves the integration of the analytic network process with subjective… More >

Canghong Jin^1,*, Jiapeng Chen¹, Shuyu Wu¹, Hao Wu², Shuoping Wang¹, Jing Ying³

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 873-891, 2024, DOI:10.32604/cmes.2023.043230

Abstract Time series data plays a crucial role in intelligent transportation systems. Traffic flow forecasting represents a precise estimation of future traffic flow within a specific region and time interval. Existing approaches, including sequence periodic, regression, and deep learning models, have shown promising results in short-term series forecasting. However, forecasting scenarios specifically focused on holiday traffic flow present unique challenges, such as distinct traffic patterns during vacations and the increased demand for long-term forecastings. Consequently, the effectiveness of existing methods diminishes in such scenarios. Therefore, we propose a novel long-term forecasting model based on scene matching and embedding fusion representation to… More >

Chaoji Liu¹, Xingqiao Liu^1,*, Chong Chen², Kang Zhou¹

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 405-440, 2024, DOI:10.32604/cmes.2023.031040

Abstract Pose-invariant facial expression recognition (FER) is an active but challenging research topic in computer vision. Especially with the involvement of diverse observation angles, FER makes the training parameter models inconsistent from one view to another. This study develops a deep global multiple-scale and local patches attention (GMS-LPA) dual-branch network for pose-invariant FER to weaken the influence of pose variation and self-occlusion on recognition accuracy. In this research, the designed GMS-LPA network contains four main parts, i.e., the feature extraction module, the global multiple-scale (GMS) module, the local patches attention (LPA) module, and the model-level fusion model. The feature extraction module… More >

Zheng Shi, Wanru Song^*, Junhao Shan, Feng Liu

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3467-3488, 2023, DOI:10.32604/cmc.2023.045849

Abstract Visible-infrared Cross-modality Person Re-identification (VI-ReID) is a critical technology in smart public facilities such as cities, campuses and libraries. It aims to match pedestrians in visible light and infrared images for video surveillance, which poses a challenge in exploring cross-modal shared information accurately and efficiently. Therefore, multi-granularity feature learning methods have been applied in VI-ReID to extract potential multi-granularity semantic information related to pedestrian body structure attributes. However, existing research mainly uses traditional dual-stream fusion networks and overlooks the core of cross-modal learning networks, the fusion module. This paper introduces a novel network called the Augmented Deep Multi-Granularity Pose-Aware Feature… More >

Jun Shu^1,2, Shuai Wang^1,2, Shiqi Yu^1,2, Jie Zhang^3,*

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2677-2697, 2023, DOI:10.32604/cmc.2023.045818

Abstract Traditional models for semantic segmentation in point clouds primarily focus on smaller scales. However, in real-world applications, point clouds often exhibit larger scales, leading to heavy computational and memory requirements. The key to handling large-scale point clouds lies in leveraging random sampling, which offers higher computational efficiency and lower memory consumption compared to other sampling methods. Nevertheless, the use of random sampling can potentially result in the loss of crucial points during the encoding stage. To address these issues, this paper proposes cross-fusion self-attention network (CFSA-Net), a lightweight and efficient network architecture specifically designed for directly processing large-scale point clouds.… More >

Chaoqian He^1,2, Runfang Hao^1,2,*, Kun Yang^1,2, Zhongyun Yuan^1,2, Shengbo Sang^1,2, Xiaorui Wang^1,2

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3423-3442, 2023, DOI:10.32604/cmc.2023.045718

Abstract Intelligent fault diagnosis in modern mechanical equipment maintenance is increasingly adopting deep learning technology. However, conventional bearing fault diagnosis models often suffer from low accuracy and unstable performance in noisy environments due to their reliance on a single input data. Therefore, this paper proposes a dual-channel convolutional neural network (DDCNN) model that leverages dual data inputs. The DDCNN model introduces two key improvements. Firstly, one of the channels substitutes its convolution with a larger kernel, simplifying the structure while addressing the lack of global information and shallow features. Secondly, the feature layer combines data from different sensors based on their… More >