Chunlei Ruan^1,2,*, Cengceng Dong¹, Zeyue Zhang¹, Boyu Chen¹, Zhijun Liu¹

CMES-Computer Modeling in Engineering & Sciences, Vol., , DOI:10.32604/cmes.2024.050003

Abstract Transient heat conduction problems widely exist in engineering. In previous work on the peridynamic differential operator (PDDO) method for solving such problems, both time and spatial derivatives were discretized using the PDDO method, resulting in increased complexity and programming difficulty. In this work, the forward difference formula, the backward difference formula, and the centered difference formula are used to discretize the time derivative, while the PDDO method is used to discretize the spatial derivative. Three new schemes for solving transient heat conduction equations have been developed, namely, the forward-in-time and PDDO in space (FT-PDDO) scheme, the backward-in-time and PDDO in… More >

Sandeep Dasari, Rajesh Kaluri^*

CMES-Computer Modeling in Engineering & Sciences, Vol., , DOI:10.32604/cmes.2024.049152

Abstract The increasing data pool in finance sectors forces machine learning (ML) to step into new complications. Banking data has significant financial implications and is confidential. Combining users data from several organizations for various banking services may result in various intrusions and privacy leakages. As a result, this study employs federated learning (FL) using a flower paradigm to preserve each organization’s privacy while collaborating to build a robust shared global model. However, diverse data distributions in the collaborative training process might result in inadequate model learning and a lack of privacy. To address this issue, the present paper proposes the implementation… More > Graphic Abstract

Yujing Ma^1,4, Zhongwang Wang², Jieyuan Zhang³, Ruijin Huo^1,4, Xiaohui Yuan^1,4,*

CMES-Computer Modeling in Engineering & Sciences, Vol., , DOI:10.32604/cmes.2024.048488

Abstract In this paper, an adaptive polynomial chaos expansion method (PCE) based on the method of moments (MoM) is proposed to construct surrogate models for electromagnetic scattering and further sensitivity analysis. The MoM is applied to accurately solve the electric field integral equation (EFIE) of electromagnetic scattering from homogeneous dielectric targets. Within the bistatic radar cross section (RCS) as the research object, the adaptive PCE algorithm is devoted to selecting the appropriate order to construct the multivariate surrogate model. The corresponding sensitivity results are given by the further derivative operation, which is compared with those of the finite difference method (FDM).… More >

Lassaad K. Smirani¹, Leila Jamel^2,*, Latifah Almuqren²

CMES-Computer Modeling in Engineering & Sciences, Vol., , DOI:10.32604/cmes.2024.047551

Abstract This study presents a layered generalization ensemble model for next generation radio mobiles, focusing on supervised channel estimation approaches. Channel estimation typically involves the insertion of pilot symbols with a well-balanced rhythm and suitable layout. The model, called Stacked Generalization for Channel Estimation (SGCE), aims to enhance channel estimation performance by eliminating pilot insertion and improving throughput. The SGCE model incorporates six machine learning methods: random forest (RF), gradient boosting machine (GB), light gradient boosting machine (LGBM), support vector regression (SVR), extremely randomized tree (ERT), and extreme gradient boosting (XGB). By generating meta-data from five models (RF, GB, LGBM, SVR,… More >

Samar M. Alqhtani¹, Toufique A. Soomro^2,*, Faisal Bin Ubaid³, Ahmed Ali⁴, Muhammad Irfan⁵, Abdullah A. Asiri⁶

CMES-Computer Modeling in Engineering & Sciences, Vol., , DOI:10.32604/cmes.2024.051475

Abstract Cancer-related to the nervous system and brain tumors is a leading cause of mortality in various countries. Magnetic resonance imaging (MRI) and computed tomography (CT) are utilized to capture brain images. MRI plays a crucial role in the diagnosis of brain tumors and the examination of other brain disorders. Typically, manual assessment of MRI images by radiologists or experts is performed to identify brain tumors and abnormalities in the early stages for timely intervention. However, early diagnosis of brain tumors is intricate, necessitating the use of computerized methods. This research introduces an innovative approach for the automated segmentation of brain… More >

Rui Zhang¹, Lixiang Wang^2,*, Jing Li^1,4, Chun Feng², Yiming Zhang^1,3,4,*

CMES-Computer Modeling in Engineering & Sciences, Vol., , DOI:10.32604/cmes.2024.049885

Abstract Perforation is a pivotal technique employed to establish main flow channels within the reservoir formation at the outset of hydraulic fracturing operations. Optimizing perforation designs is critical for augmenting the efficacy of hydraulic fracturing and boosting oil or gas production. In this study, we employ a hybrid finite-discrete element method, known as the continuous–discontinuous element method (CDEM), to simulate the initiation of post-perforation hydraulic fractures and to derive enhanced design parameters. The model incorporates the four most prevalent perforation geometries, as delineated in an engineering technical report. Real-world perforations deviate from the ideal cylindrical shape, exhibiting variable cross-sectional profiles that… More >

Xiaofeng Yang^1,3,4, Wei Wang^1,3,4,*, Yujie Shen^2,4, Changning Liu^1,3,4, Tianyi Zhang^1,4

CMES-Computer Modeling in Engineering & Sciences, Vol., , DOI:10.32604/cmes.2024.049837

Abstract This paper addresses the impact of vertical vibration negative effects, unbalanced radial forces generated by the static eccentricity of the hub motor, and road excitation on the suspension performance of Hub Motor Driven Vehicle (HMDV). A dynamic inertial suspension based on Active Disturbance Rejection Control (ADRC) is proposed, combining the vertical dynamic characteristics of dynamic inertial suspension with the features of ADRC, which distinguishes between internal and external disturbances and arranges the transition process. Firstly, a simulation model of the static eccentricity of the hub motor is established to simulate the unbalanced radial electromagnetic force generated under static eccentricity. A… More >

Awais Khan¹, Chomyong Kim², Jung-Yeon Kim², Ahsan Aziz¹, Yunyoung Nam^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol., , DOI:10.32604/cmes.2024.049618

Abstract Sleep posture surveillance is crucial for patient comfort, yet current systems face difficulties in providing comprehensive studies due to the obstruction caused by blankets. Precise posture assessment remains challenging because of the complex nature of the human body and variations in sleep patterns. Consequently, this study introduces an innovative method utilizing RGB and thermal cameras for comprehensive posture classification, thereby enhancing the analysis of body position and comfort. This method begins by capturing a dataset of sleep postures in the form of videos using RGB and thermal cameras, which depict six commonly adopted postures: supine, left log, right log, prone… More > Graphic Abstract

Sen-Hui Wang^1,2,*, Xi Kang¹, Cheng Wang¹, Tian-Bing Ma¹, Xiang He², Ke Yang^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol., , DOI:10.32604/cmes.2024.049281

Abstract Accurately predicting the remaining useful life (RUL) of bearings in mining rotating equipment is vital for mining enterprises. This research aims to distinguish the features associated with the RUL of bearings and propose a prediction model based on these selected features. This study proposes a hybrid predictive model to assess the RUL of rolling element bearings. The proposed model begins with the pre-processing of bearing vibration signals to reconstruct sixty time-domain features. The hybrid model selects relevant features from the sixty time-domain features of the vibration signal by adopting the RReliefF feature selection algorithm. Subsequently, the extreme learning machine (ELM)… More >

Xingzi Yang¹, Wei Gao², Xiaodu Wang¹, Xiaowei Zeng^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol., , DOI:10.32604/cmes.2024.049371

Abstract The bioinspired nacre or bone structure represents a remarkable example of tough, strong, lightweight, and multifunctional structures in biological materials that can be an inspiration to design bioinspired high-performance materials. The bioinspired structure consists of hard grains and soft material interfaces. While the material interface has a very low volume percentage, its property has the ability to determine the bulk material response. Machine learning technology nowadays is widely used in material science. A machine learning model was utilized to predict the material response based on the material interface properties in a bioinspired nanocomposite. This model was trained on a comprehensive… More >