Kai Jiang, Bin Cao^*, Jing Fan

CMES-Computer Modeling in Engineering & Sciences, Vol., , DOI:10.32604/cmes.2023.046348

Abstract Multimodal sentiment analysis utilizes multimodal data such as text, facial expressions and voice to detect people’s attitudes. With the advent of distributed data collection and annotation, we can easily obtain and share such multimodal data. However, due to professional discrepancies among annotators and lax quality control, noisy labels might be introduced. Recent research suggests that deep neural networks (DNNs) will overfit noisy labels, leading to the poor performance of the DNNs. To address this challenging problem, we present a Multimodal Robust Meta Learning framework (MRML) for multimodal sentiment analysis to resist noisy labels and correlate distinct modalities simultaneously. Specifically, we… More >

Weiliang Gao¹, Shixu Jia², Tingting Zhao^2,3,*, Zhiyong Wang²

CMES-Computer Modeling in Engineering & Sciences, Vol., , DOI:10.32604/cmes.2023.046188

Abstract The mechanical properties of interfacial transition zones (ITZs) have traditionally been simplified by reducing the stiffness of cement in previous simulation methods. A novel approach based on the discrete element method (DEM) has been developed for modeling concrete. This new approach efficiently simulates the meso-structure of ITZs, accurately capturing their heterogeneous properties. Validation against established uniaxial compression experiments confirms the precision of this model. The proposed model can model the process of damage evolution containing cracks initiation, propagation and penetration. Under increasing loads, cracks within ITZs progressively accumulate, culminating in macroscopic fractures that traverse the mortar matrix, forming the complex,… More >

Zhou Ji¹, Mengmeng Zhou², Qiang Wang², Jiandong Huang^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol., , DOI:10.32604/cmes.2023.046025

Abstract To improve the prediction accuracy of the International Roughness Index (IRI) of Jointed Plain Concrete Pavements (JPCP) and Continuously Reinforced Concrete Pavements (CRCP), a machine learning approach is developed in this study for the modelling, combining an improved Beetle Antennae Search (MBAS) algorithm and Random Forest (RF) model. The 10-fold cross-validation was applied to verify the reliability and accuracy of the model proposed in this study. The importance scores of all input variables on the IRI of JPCP and CRCP were analysed as well. The results by the comparative analysis showed the prediction accuracy of the IRI of the newly… More > Graphic Abstract

Mingzhe Huang, Mi Xiao^*, Liang Gao, Mian Zhou, Wei Sha, Jinhao Zhang

CMES-Computer Modeling in Engineering & Sciences, Vol., , DOI:10.32604/cmes.2023.045735

Abstract Cellular thin-shell structures are widely applied in ultralightweight designs due to their high bearing capacity and strength-to-weight ratio. In this paper, a full-scale isogeometric topology optimization (ITO) method based on Kirchhoff–Love shells for designing cellular tshin-shell structures with excellent damage tolerance ability is proposed. This method utilizes high-order continuous nonuniform rational B-splines (NURBS) as basis functions for Kirchhoff–Love shell elements. The geometric and analysis models of thin shells are unified by isogeometric analysis (IGA) to avoid geometric approximation error and improve computational accuracy. The topological configurations of thin-shell structures are described by constructing the effective density field on the control… More >

Qing Ai^1,2, Hao Tian^2,3,*, Hui Wang^1,*, Qing Lang¹, Xingchun Huang¹, Xinghong Jiang⁴, Qiang Jing⁵

CMES-Computer Modeling in Engineering & Sciences, Vol., , DOI:10.32604/cmes.2023.045251

Abstract Structural Health Monitoring (SHM) systems have become a crucial tool for the operational management of long tunnels. For immersed tunnels exposed to both traffic loads and the effects of the marine environment, efficiently identifying abnormal conditions from the extensive unannotated SHM data presents a significant challenge. This study proposed a model-based approach for anomaly detection and conducted validation and comparative analysis of two distinct temporal predictive models using SHM data from a real immersed tunnel. Firstly, a dynamic predictive model-based anomaly detection method is proposed, which utilizes a rolling time window for modeling to achieve dynamic prediction. Leveraging the assumption… More >

Weiya Shi^1,*, Shaowen Zhang², Shiqiang Zhang²

CMES-Computer Modeling in Engineering & Sciences, Vol., , DOI:10.32604/cmes.2023.044863

Abstract In recent years, there has been extensive research on object detection methods applied to optical remote sensing images utilizing convolutional neural networks. Despite these efforts, the detection of small objects in remote sensing remains a formidable challenge. The deep network structure will bring about the loss of object features, resulting in the loss of object features and the near elimination of some subtle features associated with small objects in deep layers. Additionally, the features of small objects are susceptible to interference from background features contained within the image, leading to a decline in detection accuracy. Moreover, the sensitivity of small… More >

Xuechuan Wang¹, Wei He^1,*, Haoyang Feng¹, Satya N. Atluri²

CMES-Computer Modeling in Engineering & Sciences, Vol., , DOI:10.32604/cmes.2023.043068

Abstract Although predictor-corrector methods have been extensively applied, they might not meet the requirements of practical applications and engineering tasks, particularly when high accuracy and efficiency are necessary. A novel class of correctors based on feedback-accelerated Picard iteration (FAPI) is proposed to further enhance computational performance. With optimal feedback terms that do not require inversion of matrices, significantly faster convergence speed and higher numerical accuracy are achieved by these correctors compared with their counterparts; however, the computational complexities are comparably low. These advantages enable nonlinear engineering problems to be solved quickly and accurately, even with rough initial guesses from elementary predictors.… More > Graphic Abstract

Fangyi Li^*, Dachang Zhu^*, Huimin Shi

CMES-Computer Modeling in Engineering & Sciences, Vol., , DOI:10.32604/cmes.2023.031332

Abstract In time-variant reliability problems, there are a lot of uncertain variables from different sources. Therefore, it is important to consider these uncertainties in engineering. In addition, time-variant reliability problems typically involve a complex multilevel nested optimization problem, which can result in an enormous amount of computation. To this end, this paper studies the time-variant reliability evaluation of structures with stochastic and bounded uncertainties using a mixed probability and convex set model. In this method, the stochastic process of a limit-state function with mixed uncertain parameters is first discretized and then converted into a time-independent reliability problem. Further, to solve the… More >

Wei Zhong¹, Zhenfang Xin², Lihua Wang^1,*, Haiping Liu²

CMES-Computer Modeling in Engineering & Sciences, Vol., , DOI:10.32604/cmes.2023.031271

Abstract Flash boiling atomization (FBA) is a promising approach for enhancing spray atomization, which can generate a fine and more evenly distributed spray by increasing the fuel injection temperature or reducing the ambient pressure. However, when the outlet speed of the nozzle exceeds 400 m/s, investigating high-speed flash boiling atomization (HFBA) becomes quite challenging. This difficulty arises from the involvement of many complex physical processes and the requirement for a very fine mesh in numerical simulations. In this study, an HFBA model for gasoline direct injection (GDI) is established. This model incorporates primary and secondary atomization, as well as vaporization and… More >

Afraz Hussain Majeed¹, Rashid Mahmood², Sayed M. Eldin³, Imran Saddique^4,5,*, S. Saleem⁶, Muhammad Jawad⁷

CMES-Computer Modeling in Engineering & Sciences, Vol., , DOI:10.32604/cmes.2023.030255

Abstract This study explains the entropy process of natural convective heating in the nanofluid-saturated cavity in a heated fin and magnetic field. The temperature is constant on the Y-shaped fin, insulating the top wall while the remaining walls remain cold. All walls are subject to impermeability and non-slip conditions. The mathematical modeling of the problem is demonstrated by the continuity, momentum, and energy equations incorporating the inclined magnetic field. For elucidating the flow characteristics Finite Element Method (FEM) is implemented using stable FE pair. A hybrid fine mesh is used for discretizing the domain. Velocity and thermal plots concerning parameters are… More >