Jinrong Li^1,2, Chunhua Wei², Lei Liang^2,3,*, Zhisheng Gao^1,*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3259-3280, 2025, DOI:10.32604/cmc.2025.063547 - 03 July 2025

Abstract The Pressure Sensitive Paint Technique (PSP) has gained attention in recent years because of its significant benefits in measuring surface pressure on wind tunnel models. However, in the post-processing process of PSP images, issues such as pressure taps, paint peeling, and contamination can lead to the loss of pressure data on the image, which seriously affects the subsequent calculation and analysis of pressure distribution. Therefore, image inpainting is particularly important in the post-processing process of PSP images. Deep learning offers new methods for PSP image inpainting, but some basic characteristics of convolutional neural networks (CNNs)… More >

Yu-Shiuan Tsai^*, Yuk-Hang Sit

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3065-3090, 2025, DOI:10.32604/cmes.2025.064783 - 30 June 2025

Abstract To improve small object detection and trajectory estimation from an aerial moving perspective, we propose the Aerial View Attention-PRB (AVA-PRB) model. AVA-PRB integrates two attention mechanisms—Coordinate Attention (CA) and the Convolutional Block Attention Module (CBAM)—to enhance detection accuracy. Additionally, Shape-IoU is employed as the loss function to refine localization precision. Our model further incorporates an adaptive feature fusion mechanism, which optimizes multi-scale object representation, ensuring robust tracking in complex aerial environments. We evaluate the performance of AVA-PRB on two benchmark datasets: Aerial Person Detection and VisDrone2019-Det. The model achieves 60.9% mAP@0.5 on the Aerial Person… More >

Naveen Chandra¹, Himadri Vaidya^2,3, Suraj Sawant⁴, Shilpa Gite^5,6, Biswajeet Pradhan^7,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3351-3375, 2025, DOI:10.32604/cmes.2025.064395 - 30 June 2025

Abstract Landslide hazard detection is a prevalent problem in remote sensing studies, particularly with the technological advancement of computer vision. With the continuous and exceptional growth of the computational environment, the manual and partially automated procedure of landslide detection from remotely sensed images has shifted toward automatic methods with deep learning. Furthermore, attention models, driven by human visual procedures, have become vital in natural hazard-related studies. Hence, this paper proposes an enhanced YOLOv5 (You Only Look Once version 5) network for improved satellite-based landslide detection, embedded with two popular attention modules: CBAM (Convolutional Block Attention Module) More >

Tao Jin^1,*, Siqi Gu¹, Zhekun Shou¹, Hong Shi², Min Zhang²

Structural Durability & Health Monitoring, Vol.19, No.4, pp. 903-918, 2025, DOI:10.32604/sdhm.2025.063887 - 30 June 2025

Abstract The traditional You Only Look Once (YOLO) series network models often fail to extract satisfactory features for road detection, due to the limited number of defect images in the dataset. Additionally, most open-source road crack datasets contain idealized cracks that are not suitable for detecting early-stage pavement cracks with fine widths and subtle features. To address these issues, this study collected a large number of original road surface images using road detection vehicles. A large-capacity crack dataset was then constructed, with various shapes of cracks categorized as either cracks or fractures. To improve the training… More >

Mingliang Deng¹, Zhao Zhang^1,*, Hongyan Zhou², Xuebo Chen²

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1189-1202, 2025, DOI:10.32604/cmc.2025.064509 - 09 June 2025

Abstract To fully explore the potential features contained in power load data, an innovative short-term power load forecasting method that integrates data mining and deep learning techniques is proposed. Firstly, a density peak fast search algorithm optimized by time series weighting factors is used to cluster and analyze load data, accurately dividing subsets of data into different categories. Secondly, introducing convolutional block attention mechanism into the bidirectional gated recurrent unit (BiGRU) structure significantly enhances its ability to extract key features. On this basis, in order to make the model more accurately adapt to the dynamic changes… More >

Mengqi Li, Xiaodong Huang^*, Lifeng Wu

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 975-995, 2025, DOI:10.32604/cmc.2025.062621 - 09 June 2025

Abstract Micro-expressions, fleeting involuntary facial cues lasting under half a second, reveal genuine emotions and are valuable in clinical diagnosis and psychotherapy. Real-time recognition on resource-constrained embedded devices remains challenging, as current methods struggle to balance performance and efficiency. This study introduces a semi-lightweight multifunctional network that enhances real-time deployment and accuracy. Unlike prior simplistic feature fusion techniques, our novel multi-feature fusion strategy leverages temporal, spatial, and differential features to better capture dynamic changes. Enhanced by Residual Network (ResNet) architecture with channel and spatial attention mechanisms, the model improves feature representation while maintaining a lightweight design. More >

Yongli Liu^1,2, Weihao Li^1,2,*, Haitao Wang^1,2,3, Taoren Du⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2261-2286, 2025, DOI:10.32604/cmes.2025.064179 - 30 May 2025

Abstract Coal dust explosions are severe safety accidents in coal mine production, posing significant threats to life and property. Predicting the maximum explosion pressure () of coal dust using deep learning models can effectively assess potential risks and provide a scientific basis for preventing coal dust explosions. In this study, a 20-L explosion sphere apparatus was used to test the maximum explosion pressure of coal dust under seven different particle sizes and ten mass concentrations (), resulting in a dataset of 70 experimental groups. Through Spearman correlation analysis and random forest feature selection methods, particle size… More >

Zhebin Sun¹, Wei Wang¹, Mingxuan Du², Tao Liang¹, Yang Liu¹, Hailong Fan³, Cuiping Li², Xingxu Zhu², Junhui Li^2,*

Energy Engineering, Vol.122, No.6, pp. 2155-2175, 2025, DOI:10.32604/ee.2025.062218 - 29 May 2025

Abstract Responding to the stochasticity and uncertainty in the power height of distributed photovoltaic power generation. This paper presents a distributed photovoltaic ultra-short-term power forecasting method based on Variational Mode Decomposition (VMD) and Channel Attention Mechanism. First, Pearson’s correlation coefficient was utilized to filter out the meteorological factors that had a high impact on historical power. Second, the distributed PV power data were decomposed into a relatively smooth power series with different fluctuation patterns using variational modal decomposition (VMD). Finally, the reconstructed distributed PV power as well as other features are input into the combined CNN-SENet-BiLSTM… More >

Yang Su^1,*, Xianrang Shi¹, Tinglun Song²

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5831-5848, 2025, DOI:10.32604/cmc.2025.064036 - 19 May 2025

Abstract Ensuring that autonomous vehicles maintain high precision and rapid response capabilities in complex and dynamic driving environments is a critical challenge in the field of autonomous driving. This study aims to enhance the learning efficiency of multi-sensor feature fusion in autonomous driving tasks, thereby improving the safety and responsiveness of the system. To achieve this goal, we propose an innovative multi-sensor feature fusion model that integrates three distinct modalities: visual, radar, and lidar data. The model optimizes the feature fusion process through the introduction of two novel mechanisms: Sparse Channel Pooling (SCP) and Residual Triplet-Attention… More >

Faheem Shaukat¹, Naveed Ejaz^1,2, Rashid Kamal^3,4, Tamim Alkhalifah^5,*, Sheraz Aslam^6,7,*, Mu Mu⁴

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5595-5622, 2025, DOI:10.32604/cmc.2025.061702 - 19 May 2025

Abstract Automated and accurate movie genre classification is crucial for content organization, recommendation systems, and audience targeting in the film industry. Although most existing approaches focus on audiovisual features such as trailers and posters, the text-based classification remains underexplored despite its accessibility and semantic richness. This paper introduces the Genre Attention Model (GAM), a deep learning architecture that integrates transformer models with a hierarchical attention mechanism to extract and leverage contextual information from movie plots for multi-label genre classification. In order to assess its effectiveness, we assess multiple transformer-based models, including Bidirectional Encoder Representations from Transformers… More >