Hyeontae Joo, Sangmin Lee, Kiseok Kim, Hwangnam Kim^*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2789-2804, 2025, DOI:10.32604/cmc.2025.066304 - 03 July 2025

Abstract As vehicular networks become increasingly pervasive, enhancing connectivity and reliability has emerged as a critical objective. Among the enabling technologies for advanced wireless communication, particularly those targeting low latency and high reliability, time synchronization is critical, especially in vehicular networks. However, due to the inherent mobility of vehicular environments, consistently exchanging synchronization packets with a fixed base station or access point is challenging. This issue is further exacerbated in signal shadowed areas such as urban canyons, tunnels, or large-scale indoor halls where other technologies, such as global navigation satellite system (GNSS), are unavailable. One-way synchronization… More >

Peicheng Shi^1,*, Yueyue Tang¹, Yi Li¹, Xinlong Dong¹, Yu Sun², Aixi Yang³

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3321-3343, 2025, DOI:10.32604/cmc.2025.065297 - 03 July 2025

Abstract In the dynamic scene of autonomous vehicles, the depth estimation of monocular cameras often faces the problem of inaccurate edge depth estimation. To solve this problem, we propose an unsupervised monocular depth estimation model based on edge enhancement, which is specifically aimed at the depth perception challenge in dynamic scenes. The model consists of two core networks: a deep prediction network and a motion estimation network, both of which adopt an encoder-decoder architecture. The depth prediction network is based on the U-Net structure of ResNet18, which is responsible for generating the depth map of the… More >

Duc Cuong Pham¹, Tien Duc Ngo², Hoai Nam Vu^1,3,*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2959-2974, 2025, DOI:10.32604/cmc.2025.064672 - 03 July 2025

Abstract Air quality estimation assesses the pollution level in the air, supports public health warnings, and is a valuable tool in environmental management. Although air sensors have proven helpful in this task, sensors are often expensive and difficult to install, while cameras are becoming more popular and accessible, from which images can be collected as data for deep learning models to solve the above task. This leads to another problem: several labeled images are needed to achieve high accuracy when deep-learning models predict air quality. In this research, we have three main contributions: (1) Collect and… More >

Ehab M. Almetwally^1,*, O. M. Khaled², H. M. Barakat³

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 2957-2990, 2025, DOI:10.32604/cmes.2025.065446 - 30 June 2025

Abstract Accelerated life tests play a vital role in reliability analysis, especially as advanced technologies lead to the production of highly reliable products to meet market demands and competition. Among these tests, progressive-stress accelerated life tests (PSALT) allow for continuous changes in applied stress. Additionally, the generalized progressive hybrid censoring (GPHC) scheme has attracted significant attention in reliability and survival analysis, particularly for handling censored data in accelerated testing. It has been applied to various failure models, including competing risks and step-stress models. However, despite its growing relevance, a notable gap remains in the literature regarding… 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 >

Zainab Ouardirhi^1,2,*, Mostapha Zbakh², Sidi Ahmed Mahmoudi¹

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 2509-2571, 2025, DOI:10.32604/cmes.2025.064283 - 30 June 2025

Abstract Object detection in occluded environments remains a core challenge in computer vision (CV), especially in domains such as autonomous driving and robotics. While Convolutional Neural Network (CNN)-based two-dimensional (2D) and three-dimensional (3D) object detection methods have made significant progress, they often fall short under severe occlusion due to depth ambiguities in 2D imagery and the high cost and deployment limitations of 3D sensors such as Light Detection and Ranging (LiDAR). This paper presents a comparative review of recent 2D and 3D detection models, focusing on their occlusion-handling capabilities and the impact of sensor modalities such More >

Hao Jiao¹, Xinyu Liu², Chen Wu¹, Chunlei Xu¹, Zhijun Zhou³, Ye Chen³, Guoqiang Sun^2,*

Energy Engineering, Vol.122, No.7, pp. 2977-2992, 2025, DOI:10.32604/ee.2025.064206 - 27 June 2025

Abstract With the continuous expansion of the power system scale and the increasing complexity of operational mode, the interaction between transmission and distribution systems is becoming more and more significant, placing higher requirements on the accuracy and efficiency of the power system state estimation to address the challenge of balancing computational efficiency and estimation accuracy in traditional coupled transmission and distribution state estimation methods, this paper proposes a collaborative state estimation method based on distribution systems state clustering and load model parameter identification. To resolve the scalability issue of coupled transmission and distribution power systems, clustering… More >

Pu Yang^1,*, Wanning Yan¹, Rong Li¹, Lei Chen², Lijie Guo²

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 699-725, 2025, DOI:10.32604/cmc.2025.064947 - 09 June 2025

Abstract Lithium-ion batteries (LIBs) have been widely used in mobile energy storage systems because of their high energy density, long life, and strong environmental adaptability. Accurately estimating the state of health (SOH) for LIBs is promising and has been extensively studied for many years. However, the current prediction methods are susceptible to noise interference, and the estimation accuracy has room for improvement. Motivated by this, this paper proposes a novel battery SOH estimation method, the Beluga Whale Optimization (BWO) and Noise-Input Gaussian Process (NIGP) Stacked Model (BGNSM). This method integrates the BWO-optimized Gaussian Process Regression (GPR)… More >

Ahmad Mwfaq Bataineh^1,2,*, Ahmad Sufril Azlan Mohamed¹

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 93-124, 2025, DOI:10.32604/cmc.2025.064250 - 09 June 2025

Abstract Advancements in deep learning have considerably enhanced techniques for Rapid Entire Body Assessment (REBA) pose estimation by leveraging progress in three-dimensional human modeling. This survey provides an extensive overview of recent advancements, particularly emphasizing monocular image-based methodologies and their incorporation into ergonomic risk assessment frameworks. By reviewing literature from 2016 to 2024, this study offers a current and comprehensive analysis of techniques, existing challenges, and emerging trends in three-dimensional human pose estimation. In contrast to traditional reviews organized by learning paradigms, this survey examines how three-dimensional pose estimation is effectively utilized within musculoskeletal disorder (MSD)… More >

Khizer Mehmood¹, Naveed Ishtiaq Chaudhary^2,*, Zeshan Aslam Khan³, Khalid Mehmood Cheema⁴, Muhammad Asif Zahoor Raja², Sultan S. Alshamrani⁵, Kaled M. Alshmrany⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1809-1841, 2025, DOI:10.32604/cmes.2025.064900 - 30 May 2025

Abstract Aquila Optimizer (AO) is a recently proposed population-based optimization technique inspired by Aquila’s behavior in catching prey. AO is applied in various applications and its numerous variants were proposed in the literature. However, chaos theory has not been extensively investigated in AO. Moreover, it is still not applied in the parameter estimation of electro-hydraulic systems. In this work, ten well-defined chaotic maps were integrated into a narrowed exploitation of AO for the development of a robust chaotic optimization technique. An extensive investigation of twenty-three mathematical benchmarks and ten IEEE Congress on Evolutionary Computation (CEC) functions… More >