Jian Wang¹, Hui Qi¹, Feilong Jiang^2,*, Biao Jiang³, Tiankui Sun⁴, Lingyi Ji¹, Yajun Zhao², Feifei Bu²

Energy Engineering, Vol.122, No.12, pp. 4873-4897, 2025, DOI:10.32604/ee.2025.069071 - 27 November 2025

Abstract To enhance power supply reliability and reduce customer outage time, Mobile Emergency Power Supply Vehicles (MEPSVs), including Mobile Diesel Generator Vehicles (MDGVs) and Mobile Energy Storage Vehicles (MESVs), have become indispensable sources for grid maintenance and disaster response. However, in practice, relying solely on MESVs is constrained by battery capacity, making it difficult to meet long-duration power demands. Conversely, using only MDGVs often results in low efficiency and high fuel consumption under fluctuating load conditions, posing challenges to achieving economical and efficient power supply. To address these issues, this paper investigates the parallel power supply… More >

Fawad Zaman^1,#, Adeel Iqbal^2,#, Bakhtiar Ali¹, Abdul Khader Jilani Saudagar^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2535-2550, 2025, DOI:10.32604/cmes.2025.072638 - 26 November 2025

Abstract Accurate estimation of the Direction-of-Arrival (DoA) of incident plane waves is essential for modern wireless communication, radar, sonar, and localization systems. Precise DoA information enables adaptive beamforming, spatial filtering, and interference mitigation by steering antenna array beams toward desired sources while suppressing unwanted signals. Traditional one-dimensional Uniform Linear Arrays (ULAs) are limited to elevation angle estimation due to geometric constraints, typically within the range [0, π]. To capture full spatial characteristics in environments with multipath and angular spread, joint estimation of both elevation and azimuth angles becomes necessary. However, existing 2D and 3D array geometries… More >

Shen-Haw Ju^*, Yi-Chen Huang

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 1631-1654, 2025, DOI:10.32604/cmes.2025.067679 - 31 August 2025

Abstract This study focuses on determining the second-order irregular wave loads in the time domain without using the Inverse Fast Fourier Transform (IFFT). Considering the substantial displacement effects that Floating Offshore Wind Turbine (FOWT) support structures undergo when subjected to wave loads, the time-domain wave method is more suitable, while the frequency-domain method requiring IFFT cannot be used for moving bodies. Nonetheless, the computational challenges posed by the considerable computer time requirements of the time-domain wave method remain a significant obstacle. Thus, the paper incorporates various numerical schemes, including parallel computing and extrapolation of wave forces… More >

Rongrong Gong^1,#, Tingxian Zhang^2,#, Yawen Wei², Dengyong Zhang², Yan Li^3,*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1221-1234, 2025, DOI:10.32604/cmc.2025.066842 - 29 August 2025

Abstract Image inpainting refers to synthesizing missing content in an image based on known information to restore occluded or damaged regions, which is a typical manifestation of this trend. With the increasing complexity of image in tasks and the growth of data scale, existing deep learning methods still have some limitations. For example, they lack the ability to capture long-range dependencies and their performance in handling multi-scale image structures is suboptimal. To solve this problem, the paper proposes an image inpainting method based on the parallel dual-branch learnable Transformer network. The encoder of the proposed model More >

Wy-Liang Cheng¹, Wei Hong Lim^1,*, Kim Soon Chong¹, Sew Sun Tiang¹, Yit Hong Choo², El-Sayed M. El-kenawy^3,4, Amal H. Alharbi⁵, Marwa M. Eid^6,7

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 2021-2050, 2025, DOI:10.32604/cmc.2025.064243 - 29 August 2025

Abstract The exponential growth of data in recent years has introduced significant challenges in managing high-dimensional datasets, particularly in industrial contexts where efficient data handling and process innovation are critical. Feature selection, an essential step in data-driven process innovation, aims to identify the most relevant features to improve model interpretability, reduce complexity, and enhance predictive accuracy. To address the limitations of existing feature selection methods, this study introduces a novel wrapper-based feature selection framework leveraging the recently proposed Arctic Puffin Optimization (APO) algorithm. Specifically, we incorporate a specialized conversion mechanism to effectively adapt APO from continuous… More >

Yasir Khan Jadoon¹, Yasir Noman Khalid¹, Muhammad Attique Khan², Jungpil Shin^3,*, Fatimah Alhayan⁴, Hee-Chan Cho⁵, Byoungchol Chang^6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 1143-1164, 2025, DOI:10.32604/cmes.2025.066984 - 31 July 2025

Abstract Real-time surveillance is attributed to recognizing the variety of actions performed by humans. Human Action Recognition (HAR) is a technique that recognizes human actions from a video stream. A range of variations in human actions makes it difficult to recognize with considerable accuracy. This paper presents a novel deep neural network architecture called Attention RB-Net for HAR using video frames. The input is provided to the model in the form of video frames. The proposed deep architecture is based on the unique structuring of residual blocks with several filter sizes. Features are extracted from each… More >

Xiaoguang Lv^1,2, Tao Liu^1,2,*, Han Qin^1,2, Ying Guo^1,2, Jingshan Pan^1,2, Dawei Zhao^1,2, Xiaoming Wu^1,2, Meihong Yang^1,2

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1417-1436, 2025, DOI:10.32604/cmc.2025.063852 - 09 June 2025

Abstract The Dynamical Density Functional Theory (DDFT) algorithm, derived by associating classical Density Functional Theory (DFT) with the fundamental Smoluchowski dynamical equation, describes the evolution of inhomogeneous fluid density distributions over time. It plays a significant role in studying the evolution of density distributions over time in inhomogeneous systems. The Sunway Bluelight II supercomputer, as a new generation of China’s developed supercomputer, possesses powerful computational capabilities. Porting and optimizing industrial software on this platform holds significant importance. For the optimization of the DDFT algorithm, based on the Sunway Bluelight II supercomputer and the unique hardware architecture… More >

Mingbo Li, Deming Lei^*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1789-1808, 2025, DOI:10.32604/cmes.2025.064886 - 30 May 2025

Abstract As a complicated optimization problem, parallel batch processing machines scheduling problem (PBPMSP) exists in many real-life manufacturing industries such as textiles and semiconductors. Machine eligibility means that at least one machine is not eligible for at least one job. PBPMSP and scheduling problems with machine eligibility are frequently considered; however, PBPMSP with machine eligibility is seldom explored. This study investigates PBPMSP with machine eligibility in fabric dyeing and presents a novel shuffled frog-leaping algorithm with competition (CSFLA) to minimize makespan. In CSFLA, the initial population is produced in a heuristic and random way, and the More >

Lianqiang Wu, Deming Lei^*, Yutong Cai

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1771-1789, 2025, DOI:10.32604/cmc.2025.063944 - 16 April 2025

Abstract Fabric dyeing is a critical production process in the clothing industry and heavily relies on batch processing machines (BPM). In this study, the parallel BPM scheduling problem with machine eligibility in fabric dyeing is considered, and an adaptive cooperated shuffled frog-leaping algorithm (ACSFLA) is proposed to minimize makespan and total tardiness simultaneously. ACSFLA determines the search times for each memeplex based on its quality, with more searches in high-quality memeplexes. An adaptive cooperated and diversified search mechanism is applied, dynamically adjusting search strategies for each memeplex based on their dominance relationships and quality. During the… More >

Penghui Liu^1,*, Tianyu Yang¹, Peng Zhang², Peiyuan Zou³

Energy Engineering, Vol.122, No.4, pp. 1387-1402, 2025, DOI:10.32604/ee.2025.062627 - 31 March 2025

Abstract The photovoltaic (PV) output process is inherently complex, often disrupted by a multitude of meteorological factors, while conventional detection methods at PV power stations prove inadequate, compromising prediction accuracy. To address this challenge, this paper introduces a power prediction method that leverages modal switching (MS), weight factor adjustment (WFA), and parallel long short-term memory (PALSTM). Initially, historical PV power station data is categorized into distinct modes based on global horizontal irradiance and converted solar angles. Correlation analysis is then employed to evaluate the impact of various meteorological factors on PV power, selecting those with strong… More >