Xin Liu¹, Zheng Zhang^2,*, Yu Zhao³, Yi Yang², Zhenning Qiao², Zhibo Xu², Xianzhi Yu²

Energy Engineering, Vol.122, No.11, pp. 4679-4702, 2025, DOI:10.32604/ee.2025.068371 - 27 October 2025

Abstract With the growing demand for offshore energy, deepwater drilling has become a vital technology in petroleum engineering. However, conventional drilling systems often face limitations such as delayed bottomhole pressure response and low control precision, particularly under narrow pressure window and complex formation conditions. To address these challenges, Dual-layer Pipe dual-gradient drilling (DGD) technology has been introduced, utilizing a dual-pipe structure and downhole lift pumps to extend the pressure control range. Despite these advantages, current DGD systems lack fast and precise bottomhole pressure control due to their reliance on indirect flow-based methods. This study proposes a… More >

Jinyu Guo^*, Xingxu Zhu, Zezhong Liu, Cuiping Li

Energy Engineering, Vol.122, No.10, pp. 3961-3983, 2025, DOI:10.32604/ee.2025.067811 - 30 September 2025

Abstract In high-renewable-energy power systems, the demand for fast-responding capabilities is growing. To address the limitations of conventional closed-loop frequency control, where the integral coefficient cannot dynamically adjust the frequency regulation command based on the state of charge (SoC) of energy storage units, this paper proposes a secondary frequency regulation control strategy based on variable integral coefficients for multiple energy storage units. First, a power-uniform controller is designed to ensure that thermal power units gradually take on more regulation power during the frequency regulation process. Next, a control framework based on variable integral coefficients is proposed… More >

Shigen Shen^1,2, Xiaojun Ji^1,*, Yimeng Liu¹

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3997-4021, 2025, DOI:10.32604/cmc.2025.069332 - 23 September 2025

Abstract The Industrial Internet of Things (IIoT) is increasingly vulnerable to sophisticated cyber threats, particularly zero-day attacks that exploit unknown vulnerabilities and evade traditional security measures. To address this critical challenge, this paper proposes a dynamic defense framework named Zero-day-aware Stackelberg Game-based Multi-Agent Distributed Deep Deterministic Policy Gradient (ZSG-MAD3PG). The framework integrates Stackelberg game modeling with the Multi-Agent Distributed Deep Deterministic Policy Gradient (MAD3PG) algorithm and incorporates defensive deception (DD) strategies to achieve adaptive and efficient protection. While conventional methods typically incur considerable resource overhead and exhibit higher latency due to static or rigid defensive mechanisms,… More >

Musulmon Lolaev¹, Anand Paul^2,*, Jeonghong Kim¹

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 4155-4171, 2025, DOI:10.32604/cmc.2025.064758 - 23 September 2025

Abstract Transfer learning is the predominant method for adapting pre-trained models on another task to new domains while preserving their internal architectures and augmenting them with requisite layers in Deep Neural Network models. Training intricate pre-trained models on a sizable dataset requires significant resources to fine-tune hyperparameters carefully. Most existing initialization methods mainly focus on gradient flow-related problems, such as gradient vanishing or exploding, or other existing approaches that require extra models that do not consider our setting, which is more practical. To address these problems, we suggest employing gradient-free heuristic methods to initialize the weights… More >

Shubham Desai, Sai Sidhardh^*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 1271-1334, 2025, DOI:10.32604/cmes.2025.068141 - 31 August 2025

Abstract The increasing integration of small-scale structures in engineering, particularly in Micro-Electro-Mechanical Systems (MEMS), necessitates advanced modeling approaches to accurately capture their complex mechanical behavior. Classical continuum theories are inadequate at micro- and nanoscales, particularly concerning size effects, singularities, and phenomena like strain softening or phase transitions. This limitation follows from their lack of intrinsic length scale parameters, crucial for representing microstructural features. Theoretical and experimental findings emphasize the critical role of these parameters on small scales. This review thoroughly examines various strain gradient elasticity (SGE) theories commonly employed in literature to capture these size-dependent effects… More >

Farhan A. Alenizi¹, Faten Khalid Karim^2,*, Alaa R. Al-Shamasneh³, Mohammad Hossein Shakoor⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 1793-1829, 2025, DOI:10.32604/cmes.2025.066023 - 31 August 2025

Abstract Deep neural networks provide accurate results for most applications. However, they need a big dataset to train properly. Providing a big dataset is a significant challenge in most applications. Image augmentation refers to techniques that increase the amount of image data. Common operations for image augmentation include changes in illumination, rotation, contrast, size, viewing angle, and others. Recently, Generative Adversarial Networks (GANs) have been employed for image generation. However, like image augmentation methods, GAN approaches can only generate images that are similar to the original images. Therefore, they also cannot generate new classes of data.… More >

Thaer Thaher^1,*, Muhammed Saffarini², Majdi Mafarja³, Abdulaziz Alashbi⁴, Abdul Hakim Mohamed⁵, Ayman A. El-Saleh⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2359-2394, 2025, DOI:10.32604/cmes.2025.065388 - 31 August 2025

Abstract Face detection is a critical component in modern security, surveillance, and human-computer interaction systems, with widespread applications in smartphones, biometric access control, and public monitoring. However, detecting faces with high levels of occlusion, such as those covered by masks, veils, or scarves, remains a significant challenge, as traditional models often fail to generalize under such conditions. This paper presents a hybrid approach that combines traditional handcrafted feature extraction technique called Histogram of Oriented Gradients (HOG) and Canny edge detection with modern deep learning models. The goal is to improve face detection accuracy under occlusions. The… More >

Huichao Cao^1,*, Honghe Du¹, Dongnian Jiang¹, Wei Li¹, Lei Du¹, Jianfeng Yang²

Structural Durability & Health Monitoring, Vol.19, No.5, pp. 1305-1325, 2025, DOI:10.32604/sdhm.2025.066002 - 05 September 2025

Abstract In the production processes of modern industry, accurate assessment of the system’s health state and traceability non-optimal factors are key to ensuring “safe, stable, long-term, full load and optimal” operation of the production process. The benzene-to-ethylene ratio control system is a complex system based on an MPC-PID double-layer architecture. Taking into consideration the interaction between levels, coupling between loops and conditions of incomplete operation data, this paper proposes a health assessment method for the dual-layer control system by comprehensively utilizing deep learning technology. Firstly, according to the results of the pre-assessment of the system layers… More >

Yu-Hsien Lin^*, Po-Cheng Chuang, Joyce Yi-Tzu Huang

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4907-4948, 2025, DOI:10.32604/cmc.2025.065995 - 30 July 2025

Abstract This study proposes an automatic control system for Autonomous Underwater Vehicle (AUV) docking, utilizing a digital twin (DT) environment based on the HoloOcean platform, which integrates six-degree-of-freedom (6-DOF) motion equations and hydrodynamic coefficients to create a realistic simulation. Although conventional model-based and visual servoing approaches often struggle in dynamic underwater environments due to limited adaptability and extensive parameter tuning requirements, deep reinforcement learning (DRL) offers a promising alternative. In the positioning stage, the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm is employed for synchronized depth and heading control, which offers stable training, reduced overestimation… More >

Haolang Feng^1,2, Yuling Chen^1,2,*, Yang Huang^1,2, Xuewei Wang³, Haiwei Sang⁴

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4469-4490, 2025, DOI:10.32604/cmc.2025.064896 - 30 July 2025

Abstract Deep neural networks remain susceptible to adversarial examples, where the goal of an adversarial attack is to introduce small perturbations to the original examples in order to confuse the model without being easily detected. Although many adversarial attack methods produce adversarial examples that have achieved great results in the white-box setting, they exhibit low transferability in the black-box setting. In order to improve the transferability along the baseline of the gradient-based attack technique, we present a novel Stochastic Gradient Accumulation Momentum Iterative Attack (SAMI-FGSM) in this study. In particular, during each iteration, the gradient information More >