Guangyu Xu^1,2, Yuxin Liu¹, Bo Yang¹, Siyu Lu^3,*, Chao Liu⁴, Junmin Lyu⁵, Wenfeng Zheng^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.074938 - 29 January 2026

Abstract Soft-tissue motion introduces significant challenges in robotic teleoperation, especially in medical scenarios where precise target tracking is critical. Latency across sensing, computation, and actuation chains leads to degraded tracking performance, particularly around high-acceleration segments and trajectory inflection points. This study investigates machine learning-based predictive compensation for latency mitigation in soft-tissue tracking. Three models—autoregressive (AR), long short-term memory (LSTM), and temporal convolutional network (TCN)—were implemented and evaluated on both synthetic and real datasets. By aligning the prediction horizon with the end-to-end system delay, we demonstrate that prediction-based compensation significantly reduces tracking errors. Among the models, TCN More >

Abdulaziz Almalaq¹, Ambe Harrison^2,*, Ibrahim Alsaleh¹, Abdullah Alassaf¹, Mashari Alangari¹

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.074815 - 29 January 2026

Abstract We present a computer-modeling framework for photovoltaic (PV) source emulation that preserves the exact single-diode physics while enabling iteration-free, real-time evaluation. We derive two closed-form explicit solvers based on the Lambert W function: a voltage-driven V-Lambert solver for high-fidelity I–V computation and a resistance-driven R-Lambert solver designed for seamless integration in a closed-loop PV emulator. Unlike Taylor-linearized explicit models, our proposed formulation retains the exponential nonlinearity of the PV equations. It employs a numerically stable analytical evaluation that eliminates the need for lookup tables and root-finding, all while maintaining limited computational costs and a small… More >

Huda Talib Najm^1,2, Ahmed Sabah Al-Araji³, Nur Syazreen Ahmad^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.071837 - 29 January 2026

Abstract Mobile service robots (MSRs) in hospital environments require precise and robust trajectory tracking to ensure reliable operation under dynamic conditions, including model uncertainties and external disturbances. This study presents a cognitive control strategy that integrates a Numerical Feedforward Inverse Dynamic Controller (NFIDC) with a Feedback Radial Basis Function Neural Network (FRBFNN). The robot’s mechanical structure was designed in SolidWorks 2022 SP2.0 and validated under operational loads using finite element analysis in ANSYS 2022 R1. The NFIDC-FRBFNN framework merges proactive inverse dynamic compensation with adaptive neural learning to achieve smooth torque responses and accurate motion control.… More >

Yewon Choi^1,2, Song Hyeon Ju², Jungsoo Nam^2,*, Min Ku Kim^1,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.071817 - 29 January 2026

Abstract The composite material layering process has attracted considerable attention due to its production advantages, including high scalability and compatibility with a wide range of raw materials. However, changes in process conditions can lead to degradation in layer quality and non-uniformity, highlighting the need for real-time monitoring to improve overall quality and efficiency. In this study, an AI-based monitoring system was developed to evaluate layer width and assess quality in real time. Three deep learning models Faster Region-based Convolutional Neural Network (R-CNN), You Only Look Once version 8 (YOLOv8), and Single Shot MultiBox Detector (SSD) were… More >

Hussain H. Al-Kayiem^1,*, Safaa M. Ali², Sundus S. Al-Azawiey³, Raed A. Jessam³

Energy Engineering, Vol.123, No.2, 2026, DOI:10.32604/ee.2025.073303 - 27 January 2026

Abstract Gas Turbines are among the most important energy systems for aviation and thermal-based power generation. The performance of gas turbine intakes with S-shaped diffusers is vulnerable to flow separation, reversal flow, and pressure distortion, mainly in aggressive S-shaped diffusers. Several methods, including vortex generators and energy promoters, have been proposed and investigated both experimentally and numerically. This paper compiles a review of experimental investigations that have been performed and reported to mitigate flow separation and restore system performance. The operational principles, classifications, design geometries, and performance parameters of S-shaped diffusers are presented to facilitate the… More > Graphic Abstract

Haokai Xie¹, Yi Lu¹, Xiaojun Ni¹, Yilei Gu¹, Sihao Fu^2,*, Wenyao Ye³, Zheren Zhang², Zheng Xu²

Energy Engineering, Vol.123, No.2, 2026, DOI:10.32604/ee.2025.070934 - 27 January 2026

Abstract With the rapid development of large-scale offshore wind farms, efficient and reliable power transmission systems are urgently needed. Hybrid high-voltage direct current (HVDC) configurations combining a diode rectifier unit (DRU) and a modular multilevel converter (MMC) have emerged as a promising solution, offering advantages in cost-effectiveness and control capability. However, the uncontrollable nature of the DRU poses significant challenges for system stability under offshore AC fault conditions, particularly due to its inability to provide fault current or voltage support. This paper investigates the offshore AC fault characteristics and fault ride-through (FRT) strategy of a hybrid… More >

Junxian Ma¹, Haonan Zhao^2,*, Zhibing Hu³, Yaru Shen³, Fan Ding³, Shouqi Jiang²

Energy Engineering, Vol.123, No.2, 2026, DOI:10.32604/ee.2025.070530 - 27 January 2026

Abstract Energy storage-equipped photovoltaic (PV-storage) systems can meet frequency regulation requirements under various operating conditions, and their coordinated support for grid frequency has become a future trend. To address frequency stability issues caused by low inertia and weak damping, this paper proposes a multi-timescale frequency regulation coordinated control strategy for PV-storage integrated systems. First, a self-synchronizing control strategy for grid-connected inverters is designed based on DC voltage dynamics, enabling active inertia support while transmitting frequency variation information. Next, an energy storage inertia support control strategy is developed to enhance the frequency nadir, and an active frequency… More >

Tong Wu, Dawei Zhou, Qingyu Ou^*, Fang Luo

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073482 - 12 January 2026

Abstract The increasing interconnection of modern industrial control systems (ICSs) with the Internet has enhanced operational efficiency, but also made these systems more vulnerable to cyberattacks. This heightened exposure has driven a growing need for robust ICS security measures. Among the key defences, intrusion detection technology is critical in identifying threats to ICS networks. This paper provides an overview of the distinctive characteristics of ICS network security, highlighting standard attack methods. It then examines various intrusion detection methods, including those based on misuse detection, anomaly detection, machine learning, and specialised requirements. This paper concludes by exploring More >

José Arturo Ramírez-Fernández¹, Henevith G. Méndez-Figueroa¹, Sebastián Ossandón^2,*, Ricardo Galván-Martínez³, Miguel Ángel Hernández-Pérez³, Ricardo Orozco-Cruz³

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072707 - 12 January 2026

Abstract In this study, artificial neural networks (ANNs) were implemented to determine design parameters for an impressed current cathodic protection (ICCP) prototype. An ASTM A36 steel plate was tested in 3.5% NaCl solution, seawater, and NS4 using electrochemical impedance spectroscopy (EIS) to monitor the evolution of the substrate surface, which affects the current required to reach the protection potential (Eprot). Experimental data were collected as training datasets and analyzed using statistical methods, including box plots and correlation matrices. Subsequently, ANNs were applied to predict the current demand at different exposure times, enabling the estimation of electrochemical More >

Haohui Su¹, Xuan Zhang^1,*, Lvjun Zheng¹, Xiaojie Shen², Hua Liao¹

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072093 - 12 January 2026

Abstract Distributed Denial-of-Service (DDoS) attacks pose severe threats to Industrial Control Networks (ICNs), where service disruption can cause significant economic losses and operational risks. Existing signature-based methods are ineffective against novel attacks, and traditional machine learning models struggle to capture the complex temporal dependencies and dynamic traffic patterns inherent in ICN environments. To address these challenges, this study proposes a deep feature-driven hybrid framework that integrates Transformer, BiLSTM, and KNN to achieve accurate and robust DDoS detection. The Transformer component extracts global temporal dependencies from network traffic flows, while BiLSTM captures fine-grained sequential dynamics. The learned… More >