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 >

Tony Thomas^*, Ravi Prakash, Soumya Pal

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4055-4104, 2025, DOI:10.32604/cmc.2025.064903 - 30 July 2025

Abstract The Internet of Medical Things (IoMT) is transforming healthcare by enabling real-time data collection, analysis, and personalized treatment through interconnected devices such as sensors and wearables. The integration of Digital Twins (DTs), the virtual replicas of physical components and processes, has also been found to be a game changer for the ever-evolving IoMT. However, these advancements in the healthcare domain come with significant cybersecurity challenges, exposing it to malicious attacks and several security threats. Intrusion Detection Systems (IDSs) serve as a critical defense mechanism, yet traditional IDS approaches often struggle with the complexity and scale… More >

Bisni Fahad Mon¹, Mohammad Hayajneh^1,2,*, Najah Abu Ali¹, Farman Ullah¹, Hikmat Ullah³, Shayma Alkobaisi⁴

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 3675-3712, 2025, DOI:10.32604/cmc.2025.061411 - 19 May 2025

Abstract In this paper, we explore the ever-changing field of Digital Twins (DT) in the Industrial Internet of Things (IIoT) context, emphasizing their critical role in advancing Industry 4.0 toward the frontiers of Industry 5.0. The article explores the applications of DT in several industrial sectors and their smooth integration into the IIoT, focusing on the fundamentals of digital twins and emphasizing the importance of virtual-real integration. It discusses the emergence of DT, contextualizing its evolution within the framework of IIoT. The study categorizes the different types of DT, including prototypes and instances, and provides an… More >

Hongxiao Wang¹, Hongshen Liu¹, Dingsen Zhang^1,*, Ziye Zhang¹, Yonghui Yue¹, Jie Chen²

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1987-2001, 2025, DOI:10.32604/cmc.2025.060384 - 16 April 2025

Abstract Despite its immense potential, the application of digital twin technology in real industrial scenarios still faces numerous challenges. This study focuses on industrial assembly lines in sectors such as microelectronics, pharmaceuticals, and food packaging, where precision and speed are paramount, applying digital twin technology to the robotic assembly process. The innovation of this research lies in the development of a digital twin architecture and system for Delta robots that is suitable for real industrial environments. Based on this system, a deep reinforcement learning algorithm for obstacle avoidance path planning in Delta robots has been developed, More >

Vidyalakshmi G¹, S Gopikrishnan^2,*, Wadii Boulila³, Anis Koubaa³, Gautam Srivastava^4,5

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 51-113, 2025, DOI:10.32604/cmes.2025.057788 - 11 April 2025

Abstract Cyber-Physical Systems (CPS) represent an integration of computational and physical elements, revolutionizing industries by enabling real-time monitoring, control, and optimization. A complementary technology, Digital Twin (DT), acts as a virtual replica of physical assets or processes, facilitating better decision making through simulations and predictive analytics. CPS and DT underpin the evolution of Industry 4.0 by bridging the physical and digital domains. This survey explores their synergy, highlighting how DT enriches CPS with dynamic modeling, real-time data integration, and advanced simulation capabilities. The layered architecture of DTs within CPS is examined, showcasing the enabling technologies and… More >

Xiyu Gao, Peng Liu, Anran Zhao, Guotai Huang, Jianhai Zhang, Liming Zhou^*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4137-4159, 2025, DOI:10.32604/cmc.2025.060194 - 06 March 2025

Abstract The High-Temperature Biaxial Testing Apparatus (HTBTA) is a critical tool for studying the damage and failure mechanisms of heat-resistant composite materials under extreme conditions. However, existing methods for managing and monitoring such apparatus face challenges, including limited real-time modeling capabilities, inadequate integration of multi-source data, and inefficiencies in human-machine interaction. To address these gaps, this study proposes a novel digital twin-driven framework for HTBTA, encompassing the design, validation, operation, and maintenance phases. By integrating advanced modeling techniques, such as finite element analysis and Long Short-Term Memory (LSTM) networks, the digital twin enables high-fidelity simulation, real-time… More >

Yao Jin^1,2,*, Jie Zhao^1,2, Xiaozhe Tan^1,2, Linghou Miao^1,2, Wenxing Yu^1,2

Digital Engineering and Digital Twin, Vol.2, pp. 131-144, 2024, DOI:10.32604/dedt.2024.048142 - 31 December 2024

Abstract Substation siting is an important foundation and a key task in power system planning. The article is based on a three-dimensional GIS platform combined with an improved BP neural network algorithm and proposes a substation siting method that is more efficient, accurate and provides a better user experience. Firstly, the BP algorithm is enhanced to improve its convergence speed and computational efficiency for a more accurate and reasonable calculation of optimal site selection. Then, a 24-item selection index system with 7 categories is proposed, which provides quantifiable data support and an evaluation basis for substation… More >

Ziyu Xu¹, Kuo Tian^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.2, pp. 1-1, 2024, DOI:10.32604/icces.2024.011245

Abstract Considering experimental testing data is costly, and sensor data is often sparse, while simulation analysis provides overall strength information with lower accuracy, a digital twin framework is proposed for full-field structural strength assessment and prediction. The framework is mainly divided into two stages. In the offline stage, the simulation model of the structure is established, and the sensor layouts are completed. Then, the DNN pre-training model is constructed based on the reduced simulation data. In the online stage, the experimentally measured data are predicted to obtain the time-series sensors data, and the traditional transfer learning… More >

Jiamin Huang^1,2, Ping Wu^2,*, Wangxin Li³, Jun Zhang², Yidong Xu²

Structural Durability & Health Monitoring, Vol.18, No.5, pp. 577-598, 2024, DOI:10.32604/sdhm.2024.050338 - 19 July 2024

Abstract With the advent of the big data era and the rise of Industrial Revolution 4.0, digital twins (DTs) have gained significant attention in various industries. DTs offer the opportunity to combine the physical and digital worlds and aid the digital transformation of the civil engineering industry. In this paper, 605 documents obtained from the search were first analysed using CiteSpace for literature visualisation, and an author co-occurrence network, a keyword co-occurrence network, and a keyword clustering set were obtained. Next, through a literature review of 86 papers, this paper summarises the current status of DT More >

Wei Wu, Liang Yu, Liping Yang^*, Yadong Zhang, Peng Wang

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 587-603, 2024, DOI:10.32604/cmc.2024.052655 - 18 July 2024

Abstract As an open network architecture, Wireless Computing Power Networks (WCPN) pose new challenges for achieving efficient and secure resource management in networks, because of issues such as insecure communication channels and untrusted device terminals. Blockchain, as a shared, immutable distributed ledger, provides a secure resource management solution for WCPN. However, integrating blockchain into WCPN faces challenges like device heterogeneity, monitoring communication states, and dynamic network nature. Whereas Digital Twins (DT) can accurately maintain digital models of physical entities through real-time data updates and self-learning, enabling continuous optimization of WCPN, improving synchronization performance, ensuring real-time accuracy, More >