Rebhi M’hamed^1,*, Himri Youcef^2,3,*, Bouchiba Bousmaha¹, Yaichi Mouaadh¹

Energy Engineering, Vol.122, No.12, pp. 4899-4918, 2025, DOI:10.32604/ee.2025.070806 - 27 November 2025

Abstract Currently, most conventional street lighting systems use a constant light mode throughout the entire night, from sunset to sunrise, which results in high energy consumption and maintenance costs. Furthermore, scientific research predicts that energy consumption for street lighting will increase in the coming years due to growing demand and rising electricity prices. The dimming strategy is a current trend and a key concept in smart street lighting systems. It involves turning on the road lights only when a vehicle or pedestrian is detected; otherwise, the control system reduces the light intensity of the lamps. Power… More >

Baiyue Song¹, Chenxi Zhang², Wei Zhang^2,*, Leiyu Wan²

Energy Engineering, Vol.122, No.12, pp. 4919-4945, 2025, DOI:10.32604/ee.2025.068971 - 27 November 2025

Abstract Hydrogen energy is a crucial support for China’s low-carbon energy transition. With the large-scale integration of renewable energy, the combination of hydrogen and integrated energy systems has become one of the most promising directions of development. This paper proposes an optimized scheduling model for a hydrogen-coupled electro-heat-gas integrated energy system (HCEHG-IES) using generative adversarial imitation learning (GAIL). The model aims to enhance renewable-energy absorption, reduce carbon emissions, and improve grid-regulation flexibility. First, the optimal scheduling problem of HCEHG-IES under uncertainty is modeled as a Markov decision process (MDP). To overcome the limitations of conventional deep… More >

Quan Li^1,*, Ziheng Zhou²

Energy Engineering, Vol.122, No.12, pp. 5001-5020, 2025, DOI:10.32604/ee.2025.068866 - 27 November 2025

Abstract This study considers the state estimation problem of the circuit breakers (CBs), solving for random abrupt changes that occurred in power systems. With the abrupt changes randomly occurring, it is represented in a Markov chain, and then the CBs can be considered as a Markov jump system (MJS). In these MJSs, the transition probabilities are obtained from historical statistical data of the random abrupt changes when the faults occurred. Considering that the traditional Kalman filter (KF) frameworks based on MJS only depend on the subsystem of MJS, but neglect the stochastic jump between different subsystems.… More > Graphic Abstract

Muflih Alhazmi¹, Waqar Ul Hassan², Saba Shaheen³, Mohammed M. A. Almazah⁴, Azmat Ullah Khan Niazi^3,*, Nafisa A. Albasheir⁵, Ameni Gargouri⁶, Naveed Iqbal⁷

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1903-1940, 2025, DOI:10.32604/cmes.2025.072973 - 26 November 2025

Abstract This paper proposes a model-based control framework for vehicle platooning systems with second-order nonlinear dynamics operating over switching signed networks, time-varying delays, and deception attacks. The study includes two configurations: a leaderless structure using Finite-Time Non-Singular Terminal Bipartite Consensus (FNTBC) and Fixed-Time Bipartite Consensus (FXTBC), and a leader—follower structure ensuring structural balance and robustness against deceptive signals. In the leaderless model, a bipartite controller based on impulsive control theory, gauge transformation, and Markovian switching Lyapunov functions ensures mean-square stability and coordination under deception attacks and communication delays. The FNTBC achieves finite-time convergence depending on initial More >

I Wayan Adi Juliawan Pawana^1,2, Vincent Abella², Jhury Kevin Lastre², Yongho Ko², Ilsun You^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2733-2760, 2025, DOI:10.32604/cmes.2025.072611 - 26 November 2025

Abstract Roaming in 5G networks enables seamless global mobility but also introduces significant security risks due to legacy protocol dependencies, uneven Security Edge Protection Proxy (SEPP) deployment, and the dynamic nature of inter-Public Land Mobile Network (inter-PLMN) signaling. Traditional rule-based defenses are inadequate for protecting cloud-native 5G core networks, particularly as roaming expands into enterprise and Internet of Things (IoT) domains. This work addresses these challenges by designing a scalable 5G Standalone testbed, generating the first intrusion detection dataset specifically tailored to roaming threats, and proposing a deep learning based intrusion detection framework for cloud-native environments.… More > Graphic Abstract

Bin Zhang¹, En-Cheng Liou^2,*, Yi-Chih Tung³, Muhammad Usman^2,4, Chiung-An Chen^2,4, Chao-Shun Yang^2,4

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2551-2571, 2025, DOI:10.32604/cmes.2025.072471 - 26 November 2025

Abstract Indoor localization is a fundamental requirement for future 6G Intelligent Sensing and Communication (ISAC) systems, enabling precise navigation in environments where Global Positioning System (GPS) signals are unavailable. Existing methods, such as map-based navigation or site-specific fingerprinting, often require intensive data collection and lack generalization capability across different buildings, thereby limiting scalability. This study proposes a cross-site, map-free indoor localization framework that uses low-frequency sub-1 GHz radio signals and a Transformer-based neural network for robust positioning without prior environmental knowledge. The Transformer’s self-attention mechanisms allow it to capture spatial correlations among anchor nodes, facilitating accurate… More >

Bo Yang^1,2, Yu Zhou³, Jiawei Tian^4,*, Xiang Zhang², Fupei Guo², Shan Liu^5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2253-2276, 2025, DOI:10.32604/cmes.2025.072031 - 26 November 2025

Abstract Accurate calibration of surgical instruments and ultrasound probes is essential for achieving high precision in image guided minimally invasive procedures. However, existing methods typically treat the calibration of the needle tip and the ultrasound probe as two independent processes, lacking an integrated calibration mechanism, which often leads to cumulative errors and reduced spatial consistency. To address this challenge, we propose a joint calibration model that unifies the calibration of the surgical needle tip and the ultrasound probe within a single coordinate system. The method formulates the calibration process through a series of mathematical models and… More >

Seong-Su Yoon, Dong-Hyuk Shin, Ieck-Chae Euom^*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2683-2706, 2025, DOI:10.32604/cmes.2025.071577 - 26 November 2025

Abstract With the continuous expansion of digital infrastructures, malicious behaviors in host systems have become increasingly sophisticated, often spanning multiple processes and employing obfuscation techniques to evade detection. Audit logs, such as Sysmon, offer valuable insights; however, existing approaches typically flatten event sequences or rely on generic graph models, thereby discarding the natural parent-child process hierarchy that is critical for analyzing multiprocess attacks. This paper proposes a structure-aware threat detection framework that transforms audit logs into a unified two-dimensional (2D) spatio-temporal representation, where process hierarchy is modeled as the spatial axis and event chronology as the More >

Abdullrahman A. Al-Shamma’a¹, Hassan M. Hussein Farh^1,*, Ridwan Taiwo², Al-Wesabi Ibrahim³, Abdulrhman Alshaabani¹, Saad Mekhilef ⁴, Mohamed A. Mohamed^5,6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1303-1347, 2025, DOI:10.32604/cmes.2025.071302 - 26 November 2025

Abstract Optimal sizing and allocation of distributed generators (DGs) have become essential computational challenges in improving the performance, efficiency, and reliability of electrical distribution networks. Despite extensive research, existing approaches often face algorithmic limitations such as slow convergence, premature stagnation in local minima, or suboptimal accuracy in determining optimal DG placement and capacity. This study presents a comprehensive scientometric and systematic review of global research focused on computer-based modelling and algorithmic optimization for renewable DG sizing and placement. It integrates both quantitative and qualitative analyses of the scholarly landscape, mapping influential research domains, co-authorship structures, the More >

Seunghan Kim^1,#, Changhoon Lim^2,#, Gwonsang Ryu³, Hyunil Kim^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2617-2630, 2025, DOI:10.32604/cmes.2025.071190 - 26 November 2025

Abstract Federated Learning enables privacy-preserving training of Transformer-based language models, but remains vulnerable to backdoor attacks that compromise model reliability. This paper presents a comparative analysis of defense strategies against both classical and advanced backdoor attacks, evaluated across autoencoding and autoregressive models. Unlike prior studies, this work provides the first systematic comparison of perturbation-based, screening-based, and hybrid defenses in Transformer-based FL environments. Our results show that screening-based defenses consistently outperform perturbation-based ones, effectively neutralizing most attacks across architectures. However, this robustness comes with significant computational overhead, revealing a clear trade-off between security and efficiency. By explicitly More >