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  • Open Access

    ARTICLE

    Equivalent Modeling with Passive Filter Parameter Clustering for Photovoltaic Power Stations Based on a Particle Swarm Optimization K-Means Algorithm

    Binjiang Hu1,*, Yihua Zhu2, Liang Tu1,2, Zun Ma3, Xian Meng3, Kewei Xu3

    Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.069777 - 27 December 2025

    Abstract This paper proposes an equivalent modeling method for photovoltaic (PV) power stations via a particle swarm optimization (PSO) K-means clustering (KMC) algorithm with passive filter parameter clustering to address the complexities, simulation time cost and convergence problems of detailed PV power station models. First, the amplitude–frequency curves of different filter parameters are analyzed. Based on the results, a grouping parameter set for characterizing the external filter characteristics is established. These parameters are further defined as clustering parameters. A single PV inverter model is then established as a prerequisite foundation. The proposed equivalent method combines the… More >

  • Open Access

    ARTICLE

    HCF-MFGB: Hybrid Collaborative Filtering Based on Matrix Factorization and Gradient Boosting

    Salahudin Robo1,2, Triyanna Widiyaningtyas1,*, Wahyu Sakti Gunawan Irianto1

    CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-19, 2026, DOI:10.32604/cmc.2025.073011 - 09 December 2025

    Abstract Recommendation systems are an integral and indispensable part of every digital platform, as they can suggest content or items to users based on their respective needs. Collaborative filtering is a technique often used in various studies, which produces recommendations by analyzing similarities between users and items based on their behavior. Although often used, traditional collaborative filtering techniques still face the main challenge of sparsity. Sparsity problems occur when the data in the system is sparse, meaning that only a portion of users provide feedback on some items, resulting in inaccurate recommendations generated by the system.… More >

  • Open Access

    ARTICLE

    Mitigating the Dynamic Load Altering Attack on Load Frequency Control with Network Parameter Regulation

    Yunhao Yu1, Boda Zhang1, Meiling Dizha1, Ruibin Wen1, Fuhua Luo1, Xiang Guo1, Zhenyong Zhang2,*

    CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-19, 2026, DOI:10.32604/cmc.2025.070577 - 09 December 2025

    Abstract Load frequency control (LFC) is a critical function to balance the power consumption and generation. The grid frequency is a crucial indicator for maintaining balance. However, the widely used information and communication infrastructure for LFC increases the risk of being attacked by malicious actors. The dynamic load altering attack (DLAA) is a typical attack that can destabilize the power system, causing the grid frequency to deviate from its nominal value. Therefore, in this paper, we mathematically analyze the impact of DLAA on the stability of the grid frequency and propose the network parameter regulation (NPR)… More >

  • Open Access

    ARTICLE

    FishTracker: An Efficient Multi-Object Tracking Algorithm for Fish Monitoring in a RAS Environment

    Yuqiang Wu1,2, Zhao Ji1, Guanqi You1, Zihan Zhang1, Chaoping Lu3, Huanliang Xu1, Zhaoyu Zhai1,*

    CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-22, 2026, DOI:10.32604/cmc.2025.070414 - 09 December 2025

    Abstract Understanding fish movement trajectories in aquaculture is essential for practical applications, such as disease warning, feeding optimization, and breeding management. These trajectories reveal key information about the fish’s behavior, health, and environmental adaptability. However, when multi-object tracking (MOT) algorithms are applied to the high-density aquaculture environment, occlusion and overlapping among fish may result in missed detections, false detections, and identity switching problems, which limit the tracking accuracy. To address these issues, this paper proposes FishTracker, a MOT algorithm, by utilizing a Tracking-by-Detection framework. First, the neck part of the YOLOv8 model is enhanced by introducing… More >

  • Open Access

    ARTICLE

    Sand Production in Unconsolidated Sandstone: Experimental Analysis of Multiphase Flow During Cyclic Injection and Production

    Tianen Liu1,2, Kun Dai1,2, Shiju Ren1,2, Chuanxiang Zhang1,2, Xiaoling Tang3,*, Jinghong Hu3,*, Yidong Cai3, Jun Lu3

    FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.12, pp. 2981-2995, 2025, DOI:10.32604/fdmp.2025.073859 - 31 December 2025

    Abstract Many mature onshore oilfields have entered a high-water-cut stage, with reservoir recovery approaching economic limits. Converting these depleted or nearly depleted reservoirs into underground gas storage (UGS) facilities offers an efficient way to leverage their substantial storage potential. During cyclic gas injection and withdrawal, however, the reservoir experiences complex three-phase flow and repeated stress fluctuations, which can induce rock fatigue, inelastic deformation, and ultimately sand production. This study uses controlled physical experiments to simulate sand production in reservoir rocks subjected to alternating gas injection and production under three-phase conditions. After preparing oil-water-saturated cores through waterflooding,… More > Graphic Abstract

    Sand Production in Unconsolidated Sandstone: Experimental Analysis of Multiphase Flow During Cyclic Injection and Production

  • Open Access

    ARTICLE

    Multivariate Lithium-ion Battery State Prediction with Channel-Independent Informer and Particle Filter for Battery Digital Twin

    Changyu Jeon, Younghoon Kim*

    CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3723-3745, 2025, DOI:10.32604/cmes.2025.073030 - 23 December 2025

    Abstract Accurate State-of-Health (SOH) prediction is critical for the safe and efficient operation of lithium-ion batteries (LiBs). However, conventional methods struggle with the highly nonlinear electrochemical dynamics and declining accuracy over long-horizon forecasting. To address these limitations, this study proposes CIPF-Informer, a novel digital twin framework that integrates the Informer architecture with Channel Independence (CI) and a Particle Filter (PF). The CI mechanism enhances robustness by decoupling multivariate state dependencies, while the PF captures the complex stochastic variations missed by purely deterministic models. The proposed framework was evaluated using the Massachusetts Institute of Technology (MIT) battery More >

  • Open Access

    ARTICLE

    Attitude Estimation Using an Enhanced Error-State Kalman Filter with Multi-Sensor Fusion

    Yu Tao1, Tian Yin2, Yang Jie1,*

    Journal on Artificial Intelligence, Vol.7, pp. 549-570, 2025, DOI:10.32604/jai.2025.072727 - 01 December 2025

    Abstract To address the issue of insufficient accuracy in attitude estimation using Inertial Measurement Units (IMU), this paper proposes a multi-sensor fusion attitude estimation method based on an improved Error-State Kalman Filter (ESKF). Several adaptive mechanisms are introduced within the standard ESKF framework: first, the process noise covariance is dynamically adjusted based on gyroscope angular velocity to enhance the algorithm’s adaptability under both static and dynamic conditions; second, the Sage-Husa algorithm is employed to estimate the measurement noise covariance of the accelerometer and magnetometer in real-time, mitigating disturbances caused by external accelerations and magnetic fields. Additionally,… More >

  • Open Access

    ARTICLE

    The Kalman Filter Design for MJS in Power System Based on Derandomization Technique

    Quan Li1,*, 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

    The Kalman Filter Design for MJS in Power System Based on Derandomization Technique

  • Open Access

    REVIEW

    A Review of Modern Strategies for Enhancing Power Quality and Hosting Capacity in Renewable-Integrated Grids: From Conventional Devices to AI-Based Solutions

    Adel A.Abou El-Ela1, Ragab A. El-Sehiemy2,3,4,*, Abdallah Nazih1, Asmaa A. Mubarak5, Eman S. Ali1

    CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1349-1388, 2025, DOI:10.32604/cmes.2025.069507 - 26 November 2025

    Abstract Distribution systems face significant challenges in maintaining power quality issues and maximizing renewable energy hosting capacity due to the increased level of photovoltaic (PV) systems integration associated with varying loading and climate conditions. This paper provides a comprehensive overview on the exit strategies to enhance distribution system operation, with a focus on harmonic mitigation, voltage regulation, power factor correction, and optimization techniques. The impact of passive and active filters, custom power devices such as dynamic voltage restorers (DVRs) and static synchronous compensators (STATCOMs), and grid modernization technologies on power quality is examined. Additionally, this paper… More >

  • Open Access

    ARTICLE

    A Filter-Based Feature Selection Framework to Detect Phishing URLs Using Stacking Ensemble Machine Learning

    Nimra Bari1, Tahir Saleem2, Munam Shah3, Abdulmohsen Algarni4, Asma Patel5,*, Insaf Ullah6,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 1167-1187, 2025, DOI:10.32604/cmes.2025.070311 - 30 October 2025

    Abstract Today, phishing is an online attack designed to obtain sensitive information such as credit card and bank account numbers, passwords, and usernames. We can find several anti-phishing solutions, such as heuristic detection, virtual similarity detection, black and white lists, and machine learning (ML). However, phishing attempts remain a problem, and establishing an effective anti-phishing strategy is a work in progress. Furthermore, while most anti-phishing solutions achieve the highest levels of accuracy on a given dataset, their methods suffer from an increased number of false positives. These methods are ineffective against zero-hour attacks. Phishing sites with… More >

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