Jiahui Liu^1,2, Lang Li^1,2,*, Di Li^1,2, Yu Ou^1,2

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4641-4657, 2024, DOI:10.32604/cmc.2024.051613

Abstract Correlation power analysis (CPA) combined with genetic algorithms (GA) now achieves greater attack efficiency and can recover all subkeys simultaneously. However, two issues in GA-based CPA still need to be addressed: key degeneration and slow evolution within populations. These challenges significantly hinder key recovery efforts. This paper proposes a screening correlation power analysis framework combined with a genetic algorithm, named SFGA-CPA, to address these issues. SFGA-CPA introduces three operations designed to exploit CPA characteristics: propagative operation, constrained crossover, and constrained mutation. Firstly, the propagative operation accelerates population evolution by maximizing the number of correct bytes… More >

Marya Iqbal¹, Yaser Hafeez¹, Nabil Almashfi², Amjad Alsirhani³, Faeiz Alserhani⁴, Sadia Ali¹, Mamoona Humayun^5,*, Muhammad Jamal⁶

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5031-5049, 2024, DOI:10.32604/cmc.2024.051371

Abstract Embracing software product lines (SPLs) is pivotal in the dynamic landscape of contemporary software development. However, the flexibility and global distribution inherent in modern systems pose significant challenges to managing SPL variability, underscoring the critical importance of robust cybersecurity measures. This paper advocates for leveraging machine learning (ML) to address variability management issues and fortify the security of SPL. In the context of the broader special issue theme on innovative cybersecurity approaches, our proposed ML-based framework offers an interdisciplinary perspective, blending insights from computing, social sciences, and business. Specifically, it employs ML for demand analysis, More >

Yunfeng Cai¹, Ran Qin¹, Jin Tang¹, Long Zhang¹, Xiaotian Bi¹, Qing Yang^2,*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4979-4994, 2024, DOI:10.32604/cmc.2024.050435

Abstract Electric power training is essential for ensuring the safety and reliability of the system. In this study, we introduce a novel Abnormal Action Recognition (AAR) system that utilizes a Lightweight Pose Estimation Network (LPEN) to efficiently and effectively detect abnormal fall-down and trespass incidents in electric power training scenarios. The LPEN network, comprising three stages—MobileNet, Initial Stage, and Refinement Stage—is employed to swiftly extract image features, detect human key points, and refine them for accurate analysis. Subsequently, a Pose-aware Action Analysis Module (PAAM) captures the positional coordinates of human skeletal points in each frame. Finally, More >

Iftikhar Naseer^1,2, Tehreem Masood^1,2, Sheeraz Akram^3,*, Zulfiqar Ali⁴, Awais Ahmad³, Shafiq Ur Rehman³, Arfan Jaffar^1,2

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4963-4977, 2024, DOI:10.32604/cmc.2024.050204

Abstract Lung cancer is a leading cause of global mortality rates. Early detection of pulmonary tumors can significantly enhance the survival rate of patients. Recently, various Computer-Aided Diagnostic (CAD) methods have been developed to enhance the detection of pulmonary nodules with high accuracy. Nevertheless, the existing methodologies cannot obtain a high level of specificity and sensitivity. The present study introduces a novel model for Lung Cancer Segmentation and Classification (LCSC), which incorporates two improved architectures, namely the improved U-Net architecture and the improved AlexNet architecture. The LCSC model comprises two distinct stages. The first stage involves… More >

Jing Gao^*, Mingxuan Ji, Hongjiang Wang, Zhongxiao Du

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5017-5030, 2024, DOI:10.32604/cmc.2024.050158

Abstract With the continuous advancement of China’s “peak carbon dioxide emissions and Carbon Neutrality” process, the proportion of wind power is increasing. In the current research, aiming at the problem that the forecasting model is outdated due to the continuous updating of wind power data, a short-term wind power forecasting algorithm based on Incremental Learning-Bagging Deep Hybrid Kernel Extreme Learning Machine (IL-Bagging-DHKELM) error affinity propagation cluster analysis is proposed. The algorithm effectively combines deep hybrid kernel extreme learning machine (DHKELM) with incremental learning (IL). Firstly, an initial wind power prediction model is trained using the Bagging-DHKELM… More >

Xiu Liu, Liang Gu^*, Xin Gong, Long An, Xurui Gao, Juying Wu

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4045-4061, 2024, DOI:10.32604/cmc.2024.048442

Abstract With the popularisation of intelligent power, power devices have different shapes, numbers and specifications. This means that the power data has distributional variability, the model learning process cannot achieve sufficient extraction of data features, which seriously affects the accuracy and performance of anomaly detection. Therefore, this paper proposes a deep learning-based anomaly detection model for power data, which integrates a data alignment enhancement technique based on random sampling and an adaptive feature fusion method leveraging dimension reduction. Aiming at the distribution variability of power data, this paper developed a sliding window-based data adjustment method for… More >

Fabien Mukundufite^1,*, Jean Marie Vianney Bikorimana¹, Alexander Kyaruzi Lugatona²

Energy Engineering, Vol.121, No.7, pp. 1805-1821, 2024, DOI:10.32604/ee.2024.051398

Abstract The governmental electric utility and the private sector are joining hands to meet the target of electrifying all households by 2024. However, the aforementioned goal is challenged by households that are scattered in remote areas. So far, Solar Home Systems (SHS) have mostly been applied to increase electricity access in rural areas. SHSs have continuous constraints to meet electricity demands and cannot run income-generating activities. The current research presents the feasibility study of electrifying Remera village with the smart microgrid as a case study. The renewable energy resources available in Remera are the key sources… More >

Penghui Liu^1,2,*, Yaning Zhang¹, Yuxing Dai², Yanzhou Sun^1,3

Energy Engineering, Vol.121, No.7, pp. 1883-1901, 2024, DOI:10.32604/ee.2024.049318

Abstract Arc grounding faults occur frequently in the power grid with small resistance grounding neutral points. The existing arc fault identification technology only uses the fault line signal characteristics to set the identification index, which leads to detection failure when the arc zero-off characteristic is short. To solve this problem, this paper presents an arc fault identification method by utilizing integrated signal characteristics of both the fault line and sound lines. Firstly, the waveform characteristics of the fault line and sound lines under an arc grounding fault are studied. After that, the convex hull, gradient product,… More >

Yan Zhang¹, Bowen Du^2,*, Benren Pan¹, Guannan Wang¹, Guoqiang Xie¹, Tong Jiang²

Energy Engineering, Vol.121, No.7, pp. 1903-1920, 2024, DOI:10.32604/ee.2024.048718

Abstract During faults in a distribution network, the output power of a distributed generation (DG) may be uncertain. Moreover, the output currents of distributed power sources are also affected by the output power, resulting in uncertainties in the calculation of the short-circuit current at the time of a fault. Additionally, the impacts of such uncertainties around short-circuit currents will increase with the increase of distributed power sources. Thus, it is very important to develop a method for calculating the short-circuit current while considering the uncertainties in a distribution network. In this study, an affine arithmetic algorithm… More >

Xin Shen¹, Jiahao Li¹, Yujun Yin¹, Jianlin Tang^2,3,*, Weibin Lin^2,3, Mi Zhou^2,3

Energy Engineering, Vol.121, No.7, pp. 1945-1961, 2024, DOI:10.32604/ee.2024.048388

Abstract Advanced carbon emission factors of a power grid can provide users with effective carbon reduction advice, which is of immense importance in mobilizing the entire society to reduce carbon emissions. The method of calculating node carbon emission factors based on the carbon emissions flow theory requires real-time parameters of a power grid. Therefore, it cannot provide carbon factor information beforehand. To address this issue, a prediction model based on the graph attention network is proposed. The model uses a graph structure that is suitable for the topology of the power grid and designs a supervised More >