Chenxu Wang^*, Jing Bian, Rui Yuan

Energy Engineering, Vol.122, No.3, pp. 985-1003, 2025, DOI:10.32604/ee.2025.059559 - 07 March 2025

Abstract Considering the uncertainty of grid connection of electric vehicle charging stations and the uncertainty of new energy and residential electricity load, a spatio-temporal decoupling strategy of dynamic reactive power optimization based on clustering-local relaxation-correction is proposed. Firstly, the k-medoids clustering algorithm is used to divide the reduced power scene into periods. Then, the discrete variables and continuous variables are optimized in the same period of time. Finally, the number of input groups of parallel capacitor banks (CB) in multiple periods is fixed, and then the secondary static reactive power optimization correction is carried out by… More >

Hairani Hairani^1,2, Triyanna Widiyaningtyas^1,*, Didik Dwi Prasetya¹, Afrig Aminuddin³

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2931-2949, 2025, DOI:10.32604/cmc.2024.060837 - 17 February 2025

Abstract An imbalanced dataset often challenges machine learning, particularly classification methods. Underrepresented minority classes can result in biased and inaccurate models. The Synthetic Minority Over-Sampling Technique (SMOTE) was developed to address the problem of imbalanced data. Over time, several weaknesses of the SMOTE method have been identified in generating synthetic minority class data, such as overlapping, noise, and small disjuncts. However, these studies generally focus on only one of SMOTE’s weaknesses: noise or overlapping. Therefore, this study addresses both issues simultaneously by tackling noise and overlapping in SMOTE-generated data. This study proposes a combined approach of… More >

Rahma Gantassi, Zaki Masood, Yonghoon Choi^*

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 327-343, 2025, DOI:10.32604/cmc.2024.058143 - 03 January 2025

Abstract Wireless sensor network (WSN) technologies have advanced significantly in recent years. Within WSNs, machine learning algorithms are crucial in selecting cluster heads (CHs) based on various quality of service (QoS) metrics. This paper proposes a new clustering routing protocol employing the Traveling Salesman Problem (TSP) to locate the optimal path traversed by the Mobile Data Collector (MDC), in terms of energy and QoS efficiency. To be more specific, to minimize energy consumption in the CH election stage, we have developed the M-T protocol using the K-Means and the grid clustering algorithms. In addition, to improve More >

Menglin Yin¹, Yong Qin^1,2,3,4,*, Jiansheng Peng^1,2,3,4

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 1329-1347, 2025, DOI:10.32604/cmc.2024.057460 - 03 January 2025

Abstract In dynamic scenarios, visual simultaneous localization and mapping (SLAM) algorithms often incorrectly incorporate dynamic points during camera pose computation, leading to reduced accuracy and robustness. This paper presents a dynamic SLAM algorithm that leverages object detection and regional dynamic probability. Firstly, a parallel thread employs the YOLOX object detection model to gather 2D semantic information and compensate for missed detections. Next, an improved K-means++ clustering algorithm clusters bounding box regions, adaptively determining the threshold for extracting dynamic object contours as dynamic points change. This process divides the image into low dynamic, suspicious dynamic, and high More >

Abdulrahman M. Abdulghani^1,*, Azizol Abdullah¹, A. R. Rahiman¹, Nor Asilah Wati Abdul Hamid^1,2, Bilal Omar Akram^3,4, Hafsa Raissouli¹

Computer Systems Science and Engineering, Vol.49, pp. 123-157, 2025, DOI:10.32604/csse.2024.058314 - 03 January 2025

Abstract This review article provides a comprehensive analysis of the latest advancements and persistent challenges in Software-Defined Wide Area Networks (SD-WANs), with a particular emphasis on the multi-objective Controller Placement Problem (CPP). As SD-WAN technology continues to gain prominence for its capacity to offer flexible and efficient network management, the task of 36optimally placing controllers—responsible for orchestrating and managing network traffic—remains a critical yet complex challenge. This review delves into recent innovations in multi-objective controller placement strategies, including clustering techniques, heuristic-based approaches, and the integration of machine learning and deep learning models. Each methodology is critically More >

Santiago Bañales^1,2,*, Raquel Dormido¹, Natividad Duro¹

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 869-907, 2025, DOI:10.32604/cmes.2024.054946 - 17 December 2024

Abstract Customer segmentation according to load-shape profiles using smart meter data is an increasingly important application to vital the planning and operation of energy systems and to enable citizens’ participation in the energy transition. This study proposes an innovative multi-step clustering procedure to segment customers based on load-shape patterns at the daily and intra-daily time horizons. Smart meter data is split between daily and hourly normalized time series to assess monthly, weekly, daily, and hourly seasonality patterns separately. The dimensionality reduction implicit in the splitting allows a direct approach to clustering raw daily energy time series… More > Graphic Abstract

Theophile Fozin Fonzin^1,2, Halilou Claude Bobo Hamadjida², Aurelle Tchagna Kouanou^2,3,*, Valery Monthe⁴, Anicet Brice Mezatio⁵, Michael Sone Ekonde⁶

Journal of Cyber Security, Vol.6, pp. 155-177, 2024, DOI:10.32604/jcs.2024.059265 - 09 January 2025

Abstract Cloud computing is a transformational paradigm involving the delivery of applications and services over the Internet, using access mechanisms through microprocessors, smartphones, etc. Latency time to prevent and detect modern and complex threats remains one of the major challenges. It is then necessary to think about an intrusion prevention system (IPS) design, making it possible to effectively meet the requirements of a cloud computing environment. From this analysis, the central question of the present study is to minimize the latency time for efficient threat prevention and detection in the cloud. To design this IPS design… More >

Juliet A. Murali^1,*, Brindha T.²

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4659-4690, 2024, DOI:10.32604/cmc.2024.058115 - 19 December 2024

Abstract Infrastructure as a Service (IaaS) in cloud computing enables flexible resource distribution over the Internet, but achieving optimal scheduling remains a challenge. Effective resource allocation in cloud-based environments, particularly within the IaaS model, poses persistent challenges. Existing methods often struggle with slow optimization, imbalanced workload distribution, and inefficient use of available assets. These limitations result in longer processing times, increased operational expenses, and inadequate resource deployment, particularly under fluctuating demands. To overcome these issues, a novel Clustered Input-Oriented Salp Swarm Algorithm (CIOSSA) is introduced. This approach combines two distinct strategies: Task Splitting Agglomerative Clustering (TSAC)… More >

Wenhao Li^1,*, Zongyang Liu^1,2, Dingcheng Ji^1,2, Yiding Liu^3,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.31, No.1, pp. 1-1, 2024, DOI:10.32604/icces.2024.011927

Abstract This research advances the field by focusing on the damage assessment of adhesively bonded joints using AE, with limited prior studies in this specific area. Through the preparation of CFRP specimens and subsequent tensile loading tests, AE signals were captured and analyzed. The study employed wavelet decomposition for noise reduction and Short-Time Fourier Transform (STFT) for signal analysis, facilitating the identification of damage-related frequencies and amplitudes. Hierarchical clustering was applied to categorize AE signals into distinct damage behaviors, utilizing a divisive approach that avoids local minima and offers unique results at each iteration. The method's… More >

Yuxuan Zhao^1,2,*, Bo Wang¹, Shu Wang¹, Wenjun Xu², Gang Ma²

Energy Engineering, Vol.121, No.12, pp. 3711-3733, 2024, DOI:10.32604/ee.2024.054032 - 22 November 2024

Abstract The output of photovoltaic power stations is significantly affected by environmental factors, leading to intermittent and fluctuating power generation. With the increasing frequency of extreme weather events due to global warming, photovoltaic power stations may experience drastic reductions in power generation or even complete shutdowns during such conditions. The integration of these stations on a large scale into the power grid could potentially pose challenges to system stability. To address this issue, in this study, we propose a network architecture based on VMD-KELM for predicting the power output of photovoltaic power plants during severe weather… More >