Tao Yan¹, Javed Rashid^2,3, Muhammad Shoaib Saleem^3,4, Sajjad Ahmad⁴, Muhammad Faheem^5,*

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2685-2708, 2024, DOI:10.32604/cmc.2024.058186 - 18 November 2024

Abstract Electricity is essential for keeping power networks balanced between supply and demand, especially since it costs a lot to store. The article talks about different deep learning methods that are used to guess how much green energy different Asian countries will produce. The main goal is to make reliable and accurate predictions that can help with the planning of new power plants to meet rising demand. There is a new deep learning model called the Green-electrical Production Ensemble (GP-Ensemble). It combines three types of neural networks: convolutional neural networks (CNNs), gated recurrent units (GRUs), and… More >

Mitra Madanchian^1,*, Hamed Taherdoost^1,2,3,4

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2133-2159, 2024, DOI:10.32604/cmc.2024.057094 - 18 November 2024

Abstract This comparative review explores the dynamic and evolving landscape of artificial intelligence (AI)-powered innovations within high-tech research and development (R&D). It delves into both theoretical models and practical applications across a broad range of industries, including biotechnology, automotive, aerospace, and telecommunications. By examining critical advancements in AI algorithms, machine learning, deep learning models, simulations, and predictive analytics, the review underscores the transformative role AI has played in advancing theoretical research and shaping cutting-edge technologies. The review integrates both qualitative and quantitative data derived from academic studies, industry reports, and real-world case studies to showcase the… More >

Chunxue Wen, Yaoquan Wei^*, Peng Wang, Jianlin Li, Jinghua Zhou, Qingyun Li

Energy Engineering, Vol.121, No.11, pp. 3241-3261, 2024, DOI:10.32604/ee.2024.051810 - 21 October 2024

Abstract A modular system of cascaded converters based on model predictive control (MPC) is proposed to meet the application requirements of multiple voltage levels and electrical isolation in renewable energy generation systems. The system consists of a Buck/Boost + CLLLC cascaded converter as a submodule, which is combined in series and parallel on the input and output sides to achieve direct-current (DC) voltage transformation, bidirectional energy flow, and electrical isolation. The CLLLC converter operates in DC transformer mode in the submodule, while the Buck/Boost converter participates in voltage regulation. This article establishes a suitable mathematical model More >

Grace Gau¹, Minerva Singh^2,3,*

Revue Internationale de Géomatique, Vol.33, pp. 427-443, 2024, DOI:10.32604/rig.2024.055752 - 11 October 2024

Abstract This study examines how socio-economic characteristics predict flood risk in London, England, using machine learning algorithms. The socio-economic variables considered included race, employment, crime and poverty measures. A stacked generalization (SG) model combines random forest (RF), support vector machine (SVM), and XGBoost. Binary classification issues employ RF as the basis model and SVM as the meta-model. In multiclass classification problems, RF and SVM are base models while XGBoost is meta-model. The study utilizes flood risk labels for London areas and census data to train these models. This study found that SVM performs well in binary… More >

Md Naeem Hossain¹, Md Mustafizur Rahman^1,2,*, Devarajan Ramasamy¹

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 951-996, 2024, DOI:10.32604/cmes.2024.056022 - 27 September 2024

Abstract Conventional fault diagnosis systems have constrained the automotive industry to damage vehicle maintenance and component longevity critically. Hence, there is a growing demand for advanced fault diagnosis technologies to mitigate the impact of these limitations on unplanned vehicular downtime caused by unanticipated vehicle breakdowns. Due to vehicles’ increasingly complex and autonomous nature, there is a growing urgency to investigate novel diagnosis methodologies for improving safety, reliability, and maintainability. While Artificial Intelligence (AI) has provided a great opportunity in this area, a systematic review of the feasibility and application of AI for Vehicle Fault Diagnosis (VFD)… More > Graphic Abstract

Shih-Lin Lin^*, Bo-Chen Lin

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3555-3572, 2024, DOI:10.32604/cmc.2024.055456 - 12 September 2024

Abstract This paper explores the application of Model Predictive Control (MPC) to enhance safety and efficiency in autonomous vehicle (AV) navigation through optimized path planning. The evolution of AV technology has progressed rapidly, moving from basic driver-assistance systems (Level 1) to fully autonomous capabilities (Level 5). Central to this advancement are two key functionalities: Lane-Change Maneuvers (LCM) and Adaptive Cruise Control (ACC). In this study, a detailed simulation environment is created to replicate the road network between Nantun and Wuri on National Freeway No. 1 in Taiwan. The MPC controller is deployed to optimize vehicle trajectories,… More >

Yang Hua^#, Jinyu Sun^#, Yuxuan Lou, Hao Zhang, Jing Shi^*, Wei Sun^*

Congenital Heart Disease, Vol.19, No.3, pp. 279-291, 2024, DOI:10.32604/chd.2024.051190 - 26 July 2024

Abstract Background: White matter lesion (WML) is common in aging brain and is associated with cognitive impairment and dementia. However, recent studies reported an association between patent foramen ovale (PFO) and WML in migraineurs, especially in young, middle-aged migraineurs. Our retrospective, case-control study aims to describe the clinical characteristics of WML in this population and to explore potential risk factors. Methods: 226 patients with migraine and PFO were consecutively initially screened. Relevant factors were selected by the least absolute shrinkage and selection operator (LASSO) regression and multivariable logistic regression model. A Nomogram was employed to visualize… More >

Monerah Alawadh^*, Ahmed Barnawi

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4995-5015, 2024, DOI:10.32604/cmc.2024.048762 - 20 June 2024

Abstract Association rule learning (ARL) is a widely used technique for discovering relationships within datasets. However, it often generates excessive irrelevant or ambiguous rules. Therefore, post-processing is crucial not only for removing irrelevant or redundant rules but also for uncovering hidden associations that impact other factors. Recently, several post-processing methods have been proposed, each with its own strengths and weaknesses. In this paper, we propose THAPE (Tunable Hybrid Associative Predictive Engine), which combines descriptive and predictive techniques. By leveraging both techniques, our aim is to enhance the quality of analyzing generated rules. This includes removing irrelevant… More >

Yuanjun Dai, Haonan Li, Baohua Li^*

Energy Engineering, Vol.121, No.6, pp. 1607-1636, 2024, DOI:10.32604/ee.2024.047255 - 21 May 2024

Abstract This paper addresses the micro wind-hydrogen coupled system, aiming to improve the power tracking capability of micro wind farms, the regulation capability of hydrogen storage systems, and to mitigate the volatility of wind power generation. A predictive control strategy for the micro wind-hydrogen coupled system is proposed based on the ultra-short-term wind power prediction, the hydrogen storage state division interval, and the daily scheduled output of wind power generation. The control strategy maximizes the power tracking capability, the regulation capability of the hydrogen storage system, and the fluctuation of the joint output of the wind-hydrogen… More >

Yunye Wan¹, Peng Chen², Yunni Xia^1,*, Yong Ma³, Dongge Zhu⁴, Xu Wang⁵, Hui Liu⁶, Weiling Li⁷, Xianhua Niu², Lei Xu⁸, Yumin Dong⁹

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1997-2017, 2024, DOI:10.32604/cmes.2024.048723 - 20 May 2024

Abstract Mobile Edge Computing (MEC) is a promising technology that provides on-demand computing and efficient storage services as close to end users as possible. In an MEC environment, servers are deployed closer to mobile terminals to exploit storage infrastructure, improve content delivery efficiency, and enhance user experience. However, due to the limited capacity of edge servers, it remains a significant challenge to meet the changing, time-varying, and customized needs for highly diversified content of users. Recently, techniques for caching content at the edge are becoming popular for addressing the above challenges. It is capable of filling… More >