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 In the current research, aiming at the problem that the forecasting model is outdated due to the continuous updating of wind power data, a short-term wind power forecasting algorithm based on Incremental Learning-Bagging Deep Hybrid Kernel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 >

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 >

Seungwoo Kang¹, Seyha Ros¹, Inseok Song¹, Prohim Tam¹, Sa Math², Seokhoon Kim^1,3,*

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1967-1983, 2023, DOI:10.32604/cmc.2023.045020

Abstract Intelligent healthcare networks represent a significant component in digital applications, where the requirements hold within quality-of-service (QoS) reliability and safeguarding privacy. This paper addresses these requirements through the integration of enabler paradigms, including federated learning (FL), cloud/edge computing, software-defined/virtualized networking infrastructure, and converged prediction algorithms. The study focuses on achieving reliability and efficiency in real-time prediction models, which depend on the interaction flows and network topology. In response to these challenges, we introduce a modified version of federated logistic regression (FLR) that takes into account convergence latencies and the accuracy of the final FL model… More >

Ying Bai^1,*, Xiaoti Ren², Hong Li¹

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2999-3018, 2023, DOI:10.32604/iasc.2023.040124

Abstract The combination of the Industrial Internet of Things (IIoT) and digital twin (DT) technology makes it possible for the DT model to realize the dynamic perception of equipment status and performance. However, conventional digital modeling is weak in the fusion and adjustment ability between virtual and real information. The performance prediction based on experience greatly reduces the inclusiveness and accuracy of the model. In this paper, a DT-IIoT optimization model is proposed to improve the real-time representation and prediction ability of the key equipment state. Firstly, a global real-time feedback and the dynamic adjustment mechanism… More >

Song Yu^*, Chen Wang

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5555-5571, 2023, DOI:10.32604/cmc.2023.038162

Abstract PM_2.5 has a non-negligible impact on visibility and air quality as an important component of haze and can affect cloud formation and rainfall and thus change the climate, and it is an evaluation indicator of air pollution level. Achieving PM_2.5 concentration prediction based on relevant historical data mining can effectively improve air pollution forecasting ability and guide air pollution prevention and control. The past methods neglected the impact caused by PM_2.5 flow between cities when analyzing the impact of inter-city PM_2.5 concentrations, making it difficult to further improve the prediction accuracy. However, factors including geographical information such… More >

Guoqing Xu¹, Changsen Xia¹, Jun Qian¹, Guo Ran³, Zilong Jin^1,2,*

Journal on Internet of Things, Vol.4, No.2, pp. 113-125, 2022, DOI:10.32604/jiot.2022.036066

Abstract Huge networks and increasing network traffic will consume more and more resources. It is critical to predict network traffic accurately and timely for network planning, and resource allocation, etc. In this paper, a combined network traffic prediction model is proposed, which is based on Prophet, evolutionary attention-based LSTM (EALSTM) network, and Gaussian process regression (GPR). According to the non-smooth, sudden, periodic, and long correlation characteristics of network traffic, the prediction procedure is divided into three steps to predict network traffic accurately. In the first step, the Prophet model decomposes network traffic data into periodic and More >

Milos Mravik¹, T. Vetriselvi², K. Venkatachalam^3,*, Marko Sarac¹, Nebojsa Bacanin¹, Sasa Adamovic¹

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 457-471, 2022, DOI:10.32604/cmc.2022.020687

Abstract At present, the prevalence of diabetes is increasing because the human body cannot metabolize the glucose level. Accurate prediction of diabetes patients is an important research area. Many researchers have proposed techniques to predict this disease through data mining and machine learning methods. In prediction, feature selection is a key concept in preprocessing. Thus, the features that are relevant to the disease are used for prediction. This condition improves the prediction accuracy. Selecting the right features in the whole feature set is a complicated process, and many researchers are concentrating on it to produce a… More >

Gen Xu, Danhe Chen, Xiang Zhang, Wenhe Liao^*

CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.2, pp. 865-878, 2020, DOI:10.32604/cmes.2020.012844

Abstract For the on-orbit flight missions, the model of orbit prediction is critical for the tasks with high accuracy requirement and limited computing resources of spacecraft. The precession-nutation model, as the main part of extended orbit prediction, affects the efficiency and accuracy of on-board operation. In this paper, the previous research about the conversion between the Geocentric Celestial Reference System and International Terrestrial Reference System is briefly summarized, and a practical concise precession-nutation model is proposed for coordinate transformation computation based on Celestial Intermediate Pole (CIP). The idea that simplifying the CIP-based model with interpolation method… More >