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 - 20 June 2024

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 In the current research, aiming at the problem that the forecasting model is outdated due to the continuous updating of wind power data, a short-term wind power forecasting algorithm based on Incremental Learning-Bagging Deep Hybrid Kernel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

Tusongjiang Kari¹, Zhiyang He¹, Aisikaer Rouzi², Ziwei Zhang³, Xiaojing Ma^1,*, Lin Du¹

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 691-705, 2023, DOI:10.32604/iasc.2023.037617 - 29 April 2023

Abstract Power transformer is one of the most crucial devices in power grid. It is significant to determine incipient faults of power transformers fast and accurately. Input features play critical roles in fault diagnosis accuracy. In order to further improve the fault diagnosis performance of power transformers, a random forest feature selection method coupled with optimized kernel extreme learning machine is presented in this study. Firstly, the random forest feature selection approach is adopted to rank 42 related input features derived from gas concentration, gas ratio and energy-weighted dissolved gas analysis. Afterwards, a kernel extreme learning… More >

Feisha Hu¹, Qi Wang^1,*, Haijian Shao^1,2, Shang Gao¹, Hualong Yu¹

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2405-2424, 2023, DOI:10.32604/cmes.2023.026732 - 09 March 2023

Abstract Unmanned Aerial Vehicles (UAVs) are widely used and meet many demands in military and civilian fields. With the continuous enrichment and extensive expansion of application scenarios, the safety of UAVs is constantly being challenged. To address this challenge, we propose algorithms to detect anomalous data collected from drones to improve drone safety. We deployed a one-class kernel extreme learning machine (OCKELM) to detect anomalies in drone data. By default, OCKELM uses the radial basis (RBF) kernel function as the kernel function of the model. To improve the performance of OCKELM, we choose a Triangular Global More > Graphic Abstract

Saud S. Alotaibi¹, Amal Al-Rasheed², Sami Althahabi³, Manar Ahmed Hamza^4,*, Abdullah Mohamed⁵, Abu Sarwar Zamani⁴, Abdelwahed Motwakel⁴, Mohamed I. Eldesouki⁶

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3305-3318, 2022, DOI:10.32604/cmc.2022.029385 - 16 June 2022

Abstract Artificial Intelligence (AI) encompasses various domains such as Machine Learning (ML), Deep Learning (DL), and other cognitive technologies which have been widely applied in healthcare sector. AI models are utilized in healthcare sector in which the machines are used to investigate and make decisions based on prediction and classification of input data. With this motivation, the current study involves the design of Metaheuristic Optimization with Kernel Extreme Learning Machine for COVID-19 Prediction Model on Epidemiology Dataset, named MOKELM-CPED technique. The primary aim of the presented MOKELM-CPED model is to accomplish effectual COVID-19 classification outcomes using… More >

Wei Sun^1,3,*, Hongji Du¹, Shoubai Nie^2,3, Xiaozheng He⁴

CMC-Computers, Materials & Continua, Vol.60, No.1, pp. 147-161, 2019, DOI:10.32604/cmc.2019.03581

Abstract Traffic sign recognition (TSR), as a critical task to automated driving and driver assistance systems, is challenging due to the color fading, motion blur, and occlusion. Traditional methods based on convolutional neural network (CNN) only use an end-layer feature as the input to TSR that requires massive data for network training. The computation-intensive network training process results in an inaccurate or delayed classification. Thereby, the current state-of-the-art methods have limited applications. This paper proposes a new TSR method integrating multi-layer feature and kernel extreme learning machine (ELM) classifier. The proposed method applies CNN to extract… More >