Research on the volatility characteristics and evolutionary mechanism of the “Asian premium” for natural gas by Jian Chai, Mingxiao Zhao, Xiaokong Zhang, Na Li, Zhefei Zhang, Zenghui Liu

“…First, this paper used the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) model to decompose and reconstruct natural gas “Asian premium.” …”
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Research on Fault Diagnosis of UAV Rotor Motor Bearings Based on WPT-CEEMD-CNN-LSTM by Xianyi Shang, Wei Li, Fang Yuan, Haifeng Zhi, Zhilong Gao, Min Guo, Bo Xin

“…Initially, the method applies multiple noise reduction processes to the original vibration signals and enhances their time–frequency resolution through Wavelet Packet Transform (WPT) and Complete Ensemble Empirical Mode Decomposition (CEEMD). …”
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Multidimensional Meteorological Variables for Wind Speed Forecasting in Qinghai Region of China: A Novel Approach by He Jiang, Luo Shihua, Yao Dong

“…The objective is to develop a hybrid model with multidimensional meteorological variables for forecasting the wind speed accurately. The complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is applied to handle the nonlinearity of the wind speed. …”
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Charging Strategies for Electric Vehicles Using a Machine Learning Load Forecasting Approach for Residential Buildings in Canada by Ahmad Mohsenimanesh, Evgueniy Entchev

“…The hybrid ML method combines similar day selection, complete ensemble empirical mode decomposition with adaptive noise, and deep neural networks. …”
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Cutting Feature Extraction Method for Ultra-High Molecular Weight Polyethylene Fiber-Reinforced Concrete Based on Feature Classification and Improved Hilbert–Huang Transform by Shanshan Hu, Jinzhao Feng, Hui Liu, Guoxin Tang, Geng’e Zhang, Fali Xiong, Shirun Zhong, Yilong Huang

“…In order to intelligently evaluate the performance of diamond saw blades when cutting UHMWPE-FRC, a feature extraction method, based on feature classification and an improved Hilbert–Huang transform (HHT), is proposed, which consider Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) and wavelet threshold de-noising. …”
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The role of hybrid models in financial decision-making: Forecasting stock prices with advanced algorithms by Xiaoyi Zhu

“…Additionally, it incorporates ensemble empirical mode decomposition, sample entropy clustering, and sea-horse optimizer as part of its methodology. …”
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A source-load collaborative stochastic optimization method considering the electricity price uncertainty and industrial load peak regulation compensation benefit by Xiaoyu Yue, Lijun Fu, Siyang Liao, Jian Xu, Deping Ke, Huiji Wang, Shuaishuai Feng, Jiaquan Yang, Xuehao He

“…This paper combines the complete ensemble empirical mode decomposition adaptive noise (CEEMDAN), whale optimization algorithm (WOA), and long short-term memory network (LSTM) to propose a CEEMDAN-WOA-LSTM prediction model for electricity price scenarios. …”
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The interplay between firms’ capabilities and ownership in explaining environmental performance by Beatriz Forés, César Camisón-Zornoza, José María Fernández-Yáñez

“…研究設計/方法/理念 – 研究人員使用基於改良完全集合經驗模態分解自適應噪聲(Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise)的聚類分析法,取得Rényi有效轉移熵,藉此得到研究結果。 …”
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