Photovoltaic Short-Term Output Power Forecast Model Based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise–Kernel Principal Component Analysis–Long Sh... by Lan Cao, Haoyu Yang, Chenggong Zhou, Shaochi Wang, Yingang Shen, Binxia Yuan

“…By comparing with other prediction models, the ICEEMDAN-KPCA-LSTM photovoltaic output power model outperforms other models.…”
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Prediction of remaining parking spaces based on EMD-LSTM-BiLSTM neural network by Changxi Ma, Xiaoting Huang, Ke Wang, Yongpeng Zhao

“…The proposed hybrid model is compared with a variety of current mainstream deep learning algorithms, and the effectiveness of the EMD-LSTM-BiLSTM method is validated. …”
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Method for compensating multi-time measurement data of distribution network based on alternating minimization matrix completion combined with VMD-ARIMA-LSTM by Xiaodan Yu, Ruijia Jiang, Xiaolong Jin, Hongjie Jia, Yunfei Mu, Wei Wei, Wanxin Tang

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An Integrated CEEMDAN to Optimize Deep Long Short-Term Memory Model for Wind Speed Forecasting by Yingying He, Likai Zhang, Tengda Guan, Zheyu Zhang

“…Experimental results indicate that the proposed method achieves minimal mean absolute percentage errors of 0.3285 and 0.1455, outperforming other popular models across multiple performance criteria.…”
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Semi-analytical dynamic modeling and impact mechanism analysis of a hard-coating cylindrical shell with arbitrary circular perforations by Jian Yang, Yue Zhang

“…Abstract In this paper, an innovative axial domain decomposition method, which uniquely integrates axial and circumferential perforation parameters, is developed for semi-analytical modeling of free vibration of a hard-coating cylindrical shell with arbitrary axial and circumferential perforations, based on the Love’s first-order shear deformation theory and Rayleigh-Ritz method. …”
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Optimal design of neural network-based fuzzy predictive control model for recommending educational resources in the context of information technology by Liu Tingting, Liu Qiyuan, Wang Xiaobei, Yang Yang

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Multi-objective stochastic model optimal operation of smart microgrids coalition with penetration renewable energy resources with demand responses by Ali Abdolahzadeh, Amir Hassannia, Farhoud Mousavizadeh, Mohammad Tolou Askari

“…A key innovation of this study is the development of an advanced hybrid solution methodology, combining the ε-constraint method for multi-objective optimization with Benders decomposition for computational efficiency. …”
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Investigation of the Dynamic Characteristics of PM_2.5 Dispersion During the Acceleration of Motor Vehicles in Urban Streets Based on Computational Fluid Dynamics and Dyn... by Jiawei Ding, Dan Mei, Bowen Liu, Mingwei Gao, Jiale Cui

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Stochastic Optimization Scheduling Method for Mine Electricity–Heat Energy Systems Considering Power-to-Gas and Conditional Value-at-Risk by Chao Han, Yun Zhu, Xing Zhou, Xuejie Wang

“…Second, a stochastic optimization scheduling model is developed for the mine electricity–heat energy system, aiming to minimize the total scheduling cost comprising day-ahead scheduling cost, expected reserve adjustment cost, and CVaR. …”
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Research on Annual Runoff Prediction Based on EMD-LSTM-ANFIS Model by HU Shunqiang, CUI Dongwen

“…To improve the accuracy of runoff prediction,this paper proposes a runoff prediction model based on the combination of empirical mode decomposition (EMD),long short-term memory (LSTM) neural network,and adaptive neuro-fuzzy inference system (ANFIS),decomposes the original runoff sequence into multiple regular component sequences through EMD,and reconstructs the phase space of each component sequence by the autocorrelation function method (AFM) and the false nearest neighbor method (FNN) to determine the input and output vectors,establishes the EMD-LSTM-ANFIS prediction model,and constructs the EMD-LSTM,EMD-ANFIS,LSTM,ANFIS as comparison models,as well as predicts and compares the annual runoff of the Longtan Station in Yunnan Province by the five models.The results show that the average relative error of the EMD-LSTM-ANFIS model for the annual runoff prediction is 3.18%,which is reduced by 55.0%、65.2%、68.1%、78.4% compared with the EMD-LSTM,EMD-ANFIS,LSTM,and ANFIS models respectively,with higher prediction accuracy and stronger generalization ability.Therefore,the EMD-LSTM-ANFIS model is feasible and reliable for runoff prediction.…”
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Complex, Temporally Variant SVD via Real ZN Method and 11-Point ZeaD Formula from Theoretics to Experiments by Jianrong Chen, Xiangui Kang, Yunong Zhang

“…Then, by using the real zeroing neurodynamics (ZN) method, matrix vectorization, Kronecker product, vectorized transpose matrix, and dimensionality reduction technique, a dynamical model, termed the continuous-time SVD (CTSVD) model, is derived and investigated. …”
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Simultaneous Incomplete Traffic Data Imputation and Similarity Pattern Discovery with Bayesian Nonparametric Tensor Decomposition by Yaxiong Han, Zhaocheng He

“…BNPTD is a hierarchical probabilistic model, which is comprised of Bayesian tensor decomposition and Dirichlet process mixture model. …”
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Application of Empirical Mode Decomposition with Local Linear Quantile Regression in Financial Time Series Forecasting by Abobaker M. Jaber, Mohd Tahir Ismail, Alsaidi M. Altaher

“…We propose a two-stage technique that combines the empirical mode decomposition (EMD) with nonparametric methods of local linear quantile (LLQ). …”
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Prediction for Coastal Wind Speed Based on Improved Variational Mode Decomposition and Recurrent Neural Network by Muyuan Du, Zhimeng Zhang, Chunning Ji

“…For outlier detection, statistical methods were employed, followed by a comparative evaluation of three models—LSTM, LSTM-KAN, and Seq2Seq-Attention—for multi-step wind speed forecasting over horizons ranging from 1 to 12 h. …”
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Ex-Ante and Ex-Post Decomposition Strategy for Ultra-Short-Term Wind Power Prediction by Peng Lu, Zhuo Li, Lin Ye, Ming Pei, Yingying Zheng, Yongning Zhao

“…Secondly, in the error correction stage, the errors produced by the preliminary prediction model are corrected by persistence methods to compensate for final prediction errors. …”
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Short-term Wind Speed Prediction Based on Wavelet Packet Decomposition and BP Neural Network by WANG Ning, LUO Rubin, LIAO Jun, LI Jun, JIANG Yi, YANG Zechuan, YUAN Junjie

“…The simulation results show that the average absolute percentage error (MAPE), root mean square error (RMSE) and mean absolute error (MAE) of the short-term wind speed prediction model based on wavelet packet decomposition are lower than those of other short-term wind speed prediction methods. …”
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Hierarchical Multi-Scale Decomposition and Deep Learning Ensemble Framework for Enhanced Carbon Emission Prediction by Yinuo Sun, Zhaoen Qu, Zhuodong Liu, Xiangyu Li

“…This paper proposes a hierarchical multi-scale decomposition and deep learning ensemble framework that addresses these limitations. …”
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Fault Feature Research of Rolling Bearing based on Empirical Mode Decomposition and Principle Component Analysis by Zheng Xin

“…It is proposed that a fault diagnosis method for rolling bearing based on empirical mode decomposition（ EMD） and multivariate statistical process control（ MSPC）,the Hilbert- Huang transformation and principal component analysis（ PCA） are combined effectively in this method. …”
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Prediction of the Remaining Useful Life of Lithium–Ion Batteries Based on Mode Decomposition and ED-LSTM by Bingzeng Song, Guangzhao Yue, Dong Guo, Hanming Wu, Yonghai Sun, Yuhua Li, Bin Zhou

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Enhanced RT-DETR with Dynamic Cropping and Legendre Polynomial Decomposition Rockfall Detection on the Moon and Mars by Panpan Zang, Jinxin He, Yongbin Yang, Yu Li, Hanya Zhang

“…Conventional visual interpretation methods that rely on orbiter imagery can be inefficient due to their massive datasets and subtle morphological signatures. …”
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