Parallel convolutional neural network and empirical mode decomposition for high accuracy in motor imagery EEG signal classification. by Jaipriya D, Sriharipriya K C

“…In this work, we propose a novel method that addresses these challenges by employing empirical mode decomposition (EMD) for feature extraction and a parallel convolutional neural network (PCNN) for feature classification. …”
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Ultra-Short-Term Photovoltaic Power Interval Forecasting Based on Time-Series Decomposition and Conformal Quantile Regression by Qianjin GUI, Wenfa XU, Xiaoyang LI, Lirong LUO, Haifeng YE, Zhengfeng WANG

“…Then, piecewise linear models, Fourier series decomposition models, and AR-Net models are respectively employed to fit the three subseries, with the Fourier series decomposition model enhancing the fitting capability for daily and seasonal periodicities of PV power. …”
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Singular Value Decomposition-Based Adaptive Sampling Approximate Message Passing Net for Sparse-View CT Reconstruction by Zhenhua Wu, Jiafei Xu, Lixia Yang

“…However, the challenges of handling a reduced number of projection views persist for both iterative estimation and deep neural reconstruction methods. In this paper, to address these challenges, we present a singular value decomposition-based adaptive sampling approximate message passing network (ASAMP-Net) sparse-view CT imaging method. …”
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Multiscale Fuzzy Temporal Pattern Mining: A Block-Decomposition Algorithm for Partial Periodic Associations in Event Data by Aihua Zhu, Haote Zhang, Xingqian Chen, Dingkun Zhu

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A multiple pattern complex event detection scheme based on decomposition and merge sharing for massive event streams by Jianhua Wang, Bang Ji, Feng Lin, Shilei Lu, Yubin Lan, Lianglun Cheng

“…The experimental results show that the proposed detection method in this article outperforms some general detection methods in detection model and detection algorithm in multiple pattern complex event detection as a whole.…”
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Optimizing Fleet Size in Point-to-Point Shared Demand Responsive Transportation Service: A Network Decomposition Approach by Fudong Xie, Ce Wang, Housheng Duan

“…To solve this practical problem, we resort to the Model Predictive Control method (MPC) and propose a network decomposition approach that first converts the transportation network to a nodal tree structure and then develops a Nodal Tree Recourse with Dependent Arc Capacities (NTRDAC) algorithm to obtain the exact value of the expected recourse functions. …”
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AMDCnet: attention-gate-based multi-scale decomposition and collaboration network for long-term time series forecasting by Shikang Hou, Song Sun, Tao Yin, Zhibin Zhang, Meng Yan

“…Furthermore, the intricate structure of multi-scale time series complicates the effective extraction of features at different temporal resolutions.MethodTo address these limitations, we propose AMDCnet, a multi-scale-based time series decomposition and collaboration network designed to enhance the model's capacity for decomposing and integrating data across varying time scales. …”
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Resource-Efficient Acoustic Full-Waveform Inversion via Dual-Branch Physics-Informed RNN with Scale Decomposition by Cai Lu, Jijun Liu, Liyuan Qu, Jianbo Gao, Hanpeng Cai, Jiandong Liang

“…The proposed dual-branch PIRNN framework was validated through full-waveform inversions on synthetic horizontal-layer models and the Marmousi model across various scales. …”
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Enhancing artificial neural network learning efficiency through Singular value decomposition for solving partial differential equations by Alfi Bella Kurniati, Maharani A. Bakar, Nur Fadhilah Ibrahim, Hanani Farhah Harun

“…In response, we introduce the matrix decomposition method into the ANN learning process, rooted in Singular Value Decomposition (SVD). …”
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SFPFMformer: Short-Term Power Load Forecasting for Proxy Electricity Purchase Based on Feature Optimization and Multiscale Decomposition by Chengfei Qi, Yanli Feng, Junling Wan, Xinying Mao, Peisen Yuan

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Secondary Hybrid Decomposition Strategy for Wind Power Prediction Using Long Short-Term Memory With Crisscross Optimization by Yoseph Mekonnen Abebe, Habtamu Kassa Bayu, Tekalign Tesfaye Mengistu, Abera Tullu, Sunghun Jung

“…Two sets of wind farm data are used to validate the accuracy of the model. A comparative study shows that the proposed SHD-LSTM-CSO model performs better than other hybrid models such as SHD-LSTM, EMD-LSTM, LSTM based on variable mode decomposition (VMD) (VMD-LSTM), Gray Worm Optimization-based backpropagation neural network (GWO-BPANN) and EMD-based Artificial Neural Network (EMD-ANN) methods.…”
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Decomposition-reconstruction-optimization framework for hog price forecasting: Integrating STL, PCA, and BWO-optimized BiLSTM. by Xiangjuan Liu, Yunlong Li, Fengtong Wang, Yujie Qin, Zhongyu Lyu

“…First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. …”
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A New Modelling and Feature Extraction Method Based on Complex Network and Its Application in Machine Fault Diagnosis by Qi Zhang, Tian Tian, Guangrui Wen, Zhifen Zhang

“…This paper proposes a new method of local feature extraction based on frequency complex network (FCN) decomposition and builds a new complex network structure feature on this basis, namely, subnetwork average degree. …”
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Colored linear inverse model: A data-driven method for studying dynamical systems with temporally correlated stochasticity by Justin Lien, Yan-Ning Kuo, Hiroyasu Ando, Shoichiro Kido

“…In real-world problems, environmental noise is often idealized as Gaussian white noise, despite potential temporal dependencies. The linear inverse model (LIM) is a class of data-driven methods that extract dynamic and stochastic information from finite time-series data of complex systems. …”
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Battery Cluster Inconsistency Detection Method and Intelligent O&M Scheme Based on Vector Error Correction Model by Yuan GUO, Xiangyang XIA, Jiahui YUE, Hui LI, Jinbo WU

“…To solve the problems of incomplete battery data and fragmented data segments leading to inaccurate detection in the actual operation data of energy storage power stations, this paper proposes a battery cluster inconsistency detection method based on the vector error correction model. …”
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Enhanced forecasting of shipboard electrical power demand using multivariate input and variational mode decomposition with mode selection by Paolo Fazzini, Giuseppe La Tona, Matteo Diez, Maria Carmela Di Piazza

“…This work contributes to ongoing efforts in optimizing decomposition methods for predictive modeling in energy management, opening new avenues for improving shipboard power grid efficiency.…”
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A robust deep learning approach for photovoltaic power forecasting based on feature selection and variational mode decomposition by Mokhtar Ali, Abdelkerim Souahlia, Abdelhalim Rabehi, Mawloud Guermoui, Ali Teta, Imad Eddine Tibermacine, Abdelaziz Rabehi, Mohamed Benghanem

“…Among the selected predictors, Global Solar Radiation (GSR) consistently proves to be the most influential. To further enhance model inputs, Variational Mode Decomposition (VMD) is applied to extract informative Intrinsic Mode Functions (IMFs) from the selected features. …”
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Research on Electric Vehicle Charging Load Prediction Methods Combining Signal Noise Reduction and Time Series Modeling by Liyun Liu, Xiaomei Xu, Jinsong Zhang, Dong Li

“…This study introduces a hybrid deep learning model combining Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Convolutional Neural Networks (CNN), Bi-directional Gated Recurrent Units (BiGRU), and Attention Mechanism (AM) to address the volatility in charging load patterns. …”
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A Novel Hybrid Approach to Forecasting Crude Oil Prices Using Local Mean Decomposition, ARIMA, and XGBoost by Jawaria Nasir, Muhammad Aamir, Soofia Iftikhar, A. B. Albidah, Ohud A. Alqasem, Maysaa E. A. Elwahab, Ilyas Khan, Wei Sin Koh

“…The hybrid LMD-SD-ARIMA-XGBOOST model overcomes overfitting by combining statistical and machine learning components, where the decomposition process isolates key features of the data, reducing noise and complexity. …”
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A Hybrid Approach Integrating Decomposition Ensemble Forecasting With Optimal Combination Selection for Air Passenger Demand Forecasting by Yi-Chung Hu, Li-Chin Shih, Yu-Jing Chiu

“…The optimal combination selection from individual decomposition ensemble models was then used to construct combined models to strengthen the accuracy of decomposition ensemble forecasting. …”
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