An Intelligent Framework for Multiscale Detection of Power System Events Using Hilbert–Huang Decomposition and Neural Classifiers by Juan Vasquez, Manuel Jaramillo, Diego Carrión

“…This study contributes a scalable and adaptable solution for automated PQ event classification under variable conditions.…”
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Photovoltaic Power Forecasting Based on Variational Mode Decomposition and Long Short-Term Memory Neural Network by Zhijian Hou, Yunhui Zhang, Xuemei Cheng, Xiaojiang Ye

“…This paper presents a hybrid forecasting model that combines Variational Mode Decomposition (VMD) and Long Short-Term Memory (LSTM). …”
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A Financial Data Analysis Method Based on Time-Series Generative Adversarial Network and Decomposition Learning by Wei Wang, Bo Li

“…In existing works, deep learning combined with stock price series decomposition is a common architecture. Inspired by this, we propose a financial data analysis method based on time series generative adversarial network (TimeGAN) and decomposition learning. …”
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Wheat Production Across East Africa: Trend, Instability, and Decomposition Analysis Using Time Series Approach by Habtamu Mossie Andualem, Mesfin Bekele Gebbisa, Zsuzsanna Bacsi

“…To analyse and estimate wheat production trends, instability with regional disparity, and decomposition across East Africa's top wheat‐producing countries, a 30‐year data series with different secondary data, mostly the FAOSTAT database, was divided into three sub‐periods: Period I (1993/94‐2002/03), Period II (2003/04‐2012/13) and Period III (2013/14‐2022/23), even though compound growth rates, a semi‐logarithmic trend model, a differential equation approach for decomposition analysis, and the Cuddy‐Della Valle Index were utilised. …”
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Socioeconomic Inequality in Chronic Complications of Type 2 Diabetes Mellitus in Iran: Concentration Index and Decomposition Approach by Sedigheh Mafakheri, Erfan Ayubi, Shiva Borzouei, Vajiheh Ramezani Doroh, Salman Khazaei

“…A binary logistic regression model was utilized to investigate the relationship between diabetes complications and independent variables. …”
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Analysis, Forecasting, and System Identification of a Floating Offshore Wind Turbine Using Dynamic Mode Decomposition by Giorgio Palma, Andrea Bardazzi, Alessia Lucarelli, Chiara Pilloton, Andrea Serani, Claudio Lugni, Matteo Diez

“…This article presents the data-driven equation-free modeling of the dynamics of a hexafloat floating offshore wind turbine based on the application of dynamic mode decomposition (DMD). …”
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A reduced simulation applied to viscoelastic fatigue of polymers using a time multi-scale approach based on partition of Unity method by Sebastian Rodriguez, Angelo Pasquale, Jad Mounayer, Diego Canales, Marianne Beringhier, Chady Ghnatios, Amine Ammar, Francisco Chinesta

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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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An Assessment of Local Geometric Uncertainties in Polysilicon MEMS: A Genetic Algorithm and POD-Kriging Surrogate Modeling Approach by Ananya Roy, Francesco Rizzini, Gabriele Gattere, Carlo Valzasina, Aldo Ghisi, Stefano Mariani

“…In this paper, an approach that combines genetic algorithms and proper orthogonal decomposition with kriging surrogate modeling was proposed to accurately predict over-etch measures through an on-chip test device. …”
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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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Combining a Multi‐Lake Model Ensemble and a Multi‐Domain CORDEX Climate Data Ensemble for Assessing Climate Change Impacts on Lake Sevan by Muhammed Shikhani, Johannes Feldbauer, Robert Ladwig, Daniel Mercado‐Bettín, Tadhg N. Moore, Artur Gevorgyan, Amalya Misakyan, Chenxi Mi, Martin Schultze, Bertram Boehrer, Tom Shatwell, Klemens Barfus, Karsten Rinke

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A synthesis of <i>Sphagnum</i> litterbag experiments: initial leaching losses bias decomposition rate estimates by H. Teickner, H. Teickner, E. Pebesma, K.-H. Knorr

“…</p> <p>We present a meta-analysis of 15 <i>Sphagnum</i> litterbag studies to estimate initial leaching losses (<span class="inline-formula"><i>l</i>₀</span>), to analyze how much <i>Sphagnum</i> <span class="inline-formula"><i>k</i>₀</span> estimates are biased when the decomposition model ignores initial leaching losses and to analyze how much the variance in <span class="inline-formula"><i>k</i>₀</span> estimates increases due to initial leaching losses even when they are estimated by the decomposition model.…”
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Novel model for medium to long term photovoltaic power prediction using interactive feature trend transformer by Xiang Liu, Qingyu Liu, Shuai Feng, Yangyang Ge, Haoran Chen, Chunling Chen

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Investigation Study of Structure Real Load Spectra Acquisition and Fatigue Life Prediction Based on the Optimized Efficient Hinging Hyperplane Neural Network Model by Lin Zhu, Benao Xing, Xingbao Li, Min Chen, Minping Jia

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Development of a deep learning based approach for multi-material decomposition in spectral CT: a proof of principle in silico study by Jayasai R. Rajagopal, Saikiran Rapaka, Faraz Farhadi, Ehsan Abadi, W. Paul Segars, Tristan Nowak, Puneet Sharma, William F. Pritchard, Ashkan Malayeri, Elizabeth C. Jones, Ehsan Samei, Pooyan Sahbaee

“…Abstract Conventional approaches to material decomposition in spectral CT face challenges related to precise algorithm calibration across imaged conditions and low signal quality caused by variable object size and reduced dose. …”
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Designing empirical fourier decomposition reinforced with multiscale increment entropy and deep learning to forecast dry bulb air temperature by Mohammed Diykh, Mumtaz Ali, Abdulhaleem H. Labban, Ramendra Prasad, Mehdi Jamei, Shahab Abdulla, Aitazaz Ahsan Farooque

“…This paper aims to design an intelligent model namely MEFD-MSIE-FCNN to forecast DBTair which integrates multivariate empirical Fourier decomposition (MEFD), multiscale increment entropy (MSIE), and FCSM model that integrates a fully connected neural network FCNN with long short-term memory (LSTM) to forecast DBTair. …”
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Climate variability and its impact on sanitation facility choice in Ethiopia by Bahre Gebru, Katarina Elofsson, Franklin Amuakwa-Mensah, George Marbuah

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Future variation and uncertainty source decomposition in deep learning bias-corrected CMIP6 global extreme precipitation historical simulation by Xiaohua Xiang, Yongxuan Li, Xiaoling Wu, Zhu Liu, Lei Wu, Biqiong Wu, Chuanxin Jin, Zhiqiang Zeng

“…Implementation of CNNs as a BC method could significantly reduce model uncertainty but at the cost of increasing the proportion of scenario uncertainty and internal variability. …”
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Blood Glucose Concentration Prediction Based on Double Decomposition and Deep Extreme Learning Machine Optimized by Nonlinear Marine Predator Algorithm by Yang Shen, Deyi Li, Wenbo Wang, Xu Dong

“…The existing blood glucose concentration prediction models often overlook the impacts of residual components after multi-scale decomposition on prediction accuracy. …”
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Stochastic Fracture Analysis Using Scaled Boundary Finite Element Methods Accelerated by Proper Orthogonal Decomposition and Radial Basis Functions by Xiaowei Shen, Haowen Hu, Zhongwang Wang, Xiuyun Chen, Chengbin Du

“…The adoption of POD and RBF significantly reduces the model order and increases computation efficiency, while maintaining the versatility and accuracy of MCs. …”
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