Short-Term Power Load Prediction Method Based on VMD and EDE-BiLSTM by Yibo Lai, Qifeng Wang, Gang Chen, Yu Bai, Peiyu Zhao, Xiaojing Liao, Shuang Wu, Changyou Men, Quan Sun

“…Aiming at the diversity and flexibility of electric loads and their inherent nonlinearity and temporality in the context of the new era. A forecasting method is proposed combining Variational Modal Decomposition (VMD) and EDE-BiLSTM. …”
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A Multi-Directional Pyranometer (CUBE-<i>i</i>) for Real-Time Direct and Diffuse Solar Irradiance Decomposition by Dong-Seok Lee

“…Conventional decomposition models (empirical and numerical decomposition models) estimate direct normal irradiance (DNI) and diffuse horizontal irradiance (DHI) from global horizontal irradiance (GHI) based on empirical correlations or physical equations. …”
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ATHEMATICAL MODEL FOR THE OPTIMISATION OF HIERARCHICAL MULTI-LEVEL PRODUCTION SYSTEMS by Yu. V. Minaeva

“…Objectives The aim of the study is to develop a mathematical model for the complex solution of various problems in designing and reconstructing the technological system of a production workshop of a machine-building enterprise.Methods Complex system theory and an aggregative decomposition approach are used as the methodological basis for modelling complex hierarchical productions, making it possible to represent a complex system in the form of a set of interconnected subsystems.Results A mathematical model designed for a complex solution of problems associated with the formation of an optimal production programme and selection ofequipment was developed. …”
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Features of Combustion of Water-fuel Emulsions with Different Methods of Introducing Water into the Combustion Zone by Kolbasenko O.V., Dymo B.V., Anastasenko S.M., Shostak O.V.

“…The main purpose of the article is to conduct research on combustion quality and determine nitrogen oxide concentrations for various methods of introducing water into the combustion chamber zone. …”
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Micro-firms’ productivity growth in Poland before and during COVID-19: Do industry and region matter? by Emilia Gosińska, Mariusz Górajski, Magdalena Ulrichs

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Sparse Multichannel Decomposition of Electrodermal Activity With Physiological Priors by Samiul Alam, Md. Rafiul Amin, Rose T. Faghih

“…Finally, we develop a block coordinate descent method for SC signal decomposition by employing a generalized cross-validation sparse recovery approach while including physiological priors. …”
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Dynamics-Guided Support Vector Machines for Response Analysis of Steel Frame Under Sine Wave Excitation by Yao Wang, Huaiman Li

“…Furthermore, a solution algorithm combining DG-SVM with static condensation and mode decomposition methods is developed to enhance computational efficiency for the analysis of multi-degree-of-freedom systems. …”
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TensorTrack: Tensor Decomposition for Video Object Tracking by Yuntao Gu, Pengfei Zhao, Lan Cheng, Yuanjun Guo, Haikuan Wang, Wenjun Ding, Yu Liu

“…To address this issue, this paper proposes an innovative method that utilizes tensor decomposition, an underexplored concept in object-tracking research. …”
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Behaviour Analysis of Modeling and Model Evaluating Methods in System Identification for a Multiprocess Station by A. Annie Steffy Beula, Geno Peter, Albert Alexander Stonier, K. Ezhil Vignesh, Vivekananda Ganji

“…Order of the model is found from Hankel matrix representation methods such as singular value decomposition and determinant method. …”
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Bearing Fault Diagnosis under Transient Conditions: Using Variational Mode Decomposition and the Symmetrized Dot Pattern-Based Convolutional Neural Network Model by Jide Jia, Jianmin Mei, Chuang Sun, Fengjuan Yang

“…An effective bearing fault diagnosis method for gearbox applications under variable operating conditions is proposed, utilizing variational mode decomposition (VMD) for feature extraction, symmetrized dot pattern (SDP) for visual representation, and convolutional neural network (CNN) for deep feature extraction and classification. …”
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Estimation of Forest Aboveground Biomass Using Multitemporal Quad-Polarimetric PALSAR-2 SAR Data by Model-Free Decomposition Approach in Planted Forest by Cong Peng, Chao Fang, Jiangping Long, Tingchen Zhang, Huanna Zheng, Zilin Ye

“…In this study, to overcome the disadvantages of common polarization decomposition methods, model-free decomposition methods were first applied to map forest AGB by extracting decomposition features from L-band quad-polarimetric PALSAR-2 images. …”
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Tensor‐structured decomposition improves systems serology analysis by Zhixin Cyrillus Tan, Madeleine C Murphy, Hakan S Alpay, Scott D Taylor, Aaron S Meyer

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Decomposition of Fuzzy Soft Sets with Finite Value Spaces by Feng Feng, Hamido Fujita, Young Bae Jun, Madad Khan

“…The notion of fuzzy soft sets is a hybrid soft computing model that integrates both gradualness and parameterization methods in harmony to deal with uncertainty. …”
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Improving agricultural commodity allocation and market regulation: a novel hybrid model based on dual decomposition and enhanced BiLSTM for price prediction by Lihua Zhang, Fushun Wang, Fushun Wang, Kejian Wang, Kejian Wang, Zhenxue He, Zhenxue He, Chen Chen, Chen Chen, Jiahao Liu, Jiahao Liu, Chao Wang, Chao Wang, Zhe Wang

“…This innovative approach first performs seasonal decomposition of the original data using the STL method, then applies the VMD method for double decomposition of the residual components, reconstructs the data based on sample entropy, and finally predicts agricultural commodity market prices using the BiLSTM network model optimized by the PSO algorithm. …”
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Combination of the Improved Diffraction Nonlocal Boundary Condition and Three-Dimensional Wide-Angle Parabolic Equation Decomposition Model for Predicting Radio Wave Propagation by Ruidong Wang, Guizhen Lu, Rongshu Zhang, Weizhang Xu

“…Numeric computation and measurement results demonstrate the computational accuracy and speed of the WA-3DPE decomposition model with the improved diffraction nonlocal BC.…”
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Research on a hybrid deep learning model based on two-stage decomposition and an improved whale optimization algorithm for air quality index prediction by Hangyu Zhou, Yongquan Yan

“…A hybrid deep learning model is developed for AQI prediction, incorporating two-stage decomposition and hyperparameter optimization. …”
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EM-DeepSD: A Deep Neural Network Model Based on Cell-Free DNA End-Motif Signal Decomposition for Cancer Diagnosis by Zhi-Yang Zhao, Chang-Ling Huang, Tong-Min Wang, Shi-Hao Zhou, Lu Pei, Wen-Hui Jia, Wei-Hua Jia

“…Furthermore, this paper compares the performance of EM-DeepSD with that of existing benchmarked methods to demonstrate its superiority. Based on the EM-DeepSD framework, we developed the EM-DeepSSA model and compared it with two benchmarked methods across different cfDNA sequencing datasets. …”
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Reference modeling as a method for solving nonlinear problems by A. B. Cheboksarov, N. Yu. Botvineva, V. A. Cheboksarov, E. V. Polovinko

“…The obtained results of analysis and modeling allow us to confidently assess the reliability of the general ideas of the reference modeling method, its design scheme, as well as the convergence of its decompositions, the similarity criteria of the system under study and the selected model. …”
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Forecasting methods in Greek coastal shipping: The case of Southwest Crete by Ioannis Sitzimis

“…The results showed that in four of the six ports the Winters’ method is best adapted. The port of Gavdos adapts better to the decomposition method and the port of Sougia to Li’s method. …”
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Prediction of Landslide Displacement Based on EMD-TAR Combined Model by CHEN Xi, GAO Yaping, TU Rui

“…This study aims to more accurately predict the displacement changes of landslides with nonlinear volatility development.The empirical mode decomposition is first employed to process the time series of monitoring surface displacement of a landslide,and then the irregularly changing displacement series is converted into modal components with regular changes,which generates displacement components at different frequencies.Each component is predicted separately so that the mutual influence of errors can be avoided.The comprehensive prediction of the changing trend of displacement series is based on the prediction of the changing trends of all components.The improved threshold autoregressive model able to well describe non-stationary harmonics is used to predict the landslide displacement components.Finally,the modal superposition yields the final predicted displacement.In this way,a combined prediction model based on empirical mode decomposition and threshold autoregressive model is established,and its prediction accuracy is verified with Baishuihe landslide data.Compared with a BP neural network model and a long short-term memory network model,the proposed model has a high prediction accuracy,which provides a new method for the prediction of landslide displacement.…”
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