Combining Kronecker-Basis-Representation Tensor Decomposition and Total Variational Constraint for Spectral Computed Tomography Reconstruction by Xuru Li, Kun Wang, Yan Chang, Yaqin Wu, Jing Liu

“…The method based on tensor decomposition can effectively remove noise by exploring the correlation of energy channels, but it is difficult for traditional tensor decomposition methods to describe the problem of tensor sparsity and low-rank properties of all expansion modules simultaneously. …”
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A double broad learning approach based on variational modal decomposition for Lithium-Ion battery prognostics by Xiaojia Wang, Xinyue Guo, Sheng Xu, Xibin Zhao

“…Therefore, in this paper, a novel model based on variational modal decomposition and double broad learning (VMD-DBL) is proposed. …”
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Domain Knowledge Decomposition for Cross-Domain Few-Shot Scene Classification From Remote Sensing Imagery by Can Li, He Chen, Yin Zhuang, Liang Chen, Lianlin Li

“…Hence, in this article, a novel CDFSSC method called domain knowledge decomposition (DKD) framework is proposed to effectively exploit domain-common and domain-specific knowledge from the pseudo-labels of target samples, improve the certainty of cross-domain representation learning, and enhance the model’s adaptability to the target domain. …”
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Gaussian Process Regression Total Nitrogen Prediction Based on Data Decomposition Technology and Several Intelligent Algorithms by WANG Yongshun, CUI Dongwen

“…Total nitrogen (TN) is one of the important indicators to reflect the degree of water pollution and measure the eutrophication status of lakes and reservoirs.To improve the accuracy of TN prediction,based on the empirical wavelet transform (EWT) and wavelet packet transform (WPT) decomposition technology,this paper proposes a Gaussian process regression (GPR) prediction model optimized by osprey optimization algorithm (OOA),rime optimization algorithm (ROA),bald eagle search (BES) and black widow optimization algorithm (BWOA) respectively.Firstly,the TN time series is decomposed into several more regular subsequence components by EWT and WPT respectively.Then,the paper briefly introduces the principles of OOA,ROA,BES,and BWOA algorithms and applies OOA,ROA,BES,and BWOA to optimize GPR hyperparameters.Finally,EWT-OOA-GPR,EWT-ROA-GPR,EWT-BES-GPR,EWT-BWOA-GPR,WPT-OOA-GPR,WPT-ROA-GPR,WPT-BES-GPR,WPT-BWOA-GPR models (EWT-OOA-GPR and other eight models for short) are established to predict the components of TN by the optimized super-parameters.The final prediction results are obtained after reconstruction,and WT-OOA-GPR,WT-ROA-GPR,WT-BES-GPR and WT-BWOA-GPR models based on wavelet transform (WT) are built.Eight models,including EWT-OOA-SVM based on support vector machine (SVM),the paper compares the unoptimized EWT-GPR,WPT-GPR models,and the uncomposed OOA-GPR,ROA-GPR,BES-GPR,and BWOA-GPR models.The models were verified by the monitoring TN concentration time series data of Mudihe Reservoir,an important drinking water source in China,from 2008 to 2022.The results are as follows.① The average absolute percentage error of eight models such as EWT-OOA-GPR for TN prediction is between 0.161% and 0.219%,and the coefficient of determination is 0.999 9,which is superior to other comparison models,with higher prediction accuracy and better generalization ability.② EWT takes into account the advantages of WT and EMD.WPT can decompose low-frequency and high-frequency signals at the same time.Both of them can decompose TN time series data into more regular modal components,significantly improving the accuracy of model prediction,and the decomposition effect is better than that of the WT method.③ OOA,ROA,BES,and BWOA can effectively optimize GPR hyperparameters and improve GPR prediction performance.…”
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An Intelligent Framework for Multiscale Detection of Power System Events Using Hilbert–Huang Decomposition and Neural Classifiers by Juan Vasquez, Manuel Jaramillo, Diego Carrión

“…The proposed model achieved a classification accuracy of 94.09% and demonstrated consistent performance across all time windows, supporting its suitability for real-time monitoring in smart distribution networks. …”
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Analysis Method of Agricultural Total Factor Productivity Based on Stochastic Block Model (SBM) and Machine Learning by Yanzi Li, Cai Chen, Fuqiang Liu, Jian Wang

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First-principle modeling of parallel-flow regenerative kilns and their optimization with genetic algorithm and gradient-based method by Michael Kreitmeir, Bruno Villela Pedras Lago, Ladislaus Schoenfeld, Sebastian Rehfeldt, Harald Klein

“…We present a one-dimensional first-principle model for parallel-flow regenerative kilns that accounts for the most important effects. …”
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Investigation of the CO2 Decomposition Capacity of the TiO2:CuO Heterojunction by Simulation and Experimentation by Thien Trinh Duc, Lam Nguyen

“…The photocatalytic characteristics and CO2 decomposition capabilities of the TiO2:CuO heterojunction were examined using modelling and experimental methods. …”
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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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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

“…The purpose of this study is to examine SES inequality in chronic complications among T2DM patients using methods of decomposing inequality. Methods: This cross-sectional study included patients with T2DM receiving care at the diabetes clinic in Hamadan City, Iran, between April and September 2023. …”
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Blood-based tri-hybrid nanofluid flow through a porous channel with the impact of thermal radiation used in drug administration by Subhalaxmi Dey, Surender Ontela, P.K. Pattnaik, S.R. Mishra

“…Further, a semi-analytical approach Adomian Decomposition Method (ADM) is proposed for the solution of the model. …”
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Enhancing Unmanned Aerial Vehicle Object Detection via Tensor Decompositions and Positive–Negative Momentum Optimizers by Ruslan Abdulkadirov, Pavel Lyakhov, Denis Butusov, Nikolay Nagornov, Dmitry Reznikov, Anatoly Bobrov, Diana Kalita

“…In this paper, we propose the Yolov8 architecture with decomposed layers via canonical polyadic and Tucker methods for accelerating the solving of the object detection problem in satellite images. …”
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Research on Four-Quadrant Input Over-current Fault Diagnosis of Electric Locomotive Based on Recursive 2-Classification Method by Jianhua WANG

“…Based on K-Means and wavelet packet decomposition, feature extraction of data-related key fields was carried out. …”
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A Model Based on Neural Network to Predict Surface Settlement During Subway Station Construction: A Case Study of the Dongba-Zhongjie Station in Beijing, China by Jiaqi Zhang, Hua Jiang, Jinsen Wang, Jili Feng

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Methods of partial replacement of nutritive vegetable oils with a synthetic film-forming substance for obtaining paint materials by V. N. Torkhovskij

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Discrete Physics-Informed Training for Projection-Based Reduced-Order Models with Neural Networks by Nicolas Sibuet, Sebastian Ares de Parga, Jose Raul Bravo, Riccardo Rossi

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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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Feature Extraction and Attribute Recognition of Aerosol Particles from In Situ Light-Scattering Measurements Based on EMD-ICA Combined LSTM Model by Heng Zhao, Yanyan Zhang, Dengxin Hua, Jiamin Fang, Jie Zhang, Zewen Yang

“…Therefore, we propose a feature extraction and attribute recognition method from in situ light-scattering measurements based on Bayesian Optimization, wavelet scattering transform, and long short-term memory neural network (BO-WST-LSTM), with empirical mode decomposition (EMD) and independent component analysis (ICA) algorithm for signal preprocessing. …”
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Branch error reduction criterion-based signal recursive decomposition and its application to wind power generation forecasting. by Fen Xiao, Siyu Yang, Xiao Li, Junhong Ni

“…To address this issue, a branch error reduction (BER) criterion is proposed in this study that is based on which a mode number adaptive VMD-based recursive decomposition method is used. This decomposition method is combined with commonly used single forecasting models and applied to the wind power generation forecasting task. …”
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Short-Term Load Forecasting Based on Complementary Ensemble Empirical Mode Decomposition and Long Short-Term Memory by Huiru ZHAO, Yihang ZHAO, Sen GUO

“…At the same time, the prediction results of complementary ensemble empirical mode decomposition and long short-term memory are compared with those of long short-term memory model under other decomposition methods, which has verified that the complementary ensemble empirical mode decomposition method is effective in improving the prediction accuracy.…”
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