A Spatial–Temporal Time Series Decomposition for Improving Independent Channel Forecasting by Yue Yu, Pavel Loskot, Wenbin Zhang, Qi Zhang, Yu Gao

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Statistical package for computing precision covariance matrices via modified Cholesky decomposition by Elías D. Niño-Ruiz, Dylan Samuel Cantillo Arrieta, Giuliano Raffaele Frieri Quiroz, Nicolas Quintero Quintero

“…We introduce a statistical package designed to compute precision covariance matrices using modified Cholesky decomposition, tailored for Atmospheric General Circulation Models (AGCMs). …”
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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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Application of an improved LSTM model based on FECA and CEEMDAN VMD decomposition in water quality prediction by Jie Long, Chong Lu, Yiming Lei, Zhong Yuan Chen, Yihan Wang

“…Abstract To address the limitations of existing water quality prediction models in handling non-stationary data and capturing multi-scale features, this study proposes a hybrid model integrating Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Variational Mode Decomposition (VMD), Long Short-Term Memory Network (LSTM), and Frequency-Enhanced Channel Attention (FECA). …”
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A Decomposition Model for HPLC-DAD Data Set and Its Solution by Particle Swarm Optimization by Lizhi Cui, Zhihao Ling, Josiah Poon, Simon K. Poon, Junbin Gao, Paul Kwan

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Improved sub-band JND model with textural decomposition and its application in perceptual image coding by Ming-kui ZHENG, Kai-xiong SU, Wei-xing WANG, Cheng-dong LAN, Xiu-zhi YANG

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Anomaly detection method for cyber physical power system based on bilateral data fusion by Tianlei Zang, Shijun Wang, Chuangzhi Li, Yunfei Liu, Yujian Xiao, Zian Wang, Xueying Yu

“…The novel model can depict data decomposition and feature extraction from both cyber and physical domains. …”
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An Enhanced TimesNet-SARIMA Model for Predicting Outbound Subway Passenger Flow with Decomposition Techniques by Tianzhuo Zuo, Shaohu Tang, Liang Zhang, Hailin Kang, Hongkang Song, Pengyu Li

“…This research introduces a hybrid forecasting approach that combines an enhanced TimesNet model, Seasonal Autoregressive Integrated Moving Average (SARIMA), and Variational Mode Decomposition (VMD) to improve passenger flow prediction. …”
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Simulation of Positive Problems in Three Dimensional ECT System by LI Yan, LI Xiang-yu, YU Dao-yang

Subjects: “…galerkin weighted residual method…”
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Linear Model and Gradient Feature Elimination Algorithm Based on Seasonal Decomposition for Time Series Forecasting by Sheng-Tzong Cheng, Ya-Jin Lyu, Yi-Hong Lin

“…This study proposes a linear time series model architecture based on seasonal decomposition. …”
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A Hybrid Model Integrating Variational Mode Decomposition and Intelligent Optimization for Vegetable Price Prediction by Gao Wang, Shuang Xu, Zixu Chen, Youzhu Li

“…Traditional forecasting methods demonstrate evident limitations in capturing the nonlinear characteristics and complex volatility patterns of price series, underscoring the necessity of developing high-precision prediction models. …”
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Rapid Prediction of Ice Accretion on Swept Wings Based on Proper Orthogonal Decomposition and Surrogate Modelling by J. Du, Q. Guo, Y. Yue, Y. Ma, H. Cheng

“…To enable rapid and accurate ice formation predictions on swept wings, this study proposes a prediction methodology integrating proper orthogonal decomposition (POD) and Kriging surrogate modelling. …”
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Photovoltaic Power Generation Forecasting Based on Secondary Data Decomposition and Hybrid Deep Learning Model by Liwei Zhang, Lisang Liu, Wenwei Chen, Zhihui Lin, Dongwei He, Jian Chen

“…Empirical tests on a PV dataset from an Australian solar power plant show that the proposed CECSVB-LSTM model significantly outperforms traditional single models and combination models with different decomposition methods, improving R² by more than 7.98% and reducing the root mean square error (RMSE) and mean absolute error (MAE) by at least 60% and 55%, respectively.…”
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Half-hourly electricity price prediction model with explainable-decomposition hybrid deep learning approach by Sujan Ghimire, Ravinesh C. Deo, Konstantin Hopf, Hangyue Liu, David Casillas-Pérez, Andreas Helwig, Salvin S. Prasad, Jorge Pérez-Aracil, Prabal Datta Barua, Sancho Salcedo-Sanz

“…Explainable Artificial Intelligence (xAI) methods were used to enhance model interpretability and trustworthiness, with optimization via the Optuna algorithm. …”
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SIP-IFVM: A Time-evolving Coronal Model with an Extended Magnetic Field Decomposition Strategy by Haopeng Wang, Liping Yang, Stefaan Poedts, Andrea Lani, Yuhao Zhou, Yuhang Gao, Luis Linan, Jiakun Lv, Tinatin Baratashvili, Jinhan Guo, Rong Lin, Zhan Su, Caixia Li, Man Zhang, Wenwen Wei, Yun Yang, Yucong Li, Xinyi Ma, Edin Husidic, Hyun-Jin Jeong, Mahdi Najafi-Ziyazi, Juan Wang, Brigitte Schmieder

“…To address this, we propose an extended magnetic field decomposition strategy and successfully implement it in an implicit MHD coronal model. …”
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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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An experimental analysis method for the incident sound field using propagation mode expansion: Study on the incident sound field in the insulation performance measurement for parti... by Yu Aida, Naohisa Inoue, Tetsuya Sakuma

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Research on sinusoidal load identification method under structural natural frequency excitation based on LSTM-CNN by HE Wenbo, SUN Hanyu, XIE Jiang, ZHANG Xiaoqiang

“…Addressing the challenge of low identification accuracy in traditional load identification methods based on the truncated singular value decomposition（TSVD）method，especially when the external load frequency approaches or reaches the natural frequency of the structure，the LSTM-CNN load identification model is proposed in this paper. …”
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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

“…Due to the class distribution of fault cause is unbalanced and there are a lot of over-current fault causes, four-quadromt over-current fault is difficult to distinguish and diagnose. A fault diagnosis method based on recursive 2-Classification was proposed. …”
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Hyperspectral Anomaly Detection Based on Intrinsic Image Decomposition and Background Subtraction by Jiao Jiao, Longlong Xiao, Chonglei Wang

“…To solve the above problems, this paper proposes a hyperspectral anomaly detection method based on intrinsic image decomposition and background subtraction. …”
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