Energy management of compound power supply based on grey wolf algorithm by Hui YI, Hui CHEN

“…Methods First, wavelet decomposition and fuzzy logic control are used to distribute the load power of the ship and optimize the charging and discharging process of the battery. …”
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A Novel Flexible Model for the Extraction of Features from Brain Signals in the Time-Frequency Domain by R. Heideklang, G. Ivanova

“…This shortcoming has inspired us to develop a new robust and flexible method for time-frequency analysis and signal feature extraction using the novel smooth natural Gaussian extension (snaGe) model. …”
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Reduced-Order Model for Performance Simulation and Conceptual Design of Rocket-Type Pulse Detonation Engines by Luis Sánchez de León, Francisco Sastre, Elena Martin, Angel Velazquez

“…A model-based method has been developed for the performance simulation and conceptual design of rocket-type pulse detonation engines (PDEs). …”
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An improved deep learning model for soybean future price prediction with hybrid data preprocessing strategy by Dingya CHEN, Hui LIU, Yanfei LI, Zhu DUAN

“…In the data preprocessing stage, futures price series are decomposed into subsequences using the ICEEMDAN (improved complete ensemble empirical mode decomposition with adaptive noise) method. The Lempel-Ziv complexity determination method was then used to identify and reconstruct high-frequency subsequences. …”
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Modeling of the process of gas movement in a channel with the presence of ice, accompanied by its thermal destruction by Tazetdinov Bulat, Tazetdinova Yulia, Gizzatullina Alina, Gimaeva Lilia

“…When constructing a mathematical model, it is assumed that warm gas is supplied at the entrance to the channel, passing through which it gives part of its energy to the channel walls, while thermal destruction of the walls occurs, and due to high pressure, the decomposition products of the channel (water and inert rock) are carried out by the flow to the surface. …”
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Applied Koopman Theory for Partial Differential Equations and Data-Driven Modeling of Spatio-Temporal Systems by J. Nathan Kutz, J. L. Proctor, S. L. Brunton

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Modelling the factors associated with quality of life in women with osteoporosis: A cross-sectional study by Rahmatollah Moradzadeh, Maryam Zamanian, Maliheh Taheri

“…Final regression coefficients were obtained based on the total effects of estimations (decompositions of effects into direct, indirect and total effects) by structural equation model (SEM) analysis. …”
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A novel hybrid model based on MPA-VMD, QRMGM and KDE for carbon price prediction by Dabin Zhang, Yufeng Ye, Yongmei Fang, Jing Zhou

“…Experimental results showed that the performance of the proposed model was validated across five aspects: the superiority of the decomposition method, point prediction accuracy, suitable prediction interval, comprehensive probability prediction performance, and training time relative to six benchmark models.…”
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A Hybrid Deep Learning Model for Link Dynamic Vehicle Count Forecasting with Bayesian Optimization by Chunguang He, Dianhai Wang, Yi Yu, Zhengyi Cai

“…This paper presents a hybrid deep learning method that combines the gated recurrent unit (GRU) neural network model with automatic hyperparameter tuning based on Bayesian optimization (BO) and the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) model. …”
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Identification of High-Order Linear Time-Invariant Models from Periodic Nonlinear System Responses by Mahmoud A. Hayajnh, Umberto Saetti, J. V. R. Prasad

“…The use of model order reduction and model-matching methods in the identification process is studied to recover the harmonic decomposition structure of the known system. …”
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A Combined Prediction Model for Hog Futures Prices Based on WOA-LightGBM-CEEMDAN by Xiang Wang, Shen Gao, Yibin Guo, Shiyu Zhou, Yonghui Duan, Daqing Wu

“…An integrated hog futures price forecasting model based on whale optimization algorithm (WOA), LightGBM, and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is proposed to overcome the limitations of a single machine learning model with low prediction accuracy and insufficient model stability. …”
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Approximate Inertial Manifold-Based Model Reduction and Vibration Suppression for Rigid-Flexible Mechanical Arms by Lisha Xu, Hua Deng, Chong Lin, Yi Zhang

“…So, accurately positioning and controlling the rigid-flexible mechanical arms could be difficult. Therefore, a model reduction method of rigid-flexible mechanical arms based on the approximate inertial manifold is put forward. …”
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Building electrical consumption patterns forecasting based on a novel hybrid deep learning model by Nasser Shahsavari-Pour, Azim Heydari, Farshid Keynia, Afef Fekih, Aylar Shahsavari-Pour

“…Specifically, the proposed model comprises three key components: (i) a mutual information-based feature selection method to identify the most significant input variables influencing energy consumption; (ii) a variational mode decomposition (VMD) approach to decompose the original energy consumption signal into intrinsic mode functions (IMFs), capturing relevant trends and eliminating noise; and (iii) a long short-term memory (LSTM) neural network to perform time-series forecasting of the target energy consumption values. …”
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Kinetics of austenite formation during continuous heating in as-cast and as-annealed conditions in a low carbon steel by Antonio Oliver-Reynoso, Octavio Vázquez-Gómez, Francisco Reyes-Calderón, Héctor J Vergara-Hernández, Martín Herrejón-Escutia, Miguel I Dávila-Pérez, Edgar López-Martínez

“…The apparent activation energies for each stage of austenite formation were calculated using the Kissinger method, with the peak temperature at the point of maximum transformation rate as the criterion. …”
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Prediction of State-of-Health and Remaining-Useful-Life of Battery Based on Hybrid Neural Network Model by Le Thi Minh Lien, Vu Quoc Anh, Nguyen Duc Tuyen, Goro Fujita

“…In the second part, with IMFs and a residual as input parameters, the SOH and RUL of different battery datasets are predicted by using the combined model CNN-BiLSTM-AM. To validate the accuracy of the proposed method, different comparative models are considered and carried out on CALCE and NASA battery degradation datasets. …”
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Tensor Signal Modeling and Channel Estimation for Reconfigurable Intelligent Surface-Assisted Full-Duplex MIMO by Alexander James Fernandes, Ioannis N. Psaromiligkos

“…We model the received signal as a tensor composed of two CANDECOMP/PARAFAC (CP) decomposition terms for the non-RIS and the RIS assisted links. …”
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An Informer-based multi-scale model that fuses memory factors and wavelet denoising for tidal prediction by Peng Lu, Yuchen He, Wenhui Li, Yuze Chen, Ru Kong, Teng Wang

“…By employing Fourier-based methods and iterative recursive decomposition strategies, we effectively separated periodic and trend components. …”
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Chaotic billiards optimized hybrid transformer and XGBoost model for robust and sustainable time series forecasting by Reham H. Mohammed, Asmaa Mohamed El-saieed

“…To fine-tune model parameters efficiently, the method incorporates the Chaotic Billiards Optimizer (CBO) alongside the Adam optimizer. …”
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Modeling Anomalous Transport of Cosmic Rays in the Heliosphere Using a Fractional Fokker–Planck Equation by José Luis Díaz Palencia

“…We derive analytical solutions using the Adomian Decomposition Method and express them in terms of Mittag-Leffler functions and Lévy stable distributions. …”
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Hyperbolic Spatial Covariance Modeling with Adaptive Signal Filtering for Robust Wi-Fi Indoor Positioning by Wenxu Wang, Mingxiang Liu

“…However, current CSI-based methods are hampered by three key limitations: susceptibility to skewed, non-Gaussian noise; informational redundancy from multi-AP configurations; and spatial discontinuities arising from Euclidean-based modeling. …”
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