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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Decomposition-reconstruction-optimization framework for hog price forecasting: Integrating STL, PCA, and BWO-optimized BiLSTM. by Xiangjuan Liu, Yunlong Li, Fengtong Wang, Yujie Qin, Zhongyu Lyu

“…First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. …”
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Enhanced forecasting of shipboard electrical power demand using multivariate input and variational mode decomposition with mode selection by Paolo Fazzini, Giuseppe La Tona, Matteo Diez, Maria Carmela Di Piazza

“…This work contributes to ongoing efforts in optimizing decomposition methods for predictive modeling in energy management, opening new avenues for improving shipboard power grid efficiency.…”
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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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A Novel Hybrid Approach to Forecasting Crude Oil Prices Using Local Mean Decomposition, ARIMA, and XGBoost by Jawaria Nasir, Muhammad Aamir, Soofia Iftikhar, A. B. Albidah, Ohud A. Alqasem, Maysaa E. A. Elwahab, Ilyas Khan, Wei Sin Koh

“…The hybrid LMD-SD-ARIMA-XGBOOST model overcomes overfitting by combining statistical and machine learning components, where the decomposition process isolates key features of the data, reducing noise and complexity. …”
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Resource-Efficient Acoustic Full-Waveform Inversion via Dual-Branch Physics-Informed RNN with Scale Decomposition by Cai Lu, Jijun Liu, Liyuan Qu, Jianbo Gao, Hanpeng Cai, Jiandong Liang

“…This study proposes a full-waveform inversion method utilizing a dual-branch PIRNN architecture to effectively minimize GPU resource consumption. …”
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Integrating 3D printing, simulations and surrogate modelling: A comprehensive study on additive manufacturing focusing on a metal twin-cantilever benchmark by C. Mallor, S. Lani, V. Zambrano, H. Ghasemi-Tabasi, S. Calvo, A. Burn

“…The reduced order method for creating the surrogate model is based on tensor decomposition and designed for easy integration into a digital twin, while preserving the underlying physics by retaining the effects of input variables on the final output. …”
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A Hybrid Approach Integrating Decomposition Ensemble Forecasting With Optimal Combination Selection for Air Passenger Demand Forecasting by Yi-Chung Hu, Li-Chin Shih, Yu-Jing Chiu

“…The optimal combination selection from individual decomposition ensemble models was then used to construct combined models to strengthen the accuracy of decomposition ensemble forecasting. …”
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Economic Model Predictive Control for Wastewater Treatment Processes Based on Global Maximum Error POD-TPWL by Zhiyu Wang, Jing Zeng, Jinfeng Liu

“…The TPWL method constructs a reduced-order model framework, while GMEC iteratively refines the linearization point selection process. …”
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A theoretical study on the decomposition of TKX-50 with different vacancy defect concentrations under shock wave loading by Jun-qing Yang, Zhi-wei Guo, Xiao-he Wang, Ga-zi Hao, Yu-bing Hu, Xiao-jun Feng, Rui Guo, Wei Jiang

“…This study investigated the impacts of different vacancy defect concentrations on the decomposition of dihydroxylammonium 5,5′-bistetrazole-1,1′-diolate (TKX-50) under shock wave loading using the ab initio molecular dynamics (AIMD) method combined with the multiscale shock technique (MSST). …”
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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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TSVD Regularization-Parameter Selection Method Based on Wilson-θ and Its Application to Vertical Wheel-Rail Force Identification of Rail Vehicles by Jiaxin Wu, Tao Zhu, Yijun Wang, Cheng Lei, Shoune Xiao

“…A parameter-selection method is proposed to improve the accuracy of the truncated singular value decomposition (TSVD) method, which is based on the Wilson-θ method and the principle of minimum response error, for dynamic load identification. …”
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A Rolling-Bearing-Fault Diagnosis Method Based on a Dual Multi-Scale Mechanism Applicable to Noisy-Variable Operating Conditions by Jing Kang, Taiyong Wang, Ye Wei, Usman Haladu Garba, Ying Tian

“…To address the performance degradation encountered by current convolutional neural network-based rolling-bearing-fault diagnosis methods due to significant noise interference and variable working conditions in industrial settings, we propose a rolling-bearing-fault diagnosis method based on dual multi-scale mechanism applicable to noisy-variable operating conditions. …”
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Off-grid DOA estimation algorithm based on unitary transform and sparse Bayesian learning by Yang GAO, Jun-li CHEN, Guang-li YANG

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SVD-LSTM-based rainfall threshold prediction for rainfall-induced landslides in Chongqing by Chao He, Chaofan Wang, Junwen Peng, Wenhui Jiang, Jing Liu

“…By utilizing Singular Value Decomposition (SVD) to decompose Long Short-Term Memory (LSTM) layer weights into two smaller matrices and adding a custom layer to the standard LSTM structure, the SVD-LSTM method reduces the dimensionality of weights in the input and intermediate layers, reducing computational complexity and accelerating model training. …”
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A Wind Power Density Forecasting Model Based on RF-DBO-VMD Feature Selection and BiGRU Optimized by the Attention Mechanism by Bixiong Luo, Peng Zuo, Lijun Zhu, Wei Hua

“…Finally, an attention mechanism is employed to identify important information from the outputs of the BiGRU model, and the Grid Search (GS) method is used to optimize the BiGRU-Attention model, yielding optimal predictions. …”
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Preparation and characterization of EPDM/silica composites prepared through non-hydrolytic sol-gel method in the absence and presence of a coupling agent by T. H. Mokhothu, A. S. Luyt, M. Messori

“…However, ethylene chloride and TESPT evaporated from the samples at temperatures below the EPDM decomposition range. The values of the Nielsen model parameters, that gave rise to a good agreement with the experimentally determined Young’s modulus values, indicated improved dispersion and reduced size of the silica aggregates in the EPDM matrix. …”
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New State Identification Method for Rotating Machinery under Variable Load Conditions Based on Hybrid Entropy Features and Joint Distribution Adaptation by Xiaoming Xue, Nan Zhang, Suqun Cao, Wei Jiang, Jianzhong Zhou, Liyan Liu

“…In this paper, a novel state identification method integrated by time-frequency decomposition, multi-information entropies, and joint distribution adaptation is proposed for rolling element bearings. …”
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Two-stage stochastic capacitated Lot-Sizing problem by Lot-Size adaptation approach by Arsalan Rahmani, Meysam Hosseini, Amir Sahami

“…The computational results indicate that the proposed method is capable of efficiently solving the model.…”
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Matrix-qubit algorithm for semantic analysis of probabilistic data by Ilya A. Surov

“…The paper presents a method for semantic data analysis by means of complex-valued matrix decomposition. …”
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