Online identification of stability region for large-scale wind farms, Part II: Construction of stability region boundary by Jia Luo, Peng Wang, Haoran Zhao, Yonghan Liu, Vladimir Terzija

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Koopman-Based Model Predictive Control of Functional Electrical Stimulation for Ankle Dorsiflexion and Plantarflexion Assistance by Mayank Singh, Noor Hakam, Trisha M. Kesar, Nitin Sharma

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YConvFormer: A Lightweight and Robust Transformer for Gearbox Fault Diagnosis with Time–Frequency Fusion by Yihang Peng, Jianjie Zhang, Songpeng Liu, Mingyang Zhang, Yichen Guo

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The superior coupled model with innovative strategy for accurate imputation of missing hydrological monitoring data in water research-A case of groundwater level data by Xian-Qi Zhang, Zi-Yu Li, Xian-Liang Liu, Xiao-Yan Wu, Xin-Yong Xu, En-Kuan Li, Bo-Wen Wang, Su-Bo Han, Shao-Bo Liu

“…This paper proposes a strategy of “Decomposition-Classification-Feature Extraction-Imputation” and establishes a coupled model integrating Extreme-point Symmetric Mode Decomposition (ESMD), Permutation Entropy (PE), Singular Value Decomposition (SVD), and Whale Optimization Algorithm-Bidirectional Long Short-Term Memory (WOA-BiLSTM) for the hydrological monitoring data imputation. …”
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Optimised Neural Network Model for Wind Turbine DFIG Converter Fault Diagnosis by Ramesh Kumar Behara, Akshay Kumar Saha

“…VMD has many advantages over other decomposition methods, notably for non-stationary signals and noise. …”
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A New Method for Estimating the Input-Energy of MDOF Structures by D. Davarnia, B. Farahmand Azar

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A Reduced-Order Algorithm for a Digital Twin Model of Ultra-High-Voltage Valve-Side Bushing Considering Spatio-Temporal Non-Uniformity by Yongsheng He, Yongfu Li, Xiangcheng Li, Yanan Yuan, Fan Yang, Zongxiang Lu

“…This proposed method reduces the calculation time to 10% of the full-order simulation model while controlling the error range of the key research area less than 0.1%. …”
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Modeling population size independent tissue epigenomes by ChIL‐seq with single thin sections by Kazumitsu Maehara, Kosuke Tomimatsu, Akihito Harada, Kaori Tanaka, Shoko Sato, Megumi Fukuoka, Seiji Okada, Tetsuya Handa, Hitoshi Kurumizaka, Noriko Saitoh, Hiroshi Kimura, Yasuyuki Ohkawa

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Fault Diagnosis of Axial Piston Pump Based on Extreme-Point Symmetric Mode Decomposition and Random Forests by Lei Yafei, Jiang Wanlu, Niu Hongjie, Shi Xiaodong, Yang Xukang

“…Aiming at fault diagnosis of axial piston pumps, a new fusion method based on the extreme-point symmetric mode decomposition method (ESMD) and random forests (RFs) was proposed. …”
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Image Characteristic-Guided Learning Method for Remote-Sensing Image Inpainting by Ying Zhou, Xiang Gao, Xinrong Wu, Fan Wang, Weipeng Jing, Xiaopeng Hu

“…Additionally, IGLL incorporates mathematical constraints into deep-learning models. A singular value decomposition (SVD) loss item is proposed to model the low-rankness characteristic, and it constrains feature consistency. …”
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DYNAMIC UNBALANCE DETECTION OF CARDAN SHATF IN HIGH-SPEED TRAIN BASED ON MODIFIED VARIATIONAL MODE DECOMPOSITION by HONG JianFeng, DING JianMing, LIN JianHui, ZHENG ShuBin

“…The Fourier spectrum of MVMD was used to detect the unbalance of Cardan shaft in high-speed train. The method and model was verified through the unbalance experiment data. …”
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Integrating Time Series Decomposition and Deep Learning: An STL-TCN-Transformer Framework for Landslide Displacement Prediction by Shuai Ren, Kamarul Hawari Ghazali

“…This study proposes an STL-TCN-Transformer model that combines time series decomposition with deep learning to predict cumulative displacement. …”
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Forecasting Influenza Trends Using Decomposition Technique and LightGBM Optimized by Grey Wolf Optimizer Algorithm by Yonghui Duan, Chen Li, Xiang Wang, Yibin Guo, Hao Wang

“…The residual sequence from the GWO-LightGBM model was then decomposed and corrected using the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) method, which led to the development of the GWO-LightGBM-CEEMDAN model. …”
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Optimization of fluid control laws through deep reinforcement learning using dynamic mode decomposition as the environment by T. Sakamoto, K. Okabayashi

“…In this study, we examine the feasibility of deriving an effective control law using a reduced-order model constructed by dynamic mode decomposition with control (DMDc). …”
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Generalized Logarithmic Tensor Nuclear Norm for Hyperspectral-Multispectral Image Fusion via Tensor Ring Decomposition by Jun Zhang, Mengling He, Chengzhi Deng

“…To address these issues, we propose a new HSI–MSI fusion model based on the generalized logarithmic tensor nuclear norm (GLTNN) under the TR decomposition framework. …”
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Combined CNN-BiLSTM-Att tourism flow prediction based on VMD-MWPE decomposition reconstruction by Maomao Luo, Danhong Chen, Xinxing Hou, Kang Luo, Tao Wang

“…These statistically significant improvements validate the model’s superior predictive capability for tourist flow forecasting. …”
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Investigation of the Effects of Length to Depth Ratio on Open Supersonic Cavities Using CFD and Proper Orthogonal Decomposition by Ibrahim Yilmaz, Ece Ayli, Selin Aradag

“…Two-dimensional compressible time-dependent Reynolds-averaged Navier-Stokes equations with k-ω turbulence model are solved. A reduced order modeling approach, Proper Orthogonal Decomposition (POD) method, is used to further analyze the flow. …”
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Enhanced Workload Prediction in Data Centers Using Two-Stage Decomposition and Hybrid Parallel Deep Learning by Dalal Alqahtani, Hamidreza Imani, Tarek El-Ghazawi

“…To improve this, we introduce CVCBM which blends signal processing techniques Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Variational Mode Decomposition (VMD) with advanced deep learning models like Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM) networks. …”
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Accelerated Convergence Method for Flow Field Based on DMD-POD Combined Reduced-Order Optimization Model by Jianhui Li, Jun Huang, Yahui Sun, Guoqiang Li

“…This method involves conducting dynamic mode decomposition (DMD) and proper orthogonal decomposition (POD) model reduction on the field snapshots. …”
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Temporal Denoising of Infrared Images via Total Variation and Low-Rank Bidirectional Twisted Tensor Decomposition by Zhihao Liu, Weiqi Jin, Li Li

“…Therefore, a novel TRN denoising approach based on total variation regularization and low-rank tensor decomposition is proposed. This method effectively suppresses temporal noise by introducing twisted tensors in both horizontal and vertical directions while preserving spatial information in diverse orientations to protect image details and textures. …”
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