Large Area Crops Mapping by Phenological Horizon Attention Transformer (PHAT) Method Using MODIS Time-Series Imagery by Quanshan Gao, Taixia Wu, Hongzhao Tang, JingYu Yang, Shudong Wang

“…To address these challenges, this article developed an advanced deep learning crop mapping method, i.e., phenological horizon attention mechanism-transformer model (PHAT) to achieve rapid and accurate multiple crops extraction over large areas. …”
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Analyzing Electromagnetic Systems on Electrically Large Platform Using a GTM-PO Hybrid Method with Synthetic Basis Functions by Shang Xiang, Gaobiao Xiao, Junfa Mao

“…Based on domain decomposition method (DDM), the proposed approach is to divide the whole problem into a GTM region and a PO region. …”
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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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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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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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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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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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Enhanced Workload Prediction in Data Centers Using Two-Stage Decomposition and Hybrid Parallel Deep Learning by Dalal Alqahtani, Hamidreza Imani, Tarek El-Ghazawi

“…After that, VMD is a method based on the center frequency applied to decompose further the high-frequency components, which may contain fluctuations that obscure underlying trends, thus enhancing the model’s overall accuracy. …”
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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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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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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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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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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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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 ANN is trained using statistical descriptors derived from the first two intrinsic mode functions (IMFs), capturing both amplitude and frequency content. The method was validated in MATLAB on the IEEE 33-bus radial distribution test system using simulated disturbances. …”
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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

“…Additionally, a tourist flow prediction method was introduced, utilizing a combination of bi-layer convolution, bidirectional long short-term memory, and an attention mechanism (CNN-BiLSTM-Att). …”
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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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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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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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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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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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