Multifractal Cross-Correlation Analysis of Carbon Emission Markets Between the European Union and China: A Study Based on the Multifractal Detrended Cross-Correlation Analysis and... by Xin Liao, Zheyu Wang, Huimin Tong

“…Using the multifractal detrended cross-correlation analysis (MF-DCCA) method and the Empirical Mode Decomposition (EMD)-MF-DCCA method, this study quantifies the dynamic interrelation between carbon emission allowance returns in the Chinese and EU markets. …”
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A Fault Diagnosis Scheme for Rolling Bearing Based on Particle Swarm Optimization in Variational Mode Decomposition by Cancan Yi, Yong Lv, Zhang Dang

“…Variational mode decomposition (VMD) is a new method of signal adaptive decomposition. …”
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Speech Enhancement Based on Constrained Low-rank Sparse Matrix Decomposition Integrated with Temporal Continuity Regularisation by Chengli SUN, Conglin YUAN

“…In this paper, we propose a temporal continuity constrained low-rank sparse matrix decomposition (TCCLSMD) based speech enhancement method. …”
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Low-Light Image Enhancement Integrating Retinex-Inspired Extended Decomposition with a Plug-and-Play Framework by Chenping Zhao, Wenlong Yue, Yingjun Wang, Jianping Wang, Shousheng Luo, Huazhu Chen, Yan Wang

“…To address these limitations, this paper presents an enhancement method by integrating a Plug-and-Play strategy into an extended decomposition model. …”
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A Frequency-Shifting Variational Mode Decomposition-Based Approach to MI-EEG Signal Classification for BCIs by Haiqin Xu, Shahzada Ali Hassan, Waseem Haider, Youchao Sun, Xiaojun Yu

“…Despite extensive research on SD techniques, these issues remain largely unresolved, emphasizing the urgent need for a more reliable and precise approach. This study proposes a novel solution through the frequency-shifting variational mode decomposition (FS-VMD) method, which overcomes the limitations of traditional SD techniques by providing better resolution of intrinsic mode functions (IMFs). …”
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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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Variable Filtered-Waveform Variational Mode Decomposition and Its Application in Rolling Bearing Fault Feature Extraction by Nuo Li, Hang Wang

“…Variational Mode Decomposition (VMD) serves as an effective method for simultaneously decomposing signals into a series of narrowband components. …”
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A voxel-based approach for simulating microbial decomposition in soil: Comparison with LBM and improvement of morphological models. by Mouad Klai, Olivier Monga, Mohamed Soufiane Jouini, Valérie Pot

“…Our method yields result similar to those of LBioS in a quarter of the computing time. …”
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Absolute Quantitative Photoacoustic Imaging for Contrast Agents Concentration Estimation Using a Spectral Decomposition Approach by Shang Gao PhD, Liudmila Serebrennikova MS, Ryo Murakami MS, Srikanth Boinapally PhD, Sangeeta Ray PhD, Martin G. Pomper MD, PhD, Haichong K. Zhang PhD

“…This study introduces a spectral decomposition method to improve absolute CA concentration estimation in PA imaging. …”
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Enhanced Singular Value Decomposition Modulation Technique to Improve Matrix Converter Input Reactive Power Control by Luis Ramon Merchan-Villalba, José Merced Lozano-García, Alejandro Pizano-Martínez, Iván Abel Hernández-Robles

“…Overall, the proposed e-SVD modulation technique lays the foundation for more reliable reactive power regulation in applications such as microgrids and distributed generation systems, contributing to the development of smarter and more resilient energy infrastructures.…”
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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

“…The fusion of a low-spatial-resolution hyperspectral image (LR-HSI) and a high-spatial-resolution multispectral image (HR-MSI) is an effective way to generate a high-resolution hyperspectral image (HR-HSI). In recent years, methods based on tensor ring (TR) decomposition have received widespread attention due to their superior performance in approximating high-dimensional data. …”
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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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Incremental Gate State Output Decomposition Model for Highway Traffic Forecasting Using Toll Collection Data by Liang Yu, Ming Li, Kaifeng Liu, Xiangping Cheng

“…The proposed method improves the ability of the RNN model to estimate traffic data series by segmenting consecutive time intervals and accumulating incremental changes across these time intervals, allowing for more precise traffic predictions. …”
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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

“…A reduced order modeling approach, Proper Orthogonal Decomposition (POD) method, is used to further analyze the flow. …”
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Modeling and nonlinear analysis of chaotic wave processes in electrochemically active neuronal media based on matrix decomposition by A. M. Krot, S. I. Pavlov

“…A general model of the origin and evolution of chaotic wave processes in electrochemically active neuronal media based on the proposed method of matrix decomposition of operators of nonlinear systems has been developed. …”
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Reduced-Order Decomposition and Coordination Approach for Markov-Based Stochastic Unit Commitment With Distributed Wind Farms by Niranjan Raghunathan, Zongjie Wang, Mikhail A. Bragin, Peter B. Luh, Bing Yan, Tianqiao Zhao, Meng Yue

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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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Fine root decomposition and nutrient release of different age Caragana intermedia plantation in alpine sandy land by Qingxue Li, Qingxue Li, Zhiqing Jia, Zhiqing Jia, Zhiqing Jia, Lingxianzi He, Lingxianzi He, Xuebin Zhao, Xiuben Yang

“…Decomposition bag method was used to study the fine root (1 mm< D ≤ 2 mm, 0.5 mm< D ≤ 1 mm and D ≤ 0.5 mm) decomposition and nutrient release of Caragana intermedia plantation with different age (4-, 9-, 11-, 16- and 22-years old) in Gonghe Basin of the Tibetan Plateau. …”
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A Hybrid Deep Learning Model Based on FFT-STL Decomposition for Ocean Wave Height Prediction by Yelian Sun, Longkun Yu, Dandan Zhu

“…To improve the accuracy of ocean wave height prediction, we developed a hybrid model that integrates decomposition and deep learning. The approach combines Fourier transform, seasonal and trend decomposition using Loess, and various deep learning models, which can more accurately capture the periodicity, trends, and random fluctuations. …”
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The impact of natural drivers and human activities on sediment flux in Hekouzhen-Longmen section of China over the last 100 years by Sinuo Fan, Xiaohui Jiang, Yuxin Lei, Xiang Su, Yuehong Li

“…To fill these research gaps, this study has collated the hydrological and meteorological data over the past century. By applying the method of sediment characteristic factor decomposition, a quantitative analysis has been conducted on the impacts of climate change and human activities on sediment changes. …”
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