Prediction of Dissolved Gas Concentration in Transformer Oil Based on Hybrid Mode Decomposition and LSTM-CNN by Tie CHEN, Zhifan ZHANG, Xianshan LI, Yifu CHEN, Hongxin LI

“…Predicting the concentration of dissolved gas in oil can help to know in advance the operation trend of transformers. A prediction method is thus proposed based on hybrid mode decomposition and LSTM-CNN network to achieve accurate gas concentration prediction. …”
Get full text

Time Series Prediction of Aerodynamic Noise Based on Variational Mode Decomposition and Echo State Network by Zhoufanxing Lei, Haiyang Meng, Jing Yang, Bin Liang, Jianchun Cheng

“…Due to the significant dynamical and non-stationary characteristics of aerodynamic noise, it is challenging to precisely predict its temporal behavior. Here, we propose a method combining variational mode decomposition (VMD) and echo state network (ESN) to accurately predict the time series of aerodynamic noise induced by flow around a cylinder. …”
Get full text

Short-term Wind Speed Prediction Based on Wavelet Packet Decomposition and BP Neural Network by WANG Ning, LUO Rubin, LIAO Jun, LI Jun, JIANG Yi, YANG Zechuan, YUAN Junjie

“…The simulation results show that the average absolute percentage error (MAPE), root mean square error (RMSE) and mean absolute error (MAE) of the short-term wind speed prediction model based on wavelet packet decomposition are lower than those of other short-term wind speed prediction methods. …”
Get full text

Differences in soil properties and the rates of decomposition of plant biomass on non-organically and organically managed soils by Triana Pitaloka, Dulbari Dulbari, Junita Barus

“…Organic farming has been advocated as a more environmentally sound and ecologically balanced alternative to the dominant agricultural model. This study compared the decomposition of plant biomass in organically and non-organically managed soil properties. …”
Get full text

Simultaneous Incomplete Traffic Data Imputation and Similarity Pattern Discovery with Bayesian Nonparametric Tensor Decomposition by Yaxiong Han, Zhaocheng He

“…BNPTD is a hierarchical probabilistic model, which is comprised of Bayesian tensor decomposition and Dirichlet process mixture model. …”
Get full text

Derivation and Numerical Assessment of a Stochastic Large–Scale Hydrostatic Primitive Equations Model by Francesco L. Tucciarone, Long Li, Etienne Mémin, Pranav Chandramouli

“…Derived from conservation principles via a stochastic Reynolds transport theorem, this approach decomposes velocity into a smooth–in–time large–scale component and a random–in–time field representing unresolved scales effects. To model the velocity noise term, we develop two data–driven methods based on Proper Orthogonal Decomposition (POD) and Dynamic Mode Decomposition (DMD) and extend this to hybrid approaches combining model– and data–driven constraints. …”
Get full text

A Novel Boundary-Type Meshless Method for Modeling Geofluid Flow in Heterogeneous Geological Media by Jing-En Xiao, Cheng-Yu Ku, Chih-Yu Liu, Wei-Chung Yeih

“…To deal with the subsurface flow problems of heterogeneous geological media, the domain decomposition method was adopted so that flux conservation and the continuity of pressure potential at the interface between two consecutive layers can be considered in the numerical model. …”
Get full text

SAR Images Despeckling Using Subaperture Decomposition and Non-Local Low-Rank Tensor Approximation by Xinwei An, Hongcheng Zeng, Zhaohong Li, Wei Yang, Wei Xiong, Yamin Wang, Yanfang Liu

“…Subaperture decomposition yields azimuth-frame subimages with high global structural similarity, which are modeled as low-rank and formed into a 3D tensor. …”
Get full text

Ex-Ante and Ex-Post Decomposition Strategy for Ultra-Short-Term Wind Power Prediction by Peng Lu, Zhuo Li, Lin Ye, Ming Pei, Yingying Zheng, Yongning Zhao

“…Secondly, in the error correction stage, the errors produced by the preliminary prediction model are corrected by persistence methods to compensate for final prediction errors. …”
Get full text

Prediction for Coastal Wind Speed Based on Improved Variational Mode Decomposition and Recurrent Neural Network by Muyuan Du, Zhimeng Zhang, Chunning Ji

“…For outlier detection, statistical methods were employed, followed by a comparative evaluation of three models—LSTM, LSTM-KAN, and Seq2Seq-Attention—for multi-step wind speed forecasting over horizons ranging from 1 to 12 h. …”
Get full text

« Décomposition fécondante » : la chimie organique et les savoirs du vivant chez Flaubert by Judith Wulf

Get full text

Hierarchical Multi-Scale Decomposition and Deep Learning Ensemble Framework for Enhanced Carbon Emission Prediction by Yinuo Sun, Zhaoen Qu, Zhuodong Liu, Xiangyu Li

“…Traditional statistical and machine learning methods struggle to capture complex multi-scale temporal patterns and long-range dependencies in emission data. …”
Get full text

Enhanced RT-DETR with Dynamic Cropping and Legendre Polynomial Decomposition Rockfall Detection on the Moon and Mars by Panpan Zang, Jinxin He, Yongbin Yang, Yu Li, Hanya Zhang

“…Conventional visual interpretation methods that rely on orbiter imagery can be inefficient due to their massive datasets and subtle morphological signatures. …”
Get full text

Assessing Vertical Equity in Defined Benefit Pension Plans: An Application to Switzerland by Tanja Kirn, Gijs Dekkers

“…We use an aligned dynamic microsimulation model to apply this method to the first pillar of the Swiss pension system and highlight the following three key effects: (1) the impact of the accrual rate on vertical equity; (2) the assessment of actuarial neutrality through the comparison of migrants with the non-migrant population; and (3) vertical equity across marital statuses. …”
Get full text

SFDformer: a frequency-based sparse decomposition transformer for air pollution time series prediction by Zhenkai Qin, Zhenkai Qin, Baozhong Wei, Caifeng Gao, Xiaolong Chen, Hongfeng Zhang, Cora Un In Wong

“…It integrates time seriesfrequency domain analysis with the decomposition of deep learning models. This approach facilitates the capture of interdependencies among high - dimensional features through time series decomposition, employs Fourier Transform to mitigate noise interference, and incorporates sparse attention mechanisms to selectively filter critical frequency components, thereby enhancing time - dependent modeling. …”
Get full text

Geny: a genotyping tool for allelic decomposition of killer cell immunoglobulin-like receptor genes by Qinghui Zhou, Mazyar Ghezelji, Ananth Hari, Ananth Hari, Michael K. B. Ford, Connor Holley, S. Cenk Sahinalp, Ibrahim Numanagić

Get full text

Short-Term Photovoltaic Power Forecasting Based on the VMD-IDBO-DHKELM Model by Shengli Wang, Xiaolong Guo, Tianle Sun, Lihui Xu, Jinfeng Zhu, Zhicai Li, Jinjiang Zhang

“…A short-term photovoltaic power forecasting method is proposed, integrating variational mode decomposition (VMD), an improved dung beetle algorithm (IDBO), and a deep hybrid kernel extreme learning machine (DHKELM). …”
Get full text

Effects of Medicare predictors in health disparities in the risk of Alzheimer's disease by Igor Akushevich, Arseniy Yashkin, Julia Kravchenko

Get full text

A Physics-Informed Neural Network Based on the Boltzmann Equation with Multiple-Relaxation-Time Collision Operators by Zhixiang Liu, Chenkai Zhang, Wenhao Zhu, Dongmei Huang

“…The micro–macro decomposition method is applied to improve learning efficiency. …”
Get full text

Applying Progressive Frequency Bands to Improve Image Quality and Training Stability of GAN by Chae-Eun Lee, Sung Hoon Jung

Get full text