A Model Based on Neural Network to Predict Surface Settlement During Subway Station Construction: A Case Study of the Dongba-Zhongjie Station in Beijing, China by Jiaqi Zhang, Hua Jiang, Jinsen Wang, Jili Feng

Get full text

A New Modelling and Feature Extraction Method Based on Complex Network and Its Application in Machine Fault Diagnosis by Qi Zhang, Tian Tian, Guangrui Wen, Zhifen Zhang

“…This paper proposes a new method of local feature extraction based on frequency complex network (FCN) decomposition and builds a new complex network structure feature on this basis, namely, subnetwork average degree. …”
Get full text

Colored linear inverse model: A data-driven method for studying dynamical systems with temporally correlated stochasticity by Justin Lien, Yan-Ning Kuo, Hiroyasu Ando, Shoichiro Kido

“…In real-world problems, environmental noise is often idealized as Gaussian white noise, despite potential temporal dependencies. The linear inverse model (LIM) is a class of data-driven methods that extract dynamic and stochastic information from finite time-series data of complex systems. …”
Get full text

Battery Cluster Inconsistency Detection Method and Intelligent O&M Scheme Based on Vector Error Correction Model by Yuan GUO, Xiangyang XIA, Jiahui YUE, Hui LI, Jinbo WU

“…To solve the problems of incomplete battery data and fragmented data segments leading to inaccurate detection in the actual operation data of energy storage power stations, this paper proposes a battery cluster inconsistency detection method based on the vector error correction model. …”
Get full text

Detecting memberships in multiplex networks via nonnegative matrix factorization and tensor decomposition by Fengqin Tang, Xiaozong Wang, Xuejing Zhao, Chunning Wang

“…Our method first clusters the layers using matrix factorization with graph regularization, followed by a tensor decomposition strategy enhanced by a corner-finding algorithm to uncover the nodes’ mixed memberships in each group. …”
Get full text

Development of novel hybrid models for the prediction of Covid-19 in Kuwait by Ahmad Aldousari, Maria Qurban, Ijaz Hussain, Maha Al-Hajeri

“…By using a multilayer model with different decomposition techniques, we developed a novel hybrid model for decomposition and prediction of corona cases in Kuwait. …”
Get full text

Metal commodity futures price forecasting based on a hybrid secondary decomposition error-corrected model by Yuetong Zhang, Ying Peng, Yuping Song

“…Finally, two predicted sequences are reconstructed to obtain the final one-step result, and based on the result of the one-step prediction, the two-step prediction is made using the sliding prediction method. The empirical results show that compared to the one-time decomposition model, the prediction accuracy of the secondary decomposition error correction model is improved by about 32%, 33%, and 22% respectively under single machine learning, ensemble machine learning, and deep learning models. …”
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

Model Decomposition-Based Approach to Optimizing the Efficiency of Wireless Power Transfer Inside a Metal Enclosure by Romans Kusnins, Sergejs Tjukovs, Janis Eidaks, Kristaps Gailis, Dmitrijs Pikulins

“…In the present study, the model decomposition method is adapted to substantially accelerate the process of finding the optimal WPT system parameters. …”
Get full text

A task decomposition and scheduling model for power IoT data acquisition with overlapping data efficiency optimization by Jindong Cui, Yuqing Wang, Zengchen Zhu, Ruotong Li

“…This paper proposes a data acquisition task decomposition and scheduling method optimized through overlapping data analysis. …”
Get full text

Improving the prediction of streamflow in large watersheds based on seasonal trend decomposition and vectorized deep learning models by Ningchang Kang, Zhaocai Wang, Anbin Zhang, Hang Chen

“…Conventional prediction methods, such as physical and statistical models, often struggle to capture the complex nonlinear and nonstationary characteristics of streamflow. …”
Get full text

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

“…To validate our VGA, we compare it with LBioS, a 3D model that integrates diffusion (via the Lattice Boltzmann method) with biodegradation, and Mosaic, a Pore Network Geometrical Modelling (PNGM) which represents the pore space using geometrical primitives. …”
Get full text

A designed predictive modelling strategy based on data decomposition and machine learning to forecast solar radiation by Mumtaz Ali, Ramendra Prasad, Salman Alamery, Aitazaz Ahsan Farooque

“…In the first stage of model design, the RLMD, a frequency resolution method, is applied to decompose the original SR time series into amplitude modulation subseries (AMs), frequency modulation subseries (FMs), and the low-frequency product functions (PFs) to reveal the internal structure of the model construction data to incrementally optimize the RLD-RF model where only PFs were used. …”
Get full text

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. …”
Get full text

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. …”
Get full text

Chemical kinetic models, reaction mechanism estimation, and thermodynamic parameters for the non-isothermal decomposition of trona ore by Ceren Eda Delikurt, Meltem Kizilca Coruh

Get full text

Comparison of dynamic mode decomposition with other data-driven models for lung cancer incidence rate prediction by L. Raymond Guo, Jifu Tan, M. Courtney Hughes

“…This study explores applying DMD in public health using lung cancer data and compares it with other machine learning models.MethodsWe analyzed lung cancer incidence data (2000–2021) from 1,013 US counties. …”
Get full text

A Hybrid Deep Learning Model Based on FFT-STL Decomposition for Ocean Wave Height Prediction by Yelian Sun, Longkun Yu, Dandan Zhu

“…The results show that the hybrid model outperforms the other methods compared in our experiments. …”
Get full text

A Novel Framework for Solar Irradiance Prediction Integrating Signal Decomposition With Hybrid Time-Series Models by Keng-Hsi Lin, Po-Yen Hsu, Po-Han Chen, Mu-Yen Chen

“…Three decomposition methods—EMD, EEMD, and CEEMDAN—were combined with models such as LSTM, Bi-LSTM, GRU, Transformer, and statistical models such as ARIMA, ARCH, and GARCH were also implemented. …”
Get full text

Model and Algorithm of Cooperative Optimization Decomposition for Short-Term Contract Electricity Considering Wind Power Uncertainty by Lingjie LIU, Jikeng LIN

“…Most of the existing contract electricity decomposition methods do not take into account the impact of wind power uncertainty and do not coordinate with the generation plan for optimization, resulting in their insufficient execution when the contract is due. …”
Get full text