TRIDENT: Text-Free Data Augmentation Using Image Embedding Decomposition for Domain Generalization by Yoonyoung Choi, Geunhyeok Yu, Hyoseok Hwang

“…Recent DG approaches use generative models like diffusion models to augment data with text prompts. …”
Get full text

Fault Diagnosis for Rotating Machinery Based on Convolutional Neural Network and Empirical Mode Decomposition by Yuan Xie, Tao Zhang

“…With features extracted from both methods combined, classification models are trained for diagnosis. …”
Get full text

An Integrated CEEMDAN to Optimize Deep Long Short-Term Memory Model for Wind Speed Forecasting by Yingying He, Likai Zhang, Tengda Guan, Zheyu Zhang

“…Experimental results indicate that the proposed method achieves minimal mean absolute percentage errors of 0.3285 and 0.1455, outperforming other popular models across multiple performance criteria.…”
Get full text

Photovoltaic Short-Term Output Power Forecast Model Based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise–Kernel Principal Component Analysis–Long Sh... by Lan Cao, Haoyu Yang, Chenggong Zhou, Shaochi Wang, Yingang Shen, Binxia Yuan

“…By comparing with other prediction models, the ICEEMDAN-KPCA-LSTM photovoltaic output power model outperforms other models.…”
Get full text

Carbon emissions from public transportation in major Chinese cities: spatiotemporal analysis, decoupling trends, and key drivers by Cai Jia, Xudong Wang, Chenglong Yang, Chengyang Qian, Zini Cao, Long Zhao, Luzhou Lin

“…This study utilized public transport data from 28 major Chinese cities from 2018 to 2022 and employed methods such as carbon emission measurement, standard deviation ellipse analysis, the Tapio decoupling model, and the LMDI decomposition method to ana-lyse the temporal and spatial evolution, decoupling states, and driving factors of public transport carbon emissions comprehensively. …”
Get full text

Prediction of remaining parking spaces based on EMD-LSTM-BiLSTM neural network by Changxi Ma, Xiaoting Huang, Ke Wang, Yongpeng Zhao

“…The proposed hybrid model is compared with a variety of current mainstream deep learning algorithms, and the effectiveness of the EMD-LSTM-BiLSTM method is validated. …”
Get full text

Semi-analytical dynamic modeling and impact mechanism analysis of a hard-coating cylindrical shell with arbitrary circular perforations by Jian Yang, Yue Zhang

“…Abstract In this paper, an innovative axial domain decomposition method, which uniquely integrates axial and circumferential perforation parameters, is developed for semi-analytical modeling of free vibration of a hard-coating cylindrical shell with arbitrary axial and circumferential perforations, based on the Love’s first-order shear deformation theory and Rayleigh-Ritz method. …”
Get full text

Investigation of the Dynamic Characteristics of PM_2.5 Dispersion During the Acceleration of Motor Vehicles in Urban Streets Based on Computational Fluid Dynamics and Dyn... by Jiawei Ding, Dan Mei, Bowen Liu, Mingwei Gao, Jiale Cui

Get full text

Optimal design of neural network-based fuzzy predictive control model for recommending educational resources in the context of information technology by Liu Tingting, Liu Qiyuan, Wang Xiaobei, Yang Yang

Get full text

Multi-objective stochastic model optimal operation of smart microgrids coalition with penetration renewable energy resources with demand responses by Ali Abdolahzadeh, Amir Hassannia, Farhoud Mousavizadeh, Mohammad Tolou Askari

“…A key innovation of this study is the development of an advanced hybrid solution methodology, combining the ε-constraint method for multi-objective optimization with Benders decomposition for computational efficiency. …”
Get full text

Method for compensating multi-time measurement data of distribution network based on alternating minimization matrix completion combined with VMD-ARIMA-LSTM by Xiaodan Yu, Ruijia Jiang, Xiaolong Jin, Hongjie Jia, Yunfei Mu, Wei Wei, Wanxin Tang

Get full text

Stochastic Optimization Scheduling Method for Mine Electricity–Heat Energy Systems Considering Power-to-Gas and Conditional Value-at-Risk by Chao Han, Yun Zhu, Xing Zhou, Xuejie Wang

“…To fully accommodate renewable and derivative energy sources in mine energy systems under supply and demand uncertainties, this paper proposes an optimized electricity–heat scheduling method for mining areas that incorporates Power-to-Gas (P2G) technology and Conditional Value-at-Risk (CVaR). …”
Get full text

Research on Annual Runoff Prediction Based on EMD-LSTM-ANFIS Model by HU Shunqiang, CUI Dongwen

“…To improve the accuracy of runoff prediction,this paper proposes a runoff prediction model based on the combination of empirical mode decomposition (EMD),long short-term memory (LSTM) neural network,and adaptive neuro-fuzzy inference system (ANFIS),decomposes the original runoff sequence into multiple regular component sequences through EMD,and reconstructs the phase space of each component sequence by the autocorrelation function method (AFM) and the false nearest neighbor method (FNN) to determine the input and output vectors,establishes the EMD-LSTM-ANFIS prediction model,and constructs the EMD-LSTM,EMD-ANFIS,LSTM,ANFIS as comparison models,as well as predicts and compares the annual runoff of the Longtan Station in Yunnan Province by the five models.The results show that the average relative error of the EMD-LSTM-ANFIS model for the annual runoff prediction is 3.18%,which is reduced by 55.0%、65.2%、68.1%、78.4% compared with the EMD-LSTM,EMD-ANFIS,LSTM,and ANFIS models respectively,with higher prediction accuracy and stronger generalization ability.Therefore,the EMD-LSTM-ANFIS model is feasible and reliable for runoff prediction.…”
Get full text

Experience in modeling inclined cracks in materials with cubic crystal structure by Karina A. Mushankova, Larisa V. Stepanova

“…The atomic stress distributions associated with the crack tip are obtained using the molecular dynamics method. Continuum distributions are obtained from the theoretical solution of the problem of determining the stress-strain state at the crack tip, based on the methods of the elasticity theory of anisotropic media and the subsequent decomposition of complex potentials by eigenfunctions. …”
Get full text

Fault Feature Research of Rolling Bearing based on Empirical Mode Decomposition and Principle Component Analysis by Zheng Xin

“…It is proposed that a fault diagnosis method for rolling bearing based on empirical mode decomposition（ EMD） and multivariate statistical process control（ MSPC）,the Hilbert- Huang transformation and principal component analysis（ PCA） are combined effectively in this method. …”
Get full text

Prediction of the Remaining Useful Life of Lithium–Ion Batteries Based on Mode Decomposition and ED-LSTM by Bingzeng Song, Guangzhao Yue, Dong Guo, Hanming Wu, Yonghai Sun, Yuhua Li, Bin Zhou

Get full text

Complex, Temporally Variant SVD via Real ZN Method and 11-Point ZeaD Formula from Theoretics to Experiments by Jianrong Chen, Xiangui Kang, Yunong Zhang

“…Then, by using the real zeroing neurodynamics (ZN) method, matrix vectorization, Kronecker product, vectorized transpose matrix, and dimensionality reduction technique, a dynamical model, termed the continuous-time SVD (CTSVD) model, is derived and investigated. …”
Get full text

Research on Multipoint Leak Location of Gas Pipeline Based on Variational Mode Decomposition and Relative Entropy by Yongmei Hao, Zhanghao Du, Juncheng Jiang, ZhiXiang Xing, Xinming Yan, Shuli Wang, Yongchao Rao

“…A multipoint leak detection and location method for urban gas pipelines based on variational mode decomposition and relative entropy was proposed. …”
Get full text

Tensor decomposition based-joint active device detection and channel estimation under frequency offset by QU Ruiyun, LIU Zujun, HUANG Beilei

“…In order to avoid the non-convexity introduced by the nonlinear between the frequency offsets and the channels, firstly, tensor decomposition was used to model the received signal from the perspective of the preamble sequence, channel, and frequency offset. …”
Get full text

Application of Empirical Mode Decomposition with Local Linear Quantile Regression in Financial Time Series Forecasting by Abobaker M. Jaber, Mohd Tahir Ismail, Alsaidi M. Altaher

“…We propose a two-stage technique that combines the empirical mode decomposition (EMD) with nonparametric methods of local linear quantile (LLQ). …”
Get full text