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

Overlapping social structures behind Brazil's cesarean section births: A decomposition analysis. by Surbhi Shrivastava, Heeju Sohn

“…We applied the Karlson-Holm-Breen (KHB) decomposition method to multivariate logistic regression models. …”
Get full text

Spatial and Temporal Evolution of Regional Energy Efficiency in China and Its Influencing Factors by Jinqiu Li, Yufeng Hu, Hui Zhang

“…To scientifically study the evolution trend in regional energy efficiency in China, this study uses convergence analysis, a spatial Gini coefficient decomposition model (no spatial consideration), and a spatial Markov chain model and spatial measurement model (spatial consideration). …”
Get full text

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

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

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

“…To solve the problem of photovoltaic power prediction in areas with large climate changes, this article proposes a hybrid Long Short-Term Memory method to improve the prediction accuracy and noise resistance. …”
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

Few-shot bearing fault diagnosis method based on an EEMD parallel neural network and a relation network by Cunsheng Zhao, Bo Tong, Chao Zhou, Qingrong Fan

“…To address these challenges, this paper proposed a few-shot bearing fault diagnosis method based on an Ensemble Empirical Mode Decomposition (EEMD) parallel neural network and a relation network (RN). …”
Get full text

A Hybrid VMD-BO-GRU Method for Landslide Displacement Prediction in the High-Mountain Canyon Area of China by Bao Liu, Jiahuan Xu, Jiangbo Xi, Chaoying Zhao, Xiaosong Feng, Chaofeng Ren, Haixing Shang

“…These findings indicate that, compared to traditional methods, our model delivers remarkable improvements in performance, offering higher prediction accuracy and greater reliability in the landslide forecasting task for the Mianshawan area.…”
Get full text

A VMD-TCN-Based Method for Predicting the Vibrational State of Scaffolding in Super High-Rise Building Construction by Ping Zhu, Gen Liu, Jian Wang, Pengfei Wang

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

A Short-Term Load Forecasting Method Considering Multiple Factors Based on VAR and CEEMDAN-CNN-BILSTM by Bao Wang, Li Wang, Yanru Ma, Dengshan Hou, Wenwu Sun, Shenghu Li

“…However, most of existing short-term load forecasting methods rely solely on the original load data or take into account a single external factor, which results in significant forecasting errors. …”
Get full text

A New Method of Intelligent Fault Diagnosis of Ship Dual-Fuel Engine Based on Instantaneous Rotational Speed by Ji Gan, Huabiao Jin, Qianming Shang, Chenxing Sheng

“…This model provides an effective new method for intelligent diagnosis of ship dual-fuel engine misfire faults to solve the traditional diagnostic methods’ limitations.…”
Get full text

Uncertainty‐aware nuclear power turbine vibration fault diagnosis method integrating machine learning and heuristic algorithm by Ruirui Zhong, Yixiong Feng, Puyan Li, Xuanyu Wu, Ao Guo, Ansi Zhang, Chuanjiang Li

“…Then, a feature extraction method integrating variational mode decomposition (VMD), L‐cliffs‐based effective mode selection, and sample entropy is devised to extract the latent features from the complex high‐dimensional feature space. …”
Get full text

Ensemble-based multiclass lung cancer classification using hybrid CNN-SVD feature extraction and selection method. by Md Sabbir Hossain, Niloy Basak, Md Aslam Mollah, Md Nahiduzzaman, Mominul Ahsan, Julfikar Haider

“…The main contribution of this research lies in its use of a hybrid CNN-SVD (Singular Value Decomposition) method and the use of a robust voting ensemble approach, which results in superior accuracy and effectiveness for mitigating potential errors. …”
Get full text