Robust Photovoltaic Power Forecasting Model Under Complex Meteorological Conditions by Yuxiang Guo, Qiang Han, Tan Li, Huichu Fu, Meng Liang, Siwei Zhang

“…To effectively mitigate these limitations, this work proposes a dual-stage feature extraction method based on Variational Mode Decomposition (VMD) and Principal Component Analysis (PCA), enhancing multi-scale modeling and noise reduction capabilities. …”
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A Survey of Post-Hoc XAI Methods From a Visualization Perspective: Challenges and Opportunities by Deepshikha Bhati, MD Amiruzzaman, Ye Zhao, Angela Guercio, Tram Le

“…Post-hoc explanation methods interpret deep learning predictions by uncovering the significance of input features, while visualization tools can contribute to a deep understanding of AI model reasoning based on these methods. …”
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A survey of data-driven fault-diagnosis methods for large-scale industrial production processes by Qianxiang YU, Qing LI, Linlin LI, Yixuan WANG

“…Traditional fault-diagnosis methods based on mechanism have limited applications because precise modeling of the systems considered is required. …”
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Synthesis, characterization, pyrolysis and thermokinetic studies of an energetic ionic liquid: 4-amino-1-methyl-1,2,4-triazolium nitrate by Santhosh G, Supriya N, Vijayalakshmi K. P, Deepthi Thomas

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Research on scraper conveyor load prediction method based on wavelet transform and BP neural network by Dan Zhang, Jiafeng Qin, Weidong Wu, Yongtao Zhu, Weijie Guo

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Gearbox Fault Diagnosis Based on the LMD Cloud Model and PSO-KELM by Zhao Xiaohui, Tan Qi, Hu Sheng, Yang Wenbin, Huan Kaixuan, Zhang Zhijie

“…To address this problem, a gearbox fault diagnosis method based on local mean decomposition (LMD) cloud model feature extraction combined with particle swarm optimization (PSO) kernel extreme learning machine (KELM) is proposed. …”
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Kinematic Calibration of Industrial Robots Based on Distance Information Using a Hybrid Identification Method by Guanbin Gao, Yuan Li, Fei Liu, Shichang Han

“…The singular value decomposition (SVD) is used to eliminate the redundant parameters of the error model. …”
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A Generalized Benders Decomposition for Mixed-Integer Nonlinear Programming: Theory and Applications by Fadiah Hasna Nadiatul Haq, Diah Chaerani, Anita Triska

“…This paper comprehensively explains how to solve mixed-integer nonlinear programming (MINLP) models using the generalized benders decomposition (GBD) method. …”
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Data Reconstruction Using Smart Sensor Placement by Farnaz Boudaghi, Danial Waleed, Luis A. Duffaut Espinosa

“…To address the uncertainties inherent in spatio-temporal field estimation, a robust data-driven control method is utilized, providing resilience against unpredictable environmental and model changes. …”
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Analytical investigation on resolution calculation method for nonlinear temperature load of steel-concrete composite girders by Chun-Ming Zhang, Wei-Hong Wu, Wei Xian, Wei Xian

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Multistep Prediction Model for Photovoltaic Power Generation Based on Time Convolution and DLinear by WANG Shuyu, LI Hao, MA Gang, YUAN Yubo, BU Qiangsheng, YE Zhigang

“…[Conclusions] Through the improved ICEEMDAN decomposition and TCN-DLinear combined model， this study effectively solves the shortcomings of traditional methods in multistep prediction and significantly improves the accuracy and reliability of photovoltaic power prediction. …”
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Short-Term Prediction Method for Gas Concentration in Poultry Houses Under Different Feeding Patterns by Yidan Xu, Guanghui Teng, Zhenyu Zhou

“…Combining the advantages of seasonal-trend decomposition using loess (STL), Granger causality (GC), long short-term memory (LSTM), and extreme gradient boosting (XGBoost), an ensemble method called the STL-GC-LSTM-XGBoost model is proposed. …”
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Prediction of polar motion and UT1-UTC based on the hybrid EEMD_LSTM model by Chenxiang Wang, Pengfei Zhang, Wenbin Shen, Jiayao Wang

“…In this paper, we propose a model that combines Ensemble Empirical Mode Decomposition (EEMD) with Long Short-Term Memory (LSTM), denoted as EEMD_LSTM. …”
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Doppler Shift Estimation Method for Frequency Diverse Array Radar Based on Graph Signal Processing by Ningbo Xie, Haijun Wang, Kefei Liao, Shan Ouyang, Hanbo Chen, Qinlin Li

“…Subsequently, spectral decomposition is conducted on the constructed signal model utilizing graph Fourier transform (GFT) techniques, enabling the extraction of the target’s Doppler shift parameter through spectral peak search. …”
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Evaluation of Modified Reflection Symmetry Decomposition Polarization Features for Sea Ice Classification by Tianlang Lan, Chengfei Jiang, Xiaofan Luo, Wentao An

“…However, traditional methods, such as Freeman–Durden (FD) and H/A/α decomposition, struggle to accurately characterize complex scattering mechanisms, limiting their ability to differentiate between various sea ice types. …”
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A Novel Methodology for the Automatic Decomposition of HAWT Wakes With K‐Means Clustering by Lorenzo Tieghi, Davide Cerbarano, Filippo De Girolamo, Valerio Francesco Barnabei, Giovanni Delibra

“…The relevance of these quantities in the clustering results is discussed and then the robustness of the method is assessed against temporal propagation of the model to 108 time steps, corresponding to three rotor revolutions. …”
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Analysis of Extraordinary Optical Transmission With Periodic Metallic Gratings Using ADE-LOD-FDTD Method by Tu-Lu Liang, Wei Shao, Sheng-Bing Shi, Haiyan Ou

“…In this paper, a frequency-dependent locally one-dimensional finite-difference time-domain (LOD-FDTD) method is developed for the extraordinary optical transmission (EOT) analysis of periodic metallic gratings. …”
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A novel dual-student reverse knowledge distillation method for magnetic tile defect detection by Jiyan Tang, Ao Zhang, Weian Liu

“…It outperforms existing methods in both pixel-level and sample-level anomaly detection tasks.…”
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A Multi-Scale Time–Frequency Complementary Load Forecasting Method for Integrated Energy Systems by Enci Jiang, Ziyi Wang, Shanshan Jiang

“…Experiments on 2021–2023 multi-energy load and meteorological data from the Arizona State University Tempe campus show that ST-ScaleFusion achieves 24 h forecasting MAE values of 667.67 kW for electric load, 1073.93 kW/h for cooling load, and 85.73 kW for heating load, outperforming models like TimesNet and TSMixer. Robust in long-step (96 h) forecasting, it reduces MAE by 30% compared to conventional methods, offering an efficient tool for real-time IES scheduling and risk decision-making.…”
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Identifying urban spatial clusters via flow dynamics: a coupled tensor-based method by Zilong Zhao, Zihan Kan, Mei-Po Kwan, Luliang Tang

“…This study develops a Graph-Laplacian Coupled Non-negative Tucker Decomposition (GCNTD) model to recognize multi-scale urban clusters in planar and network urban spaces. …”
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