Singular Vectors in Acoustic Simulation Tests of St. Paul the Apostle Church in Bochnia by Krzysztof KOSAŁA

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Research on a Joint Extraction Method of Track Circuit Entities and Relations Integrating Global Pointer and Tensor Learning by Yanrui Chen, Guangwu Chen, Peng Li

“…Next, the Tucker decomposition method is utilized to capture the semantic correlations between relations, and an Efficient Global Pointer is employed to globally predict the start and end positions of subject and object entities, incorporating relative position information through rotary position embedding (RoPE). …”
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Singularity for the macroscopic production model with Chaplygin gas by Liu Xiaoli, Guo Lihui, Hu Yanbo

“…In this article, we investigate the formation of singularities in solutions of homogeneous and inhomogeneous systems for a macroscopic production model with Chaplygin gas using the method of characteristic decomposition. …”
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A Quality Control Method based on Combination Deep Learning for Measurement Data of Complex Mountain Wind Farm by Runjin YAO, Shuaibing CHENG, Qianqian ZHAO, Wenlong LI, Dong QIAN

“…Mountainous winds exhibit strong intermittent， fluctuating， and non-stationary characteristics due to the influence of terrain， resulting in poor observation quality， which makes conventional quality control methods unable to effectively improve their observation quality.To address this issue， a quality control method （VCG） based on variational mode decomposition， convolutional neural networks， and deep learning of gated cyclic units is constructed， and a particle swarm optimization strategy and wind power reconstruction model are introduced to comprehensively improve the quality of observation data.To verify the effectiveness of this method， 10 minute wind speed and direction data of target wind turbines in six complex mountainous wind farms in Jiangxi Ganzhou， Sichuan Guangyuan， Anhui Wuhu， Hubei Huangshi， Henan Pingdingshan， and Guangxi Hezhou in 2016 was quality controlled by VCG and compared with single machine learning method， spatial regression method （SRT）， and inverse distance weighting method （IDW）.The results indicate that VCG method is suitable for quality control of observed wind data in mountainous wind farms， and has a higher error detection rate for suspicious data compared to conventional methods； The controlled data can better restore the observed background field and have a lower error rate when applied to the power generation evaluation business of wind farms； And it has the characteristics of strong terrain adaptability.…”
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Research on short-term precipitation forecasting method based on CEEMDAN-GRU algorithm by Hua Xu, Zongkai Guo, Yu Cao, Xu Cheng, Qiong Zhang, Dan Chen

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Theoretical and empirical validation of software trustworthiness measure based on the decomposition of attributes by Hongwei Tao, Yixiang Chen, Hengyang Wu

“…From the perspective of attribute decomposition, there are a variety of software trustworthiness metric models. …”
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A Hybrid LMD–ARIMA–Machine Learning Framework for Enhanced Forecasting of Financial Time Series: Evidence from the NASDAQ Composite Index by Jawaria Nasir, Hasnain Iftikhar, Muhammad Aamir, Hasnain Iftikhar, Paulo Canas Rodrigues, Mohd Ziaur Rehman

“…It incorporates LMD (Local Mean Decomposition), SD (Signal Decomposition), and sophisticated machine learning methods. …”
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Exploring the determinants of fertility rates in Ethiopia: a decomposition analysis using count regression models with a focus on urban and rural residence, based on the 2019 Ethio... by Birhan Ambachew Taye, Bantie Getnet Yirsaw, Aychew Kassa Belete, Belyu Yehualashet Weldearegay

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A Novel Method for Voltage Sag Source Location Based on HHT and GA-BP by Zhen YANG, Yuchao MA, Li LI, Xin LI, Ziying MA

“…Finally, a simulation model of a dual power supply system is used to verify the proposed method, and the simulation results show that the proposed method has high accuracy and precision in positioning and can precisely locate the voltage sag source.…”
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Advanced image preprocessing and context-aware spatial decomposition for enhanced breast cancer segmentation by G. Kalpana, N. Deepa, D. Dhinakaran

“…In this paper, we propose a new solution that integrates with AIPT (Advanced Image Preprocessing Techniques) and CASDN (Context-Aware Spatial Decomposition Network) to overcome these problems. The preprocessing pipeline apply bunch of methods including Adaptive Thresholding, Hierarchical Contrast Normalization, Contextual Feature Augmentation, Multi-Scale Region Enhancement, and Dynamic Histogram Equalization for image quality. …”
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Application of a Hybrid Model Based on CEEMDAN and IMSA in Water Quality Prediction by GUO Li-jin, WU Hao-tian

“…This study proposes a novel hybrid model for water quality prediction. [Methods] First, the original water quality sequence was decomposed using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN). …”
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Modified Non-Negative Constraint Damping Method for T2 Spectrum Inversion of Nuclear Magnetic Resonance by LEI Xin, LIAO Xinkai, LIAO Lidan

“…Numerical examples show that the new algorithm has better and more stable inversion effect than the modified truncated singular value decomposition method and damping method, especially in the case of low signal-to-noise ratio, the new algorithm has more obvious advantages, and can further optimize the NMR logging technology.…”
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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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Data‐Driven Dynamic Modal Bias Analysis and Correction for Earth System Models by S. P. McGowan, N. L. Jones, W. S. P. Robertson, S. Balasuriya

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PV Power Short-Term Forecasting Method Based on VMD-GWO-ELMAN by Na ZHANG, Qiang REN, Guangchen LIU, Liping GUO, Jingyu LI

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A novel fault diagnosis method for gearbox based on RVMD and TELM with composite chaotic grey wolf optimizer by Xuebin Huang, Anfeng Xu, Hongbing Liu, Bingcheng Ye

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Bearing Fault Diagnosis Method Based on Improved VMD and Parallel Hybrid Neural Network by Wuyi Chen, Huafeng Cai, Qiu Sun

“…In order to combat the difficulty of fault feature extraction and fault recognition in the field of bearing fault diagnosis, a bearing fault diagnosis method based on improved variational mode decomposition (VMD) and parallel hybrid neural network is proposed, which combines reweighted kurtosis (RK) with variable mode decomposition (VMD) and uses reweighted kurtosis as the evaluation index to select the decomposition times of variational mode decomposition, while removing part of the interference in the fault signal and retaining its impact characteristics. …”
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Bolt Anchorage Quality Levels Classification Method Based on HO‐VMD‐CNN‐BiLSTM by Fan Kesong, Zhang Can, Liu Shaowei, Feng Mengyin, Yan Ao, Fu Mengxiong, He Deyin, Nie Zhibin

“…In this paper, a new model named HO‐VMD‐CNN‐BiLSTM is proposed to optimize the accuracy of signal decomposition and quality classification. …”
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A Short-Term Load Interval Forecasting Method Based on EEMD-SE and PSO-KELM by Lin ZHANG, Jichun LIU

“…The proposed model was tested with the actual load data of a city in South China in different seasons under different nominal confidence, and the simulation results show that compared with other prediction methods, the proposed method has better performance in interval reliability and width.…”
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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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