Collaborative Forecasting of Multiple Energy Loads in Integrated Energy Systems Based on Feature Extraction and Deep Learning by Zhe Wang, Jiali Duan, Fengzhang Luo, Xiaoyu Qiu

“…This paper proposes a collaborative load forecasting method based on feature extraction and deep learning. …”
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2LE-BO-DeepTrade: an integrated deep learning framework for stock price prediction by Zinnet Duygu Akşehir, Erdal Kılıç

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Multi-stage Optimization Forecast of Short-term Power Load Based on VMD and PSO-SVR by Wenwu LI, Qiang SHI, Dan LI, Qunyong HU, Yun TANG, Jinchao MEI

“…To reduce the non-linearity of the short-term load sequence and improve the prediction accuracy, a short-term load forecasting model based on multi-stage optimization variational mode decomposition (VMD) and particle swarm optimization optimize support vector regression (PSO-SVR) is proposed. …”
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Smoothing Techniques for Improving COVID-19 Time Series Forecasting Across Countries by Uliana Zbezhkhovska, Dmytro Chumachenko

“…An ANOVA confirmed the statistically significant influence of the model type on the MAPE (<i>p</i> = 0.008), whereas the smoothing method alone showed no significant effect. …”
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Polarimetric SAR Ship Detection Using Context Aggregation Network Enhanced by Local and Edge Component Characteristics by Canbin Hu, Hongyun Chen, Xiaokun Sun, Fei Ma

“…Polarimetric decomposition methods are widely used in polarimetric Synthetic Aperture Radar (SAR) data processing for extracting scattering characteristics of targets. …”
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Multisensor Fault Diagnosis of Rolling Bearing with Noisy Unbalanced Data via Intuitionistic Fuzzy Weighted Least Squares Twin Support Higher-Order Tensor Machine by Shengli Dong, Yifang Zhang, Shengzheng Wang

“…Aiming at the limitations of existing multisensor fault diagnosis methods for rolling bearings in real industrial scenarios, this paper proposes an innovative intuitionistic fuzzy weighted least squares twin support higher-order tensor machine (IFW-LSTSHTM) model, which realizes a breakthrough in the noise robustness, adaptability to the working conditions, and the class imbalance processing capability. …”
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A data-driven hybrid framework for voltage transformer ratio error prediction: Addressing challenges in complex power systems by Jiuxi Cui, Zhenhua Li, Heping Lu, Feng Zhou, Haoyu Chen, Zhenxing Li

“…First, an energy entropy-optimized adaptive variational mode decomposition method is developed, which introduces convergence constraints for parameter selection, reducing randomness during the decomposition process and is validated by power spectrum analysis. …”
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Short-Term Load Forecasting of Power System Based on VMD-CNN-BIGRU by Huping YANG, Yang YU, Chao WANG, Xiangjun LI, Yitao HU, Chuchu RAO

“…The effectiveness of the proposed method is verified by simulation analysis, and the method has higher load prediction accuracy and stronger robustness than other models, which can improve the accuracy of short-term load prediction of power system.…”
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In-Situ Residual Tracking in Reduced Order Modelling by Joseph C. Slater, Chris L. Pettit, Philip S. Beran

“…Proper orthogonal decomposition (POD) based reduced-order modelling is demonstrated to be a weighted residual technique similar to Galerkin's method. …”
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Submarine Cable Vibration Signal Recognition Based on Sparrow Search Algorithm Optimized Support Vector Machine by GUO Jiaxing, QIAN Junxia, LIU Rui, MA Hongjuan

“…Firstly, the submarine cable fault signal was decomposed by the ensemble empirical module decomposition (EEMD) method, allowing for extraction of the kurtosis and energy entropy combination of each component as the training feature set, so as to avoid any potential signal distortion caused by direct noise reduction that may affect the extraction of target features. …”
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Application of a text mining method in navigation and communication for enhancing maritime safety. by Paulina Hatłas-Sowińska, Leszek Misztal

“…This paper introduces a model for the translation of natural language into ontology and vice versa in an autonomous navigation system of a sea-going vessel. …”
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Simulating Ising-Like Models on Quantum Computer by Andrey Andreev, Pavel Khrapov

“…Using special parameterizations of the state of the system of qubits and transfer matrix decomposition, free energy and magnetization are calculated for 3-chain model on a quantum computer emulator. …”
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STRUCTURAL GLOBAL SENSITIVITY METHOD BASED ON PARTIAL DERIVATIVE WHOLE DOMAIN INTEGRAL by TU LongWei, LIU Jie, LIU GuangZhao, ZHANG Zheng

“…Then,local sensitivity method based on partial derivative was extended to a global sensitivity method by integrating partial derivatives of model variables in variable sapces. …”
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Global, regional, and national burden of colorectal cancer associated with diet high in red meat, 1990 to 2021: an analysis for the global burden of disease study and prediction to... by Huijun Lei, Miaomiao Chen, Haoyu Qu, Zuomei He, Hui Zhong, Liang Li, Mengzhou Xie

“…Inequality slope and concentration indices quantified health disparities, while decomposition analyses identified drivers of burden changes. …”
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Intelligent Malfunction Identification Method in Mechanical Manufacturing Process Based on Multisensor Data by Meng Wang

“…The mechanical data of keyboard chords are acquired by sound-sensitive sensors, and the data features are extracted by wavelet packet decomposition. Based on the optimized BP, a mechanical malfunction judgment model is constructed, and various parameters in the model are calculated. …”
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A Correction Method for Hardening Artifacts in CT Images Based on Integral Invariance by Yuxin LIU, Jiaotong WEI, Xiaojie ZHAO, Ping CHEN, Jinxiao PAN

“…For imaging experiments with dual-material and multi-material models, compared with directly reconstructed CT images, the hardening artifacts in the reconstructed images were significantly reduced using this method, indicating its effectiveness.…”
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Hybrid precoding method for mmWave massive MIMO systems based on LFM by Haoyi CHEN, Yifan DING, Ying CHENG

“…Analog-digital hybrid precoding is a key technology for millimeter wave massive MIMO systems that reduce hardware costs while balancing system performance.However,the traditional hybrid precoding scheme often needed to find a suitable codebook for precoding,and some codebooks were not easy to obtain or had deviations in actual situations.An analog-digital hybrid precoding method based on latent factor model (LFM) in machine learning without codebook was proposed for this problem.The LFM decomposition and stochastic gradient descent method were used to approximate the designed precoding matrix to the optimal full digital precoding matrix for good performance.The simulation results show that compared with the hybrid precoding design method based on orthogonal matching pursuit (OMP) algorithm,this method not only does not need a codebook,but also has better performance than the hybrid precoding algorithm based on OMP algorithm,which is closer to optimal full digital precoding method.…”
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FUNCTIONAL MODELING OF CARBON SORBENTS SYNTHESIS CONTROL by V. V. Burlyaev, E. V. Burlyaeva, A. I. Nikolaev, B. V. Peshnev

“…The control processes (analysis of raw material properties and matrix selection), technological processes (raw material preparation) and mixed processes are distinguished as a result of decomposition. The model includes a consistent description of the technological parameters selection (temperature, gas flow and time) for both stages of the synthesis process. …”
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Extraction of Fetal Electrocardiogram by Combining Deep Learning and SVD-ICA-NMF Methods by Said Ziani, Yousef Farhaoui, Mohammed Moutaib

“…It is based on the Convolutional Neural Network (CNN) combined with advanced mathematical methods, such as Independent Component Analysis (ICA), Singular Value Decomposition (SVD), and a dimension-reduction technique like Nonnegative Matrix Factorization (NMF). …”
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A load margin calculation method considering optimized reactive power support by Yuejian Wu, Xiaoming Dong, Tianguang Lu, Shunxiang Yu, Chengfu Wang, Zhengshuo Li

“…Employment of the LU decomposition method allows the CPF predictor coupling with the sensitivity calculation of voltage to reactive power changes (VQ Sensitivity), decreasing the computational burden in identifying the limit-induced bifurcation (LIB). …”
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