Short-term load forecasting based on multi-frequency sequence feature analysis and multi-point modified FEDformer by Kaiyuan Hou, Xiaotian Zhang, Junjie Yang, Jiyun Hu, Guangzhi Yao, Jiannan Zhang

“…Given the complexity and dynamic nature of short-term load sequence data, coupled with prevalent errors in traditional forecasting methods, this study introduces a novel approach for short-term load forecasting. …”
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A non-intrusive load monitoring algorithm based on real-time feature extraction and deep learning model by Behrooz Taheri, Mostafa Sedighizadeh, Mohammad Reza Nasiri, Alireza Sheikhi Fini

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SFPFMformer: Short-Term Power Load Forecasting for Proxy Electricity Purchase Based on Feature Optimization and Multiscale Decomposition by Chengfei Qi, Yanli Feng, Junling Wan, Xinying Mao, Peisen Yuan

“…Finally, we utilize a depthwise separable convolution block to extract features from power load data, which efficiently captures the pattern of change in load. …”
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Monopulse Feature Extraction and Fault Diagnosis Method of Rolling Bearing under Low-Speed and Heavy-Load Conditions by Chang Liu, Gang Cheng, Xihui Chen, Yong Li

“…According to the rolling bearing local fault vibration mechanism, a monopulse feature extraction and fault diagnosis method of rolling bearing under low-speed and heavy-load conditions based on phase scan and CNN is proposed. …”
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Design Features of a Removable Module Intended for Securing Containers When Transported in an Open Wagon by Alyona Lovska, Juraj Gerlici, Ján Dižo, Mykhailo Pavliuchenkov, Pavlo Rukavishnikov

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An optimized method for short-term load forecasting based on feature fusion and ConvLSTM-3D neural network by Xiaofeng Yang, Shousheng Zhao, Kangyi Li, Wenjin Chen, Si Zhang, Jingwei Chen

Subjects: “…short-term load forecasting…”
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An electrical load forecasting model based on a novel closed loop neural networks and interaction gain feature selection by Gholamreza Memarzadeh, Faezeh Amirteimoury, Hossein Noori, Farshid Keynia

“…Accurate short-term load forecasting, particularly for normal and peak load values, plays a critical role in ensuring the reliable and efficient operation of modern power systems. …”
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A multi-modal feature combination mechanism for identification of harmonic load in distribution networks based on artificial intelligence models by Renzeng Yang, Shuang Peng, Gang Yao

“…The most suitable intrinsic mode sequences are selected as input features for sequential neural networks training. Finally, a multi-modal feature tensor combination mechanism that integrates reshaped vector layers into the sequential neural networks architecture is introduced, enabling adaptive extraction of spatial–temporal characteristics and significantly improving the accuracy of harmonic load identification without prior knowledge of their spectral features.…”
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Effects of varying loading rates on the Brazilian splitting characteristics of coal-rock composites by CHEN Yan, WANG Jiahao, DENG Liangtao, HONG Zijie, RONG Tenglong, HOU Zhiqiang

“…ObjectivesTo investigate the tensile characteristics of coal-rock composite structures during the mining process， the study takes coal-rock composite specimens as the research object.MethodsBrazilian splitting tests were conducted on coal-rock composite specimens under varying loading rates using the RMT150B testing machine. The effects of loading rates on the strength characteristics， failure modes， energy features， and crack evolution during splitting failure were analyzed.ResultsThe stress-strain curves of the coal-rock composites generally followed four stages： compaction， elasticity， yield， and failure. …”
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Multi-Dimensional AE Signal Features in Eccentrically Loaded Concrete Structures: A Machine Learning Classification for Damage Progression by Shilong Ding, Alipujiang Jierula, Abudusaimaiti Kali, Tong Han, Tae-Min Oh

“…This study employed K-means clustering algorithm and Gaussian mixture models (GMMs) to analyze AE signal features from reinforced concrete (RC) columns undergoing failure under the eccentric compression loading of different eccentricity. …”
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Identification of Two Vulnerability Features: A New Framework for Electrical Networks Based on the Load Redistribution Mechanism of Complex Networks by Xiaoguang Wei, Shibin Gao, Tao Huang, Tao Wang, Wenli Fan

“…This paper proposes a new framework to analyze two vulnerability features, impactability and susceptibility, in electrical networks under deliberate attacks based on complex network theory: these two features are overlooked but vital in vulnerability analyses. …”
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Short-Term Power Load Forecasting Using an Improved Model Integrating GCN and Transformer by Man Wu, Wanyi Feng, Xinya Li, Yunan Liu, Chuxin Cao

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A Multi-stage Scenario Tree Generation Method for Wind-Solar Load Based on Complex Feature Extraction and Sinkhorn Distance by Rui WANG, Zhixin FU, Jian WANG, Haoming LIU

“…Firstly, to enhance the clustering efficiency of wind-solar load scenarios, a method based on stacked sparse autoencoders for feature extraction of wind-solar load scenarios was introduced. …”
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A Comparative Study of Machine Learning Models for Short-Term Load Forecasting by Etna Vianita, Henri Tantyoko

Subjects: “…short-term load forecasting, machine learning models, lag features, electricity demand prediction, model evaluation…”
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Load recognition method based on convolutional neural network and attention mechanism by ZHAO Yitao, LI Zhao, LIU Xinglong, LUO Zhao, WANG Gang, SHEN Xin

Subjects: “…non-intrusive load monitoring (nilm)…”
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Feature-informed machine learning for detecting material deformation and failure in aluminum pipes under bending load using acoustic emission sensors by Xiaowei Zuo, Nicholas Satterlee, Chang-Whan Lee, In-Gyu Choi, Choon-Wook Park, John S. Kang

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New State Identification Method for Rotating Machinery under Variable Load Conditions Based on Hybrid Entropy Features and Joint Distribution Adaptation by Xiaoming Xue, Nan Zhang, Suqun Cao, Wei Jiang, Jianzhong Zhou, Liyan Liu

“…Finally, five cases with the training and testing set under variable load conditions were used to demonstrate the performance of the proposed method, and comparisons with some other diagnosis models combined with the same features and other dimensionality reduction methods were also discussed. …”
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The association of cycle threshold value with clinical features in patients infected with Omicron variant by Wen Yang, Tao Tao, Jianping Zhang, Yuting Yao, Min Chen, Mingming Liu, Meiying Wu, Wei Lei

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Features of bioelectric brain activity in people with different level of situational anxiety when performing non-verbal tasks by S. I. Pavlenko, D. S. Gromova, O. A. Vedyasova, A. M. Kovalev, A. I. Budaev

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SOME FEATURES OF CURRENT TECHNOGENIC MOVEMENTS OF THE EARTH’S CRUST by V. Yu. Timofeev, D. G. Ardyukov, A. V. Timofeev, E. V. Boyko, M. G. Valitov, E. N. Kalish, Yu. F. Stus, D. A. Nosov, I. S. Sizikov

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