ON STRENGTHENING TREATMENT FEATURES UNDER VIBROWAVE LOADING by Anatoly Prokofyevich Babichev, Nikolay Sergeyevich Koval, Alexandra Vladimirovna Alexeyenko, Dmitry Valeryevich Maximov

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Load Forecasting Based on Multiple Load Features and TCN-GRU Neural Network by Haofeng ZHENG, Guohua YANG, Wenjun KANG, Zhiyuan LIU, Shitao LIU, Hong WU, Honghao ZHANG

“…To improve the prediction accuracy, a multi-load feature combination (MLFC) is proposed, and a load prediction framework is constructed by combining Temporal Convolutional Network (TCN) and Gated Recurrent Unit (GRU). …”
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Probabilistic-based analysis for damaging features of fatigue strain loadings by M. F. Mod Yunoh, S. Abdullah, M. H. M. Saad, Z. M. Nopiah, M. Z. Nuawi, S. S. K. Singh

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Geographical features and management strategies for microplastic loads in freshwater lakes by Huike Dong, Ruixuan Zhang, Xiaoping Wang, Jiamin Zeng, Lei Chai, Xuerui Niu, Li Xu, Yunqiao Zhou, Ping Gong, Qianxue Yin

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Problems of power signal feature modeling for complex fluctuating load by WANG Xuewei, WU Di, YUAN Ruiming, MA Jian, LI Wenwen

“…The model reflects four typical characteristics of complex fluctuating load, which can shorten the test time and cost. Four types of problems in the feature modeling of power reference signal are proposed including feature acquisition, data preprocessing, characteristic analysis and extraction, and model use scheme. …”
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Power Load Classification Based on Feature Weighted Fuzzy Clustering by Zongbiao MA, Su'an XU, Shaobin ZHU, Jing WANG

Subjects: “…load classification…”
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Characteristic features of an active polar filament pushing a load by Prabhakar Maurya, Shalabh Kumar Anand, Sunil Pratap Singh

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Important features of EMG signal under simple load conditions by M. Sc. Sumia H. Jaffer, Dr. Nebras H. Ghaeb

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A Novel Feature Extraction Method for Nonintrusive Appliance Load Monitoring by Khaled Chahine, Khalil El Khamlichi Drissi

“…These complex numbers are shown to be characteristic of the considered load and can thus serve as features in any subsequent classification module. …”
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Short-Term Load Forecasting Based on Feature Selection and Combination Model by Yusong XU, Shanhua ZOU, Xianling LU

“…A short-term load forecasting method based on feature selection and combination model is proposed. …”
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Technical features of controlled cooling of circular workpieces after ring rolling by V. E. Antonyuk, S. G. Sandomirski, V. V. Yavorski

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Mechanical Response of Mudstone Based on Acoustic Emission Fractal Features by Xianyin Chang, Yunpei Liang, Qican Ran

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Features of simulation of operator control of unmanned aerial vehicle and its target load by P. I. Savelov, Penghao Gu, A. A. Lobaty

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Non-intrusive load monitoring based on time-enhanced multidimensional feature visualization by Tie Chen, Yimin Yuan, Jiaqi Gao, Shinan Guo, Pingping Yang

“…Abstract In the research of non-intrusive load monitoring (NILM), the temporal characteristics of V–I trajectories are often overlooked, and using a single feature for identification may lead to insignificant differences between similar loads. …”
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Incorporated flexible load forecasting based on non-intrusive load monitoring: a TCN-based meta learning approach by Yun Zhang, Quanyan Shu, Feng Ding, Feng Liu, Shuiming Jiang, Wenlong Wu

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Influence of physical loading on behavioral reactions of rats in cerebral hypoperfusion by V. V. Сhrishtop, T. A. Rumyantseva, V. G. Nikonorova, I. V. Fateev

“…The aim of the study was to identify the typological features of the effect of physical activity on the behavioral reactions of rats during cerebral hypoperfusion. …”
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Bucket working bodies with conveyor bottom: systematics and design features by Yu. M. Lyashenko, E. A. Revyakina, A. Yu. Lyashenko

“…Advanced design diagrams of loading bodies were searched on the basis of the accumulated experience and the study of the morphological features of the existing equipment. …”
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UniLF: A novel short-term load forecasting model uniformly considering various features from multivariate load data by Shiyang Zhou, Qingyong Zhang, Peng Xiao, Bingrong Xu, Geshuai Luo

“…Although various deep learning methods have achieved good results in STLF, they usually model load features only from a limited perspective, i.e., they do not uniformly utilize the three features of multivariate load data: the influence of covariates, multiscale features and local-global variations. …”
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Non-Intrusive Load Identification Based on Multivariate Features and Information Entropy-Weighted Ensemble by Yue Liu, Wenxia You, Miao Yang

“…In non-intrusive load monitoring (NILM), single-dimensional features exhibit limited representational capacity, while feature fusion at the feature layer often leads to information loss due to dimensional transformation, as well as the risk of dimensional explosion caused by the newly added features. …”
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SOME FEATURES OF DESIGNING TRUSSED RAFTER OF DOUBLE ANGLES by O. Yu. Marakhina, D. A. Kuzmin

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