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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Machine learning for brain tumor classification: evaluating feature extraction and algorithm efficiency by Krishan Kumar, Kiran Jyoti, Krishan Kumar

“…Our analysis revealed that Random Forest emerged as the most effective classifier by achieving an accuracy of 99% with image loading feature extraction method based on different metrics, closely followed by SVM and Logistic Regression. …”
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Construction and finite element simulation of a square laser loading model that can replace circular spot superposition in laser peening forming by Xu PEI, Boyong SU, Liang WANG

“…In order to explore an efficient, accurate, and convenient finite element simulation method for laser peening forming, a square laser loading model was constructed by studying the loading feature unit of the circular spot superposition model, which can replace the circular spots overlay laser peening forming. and Finite element simulation was conducted on 7075 aluminum alloy flat plate model. …”
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Non-intrusive Load Identification Method Based on the Online Self-organizing Incremental Neural Network—Non-intrusive load identification method based on the online self-organizing... by Zhengwei HU, Zhihong WANG, Ruixin CHANG, Zhiyuan XIE, Wangbin CAO

“…This method included two steps, which are the load feature extraction and the load feature classification with the equipment identification. …”
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CLSTM-MT (a Combination of 2-Conv CNN and BiLSTM Under the Mean Teacher Collaborative Learning Framework): Encryption Traffic Classification Based on CLSTM (a Combination of 2-Conv... by Xiaozong Qiu, Guohua Yan, Lihua Yin

“…This study addresses three key challenges: First, existing methods fail to explore the potential relationship between flow load features and sequence features during feature extraction. …”
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基于峭度和小波包能量特征的齿轮箱早期故障诊断及抗噪研究 by 陈水宣, 邹俊, 易际明, 谢丹

“…It is difficult to dectect the gear fault signal in early stage because of weak intensity and strong interference.To solve this problem,a method for incipient fault diagnosis of gears is proposed based on vibration signals using kurtosis,wavelet packet energy features extraction and discriminative weighted probabilistic neural networks.The method uses the advantages of the kurtosis statistics on the impact load feature extraction method in feature extraction and reserves the merit of wavelet packet decomposition in extracting energy characteristics of various frequency bands.Meanwhile,the discriminative weight probabilistic neural network（DWPNN） is introduced to solve the problem of the scene noise pollution.The experimental results show that the method achieves a good identification of incipient faults of gears and has strong robustness against noise disturbance.…”
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Two-stage Non-Intrusive Load Monitoring method for multi-state loads. by Lei Wang, Xia Han, Yushu Cheng, Jiaqi Ma, Xuerui Zhang, Xiaoqing Han

“…The V-I trajectories of loads are at first classified with Resnet18. Then, load features with low redundancy is obtained through the Max-Relevance and Min-Redundancy (mRMR) feature selection algorithm from various operating states of loads that were not successfully classified. …”
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Models of the macroseismic field earthquakes and their influence on seismic hazard assessment values for Central Asia by T. U. Artikov, R. S. Ibragimov, T. L. Ibragimova, M. A. Mirzaev

“…The model takes into account seismic load features determined by various depths of earthquakes. …”
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Dynamic Reconfiguration of Active Distribution Network Based on Improved Equilibrium Optimizer by Chaoxue Wang, Yue Zhang

“…First, an enhanced fuzzy C-means clustering method is proposed for load period partitioning, which integrates spatiotemporal load features and optimal network structure similarity to improve clustering accuracy. …”
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THE ACCELERATION OF FOUR COLUMN BENCH FOR VEHICLE LABORATORY ROAD SIMULATION TEST IN THE TARGET SPECTRUM METHOD RESEARCH by WANG Zhe, ZHENG SongLin, FENG JinZhi, YU JiaWei, MA ZhaGen

“…The effectiveness of accelerated spectrum was theoretically verified from amplitude domain,pseudo damage and frequency domain.The results showed that,on the basis of retaining damage,together with ensuring the damage of load features and domain features,this method had achieved the acceleration. …”
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About some approaches modeling of the vehicle on simulator by V. G. Mikhailov

“…The purpose of the given work is consideration and working off of approaches, techniques, schemes of realisation of modelling of the vehicle on simulators on the basis of a choice of more perfect models of movement, fluctuations, roadability of the vehicle in package Matlab/Simulink for reception and an estimation of parametres of the vehicle, it loading. Features of imitating modelling of the vehicle on a simulator, requirements to it, using methods of such modelling on the basis of computer models, hydropulsators and a moving platform with the monitors/projectors, simulating road conditions where reactions to it of the driver, movements essentially influencing modes and loading the vehicle with the further use of the received data at bench tests are considered are considered. …”
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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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Fatigue Load Prediction of Wind Turbine Drive Train Based on CNN-BiLSTM by Xiaodong WANG, Qing LI, Deyi FU, Yingming LIU, Ruojin WANG

“…First, we construct a fatigue load feature database using simulation data from OpenFAST under rated wind speed conditions and above, which is subsequently used for training and testing the model. …”
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An Application of Visual-Observation Unmanned Aerial Vehicles in Live Firing Range Tests by Maciej MISZCZAK, Piotr RULIŃSKI, Bohdan ZARZYCKI, Michał KUC

“…One of the two drone’s loads included a VIS light video camera, and the other one’s load featured a thermal imaging (IR) video camera. As a part of the same application, both drones were used to visually monitor the flight path of an experimental short-range rocket missile, which featured an inertial guidance head with an onboard flight recorder. …”
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Quantitative Assessment Method for Industrial Demand Response Potential Integrating STL Decomposition and Load Step Characteristics by Zhuo-Wei Yang, Kai Chang, Ming-Di Shao, Hao Lei, Zhi-Wei Liu

“…A GPR-based nonlinear mapping between extracted load features and response potential enables precise quantification and robust uncertainty estimation. …”
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Contribution of Suspension Bogies’ Aerodynamic Loads to the Dynamic Characteristics of a High-temperature Superconducting Maglev Train Running under Crosswind by Z. P. Li, X. F. Wang, Y. M. Pan, Y. Ding, P. F. Liu, Z. G. Deng

“…This research delves into the aerodynamic load features of the suspension bogies on HTS maglev trains when operating under various crosswind conditions. …”
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Design and test of high power X-band dry load for SXFEL and SHINE by GAO Zihe, TAN Jianhao, WANG Cheng, HUANG Xiaoxia, FANG Wencheng

“…Finally, the loads were manufactured and their RF parameters were measured using a vector network analyzer both in the clamping state and after welding.ResultsThe two X-band loads feature a waveguide structure with periodic grooves, and are operated at 11.424 GHz and 11.988 GHz respectively. …”
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Bioinspired surface structures for added shear stabilization in suction discs by Alyssa M. Hernandez, Jessica A. Sandoval, Michelle C. Yuen, Robert J. Wood

“…However, the design that withstood the highest shear load featured an intermediate pad size and channel spacing, potentially highlighting a balance between overall surface area and fluid channeling. …”
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Optimal Statistical Feature Subset Selection for Bearing Fault Detection and Severity Estimation by Chhaya Grover, Neelam Turk

“…Two datasets are derived from a publicly available database of Case Western Reserve University to identify the capability of features in fault identification under various fault sizes and motor loads. Features have been investigated using a two-step approach—filter-based ranking with 3 metrics followed by feature subset selection with 11 search techniques. …”
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