Results 71 to 80 of about 107,979 (335)

Research on parameter identification of shaking table systems based on the RLS method.

PLoS ONE, 2022
It is difficult to accurately establish a model of the real mesa system. Furthermore, a model of a seismic simulation vibration table array system is critical to increasing the accuracy of seismic testing in laboratory settings.
Chunhua Gao, Yanping Yang, Mengyuan Qin, Cun Li, Zihan Yuan   +4 more
doaj   +1 more source

A New Method of Insulation Detection on Electric Vehicles Based on a Variable Forgetting Factor Recursive Least Squares Algorithm [PDF]

, 2021
Zhenbin Chen, Weiya Cui, Xiangyu Cui, Huimin Qiao, Hao Lu, Na Qiu   +5 more
openalex   +1 more source

Machine Learning‐Driven Prediction and Optimization of Cu‐Based Catalysts for CO2 Hydrogenation to Methanol

Carbon and Hydrogen, EarlyView.
A machine‐learning framework integrating multimodel prediction, feature selection, and SHAP interpretability is developed to uncover structure–performance relationships of Cu‐based CO2‐to‐methanol catalysts. The optimized XGBoost model and an online prediction platform enable accurate selectivity prediction and data‐driven catalyst design.
Conglong Su, Zhangkai Jing, Qiuming Zhou, Yuan Zhu, Sishi Ye, Tianye Wang, Kuo Chen, Xuebin Luan, Zhigang Tang, Run Xu, Chaopeng Hou, Hongwei Li   +11 more
wiley   +1 more source

New Hybrid Technique for Impulsive Noise Suppression in OFDM Systems [PDF]

Mehran University Research Journal of Engineering and Technology, 2017
In this paper, a new hybrid technique employing RS (Reed Solomon) coding and adaptive filter for impulsive noise suppression in OFDM (Orthogonal Frequency Division Multiplexing) systems is presented.
ALINA MIRZA, AYESHA ZEB, SHAHZAD AMIN SHEIKH   +2 more
doaj  

Data‐driven simulation of crude distillation using Aspen HYSYS and comparative machine learning models

The Canadian Journal of Chemical Engineering, EarlyView.
Integrated Aspen HYSYS–machine learning framework for predicting product yields and quality variables. Abstract Crude oil refining is a complex process requiring precise modelling to optimize yield, quality, and efficiency. This study integrates Aspen HYSYS® simulations with machine learning techniques to develop predictive models for key refinery ...
Aldimiro Paixão Domingos, Heleno Bispo, Thiago Vaz da Costa, Sérgio Mauro da Silva Neiro   +3 more
wiley   +1 more source

Dynamic Parameter Identification of a Hip Exoskeleton Using RLS-GA

Actuators
Lower-limb exoskeletons require accurate dynamic models to achieve stable and compliant human–robot interactions. However, least-squares-based identification often relies on demanding experiments and may yield limited accuracy for exoskeletons with non ...
Wentao Sheng, Yunxia Cao, Farzan Ghalichi, Li Ding, Tianyu Gao   +4 more
doaj   +1 more source

Recursive Least Squares Advantage Actor-Critic Algorithms

, 2022
13 pages, 2 figures, 4 tables, IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS: SYSTEMS, under ...
Wang, Yuan, Zhang, Chunyuan, Yu, Tianzong, Ma, Meng   +3 more
openaire   +2 more sources

Algorithm for online estimation of power system parameters [PDF]

, 2013
Power system parameters are having great contribution in the operating, monitoring and controlling of electric device. Frequency and harmonics are the two vital parameters which influence different relay functionality of power system. This study was made
Pradhan, Rosy
core  

A novel construction approach for the pore structure model principally focuses on the controllable roundness of rock particles

Deep Underground Science and Engineering, EarlyView.
Particle rounding effects comparison of different methods: (a), (b), and (c) are the rounding results using the B‐spline curve method with different R; (d), (e), and (f) are the results of vertex rounding substitution method with different rounding radii.
Jiabin Dong, Ning Yang, Yi Jin, Ying Wu, Dandan Liu, Junling Zheng, Jingyan Zhao   +6 more
wiley   +1 more source

Rockburst prediction based on data preprocessing and hyperband‐RNN‐DNN

Deep Underground Science and Engineering, EarlyView.
A data preprocessing workflow is proposed to address challenges in rockburst data analysis. Coupled algorithms preprocess the data set, and hyperband optimization is used to enhance RNN performance. Results show that preprocessing improves accuracy, while dense layers enhance model stability and prediction performance.
Yong Fan, Chenxi Yin, Guangdong Yang, Shengyong Ding, Bin Tian   +4 more
wiley   +1 more source