Design and validation of electro-hydraulic pumping unit for smart manufacturing

Zhu, Deming; Wang, Mingkang; Fu, Yongling

doi:10.1007/s00170-022-09148-6

Cited by 2 publications

(1 citation statement)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The wrapper feature selection involved the simulated annealing algorithm (SA), genetic algorithm (GA), particle swarm optimization algorithm (PSO), ant colony optimization (ACO), and artificial bee colony algorithm (ABC). [31][32][33][34][35] In order to avoid local optimization and solve the problem of limited iterations, it was necessary to train and test repeatedly.…”

Section: Feature Dimensionality Reductionmentioning

confidence: 99%

Study of the Immediately Detection of Mild Traumatic Brain Injury by Feature Engineering on Electroencephalography

Zhou,

Hu,

Ning

et al. 2023

Advanced Biology

View full text Add to dashboard Cite

The electroencephalographic (EEG) diagnosis of mild traumatic brain injury (mTBI) is not usually timely, and the detection is often performed several hours or days after the trauma, leading to a decrease in the accuracy of its detection. In this study, EEG signals are recorded immediately after mTBI by connecting a bipolar single lead to injured animals. And three types of EEG features, namely time domain, frequency domain, and nonlinear dynamics, are screened for optimal feature subset in mTBI detection. First, EEG signals of animals are recorded before and after establishing the animal model of mTBI. Second, signal preprocessing, feature extraction, and feature preprocessing are performed to obtain the full‐feature dataset, and 1442 feature subsets are obtained by 15 feature reduction algorithms extracted from combinations of 47 features. Ultimately, the support vector machines and K‐nearest neighbor algorithms are trained and tested respectively, and their performance is comprehensively compared to determine the optimal feature subset for mTBI detection. In the EEG dataset collected in this study, a total of eight feature subsets extracted from combinations of original 47 features and classification models with 100% accuracy are obtained. This study shows the perspective of immediately detecting mTBI based on a bipolar single‐lead EEG.

show abstract