Application of GWO-SVM Algorithm in Arc Detection of Pantograph

Li, Bin; Luo, Chenyu; Wang, Zhiyong

doi:10.1109/access.2020.3025714

Cited by 22 publications

(6 citation statements)

References 19 publications

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“…According to literature, Gray Wolf Optimization (GWO) algorithm is the best one that makes balance between exploration or global research and exploitation or local research [ 33 ]. It has also simple structure, fewer parameters and strong and rapid convergence compared to other bio-inspired algorithms like particle swarm optimization (PSO) or genetic algorithm (GA) [ 34 ].…”

Section: Methodsmentioning

confidence: 99%

Bladder Wall Segmentation and Characterization on MR Images: Computer-Aided Spina Bifida Diagnosis

et al. 2022

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(1) Background: Segmentation of the bladder inner’s wall and outer boundaries on Magnetic Resonance Images (MRI) is a crucial step for the diagnosis and the characterization of the bladder state and function. This paper proposes an optimized system for the segmentation and the classification of the bladder wall. (2) Methods: For each image of our data set, the region of interest corresponding to the bladder wall was extracted using LevelSet contour-based segmentation. Several features were computed from the extracted wall on T2 MRI images. After an automatic selection of the sub-vector containing most discriminant features, two supervised learning algorithms were tested using a bio-inspired optimization algorithm. (3) Results: The proposed system based on the improved LevelSet algorithm proved its efficiency in bladder wall segmentation. Experiments also showed that Support Vector Machine (SVM) classifier, optimized by Gray Wolf Optimizer (GWO) and using Radial Basis Function (RBF) kernel outperforms the Random Forest classification algorithm with a set of selected features. (4) Conclusions: A computer-aided optimized system based on segmentation and characterization, of bladder wall on MRI images for classification purposes is proposed. It can significantly be helpful for radiologists as a part of spina bifida study.

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Section: Methodsmentioning

confidence: 99%

Bladder Wall Segmentation and Characterization on MR Images: Computer-Aided Spina Bifida Diagnosis

et al. 2022

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“…The dataset, accessible in [43], is comprised of recordings of 15 subjects (aged [24][25][26][27][28][29][30][31][32][33][34][35] watching video clips and doing public speaking and mental arithmetic tasks. The dataset is recorded with a wrist-based device (including the following sensors: photoplethysmography, accelerometer, electrodermal activity, and body temperature) and a chest-based device (including the following sensors: ECG, accelerometer, electromyogram, respiration, and body temperature).…”

Section: B Description Of Wesad Datasetmentioning

confidence: 99%

A Novel Exploitative and Explorative GWO-SVM Algorithm for Smart Emotion Recognition

Yan¹,

Lin²,

Lin³

et al. 2023

Preprint

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Emotion recognition or detection is broadly utilized in patient-doctor interactions for diseases such as schizophrenia and autism and the most typical techniques are speech detection and facial recognition. However, features extracted from these behavior-based emotion recognitions are not reliable since humans can disguise their emotions. Recording voices or tracking facial expressions for a long term is also not efficient. Therefore, our aim is to find a reliable and efficient emotion recognition scheme, which can be used for non-behavior-based emotion recognition in real-time. This can be solved by implementing a single-channel electrocardiogram (ECG) based emotion recognition scheme in a lightweight embedded system. However, existing schemes have relatively low accuracy. For instance, the accuracy is about 82.78% by using a least squares support vector machine (SVM). Therefore, we propose a reliable and efficient emotion recognition scheme-exploitative and explorative grey wolf optimizer based SVM (X-GWO-SVM) for ECG-based emotion recognition. Two datasets, one raw self-collected iRealcare dataset, and the widely-used benchmark WESAD dataset are used in the X-GWO-SVM algorithm for emotion recognition. Leave-single-subject-out cross-validation yields a mean accuracy of 93.37% for the iRealcare dataset and a mean accuracy of 95.93% for the WESAD dataset. This work demonstrates that the X-GWO-SVM algorithm can be used for emotion recognition and the algorithm exhibits superior performance in reliability compared to the use of other supervised machine learning methods in earlier works. It can be implemented in a lightweight embedded system, which is much more efficient than existing solutions based on deep neural networks.

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“…The results showed that GWO could quickly and accurately identify the pantograph arc. The obtained classification model was more accurate than the commonly used Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithm [ 35 ]. Almomani (2020) proposed a feature selection model for NIDS.…”

Section: Related Workmentioning

confidence: 99%

Automatic fault detection of sensors in leather cutting control system under GWO-SVM algorithm

Luo

Jiao

2021

PLoS ONE

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The purposes are to meet the individual needs of leather production, improve the efficiency of leather cutting, and increase the product’s competitiveness. According to the existing problems in current leather cutting systems, a Fault Diagnosis (FD) method combining Convolutional Neural Network (CNN) and the Support Vector Machine (SVM) of Gray Wolf Optimizer (GWO) is proposed. This method first converts the original signal into a scale spectrogram and then selects the pre-trained CNN model, AlexNet, to extract the signal scale spectrogram’s features. Next, the Principal Component Analysis (PCA) reduces the obtained feature’s dimensionality. Finally, the normalized data are input into GWO’s SVM classifier to diagnose the bearing’s faults. Results demonstrate that the proposed model has higher cutting accuracy than the latest fault detection models. After model optimization, when c is 25 and g is 0.2, the model accuracy can reach 99.24%, an increase of 66.96% compared with traditional fault detection models. The research results can provide ideas and practical references for improving leather cutting enterprises’ process flow.

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Application of GWO-SVM Algorithm in Arc Detection of Pantograph

Cited by 22 publications

References 19 publications

Bladder Wall Segmentation and Characterization on MR Images: Computer-Aided Spina Bifida Diagnosis

Bladder Wall Segmentation and Characterization on MR Images: Computer-Aided Spina Bifida Diagnosis

A Novel Exploitative and Explorative GWO-SVM Algorithm for Smart Emotion Recognition

Automatic fault detection of sensors in leather cutting control system under GWO-SVM algorithm

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