Real-time object detection in agricultural/remote environments using the multiple-expert colour feature extreme learning machine (MEC-ELM)

2018 ◽  
Vol 98 ◽  
pp. 183-191 ◽  
Author(s):  
Edmund J. Sadgrove ◽  
Greg Falzon ◽  
David Miron ◽  
David W. Lamb
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Extreme Learning Machine ◽  
Colour Feature ◽  
Learning Machine

scholarly journals Erratum to: Real-time transient stability status prediction using cost-sensitive extreme learning machine

2015 ◽  
Vol 27 (2) ◽  
pp. 333-333
Author(s):  
Zhen Chen ◽  
Xianyong Xiao ◽  
Changsong Li ◽  
Yin Zhang ◽  
Qingquan Hu
Keyword(s):  
Real Time ◽  
Extreme Learning Machine ◽  
Transient Stability ◽  
Learning Machine

A Real-Time Visual Tracking Approach Using Sparse Autoencoder and Extreme Learning Machine

2015 ◽  
Vol 03 (04) ◽  
pp. 267-275
Author(s):  
Liang Dai ◽  
Yuesheng Zhu ◽  
Guibo Luo ◽  
Chao He ◽  
Hanchi Lin
Keyword(s):  
Real Time ◽  
Extreme Learning Machine ◽  
Visual Tracking ◽  
Computational Cost ◽  
Real Time Tracking ◽  
Sparse Autoencoder ◽  
Selection For ◽  
Learning Machine ◽  
Hidden Layer ◽  
Online Process

Visual tracking algorithm based on deep learning is one of the state-of-the-art tracking approaches. However, its computational cost is high. To reduce the computational burden, in this paper, A real-time tracking approach is proposed by using three modules: a single hidden layer neural network based on sparse autoencoder, a feature selection for simplifying the network and an online process based on extreme learning machine. Our experimental results have demonstrated that the proposed algorithm has good performance of robust and real-time.

Real-time fault diagnosis for gas turbine generator systems using extreme learning machine

2014 ◽  
Vol 128 ◽  
pp. 249-257 ◽  
Author(s):  
Pak Kin Wong ◽  
Zhixin Yang ◽  
Chi Man Vong ◽  
Jianhua Zhong
Keyword(s):  
Fault Diagnosis ◽  
Real Time ◽  
Gas Turbine ◽  
Extreme Learning Machine ◽  
Turbine Generator ◽  
Learning Machine
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