Real-time forecasting of near-field tsunami waveforms at coastal areas using a regularized extreme learning machine

2016 ◽  
Vol 109 ◽  
pp. 1-8 ◽  
Author(s):  
Iyan E. Mulia ◽  
Toshiyuki Asano ◽  
Akio Nagayama
Keyword(s):  
Real Time ◽  
Extreme Learning Machine ◽  
Near Field ◽  
Coastal Areas ◽  
Tsunami Waveforms ◽  
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

scholarly journals Wavefront Restoration Technology of Dynamic Non-Uniform Intensity Distribution Based on Extreme Learning Machine

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3877
Author(s):  
Haiqi Lin ◽  
Xing He ◽  
Shuai Wang ◽  
Ping Yang
Keyword(s):  
Root Mean Square Error ◽  
Extreme Learning Machine ◽  
Intensity Distribution ◽  
Near Field ◽  
Wavefront Sensor ◽  
Mean Square ◽  
Initial Value ◽  
Machine Method ◽  
Simulation Results ◽  
Learning Machine

Non-uniform intensity distribution of laser near-field beam results in the irregular shape of the spot in the wavefront sensor. The intensity of some sub-aperture spots may be too weak to be detected, and the accuracy of wavefront restoration is seriously affected. Therefore, an extreme learning machine method is proposed to realize high precision wavefront restoration under dynamic non-uniform intensity distribution. The simulation results show that this method has better accuracy of wavefront restoration than the classical modal algorithm under dynamic non-uniform intensity distribution. The root mean square error of the residual wavefront for the proposed method is only 2.9% of the initial value.

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