scholarly journals Flexible non-linear physical security coding scheme combined with chaotic neural network for OFDM-WDM-PON

2021 ◽  
Author(s):  
Mingye Li ◽  
Jianxin Ren ◽  
Yaya Mao ◽  
xiumin song ◽  
shuaidong chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Physical Security ◽  
Chaotic Neural Network ◽  
Coding Scheme ◽  
Non Linear ◽  
Wdm Pon

scholarly journals A Hybrid Hidden Markov Model for Pipeline Leakage Detection

2021 ◽  
Vol 11 (7) ◽  
pp. 3138
Author(s):  
Mingchi Zhang ◽  
Xuemin Chen ◽  
Wei Li
Keyword(s):  
Neural Network ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Deep Neural Network ◽  
Hidden Markov ◽  
Gaussian Mixture ◽  
State Sequence ◽  
Non Linear

In this paper, a deep neural network hidden Markov model (DNN-HMM) is proposed to detect pipeline leakage location. A long pipeline is divided into several sections and the leakage occurs in different section that is defined as different state of hidden Markov model (HMM). The hybrid HMM, i.e., DNN-HMM, consists of a deep neural network (DNN) with multiple layers to exploit the non-linear data. The DNN is initialized by using a deep belief network (DBN). The DBN is a pre-trained model built by stacking top-down restricted Boltzmann machines (RBM) that compute the emission probabilities for the HMM instead of Gaussian mixture model (GMM). Two comparative studies based on different numbers of states using Gaussian mixture model-hidden Markov model (GMM-HMM) and DNN-HMM are performed. The accuracy of the testing performance between detected state sequence and actual state sequence is measured by micro F1 score. The micro F1 score approaches 0.94 for GMM-HMM method and it is close to 0.95 for DNN-HMM method when the pipeline is divided into three sections. In the experiment that divides the pipeline as five sections, the micro F1 score for GMM-HMM is 0.69, while it approaches 0.96 with DNN-HMM method. The results demonstrate that the DNN-HMM can learn a better model of non-linear data and achieve better performance compared to GMM-HMM method.

scholarly journals Excitation Forces Estimation for Non-linear Wave Energy Converters: A Neural Network Approach

2020 ◽  
Vol 53 (2) ◽  
pp. 12334-12339
Author(s):  
M. Bonfanti ◽  
F. Carapellese ◽  
S.A. Sirigu ◽  
G. Bracco ◽  
G. Mattiazzo
Keyword(s):  
Neural Network ◽  
Wave Energy ◽  
Linear Wave ◽  
Network Approach ◽  
Neural Network Approach ◽  
Wave Energy Converters ◽  
Non Linear ◽  
Energy Converters

scholarly journals Prediction Model of Weekly Retail Price for Eggs Based on Chaotic Neural Network

2013 ◽  
Vol 12 (12) ◽  
pp. 2292-2299 ◽  
Author(s):  
Zhe-min LI ◽  
Li-guo CUI ◽  
Shi-wei XU ◽  
Ling-yun WENG ◽  
Xiao-xia DONG ◽  
...  
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Retail Price ◽  
Chaotic Neural Network

A novel memristive electronic synapse-based Hermite chaotic neural network with application in cryptography

2015 ◽  
Vol 166 ◽  
pp. 487-495 ◽  
Author(s):  
Xinli Shi ◽  
Shukai Duan ◽  
Lidan Wang ◽  
Tingwen Huang ◽  
Chuandong Li
Keyword(s):  
Neural Network ◽  
Chaotic Neural Network ◽  
Electronic Synapse
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