A multi-layer perceptron based non-linear mixture model to estimate class abundance from mixed pixels

2011 ◽  
Author(s):  
U Kumar ◽  
S K Raja ◽  
C Mukhopadhyay ◽  
T V Ramachandra
Keyword(s):  
Mixture Model ◽  
Multi Layer Perceptron ◽  
Non Linear ◽  
Mixed Pixels ◽  
Linear Mixture Model

scholarly journals A Neural Network Based Hybrid Mixture Model to Extract Information from Non-linear Mixed Pixels

Information ◽  
2012 ◽  
Vol 3 (3) ◽  
pp. 420-441 ◽  
Author(s):  
Uttam Kumar ◽  
Kumar S. Raja ◽  
Chiranjit Mukhopadhyay ◽  
T.V. Ramachandra
Keyword(s):  
Neural Network ◽  
Mixture Model ◽  
Non Linear ◽  
Mixed Pixels ◽  
Extract Information

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.

Analysis of Forest Foliage Spectra Using a Multivariate Mixture Model

1997 ◽  
Vol 5 (3) ◽  
pp. 167-173 ◽  
Author(s):  
Christine A. Hlavka ◽  
David L. Peterson ◽  
Lee F. Johnson ◽  
Barry Ganapol
Keyword(s):  
Mixture Model ◽  
Near Infrared ◽  
Photon Absorption ◽  
Chemical Measurements ◽  
Near Infrared Spectra ◽  
Photon Absorption And Scattering ◽  
Linear Mixture Model ◽  
Wet Chemical ◽  
Nitrogen Bonds ◽  
Reflectance Measurements

Wet chemical measurements and near infrared spectra of dry ground leaf samples were analysed to test a multivariate regression technique for estimating component spectra. The technique is based on a linear mixture model for log(1/ R) pseudoabsorbance derived from diffuse reflectance measurements. The resulting unmixed spectra for carbohydrates, lignin and protein resemble the spectra of extracted plant carbohydrates, lignin and protein. The unmixed protein spectrum has prominent absorption peaks at wavelengths that have been associated with nitrogen bonds. It therefore appears feasible to incorporate the linear mixture model in whole leaf models of photon absorption and scattering so that effects of varying nitrogen and carbon concentration on leaf reflectance may be simulated.

scholarly journals The Autoregressive Linear Mixture Model: A Time-Series Model for an Instantaneous Mixture of Network Processes

2020 ◽  
Vol 68 ◽  
pp. 4481-4496
Author(s):  
Addison W. Bohannon ◽  
Vernon J. Lawhern ◽  
Nicholas R. Waytowich ◽  
Radu V. Balan
Keyword(s):  
Time Series ◽  
Mixture Model ◽  
Time Series Model ◽  
Linear Mixture Model
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