An artificial neural network based approach for power spectrum estimation and simulation of stochastic processes subject to missing data

In this study, observation on the differences in features quality of EEG records as a result of training on subjects has been made. The features of EEG records were extracted using two different methods, the root mean square which is acquired from the range between 0.5 and 5 seconds and the average of power spectrum estimation from the frequency range between 20 and 40Hz. All of the data consists of a 4-channel recording and produce good quality classification on artificial neural network, with each of which generates training data accuracy over 90%. However, different results are occured when the trained system is tested on other test data. The test results show that the two systems which are trained using training data with object with color background produce higher accuracy than the other two systems which are trained using training data with object without background color, 63.98% and 60.22% compared to 59.68% and 56.45% accuracy respectively. From the use of the features on the artificial neural network classification system, it can be concluded that the training system using EEG data records derived from the visualization of object with color background produces better features than the visualization of object without color background.

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Compressive sensing based stochastic process power spectrum estimation subject to missing data

Probabilistic Engineering Mechanics ◽

10.1016/j.probengmech.2015.09.015 ◽

2016 ◽

Vol 44 ◽

pp. 66-76 ◽

Cited By ~ 28

Author(s):

Liam Comerford ◽

Ioannis A. Kougioumtzoglou ◽

Michael Beer

Keyword(s):

Stochastic Process ◽

Missing Data ◽

Power Spectrum ◽

Compressive Sensing ◽

Spectrum Estimation ◽

Power Spectrum Estimation

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Analysing 21cm signal with artificial neural network

Proceedings of the International Astronomical Union ◽

10.1017/s174392131701081x ◽

2017 ◽

Vol 12 (S333) ◽

pp. 39-42

Author(s):

Hayato Shimabukuro ◽

Benoit Semelin

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Power Spectrum ◽

Machine Learning Techniques ◽

Inversion Method ◽

Observation Data ◽

The Neural Network ◽

Learning Techniques ◽

Artificial Neural ◽

Artificial Neural Network Ann

AbstractThe 21cm signal at epoch of reionization (EoR) should be observed within next decade. We expect that cosmic 21cm signal at the EoR provides us both cosmological and astrophysical information. In order to extract fruitful information from observation data, we need to develop inversion method. For such a method, we introduce artificial neural network (ANN) which is one of the machine learning techniques. We apply the ANN to inversion problem to constrain astrophysical parameters from 21cm power spectrum. We train the architecture of the neural network with 70 training datasets and apply it to 54 test datasets with different value of parameters. We find that the quality of the parameter reconstruction depends on the sensitivity of the power spectrum to the different parameter sets at a given redshift and also find that the accuracy of reconstruction is improved by increasing the number of given redshifts. We conclude that the ANN is viable inversion method whose main strength is that they require a sparse extrapolation of the parameter space and thus should be usable with full simulation.

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Development of missing data prediction model for carbon monoxide

Malaysian Journal of Fundamental and Applied Sciences ◽

10.11113/mjfas.v15n2019.969 ◽

2019 ◽

Vol 15 (1) ◽

pp. 13-17

Author(s):

Nurul Latiffah Abd Rani ◽

Azman Azid ◽

Muhamad Shirwan Abdullah Sani ◽

Mohd Saiful Samsudin ◽

Ku Mohd Kalkausar Ku Yusof ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Carbon Monoxide ◽

Missing Data ◽

Prediction Model ◽

Optimum Number ◽

Data Prediction ◽

Artificial Neural ◽

Input Variables ◽

Missing Data Prediction

Carbon monoxide (CO) is one of the most important pollutants since it is selected for API calculation. Therefore, it is paramount to ensure that there is no missing data of CO during the analysis. There are numbers of occurrences that may contribute to the missing data problems such as inability of the instrument to record certain parameters. In view of this fact, a CO prediction model needs to be developed to address this problem. A dataset of meteorological and air pollutants value was obtained from the Air Quality Division, Department of Environment Malaysia (DOE). A total of 113112 datasets were used to develop the model using sensitivity analysis (SA) through artificial neural network (ANN). SA showed particulate matter (PM10) and ozone (O3) were the most significant input variables for missing data prediction model of CO. Three hidden nodes were the optimum number to develop the ANN model with the value of R2 equal to 0.5311. Both models (artificial neural network-carbon monoxide-all parameters (ANN-CO-AP) and artificial neural network-carbon monoxide-leave out (ANN-CO-LO)) showed high value of R2 (0.7639 and 0.5311) and low value of RMSE (0.2482 and 0.3506), respectively. These values indicated that the models might only employ the most significant input variables to represent the CO rather than using all input variables.

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