Model Order Reduction (MOR) of Function‐Perfusion‐Growth Simulation in the Human Fatty Liver via Artificial Neural Network (ANN)

Abstract Catalysed diesel particulate filters (DPF) have been described as multifunctional reactor systems. Integration of selective catalytic reduction (SCR) functionality in the DPF creates an SCR-in-DPF system that achieves nitrous oxides (NOx) treatment along with particulate matter (PM) collection. The physical and chemical aspects of the integrated SCR-filter complicate system modelling. The goal of this work is to develop low-complexity model of the SCR-filter system which retains high fidelity. A high-fidelity model of the SCR-coated filter has been developed and validated. The performance of the model was described in a previous paper. Model complexity reduction is attempted in this paper. The objective is to achieve simulation times that can support the deployment of the model for online system control in an engine control unit. Two approaches were taken for the SCR-coated filter model order reduction (MOR): a “grey-box” approach via proper orthogonal decomposition (POD) and a “black box” approach via artificial neural network (ANN) function approximation. The POD method is shown to deliver a significant MOR while maintaining a high degree of fidelity but with less than 5% improvement in simulation time. The ANN method delivers a substantial MOR with reduction of three orders of magnitude in simulation time. The accuracy of the ANN model is satisfactory with good generalisation to new test data but noticeably inferior to the POD method.

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Artificial Neural Network and Data Dimensionality Reduction based on Machine Learning Methods for PMSM Model Order Reduction

IEEE Access ◽

10.1109/access.2021.3095668 ◽

2021 ◽

pp. 1-1

Author(s):

Maria Raluca Raia ◽

Mircea Ruba ◽

Raul Octavian Nemes ◽

Claudia Martis

Keyword(s):

Neural Network ◽

Machine Learning ◽

Artificial Neural Network ◽

Dimensionality Reduction ◽

Model Order Reduction ◽

Order Reduction ◽

Learning Methods ◽

Model Order ◽

Machine Learning Methods ◽

Data Dimensionality Reduction

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How to estimate and predict the expenses incurred by diabetes treatment using Artificial Neural Network (ANN)

Journal of Biology and Today s World ◽

10.15412/j.jbtw.01030101 ◽

2014 ◽

Vol 3 (1) ◽

Author(s):

M. Mahboubi ◽

M. Rahimi ◽

M. Mohebbi ◽

F. Ghahramani

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Diabetes Treatment ◽

Artificial Neural ◽

Artificial Neural Network Ann

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Application of the Artificial Neural Network (ANN) Method as MPPT Photovoltaic for DC Source Storage

International Review of Automatic Control (IREACO) ◽

10.15866/ireaco.v12i3.16455 ◽

2019 ◽

Vol 12 (3) ◽

pp. 145 ◽

Cited By ~ 1

Author(s):

Epyk Sunarno ◽

Ramadhan Bilal Assidiq ◽

Syechu Dwitya Nugraha ◽

Indhana Sudiharto ◽

Ony Asrarul Qudsi ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Artificial Neural ◽

Artificial Neural Network Ann

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LOAD SCHEDULING FOR SMART HOME USING DAY-AHEAD PREDICTION FROM ARTIFICIAL NEURAL NETWORK (ANN)

International Journal of Advanced Trends in Computer Science and Engineering ◽

10.30534/ijatcse/2020/9291.42020 ◽

2020 ◽

Vol 9 (1.4) ◽

pp. 658-663

Author(s):

Joharry S. H.

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Smart Home ◽

Load Scheduling ◽

Artificial Neural ◽

Artificial Neural Network Ann

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Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

Engineering and Technology Journal ◽

10.30684/etj.v38i4a.399 ◽

2020 ◽

Vol 38 (4A) ◽

pp. 510-514

Author(s):

Tay H. Shihab ◽

Amjed N. Al-Hameedawi ◽

Ammar M. Hamza

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Artificial Neural Network ◽

Random Forest ◽

Satellite Image ◽

Landsat 8 ◽

Optical Remote Sensing ◽

Data Set ◽

Artificial Neural ◽

Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019. They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

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