Estimation of the Dispersion Coefficient in Natural Rivers Using a Granular Computing Model

2017 ◽  
Vol 143 (5) ◽  
pp. 04017001 ◽  
Author(s):  
Roohollah Noori ◽  
Behzad Ghiasi ◽  
Hossien Sheikhian ◽  
Jan Franklin Adamowski
Keyword(s):  
Granular Computing ◽  
Dispersion Coefficient ◽  
Computing Model ◽  
Natural Rivers

scholarly journals PERFORMANCE ANALYSIS OF GRANULAR COMPUTING MODEL IN SOFT COMPUTING PARADIGM FOR MONITORING OF FETAL ECHOCARDIOGRAPHY

2019 ◽  
Vol 2019 (1) ◽  
pp. 14-23 ◽  
Author(s):  
Sathesh A
Keyword(s):  
Heart Rate ◽  
Fetal Heart Rate ◽  
Granular Computing ◽  
Fetal Heart ◽  
Fetal Echocardiography ◽  
Detection Accuracy ◽  
Second Trimester ◽  
Data Set ◽  
Computing Model ◽  
Computing Paradigm

The monitoring of fetal heart being essential in the second trimester of the prenatal periods. The abnormalities in the child heart rate has to be identified in the early stages, so as to take essential remedies for the babies in the womb, or would enable the physician to be ready for he complication on the delivery and the further treatment after the baby is received. The traditional methodologies being ineffective in detecting the abnormalities leading to fatalities, paves way for the granular computing based fuzzy set, that requires only a limited set of data for training, and helps in the eluding of the unwanted data set that are far beyond the optimal. Further the methods performance is analyzed to evident the improvement in the fetal heart rate detection in terms of prediction accuracy and the detection accuracy.

A New Granular Computing Model Based on Algebraic Structure

2019 ◽  
Vol 28 (1) ◽  
pp. 136-142 ◽  
Author(s):  
Linshu CHEN ◽  
Jiayang WANG ◽  
Weicheng WANG ◽  
Li LI
Keyword(s):  
Algebraic Structure ◽  
Granular Computing ◽  
Computing Model ◽  
Model Based

Prediction of longitudinal dispersion coefficients in natural rivers using genetic algorithm

2009 ◽  
Vol 40 (6) ◽  
pp. 544-552 ◽  
Author(s):  
Rajeev Ranjan Sahay ◽  
Som Dutta
Keyword(s):  
Field Data ◽  
Dispersion Coefficient ◽  
Shear Velocity ◽  
Longitudinal Dispersion ◽  
Performance Indices ◽  
Longitudinal Dispersion Coefficient ◽  
Mean Velocity ◽  
Cross Sectional ◽  
New Formula ◽  
Natural Rivers

A new expression for the prediction of longitudinal dispersion coefficient in natural rivers, using genetic algorithms, is proposed. The expression uses hydraulic and geometric characteristics of rivers, which are readily available. For performance evaluation, using published field data, results of coefficient prediction by the new expression and by the other reported expressions are compared. According to various performance indices, it is concluded that the new formula predicts the longitudinal dispersion coefficient more accurately. Sensitive analysis performed on input parameters indicates the ratio of the cross-sectional mean velocity to the bottom shear velocity to be the most influencing parameter for accurate prediction of the longitudinal dispersion coefficient.

Sign in / Sign up

Export Citation Format

Share Document