scholarly journals Statistical analysis of wastewater monitoring for maximum peak factor estimation

2021 ◽  
Vol 1981 (1) ◽  
pp. 012013
Author(s):  
N J Cely-Calixto ◽  
C A Bonilla-Granados ◽  
J P Rojas-Suárez
Keyword(s):  
Statistical Analysis ◽  
Maximum Peak ◽  
Peak Factor ◽  
Factor Estimation

Peak factor estimation in hurricane surface winds

2012 ◽  
Vol 102 ◽  
pp. 1-13 ◽  
Author(s):  
J.A. Balderrama ◽  
F.J. Masters ◽  
K.R. Gurley
Keyword(s):  
Surface Winds ◽  
Peak Factor ◽  
Factor Estimation

scholarly journals ECONOMIC AND STATISTICAL ANALYSIS OF FARMERS' ACTIVITY

2018 ◽  
pp. 287-294
Author(s):  
Natalia Ruchynska
Keyword(s):  
Mathematical Modeling ◽  
Statistical Analysis ◽  
System Analysis ◽  
Production Costs ◽  
Mathematical Methods ◽  
Index Method ◽  
Managerial Decisions ◽  
Scientific Approach ◽  
Factor Estimation

Introduction. Improvement of the process of making managerial decisions and, accordingly, improvement of the quality of the decisions made is achieved through the use of scientific approach, models and methods of decision-making. Methods of economic and mathematical modeling allow to solve a number of issues related to the development of alternative areas of activity, optimization of the structure, production costs and sales of farm products. Therefore, it is expedient to use them for making managerial decisions at farms. Purpose. The article aims to carry out the economic and statistical analysis of the activity of the farm and the feasibility of applying economic and mathematical methods in the process of managing farms in modern conditions of farming. The application of economic and mathematical methods and models for making managerial decisions is considered on the example of the activity of the farm "Horizon" of the Veselinovsky district of the Mykolaiv region, which specializes in the cultivation of grain crops (except rice), legumes and seeds of oilseeds. Methods. In the course of the study, methods of system analysis, index method of factor estimation, and economic and mathematical modeling have been used. Results. On the basis of economic indicators of the activity of the farm "Horizon" during the reporting period, the economic and mathematical model of optimization of sown areas has been created. It contributes to the correction of managerial decisions for improving the efficiency of economic activity.

Analisis Tren Kebakaran Hutan dan Lahan di Indonesia Periode Tahun 2015-2019

2020 ◽  
Vol 6 (1) ◽  
pp. 1
Author(s):  
Arisman Arisman
Keyword(s):  
Statistical Analysis ◽  
Spatial Analysis ◽  
Land Area ◽  
Maximum Peak ◽  
The Government ◽  
Descriptive Statistical Analysis

Forest and land fires are disaster events that continue to recur every year. This study aims to determine patterns / trends in the occurrence of forest and land fires in Indonesia. The method used in this study is a descriptive statistical analysis and spatial analysis. The results showed that the occurrence of forest and land fires had the same pattern where the maximum peak of forest and land fires occurred in September. Another trend shown in the last five years is forest and land fires are fluctuative in which the frequency of events increases, the number of provinces affected also increases, but the area of land affected has an average of around 24.3% of the land area of Indonesia. Spatial analysis result shown the pattern of hotspot occurred in main province consist of Central Borneo, West Borneo, South Sumatera, Riau and Jambi. From this study it can be seen that the problem of forest and land fires is still high and need attention from the government

Robustness of a Neural Network Model for Power Peak Factor Estimation in Protection Systems

2006 ◽  
Author(s):  
Rose Mary G. P. Souza ◽  
Joa˜o M. L. Moreira
Keyword(s):  
Neural Network ◽  
Relative Error ◽  
Safety Margin ◽  
Maximum Error ◽  
Maximum Relative Error ◽  
Peak Factor ◽  
Protection Systems ◽  
Power Peak ◽  
Input Variables ◽  
Factor Estimation

This work presents results of robustness verification of artificial neural network correlations that improve the real time prediction of the power peak factor for reactor protection systems. The input variables considered in the correlation are those available in the reactor protection systems, namely, the axial power differences obtained from measured ex-core detectors, and the position of control rods. The correlations, based on radial basis function (RBF) and multilayer perceptron (MLP) neural networks, estimate the power peak factor, without faulty signals, with average errors between 0.13%, 0.19% and 0.15%, and maximum relative error of 2.35%. The robustness verification was performed for three different neural network correlations. The results show that they are robust against signal degradation, producing results with faulty signals with a maximum error of 6.90%. The average error associated to faulty signals for the MLP network is about half of that of the RBF network, and the maximum error is about 1% smaller. These results demonstrate that MLP neural network correlation is more robust than the RBF neural network correlation. The results also show that the input variables present redundant information. The axial power difference signals compensate the faulty signal for the position of a given control rod, and improves the results by about 10%. The results show that the errors in the power peak factor estimation by these neural network correlations, even in faulty conditions, are smaller than the current PWR schemes which may have uncertainties as high as 8%. Considering the maximum relative error of 2.35%, these neural network correlations would allow decreasing the power peak factor safety margin by about 5%. Such a reduction could be used for operating the reactor with a higher power level or with more flexibility. The neural network correlation has to meet requirements of high integrity software that performs safety grade actions. It is shown that the correlation is a very simple algorithm that can be easily codified in software. Due to its simplicity, it facilitates the necessary process of validation and verification.

Neural network correlation for power peak factor estimation

2006 ◽  
Vol 33 (7) ◽  
pp. 594-608 ◽  
Author(s):  
Rose Mary G.P. Souza ◽  
João M.L. Moreira
Keyword(s):  
Neural Network ◽  
Peak Factor ◽  
Power Peak ◽  
Factor Estimation

Statistical analysis of classification

1966 ◽  
Vol 24 ◽  
pp. 188-189
Author(s):  
T. J. Deeming
Keyword(s):  
Multivariate Analysis ◽  
Statistical Analysis ◽  
Classification Scheme ◽  
Analytical Procedure ◽  
Narrow Band ◽  
Scientific Research ◽  
Maximum Amount ◽  
Amount Of Information

If we make a set of measurements, such as narrow-band or multicolour photo-electric measurements, which are designed to improve a scheme of classification, and in particular if they are designed to extend the number of dimensions of classification, i.e. the number of classification parameters, then some important problems of analytical procedure arise. First, it is important not to reproduce the errors of the classification scheme which we are trying to improve. Second, when trying to extend the number of dimensions of classification we have little or nothing with which to test the validity of the new parameters.Problems similar to these have occurred in other areas of scientific research (notably psychology and education) and the branch of Statistics called Multivariate Analysis has been developed to deal with them. The techniques of this subject are largely unknown to astronomers, but, if carefully applied, they should at the very least ensure that the astronomer gets the maximum amount of information out of his data and does not waste his time looking for information which is not there. More optimistically, these techniques are potentially capable of indicating the number of classification parameters necessary and giving specific formulas for computing them, as well as pinpointing those particular measurements which are most crucial for determining the classification parameters.

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