PREDICTION OF WEATHER-RELATED FAILURES OF OVERHEAD DISTRIBUTION FEEDERS

2005 ◽  
Vol 20 (1) ◽  
pp. 117-125 ◽  
Author(s):  
Yujia Zhou ◽  
Anil Pahwa ◽  
Sanjoy Das
Keyword(s):  
Linear Regression ◽  
Bayesian Network ◽  
Regression Function ◽  
Conditional Probabilities ◽  
Weather Factors ◽  
Distribution Feeder

This article presents two methods for predicting weather-related overhead distribution feeder failures. The first model is based on linear regression, which uses a regression function to determine the correlation between the weather factors and overhead feeder failures. The second method is based on a one-layer Bayesian network, which uses conditional probabilities to model the correlation. Both methods are discussed and followed by tests to assess their performance. The results obtained using these methods are discussed and compared.

Bayesian Networks

2011 ◽  
pp. 89-93 ◽  
Author(s):  
Ahmad Bashir ◽  
Latifur Khan ◽  
Mamoun Awad
Keyword(s):  
Neural Networks ◽  
Bayesian Networks ◽  
Bayesian Network ◽  
Decision Trees ◽  
Graphical Model ◽  
Conditional Probabilities ◽  
Rule Bases

A Bayesian network is a graphical model that finds probabilistic relationships among variables of a system. The basic components of a Bayesian network include a set of nodes, each representing a unique variable in the system, their inter-relations, as indicated graphically by edges, and associated probability values. By using these probabilities, termed conditional probabilities, and their interrelations, we can reason and calculate unknown probabilities. Furthermore, Bayesian networks have distinct advantages compared to other methods, such as neural networks, decision trees, and rule bases, which we shall discuss in this paper.

Experimental Study of Short-Term Creep Characteristics Base on Step Loading-Unloading Method for Hard Rock

2013 ◽  
Vol 774-776 ◽  
pp. 86-93
Author(s):  
Fu Jun Zhang ◽  
Chuan Xiao Liu
Keyword(s):  
Linear Regression ◽  
Axial Strain ◽  
Radial Strain ◽  
Strain Curve ◽  
Regression Function ◽  
Nonlinear Creep ◽  
Linear Regression Function ◽  
Term Creep ◽  
Sandstone Specimen ◽  
Short Term Creep

Based on experimental results of uniaxial compression and short-term creep using 8-step loading-unloading method, fine sandstone specimen, which lower creep limit is 27MPa, present typical brittle breakage properties of hard rock. The correlative coefficients of linear regression function for isochronous stress-strain curve are all higher than 0. 92, and the ratio of long-term strength to instantaneous strength reaches 94. 39%，which indicate that the whole creep of fine sandstone specimen is weak. The average correlative coefficients of linear regression function for isochronous stress- axial strain curve are 3. 92% higher than that of average correlative coefficients of linear regression function for isochronous stress- radial strain curve, so nonlinear creep property of the fine sandstone specimen in axial direction is correspondingly weaker than that in radial direction. Negative Gauss distribution can be applied collectively to nonlinear creep of fine sandstone specimen, which has obvious time effect.With increasing loading, the reduction degrees of average correlative coefficients of linear fitting functions of isochronous stress-axial strain curve and isochronous stress-radial strain curve are 0. 97% and 0. 67% respectively, which indicates the linear correlation decreases commonly. Thus, the degree of nonlinear creep for fine sandstone specimen increases along with loading stress with obvious stress effect.

scholarly journals Temperature dependence of blood surface tension

2007 ◽  
pp. S93-S98
Author(s):  
J Rosina ◽  
E Kvašňák ◽  
D Šuta ◽  
H Kolářová ◽  
J Málek ◽  
...  
Keyword(s):  
Surface Tension ◽  
Linear Regression ◽  
Blood Serum ◽  
Whole Blood ◽  
Healthy Subjects ◽  
Regression Function ◽  
Sediment Surface ◽  
Linear Regression Equation ◽  
Linear Regression Function ◽  
Ring Method

Whole blood surface tension of 15 healthy subjects recorded by the ring method was investigated in the temperature range from 20 to 40 degrees C. The surface tension omega as a function of temperature t ( degrees C) is described by an equation of linear regression as omega(t) = (-0.473 t + 70.105) x 10(-3) N/m. Blood serum surface tension in the range from 20 to 40 degrees C is described by linear regression equation omega(t) = (-0.368 t + 66.072) x 10(-3) N/m and linear regression function of blood sediment surface tension is omega(t) = (-0.423 t + 67.223) x10(-3) N/m.

scholarly journals METODA ZANURZANIA REGRESJI W PRZYPADKU WYSTĘPOWANIA OBSERWACJI NIETYPOWYCH

2019 ◽  
Vol 20 (2) ◽  
pp. 83-92
Author(s):  
Małgorzata Kobylińska
Keyword(s):  
Linear Regression ◽  
Least Squares ◽  
Least Squares Method ◽  
Regression Function ◽  
Maximum Depth ◽  
Two Dimensional ◽  
Structural Elements ◽  
Linear Regression Function ◽  
Regression Functions

This paper presents the application of the regression maximum depth for the estimation of linear regression function structural elements. For two-dimensional sets including untypical observations, regression functions were developed using the classical least squares method and a method based on the concept of observation depth measure in a sample. The effect of untypical observations on the estimated models has been noted.

scholarly journals Pengaruh Citra Bank dan Promosi Undian Ghendis Manis Terhadap Keputusan Petani Tebu Menabung di Bank

2017 ◽  
Vol 1 (1) ◽  
pp. 76-87
Author(s):  
Umar Makruf ◽  
Hesti Budiwati
Keyword(s):  
Linear Regression ◽  
Quantitative Research ◽  
Linear Regression Analysis ◽  
Regression Function ◽  
Multiple Linear Regression Analysis ◽  
Coefficient Of Determination ◽  
Simple Linear Regression ◽  
Effect Relation ◽  
Saving Decisions ◽  
Branch Office

The study aims to knowed influence of bank image and promotion on gamling gendis manis to farming dicision to saving in Lumajang Branch office of BRI. Either partially of simultaneously. This research is quantitative research to find the associative relationship that are causal or the research to ask the question the relations between two or more variable and cause effect relation. The study aims to find evidence of the influence of bank image and promotion on saving decisions of Lumajang Barnch Office of Bank Rakyat Indonesia either partially or simultaneously. This study tested the hypothesis that there is a bank image effect on the saving decisions, there is promotion on saing decisions, and there is the influence of bank image and promotion to work simultaneously on the performance of employees at the National Narcotics Agency office Lumajang. The study, of 45 respondents using multiple linear regression analysis is the bank image there is an influence on saving decisions, there is work promotion on saving decisions, and there is the influence of bank image and promotion to work simultaneously on the decisions of saving at the Lumajang Barnch Office of Bank Rakyat Indonesia. Simple linear regression function generated is Y= 4,096 + 0,345X1 + 0,183X2. The coefficient of determination showed that 53,5 of saving decisions can be explained by the bank image and promotion t, while the remaining 46,5% of saving decisions is influenced by other variables not examined in this study.

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