scholarly journals Flicker spreading in a transmission network

2020 ◽  
Vol 69 (1) ◽  
pp. 18-22
Author(s):  
Tonko Garma ◽  
Denisa Galzina
Keyword(s):  
Linear Regression ◽  
Predictive Model ◽  
Cost Effective ◽  
Transmission Network ◽  
Transmission Grid ◽  
Key Concepts ◽  
Regression Approach ◽  
Non Linear

This paper reports the flicker spreading in the transmission network. Chapter 1 presents introduction containing brief background and key concepts, followed by description of the corresponding instrumentation in Chapter 2.  Key contribution of the paper is elaborated in Chapters 3 and 4.  Chapter 3 reports measurements of the flicker magnitude along the 400 kV, 220 kV and 110 kV transmission grid for various distances from flicker origin on 400 kV grid, and Chapter 4 gives cost-effective predictive model, enabling estimation of the flicker magnitude for arbitrary selected origin-to-spot distance base on non-linear regression approach. Paper is extension of the work presented at Smagrimet 2019 conference.

Prediction of Surface Roughness and Optimization of Cutting Parameters in CNC Turning of Rotational Features

2020 ◽  
Vol 38 (8A) ◽  
pp. 1143-1153
Author(s):  
Yousif K. Shounia ◽  
Tahseen F. Abbas ◽  
Raed R. Shwaish
Keyword(s):  
Surface Roughness ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Feed Rate ◽  
Cutting Parameters ◽  
Spindle Speed ◽  
Depth Of Cut ◽  
Non Linear ◽  
Optimization Of Cutting Parameters

This research presents a model for prediction surface roughness in terms of process parameters in turning aluminum alloy 1200. The geometry to be machined has four rotational features: straight, taper, convex and concave, while a design of experiments was created through the Taguchi L25 orthogonal array experiments in minitab17 three factors with five Levels depth of cut (0.04, 0.06, 0.08, 0.10 and 0.12) mm, spindle speed (1200, 1400, 1600, 1800 and 2000) r.p.m and feed rate (60, 70, 80, 90 and 100) mm/min. A multiple non-linear regression model has been used which is a set of statistical extrapolation processes to estimate the relationships input variables and output which the surface roughness which prediction outside the range of the data. According to the non-linear regression model, the optimum surface roughness can be obtained at 1800 rpm of spindle speed, feed-rate of 80 mm/min and depth of cut 0.04 mm then the best surface roughness comes out to be 0.04 μm at tapper feature at depth of cut 0.01 mm and same spindle speed and feed rate pervious which gives the error of 3.23% at evolution equation.

An Epidemic Avian Influenza Prediction Model Based on Google Trends

2019 ◽  
Vol 16 (4) ◽  
pp. 303-310 ◽  
Author(s):  
Yi Lu ◽  
Shuo Wang ◽  
Jianying Wang ◽  
Guangya Zhou ◽  
Qiang Zhang ◽  
...  
Keyword(s):  
Linear Regression ◽  
Avian Influenza ◽  
Prediction Model ◽  
Multiple Linear Regression ◽  
Google Trends ◽  
Health Providers ◽  
Human Beings ◽  
Success Rates ◽  
New Model ◽  
Regression Approach

The occurrence of epidemic avian influenza (EAI) not only hinders the development of a country's agricultural economy, but also seriously affects human beings’ life. Recently, the information collected from Google Trends has been increasingly used to predict various epidemics. In this study, using the relevant keywords in Google Trends as well as the multiple linear regression approach, a model was developed to predict the occurrence of epidemic avian influenza. It was demonstrated by rigorous cross-validations that the success rates achieved by the new model were quite high, indicating the predictor will become a very useful tool for hospitals and health providers.

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