Customization and optimization of SSD-based neural network model for detection of external force damage on transmission lines

2019 ◽  
Author(s):  
Yingying Chi ◽  
Rui Liu ◽  
Wenpeng Cui ◽  
Haifeng Zhang ◽  
Yidong Yuan
Keyword(s):  
Neural Network ◽  
Network Model ◽  
External Force ◽  
Neural Network Model ◽  
Transmission Lines

Neural Network Model to Estimate Resistivity of Ground Enhancers Reinforced with Graphene Nano Particles for Transmission Lines

2019 ◽  
Vol 58 ◽  
pp. 139-150
Author(s):  
Isaias Ramirez-Vazquez ◽  
Miguel Ramirez-Gonzalez ◽  
J. Eduardo Salgado-Talavera
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Transmission Lines ◽  
Nano Particles ◽  
Grounding System ◽  
Surge Arresters ◽  
Lightning Impulse ◽  
Graphene Nanoparticles ◽  
Impulse Current

For many high voltage transmission lines, lightning is the first cause of outages. Different alternatives have been used to diminish these outages, like the use of counterpoise wires, installation of surge arresters, and the improvement of the grounding system using ground enhancers or chemical enhancers. In this paper, graphene nanoparticles were used to reformulate commercial ground enhancers. The results of this research end up in an improvement factor of up to 100 times the reduction in resistivity, when graphene nanoparticles are used. After lightning current impulse tests done on both types of samples, the performance of the un-reformulated ground enhancer samples shows a faster deterioration than the graphene reinforced ground enhancer samples. In order to establish a criterion to quantitatively rank the chemical ground enhancers ́ performance after the lightning impulse current tests, a neural network model was developed.

Neural Network Model to Calculate the Creep Data Using Size

2017 ◽  
Vol 7 (8) ◽  
pp. 257
Author(s):  
Seetharam .K ◽  
Sharana Basava Gowda ◽  
. Varadaraj
Keyword(s):  
Neural Network ◽  
Risk Factors ◽  
Software Engineering ◽  
Network Model ◽  
Neural Network Model ◽  
Software Metrics ◽  
Creep Data ◽  
Engineering Software ◽  
Function Points ◽  
Different Levels

In Software engineering software metrics play wide and deeper scope. Many projects fail because of risks in software engineering development[1]t. Among various risk factors creeping is also one factor. The paper discusses approximate volume of creeping requirements that occur after the completion of the nominal requirements phase. This is using software size measured in function points at four different levels. The major risk factors are depending both directly and indirectly associated with software size of development. Hence It is possible to predict risk due to creeping cause using size.

Estimating nitrogen contents of apple leaves based on hyperspectral parameter models at different phenophases

2016 ◽  
Vol 6 (2) ◽  
pp. 942-952
Author(s):  
Xicun ZHU ◽  
Zhuoyuan WANG ◽  
Lulu GAO ◽  
Gengxing ZHAO ◽  
Ling WANG
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Shoot Growth ◽  
Hyperspectral Data ◽  
Estimation Model ◽  
Apple Leaves ◽  
Bp Neural Network Model ◽  
Nitrogen Contents

The objective of the paper is to explore the best phenophase for estimating the nitrogen contents of apple leaves, to establish the best estimation model of the hyperspectral data at different phenophases. It is to improve the apple trees precise fertilization and production management. The experiments were done in 20 orchards in the field, measured hyperspectral data and nitrogen contents of apple leaves at three phenophases in two years, which were shoot growth phenophase, spring shoots pause growth phenophase, autumn shoots pause growth phenophase. The study analyzed the nitrogen contents of apple leaves with its original spectral and first derivative, screened sensitive wavelengths of each phenophase. The hyperspectral parameters were built with the sensitive wavelengths. Multiple stepwise regressions, partial least squares and BP neural network model were adopted in the study. The results showed that 551 nm, 716 nm, 530 nm, 703 nm; 543 nm, 705 nm, 699 nm, 756 nm and 545 nm, 702 nm, 695 nm, 746 nm were sensitive wavelengths of three phenophases. R551+R716, R551*R716, FDR530+FDR703, FDR530*FDR703; R543+R705, R543*R705, FDR699+FDR756, FDR699*FDR756and R545+R702, R545*R702, FDR695+FDR746, FDR695*FDR746 were the best hyperspectral parameters of each phenophase. Of all the estimation models, the estimated effect of shoot growth phenophase was better than other two phenophases, so shoot growth phenophase was the best phenophase to estimate the nitrogen contents of apple leaves based on hyperspectral models. In the three models, the 4-3-1 BP neural network model of shoot growth phenophase was the best estimation model. The R2 of estimated value and measured value was 0.6307, RE% was 23.37, RMSE was 0.6274.

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