An approach for reliability prediction of instrumentation & control cables by artificial neural networks and Weibull theory for probabilistic safety assessment of NPPs

2018 ◽  
Vol 170 ◽  
pp. 31-44 ◽  
Author(s):  
T.V. Santhosh ◽  
V. Gopika ◽  
A.K. Ghosh ◽  
B.G. Fernandes
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Safety Assessment ◽  
Reliability Prediction ◽  
Probabilistic Safety Assessment ◽  
Artificial Neural ◽  
Weibull Theory ◽  
Probabilistic Safety

Reliability prediction of I&C cable insulation materials by DSC and Weibull theory for probabilistic safety assessment of NPPs

2016 ◽  
Vol 296 ◽  
pp. 51-61 ◽  
Author(s):  
T.V. Santhosh ◽  
V. Gopika ◽  
A.K. Ghosh ◽  
B.G. Fernandes ◽  
K.A. Dubey
Keyword(s):  
Safety Assessment ◽  
Reliability Prediction ◽  
Cable Insulation ◽  
Probabilistic Safety Assessment ◽  
Insulation Materials ◽  
Weibull Theory ◽  
Probabilistic Safety

AN EFFECTIVE EARLY SOFTWARE RELIABILITY PREDICTION PROCEDURE FOR PROCESS ORIENTED DEVELOPMENT AT PROTOTYPE LEVEL EMPLOYING ARTIFICIAL NEURAL NETWORKS

2011 ◽  
Vol 18 (03) ◽  
pp. 237-250 ◽  
Author(s):  
K. KRISHNA MOHAN ◽  
A. K. VERMA ◽  
A. SRIVIDYA
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Software Reliability ◽  
Heterogeneous Systems ◽  
Reliability Prediction ◽  
Feed Forward Neural Network ◽  
Distributed Components ◽  
Software Reliability Prediction ◽  
Artificial Neural ◽  
Process Oriented

Reliability of a software product should be tracked during the software lifecycle right from the architectural phase to its operational phase. Heterogeneous systems consist of several globally distributed components, thus rendering their reliability evaluation more complex with respect to the conventional methods. In this context, reliability prediction of software process oriented systems assumes prime importance. It is important to take into account the proven processes like Rational Unified Process (RUP) to mitigate risks and increase the reliability of systems while building distributed based applications. This paper presents an algorithm using feed-forward neural network for early qualitative software reliability prediction. The inputs for neural networks consist of techno-complexity, practitioner's level, creation effort, size and leakage defects. The number of defects detected in each cycle can be predicted by using Artificial Neural Networks (ANN). Illustrative examples prove the effectiveness of the methodology employed.

scholarly journals The safety assessment of lifting equipment using artificial intelligence methods

2020 ◽  
Vol 224 ◽  
pp. 02018
Author(s):  
A Lyapin
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Safety Assessment ◽  
Data Sources ◽  
Artificial Intelligence Methods ◽  
Artificial Neural ◽  
Lifting Equipment

The article is devoted to the problem of using artificial neural networks to assess the risk of developing emergencies during the operation of lifting crane equipment. The data sources are telemetric measurements from microcontroller load limiters, as well as data from technical and daily inspections of equipment condition, in the last case the data may be fuzzy.

Determination of S-N curves with the application of artificial neural networks

1999 ◽  
Vol 22 (8) ◽  
pp. 723-728 ◽  
Author(s):  
Artymiak ◽  
Bukowski ◽  
Feliks ◽  
Narberhaus ◽  
Zenner
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Artificial Neural

Artificial neural networks for diagnosis and prognosis in prostate cancer

2002 ◽  
Vol 20 (2) ◽  
pp. 89-95 ◽  
Author(s):  
Guido Schwarzer ◽  
Martin Schumacher
Keyword(s):  
Prostate Cancer ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Diagnosis And Prognosis ◽  
Artificial Neural
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