Uncertainty quantification methods for neural networks pattern recognition

On-line monitoring techniques have attracted increasing attention as a promising strategy for improving safety, maintaining availability and reducing the cost of operation and maintenance. In particular, pattern recognition tools such as artificial neural networks are today largely adopted for sensor validation, plant component monitoring, system control, and fault-diagnostics based on the data acquired during operation. However, classic artificial neural networks do not provide an error context for the model response, whose robustness remains thus difficult to estimate. Indeed, experimental data generally exhibit a time/space-varying behaviour and are hence characterized by an intrinsic level of uncertainty that unavoidably affects the performance of the tools adopted and undermines the accuracy of the analysis. For this reason, the propagation of the uncertainty and the quantification of the so called margins of uncertainty in output are crucial in making risk-informed decision. The current study presents a comparison between two different approaches for the quantification of uncertainty in artificial neural networks. The first technique presented is based on the error estimation by a series association scheme, the second approach couples Bayesian model selection technique and model averaging into a unified framework. The efficiency of these two approaches are analysed in terms of their computational cost and predictive performance, through their application to a nuclear power plant fault diagnosis system

Estimation of Radioactivity Release Activity Using Non-Linear Kalman Filter-Based Estimation Techniques

The estimation of radioactivity release following an accident in a nuclear power plant is crucial due to its short and long-term impacts on the surrounding population and the environment. In the case of any accidental release, the activity needs to be estimated quickly and reliably to effectively plan a rapid emergency response and design an appropriate evacuation strategy. The accurate prediction of incurred dose rate during normal or accident scenario is another important aspect. In this article, three different non-linear estimation techniques, extended Kalman filter, unscented Kalman filter, and cubature Kalman filter are proposed in order to estimate release activity and to improve the prediction of dose rates. Radionuclide release rate, average wind speed, and height of release are estimated using the dose rate monitors data collected in proximity of the release point. Further, the estimates are employed to improve the prediction of dose rates. The atmospheric dispersion phenomenon of radioactivity release is modelled using the Gaussian plume model. The Gaussian plume model is then employed for the calculation of dose rates. A variety of atmospheric and accident related scenarios for single source and multiple sources are studied in order to assess the efficacy of the proposed filters. Statistical measures have been used in order to validate the performance of the proposed approaches

Modelling the effect of hydrogen on crack growth in zirconium

Via molecular dynamics simulations, the effects of hydrogen on stress evolution of -zirconium and crack propagation in monocrystalline and multiple grained zirconium systems are investigated. Diffusion barriers are shown to reduce when strain is applied, which then causes hydrogen to accumulate at surfaces and grain boundaries. Crack growth is considered for a range of -zirconium systems, both with and without hydrogen, strained in multiple directions. The effects of crystal orientation are shown to be of high influence on the stress evolution of -zirconium irrespective of hydrogen content. Crack growth velocity is increased the most by hydrogen for -zirconium when uniaxial strain is applied in the [0 0 0 1] direction. Simulations are conducted investigating the effects of single grain boundaries in normal and parallel orientations to crack growth showing a high importance on the location of interstitial hydrogen in crack growth behaviour. In addition, larger scale simulations show the effects of multiple grain boundaries and hydrogen content in the evolution of cracks

Short-Term History Based Authentication Using Smartphone Sensors and Apps

Secrete-QA could be a security primarily based application. A security question is asked once the user fails to login or forgets his/her password and to reset the password. A security question could be a secondary authentication, but these queries is guessed simply by an admirer or exposed to a strangers who might recognize our personal info like friends, relations or those that will access our personal info through public on-line tools. We will build these security queries additional customized by using the privilege of accessing our Smartphone sensors and apps while not affecting the user’s privacy. Three types of questions are generated that is, Yes/No questions, Multiple Choice questions and WH questions. An android app is created for the frequent updating of the Smartphone data and questions are asked in the web part using this updated data.The questions are generated by considering the legacy apps, GPS, calendar, app details etc.These have the mostmemorability to the users and high robustness to attacks

A constraint-based genetic algorithm for optimizing neural network architectures for detection of loss of coolant accidents of nuclear power plants

© 2018 Elsevier B.V. The loss of coolant accident (LOCA) of a nuclear power plant (NPP) is a severe accident in the nuclear energy industry. Nowadays, neural networks have been trained on nuclear simulation transient datasets to detect LOCA. This paper proposes a constraint-based genetic algorithm (GA) to find optimised 2-hidden layer network architectures for detecting LOCA of a NPP. The GA uses a proposed constraint satisfaction algorithm called random walk heuristic to create an initial population of neural network architectures of high performance. At each generation, the GA population is split into a sub-population of feature subsets and a sub-population of 2-hidden layer architectures to breed offspring from each sub-population independently in order to generate a wide variety of network architectures. During breeding 2-hidden layer architectures, a constraint-based nearest neighbor search algorithm is proposed to find the nearest neighbors of the offspring population generated by mutation. The results showed that for LOCA detection, the GA-optimised network outperformed a random search, an exhaustive search and a RBF kernel support vector regression (SVR) in terms of generalization performance. For the skillcraft dataset of the UCI machine learning repository, the GA-optimised network has a similar performance to the RBF kernel SVR and outperformed the other approaches

Reliability prediction of semiconductor devices using modified physics of failure approach

Traditional approaches like MIL-HDBK, Telcordia, and PRISM etc. have limitation in accurately predicting the reliability due to advancement in technology, process, materials etc. As predicting the reliability is the major concern in the field of electronics, physics of failure approach gained considerable importance as it involves investigating the root-cause which further helps in reliability growth by redesigning the structure, changing the parameters at manufacturer level and modifying the items at circuit level. On the other hand, probability and statistics methods provide quantitative data with reliability indices from testing by experimentation and by simulations. In this paper, qualitative data from PoF approach and quantitative data from the statistical analysis is combined to form a modified physics of failure approach. This methodology overcomes some of the challenges faced by PoF approach as it involves detailed analysis of stress factors, data modeling and prediction. A decision support system is added to this approach to choose the best option from different failure data models, failure mechanisms, failure criteria and other factors.Validerad; 2013; 20130312 (aditha