Advanced NDE and QA for Ensuring Quality and Integrity of Critical Components in Nuclear Industry

Energy is the enabler for all social and economic devel-opments and growth of civilization. For a country like India with a population of about 1.2 billion and a average growth rate of about 8% annually (likely to increase further), energy is a central issue. Based on the proj-ected demand and India's energy resource base, it is now well established that nuclear energy using the vast thor-ium reserves is the only viable alternatively presently that could ensure the energy security of the nation. The key to the successful utilisation of the vast thorium reserves lies in the development of fast breeder reactors

Comparing ROC Curves Derived From Regression Models

In constructing predictive models, investigators frequently assess the incremental value of a predictive marker by comparing the ROC curve generated from the predictive model including the new marker with the ROC curve from the model excluding the new marker. Many commentators have noticed empirically that a test of the two ROC areas often produces a non-significant result when a corresponding Wald test from the underlying regression model is significant. A recent article showed using simulations that the widely-used ROC area test [1] produces exceptionally conservative test size and extremely low power [2]. In this article we show why the ROC area test is invalid in this context. We demonstrate how a valid test of the ROC areas can be constructed that has comparable statistical properties to the Wald test. We conclude that using the Wald test to assess the incremental contribution of a marker remains the best strategy. We also examine the use of derived markers from non-nested models and the use of validation samples. We show that comparing ROC areas is invalid in these contexts as well

Statistical characteristics of the total ion density in the topside ionosphere during the period 1996-2004 using empirical orthogonal function (EOF) analysis

International audienceWe have applied the empirical orthogonal function (EOF) analysis to examine the climatology of the total ion density Ni at 840 km during the period 1996-2004, obtained from the Defense Meteorological Satellite Program (DMSP) spacecraft. The data set for each of the local time (09:30 LT and 21:30 LT) is decomposed into a time mean plus the sum of EOF bases Ei of space, multiplied by time-varying EOF coefficients Ai. Physical explanations are made on the first three EOFs, which together can capture more than 95% of the total variance of the original data set. Results show that the dominant mode that controls the Ni variability is the solar EUV flux, which is consistent with the results of Rich et al. (2003). The second EOF, associated with the solar declination, presents an annual (summer to winter) asymmetry that is caused by the transequatorial winds. The semiannual variation that appears in the third EOF for the evening sector is interpreted as both the effects of the equatorial electric fields and the wind patterns. Both the annual and semiannual variations are modulated by the solar flux, which has a close relationship with the O+ composition. The quick convergence of the EOF expansion makes it very convenient to construct an empirical model for the original data set. The modeled results show that the accuracy of the prediction depends mainly on the first principal component which has a close relationship with the solar EUV flux

Estimating the Empirical Lorenz Curve and Gini Coefficient in the Presence of Error

The Lorenz curve is a graphical tool that is widely used to characterize the concentration of a measure in a population, such as wealth. It is frequently the case that the measure of interest used to rank experimental units when estimating the empirical Lorenz curve, and the corresponding Gini coefficient, is subject to random error. This error can result in an incorrect ranking of experimental units which inevitably leads to a curve that exaggerates the degree of concentration (variation) in the population. We explore this bias and discuss several widely available statistical methods that have the potential to reduce or remove the bias in the empirical Lorenz curve. The properties of these methods are examined and compared in a simulation study. This work is motivated by a health outcomes application which seeks to assess the concentration of black patient visits among primary care physicians. The methods are illustrated on data from this study

Autonomy Infused Teleoperation with Application to BCI Manipulation

Robot teleoperation systems face a common set of challenges including latency, low-dimensional user commands, and asymmetric control inputs. User control with Brain-Computer Interfaces (BCIs) exacerbates these problems through especially noisy and erratic low-dimensional motion commands due to the difficulty in decoding neural activity. We introduce a general framework to address these challenges through a combination of computer vision, user intent inference, and arbitration between the human input and autonomous control schemes. Adjustable levels of assistance allow the system to balance the operator's capabilities and feelings of comfort and control while compensating for a task's difficulty. We present experimental results demonstrating significant performance improvement using the shared-control assistance framework on adapted rehabilitation benchmarks with two subjects implanted with intracortical brain-computer interfaces controlling a seven degree-of-freedom robotic manipulator as a prosthetic. Our results further indicate that shared assistance mitigates perceived user difficulty and even enables successful performance on previously infeasible tasks. We showcase the extensibility of our architecture with applications to quality-of-life tasks such as opening a door, pouring liquids from containers, and manipulation with novel objects in densely cluttered environments

Automated Defect Detection and Characterization on Pulse Thermography Images Using Computer Vision Techniques

Defect detection and characterization plays a vital role in predicting the life span of materials. Defect detection using appropriate inspection technologies at various phases has gained huge importance in metal production lines. It can be accomplished through wise application of non-destructive testing and evaluation (NDE). It is important to characterize defects at an early stage in order to be able to overcome them or take corrective measures. Pulse thermography is a modern NDE method that can be used for defect detection in metal objects. Only a limited amount of work has been done on automated detection and characterization of defects due to thermal diffusion. This paper proposes a system for automatic defect detection and characterization in metal objects using pulse thermography images as well as various image processing algorithms and mathematical tools. An experiment was carried out using a sequence of 250 pulse thermography images of an AISI 316 L stainless steel sheet with synthetic defects. The proposed system was able to detect and characterize defects sized 10 mm, 8 mm, 6 mm, 4 mm and 2 mm with an average accuracy of 96%, 95%, 84%, 77%, 54% respectively. The proposed technique helps in the effective and efficient characterization of defects in metal objects