Evaluating the effectiveness of a priori information on process measures in a virtual reality inspection task

Due to the nature of the complexity of the aircraft maintenance industry, much emphasis has been placed on improving aircraft inspection performance. One proven technique for improving inspection performance is the use of training. Several strategies have been implemented for training, one of which is giving feedforward information. The use of a priori (feedforward) information is known to positively affect inspection performance (Ernst and Yovits, 1972; Long and Rourke, 1989; McKernan, 1989; Gramopadhye et al., 1997). This information can consist of knowledge about defect characteristics (types, severity/criticality, and location) and the probability of occurrence. Although several studies have been conducted that demonstrate the usefulness of feedforward as a training strategy, there are certain research issues that need to be addressed. This study evaluates the effects of feedforward information on process measures in a simulated 3-dimensional environment (aircraft cargo bay) by the use of virtual realityPeer Reviewe

Evaluating the effectiveness of a priori information on process measures in a virtual reality inspection task

Abstract: Due to the nature of the complexity of the aircraft maintenance industry, much emphasis has been placed on improving aircraft inspection performance. One proven technique for improving inspection performance is the use of training. Several strategies have been implemented for training, one of which is giving feedforward information. The purpose of this study evaluates the effects of feedforward information on process measures in a simulated 3-dimensional environment (aircraft cargo bay) by the use of virtual reality. The study was conducted using six subjects performing inspection in a simulated aircraft cargo bay. Results show that the use of feedforward information positively impact inspection performance in terms of process measures (fixation points, fixation durations, and area covered)

A logistic approximation to the cumulative normal distribution

This paper develops a logistic approximation to the cumulative normal distribution. Although the literature contains a vast collection of approximate functions for the normal distribution, they are very complicated, not very accurate, or valid for only a limited range. This paper proposes an enhanced approximate function. When comparing the proposed function to other approximations studied in the literature, it can be observed that the proposed logistic approximation has a simpler functional form and that it gives higher accuracy, with the maximum error of less than 0.00014 for the entire range. This is, to the best of the authors’ knowledge, the lowest level of error reported in the literature. The proposed logistic approximate function may be appealing to researchers, practitioners and educators given its functional simplicity and mathematical accurac