GKN (Norway)

Pseudo-isochromatic plates with varying background lightness were used in a display-based colour vision experiment. As in previous work, the background was found to have a small effect on the ability of observers to identify the patterns on the plates. The time taken to recognize the patterns was significantly affected for both colour-normal and colour vision deficient observers, with darker backgrounds associated with shorter response times and white backgrounds having the longest response times, suggesting a greater degree of task difficulty. Possible reasons for the results when test plates were presented with a white background are suggested, including lower apparent colourfulness and greater difficulty in visual integration of the figure.

IntroductionSystems Engineering as a profession emerged from the telecommunications and space programs in the United States in the 1950s. Motivating this emergence was the need for these systems to be safe, reliable, robust, and able to handle unforeseen events. Practicing systems engineers wrote the earliest Systems Engineering literature (Chestnut, 1967; Goode & Machol,

Abstract Modeling and visualization is a common tool in the systems engineering process used to validate assumptions, increase understanding, synchronize views, and support decisions. The architect can apply models to support different purposes through all phases of a project. In this paper, we analyze model usage in production line improvement projects at GKN Aerospace Norway (GAN). We apply modeling in the understanding phase, exploration phase and verification phase of various projects. Our goal is to identify factors in the applied models that affect the models' success rate. Further, we explore how the importance of the identified factors varies in the different modeling phases. We also discuss how much effort the architect should invest in model creation.Among the impact factors we discuss are: a models ability to be instantly edited, visual attributes like how close to reality the model appears, a model's level of details and assumptions, and how personally relevant the model content is. Observations and feedback captured during this study indicate how the importance of each identified impact factor varies relative to the modeling phase. We also suggest that the resources spent on model creation should be in accordance with the intended value of the output from the model.By understanding how and when to use the impact factors discussed in this study, the architect will be better suited to make the proper design choices during model creation, and be able to provide more effective model‐assisted communication.