|
曝光台 注意防骗 网曝天猫店富美金盛家居专营店坑蒙拐骗欺诈消费者
It should be clear that some key assumptions are made in the development of this model, such as which parts of the simulator system provide which cues, and the adjustment rules are somewhat of an art. The bottom line is that today’s models are incomplete and certainly not validated. The lack of adequate analytical models has been noted by others. Breuhaus pointed out that it is a dubious assump-tion that one knows all of the important cues and their interrelationships (ref. 72). Heffley et al. stated that when a modeler begins to assemble all of the components that are believed to influence the pilot-vehicle loop in simula-tion that one notices the fragmentary nature of, and the serious gaps in, the quantification of the component characteristics (ref. 73). More empirical results from systematic and realistic investigations are needed before useful analytical models can be created.
Altitude, ft
12
10
8
6
4
2
0
-2
Figure B5. Predicted phase-plane responses.
Appendix C—Example of Repeated-Measures Analysis
A researcher wants to know if the differences among collected data are due to a manipulated experimental factor, or if they are due to random effects. If data are taken from a random sample of individuals, each of whom has experienced a different combination of the experimental factors, then experimental error may be attributed to two effects. The first effect is sampling error and the second effect is error resulting from differences among the individuals.
Repeated-measures allows the differences among individuals to be accounted for in the analysis. This is accomplished by having each individual experience each experimental manipulation. It is a technique often used when subjects are available for a long period of time, such as in a research institution. Details of repeated-measures theory may be found in Myers (ref. 54). Only a brief overview will be given here. An example of the data processing involved is given next.
The example uses the pilot compensation ratings for Task 1 in section 3 (15° yaw rotational capture). A plot of the data is given in figure 15. Table C1 contains the average compensation rating given by each pilot for all of the combinations of translational and rotational motion. The first column gives the pilot number, 1–6. The second column indicates if translational motion was present. A 1 means translational motion was present, and a zero means it was not. Column three indicates if rotational motion was present. Column four gives the average compensation rating, where the average is taken over the repeated runs performed by each pilot. Here the values correspond to 0 = minimal, 1 = moderate, 2 = consid-erable, and 3 = maximum tolerable.
The purpose of the statistical analysis is to determine if pilots provide better compensation ratings for particular motion configurations. Specifically, it can be determined if the ratings are influenced by the presence of transla-tional motion, by the rotational motion, or by a combi-nation of the two motions. Although figure 15 suggests that the translational motion is likely to be the dominant factor, the statistical analysis provides quantitative information on how often such variations are due to random effects. So, the analysis indicates how solid the inferences drawn from the data are.
直升机网 www.helicopter.cn
直升机翻译 www.aviation.cn
本文链接地址：Helicopter Flight Simulation Motion Platform Requirements(65)
|
