Work Measurement Decision Diagram Development and Application at NASA\u27s Kennedy Space Center

This paper presents a decision flow diagram developed at NASA\u27s Kennedy Space Center for the selection of the appropriate work measurement methodologies for Space Shuttle processing

Lightning forecast from chaotic and incomplete time series using wavelet de-noising and spatiotemporal kriging

Purpose – Present a method to impute missing data from a chaotic time series, in this case lightning prediction data, and then use that completed dataset to create lightning prediction forecasts. Design/methodology/approach – Using the technique of spatiotemporal kriging to estimate data that is autocorrelated but in space and time. Using the estimated data in an imputation methodology completes a dataset used in lightning prediction. Findings – The techniques provided prove robust to the chaotic nature of the data, and the resulting time series displays evidence of smoothing while also preserving the signal of interest for lightning prediction. Research limitations/implications – The research is limited to the data collected in support of weather prediction work through the 45th Weather Squadron of the United States Air Force. Practical implications – These methods are important due to the increasing reliance on sensor systems. These systems often provide incomplete and chaotic data, which must be used despite collection limitations. This work establishes a viable data imputation methodology. Social implications – Improved lightning prediction, as with any improved prediction methods for natural weather events, can save lives and resources due to timely, cautious behaviors as a result of the predictions. Originality/value – Based on the authors’ knowledge, this is a novel application of these imputation methods and the forecasting methods

Sensitivity Of A Cusum Chart For Dispersion To The Independence Assumption

Most control charts for variables data are constructed upon the assumption that the observations from a process are independent. In most applications however this assumption does not hold. The performance of process control schemes have to be evaluated in such circumstances. However most of the work published deal with shifts in the location of correlated processes. In this paper, the ARL performance of a CUSUM chart for process dispersion is analyzed, when the process does not comply with the assumption of independence. The ARL performance of the CUSUM chart for dispersion derived assuming independent observations is compared with that when the chart is used with autoregressive processes AR(1) with different degrees of correlation. It is found that the correlation structure increases the rate of false alarms of the CUSUM chart for oe . Also this chart is found much less sensitive in detecting changes in the process dispersion. INTRODUCTION Statistical Process Control (SPC) has been a ..