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- W2420107011 abstract "This paper is a further study of stochastic simulation of helicopter accidents, based on previous work Analysis of Using Monte Carlo Simulation on The State of Helicopter Autorotation‟. The goal of the current paper is to devise an analysis method for data mining the previously obtained simulation results, which are nonparametric and multidimensional. The proposed method uses the concepts of Convex Hull and Euclidean Distance, which have been well studied for mining data without a foreknown distribution. Novel approaches, Distance-Based Ratio Rank and Ratio of Distance Sum to Data Range, are used to investigate the distribution characteristics of the database and make qualitative and quantitative predictions of the simulation data. The novel methodology allows for rapid qualitative assessment of two characteristics of nonparametric data: homogeneity and centralisation. Application of the methodology to the helicopter crash simulation meta-models revealed that all sets of crash data, regardless of flight variables and initial conditions, contained qualitatively identical distributions. The underlying consistency hints at a fundamental property of stochastic crash data which may be used to generate future stochastic crash conditions." @default.
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- W2420107011 date "2015-01-01" @default.
- W2420107011 modified "2023-09-26" @default.
- W2420107011 title "A methodology of integrating convex hull into data mining for simulation of helicopter autorotation" @default.
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