Papers by Keyword: Parameter Fitting

Abstract: In view of the randomness and fuzziness of the parameters of rock’s shear strength, first, use symmetric triangular fuzzy numbers which can reflect the interval features of parameters express the parameters of rock’s shear strength. Second, put forward to a new determination method for parameters of rock’s shear strength through least absolute linear regression based on symmetric triangular fuzzy numbers according to criteria of least absolute. Finally, the analysis of practical engineering computation and comparison to other methods shows that the new method is reasonable.

Abstract: Influenced by a variety of factors, such as driving skill, traffic composition, signal control, traffic management, and traffic environment and so on, the traffic flow characteristic parameter statistics embodies multi-state characteristic which is incapable to describe precisely by using a single distribution model. In order to fit the headway characteristic parameter of multi-state traffic flow, based on the denseness of mixture Gamma distribution, the data could be fitted more accurately with the method of expectation maximization algorithm and by means of controlling the number of branches under the condition of precision beyond 95%. Take actual survey data as the example, this paper compares the fitting errors of negative exponential distribution, Erlang distribution and mixture Gamma distribution and the result shows that the fitting result based on mixture Gamma distribution is closer to actual case.

Abstract: Diffusivity [D] matrices are reported for the ternary Ni-Cr-Mo alloy in the approximate single-phase compositional range of Hastelloy C-22 (Alloy 22), surrounding Ni-63 wt.%, Cr-22.3 wt.%, and Mo-14.7 wt.%. These data will contribute to our understanding of the long-term phase stability of Alloy 22, and its potential use as a corrosion barrier in nuclear waste packages. Experimental diffusion couple data were obtained at selected temperatures from a series of diffusion couples, and evaluated, assuming constant diffusivity. Our approach treats the process as an optimization problem that simultaneously considers concentration profile data from numerous diffusion couples surrounding a single end-point composition. We make use of the mathematical characteristics of the analytical solution to this problem, reducing the number of parameters to be fitted. The parameter fitting is accomplished using a combination of heuristic and deterministic methods. Discussion of the sources and magnitudes of uncertainty in the diffusivity values is included.