Papers by Author: Ji Lu

Abstract: Elman Neural Network is a typical neural-network which shares the characteristics of multiple-layer and dynamic recurrent, and it’s more suitable than BP Neural Network when it’s applied to forecast the short-term load with periodicity and similarity. To solve the problem that Elman Neural Network lacks learning efficiency, GA-Elman model is established by optimizing the weights and thresholds using Genetic Algorithm. An example is then given to prove the effectiveness of GA-Elman model, using the load data of a certain region. Relative error and MSE have been considered as criterions to analyze the results of load forecasting. By comparing the results calculated by BP, Elman and GA-Elman model, the effectiveness of GA-Elman model is verified, which will improve the accuracy of short-term load forecasting.

Abstract: Machinability of ceramics is a comprehensive behavior resulting from the interactions of several factors. In this paper, the fuzzy comprehensive evaluation system was applied to establish a model for machinabilities of several dental ceramics, including zirconia, empress feldspar, empress toughening leucite and lithium disilicate glass. The hardness (HV), fracture toughness (KIC) and elastic modulus (E) of ceramic samples were used in evaluation model with different weighted indexes. The ceramics could be thus divided into 4 groups of “easiest”, “easy”, “difficult” and “most difficult”, based on evaluated results. Lithium disilicate glass belongs to “easiest”, empress feldspar and empress toughening leucite are at the “easy” level, and zirconia is “difficult” to be machined compared with other counterparts. The evaluations by established model are well supported by the practical machining experiments while these dental ceramics being undergone cutting. This implies that the derived evaluation model is an easy and simple way to estimate machinability of dental ceramics.