Advanced Materials Research Vols. 542-543

Abstract: For the completeness and accuracy of customers' requirements information in the mass customization paradigm, a method of ontology-driven personal requirements elicitation based on scenario was proposed. Firstly, customer scenario model and product requirements model based on ontology theory were constructed respectively. Association rules were mined with Apriori algorithm using the method of metarule. Scenario ontology was mapped to requirement ontology completely. Then, customers' personal requirements information was elicited completely and accurately. Finally, industrial case study has been performed to demonstrate the practicality and effectiveness of the proposed approach.

Abstract: Maximum likelihood estimation (MLE) is a commonly used method of Weibull distribution, but it needs to calculate the transcendental equations which is based on the computer programming. In this paper, we established a new MLE model of Weibull distribution in a case of random censoring. And this model was proved feasible and correct via three examples. At the same time, the model can be used for a variety of censored data and complete life data of Weibull distribution of MLE.

Abstract: Heterogeneous strategy, population isolation, arithmetic crossover and optimum reserved strategy are used to improve micro-genetic algorithm (mGA) in this paper. Heterogeneous strategy is used to improve the probability of convergence to global optimal solution and quicken up the convergence. Reset frequency is decreased while the global and local searching capabilities of mGA between two resets are enhanced, which makes mGA searching the parameter space intelligently as the mode recognition information is preserved as much as possible. Adaptive random mutation, which used existing genetic information of the current groups, is used to increase efficient search. Finally, standard functions testing demonstrate that the improved mGA can find better optimum solutions with less computing cost than standard genetic algorithm (SGA).