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Customer demands play a very important role in the evaluation and decision of product design scheme. This paper introduces a method for converting customer demands knowledge into product configuration knowledge, and applies this method to evaluate product design scheme. The importance degree of customer demands was analyzed through analytic hierarchy process (AHP) method and prior weight of was calculated. Then prior weight was combined with revised weight, which was gained by the entropy theory. In this way, the result avoids the disadvantage of AHP of over-subjective and the disadvantage of entropy method of absolute impersonal without the support of experts. The method optimized the weight of each product design evaluation index and truly reflected the customer demands. Finally, an example of stroller design was given to verify the feasibility and advantages of the method. Copyright 2013 International Journal of Advancements in Computing Technology is the property of Advanced Institute of Convergence Information Technology Language: en

Customer involvement become more and more important in product design and the most important problem is how to turn the customer demands into design knowledge, which makes the customer demands segmentation a hot topic in academic. A method for customer demands segmentation based on fuzzy clustering and trigonometric functions was proposed. Firstly, a customer-classified framework based on fuzzy clustering was presented. Then, 7 steps for partitioning clustering customer demand based on trigonometric functions were designed. Finally, an example of customer demands groups segmentation in stroller design was given to verify the feasibility and advantages of the method.

In this paper, we construct a Cloud-Neural Network Model in order to solve the problem that limited dividing of the evaluation factor of product satisfaction. Based on the aim of not changing the ambiguity and randomness of indicators, we achieve the reasonable exchange between qualitative and quantitative through uncertain reasoning of cloud, and then, combined with the learning ability of BP Neural Network, eliminate the randomness phenomenon of the design parameters and satisfaction evaluation given by the user, at last, we cloud get the quantify, accurately parameters and satisfaction evaluation.

In the context of the financial crisis now university students’ employment are in a dilemma that is enterprises need talents but students are difficult to look for a job. The reason appearing this stituation relates to the context of the whole society, but through our research and analysis, we found our teaching also certainly separate from the actual situation, this article mainly analyze the employment situation of university students, the problems. From a concrete curriculum of computer teaching, the teaching practice of database theory, we analyze how to establish properly to improve the combination of curriculum refrom and the practical application.