Earticle

초록

영어

Cloud manufacturing combined with information manufacturing, the Internet of things(IoT), cloud computing, and semantic web technology. Through extending and changing the service technology and network manufacturing, it makes the manufacturing resources and manufacturing capabilities virtualization and servicization. It can make centralized and unified intelligent management for manufacturing. Therefore, the establishment of appropriate cloud manufacturing QoS(quality of service) model is the basis and prerequisite for the development of related research of cloud manufacturing. The existing QoS model ignored the effect of time on QoS. This directly leads to the lack of accuracy of modeling, and further affects the implementation effect of the follow-up study on the prediction, service selection and so on. With cloud manufacturing evaluation criteria, this paper proposes a five dimensional QoS evaluation criteria and the corresponding calculation formula, it is consistent with the cloud manufacturing background. Secondly, we establish the QoS prediction model based on the support vector machine with arbitrary penalty band. This model can cover the shortage for the previous studies, and effectively solve the problem of the dimension of QoS prediction in cloud manufacturing. Thirdly, this paper predicts 5 important QoS indexes. The experimental data from a cloud manufacturing provider's historical data, and the experimental use a large number of historical QoS value to train the support vector machine with arbitrary penalty band. Network training is terminated when the coefficient is greater than 0.995. Then, the QoS value of the seven nodes in the future is predicted by the QoS value of the sample data set. Finally, the experiment shows that the support vector regression machine with arbitrary penalty has a good prediction effect on QoS of cloud manufacturing. But the artificial neural network and the gray forecast model has poor effect on it.

목차

Abstract
 1. Introduction
 2. Basic Knowledge of Cloud Manufacturing Services and Qos
 3. Support Vector Machine
  3.1. Basic Knowledge
  3.2. Support Vector Machine with Arbitrary Penalty Band(APB-SVM)
 4. The Simulation and Result Analysis
 5. Conclusion
 References

키워드

저자

• Shumei Liu [ Hengshui University Center of Modern Educational Technology Hengshui, Hebei 053000 China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of u- and e- Service, Science and Technology

• 간기

  격월간

• pISSN

  2005-4246

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605