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Yizhong Ma
Researcher at Nanjing University of Science and Technology
Publications - 64
Citations - 703
Yizhong Ma is an academic researcher from Nanjing University of Science and Technology. The author has contributed to research in topics: Computer science & Bayesian probability. The author has an hindex of 11, co-authored 46 publications receiving 440 citations. Previous affiliations of Yizhong Ma include Nanjing University of Aeronautics and Astronautics.
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Ensemble of Surrogates for Dual Response Surface Modeling in Robust Parameter Design
TL;DR: This article introduced support vector regression, kriging model, and radial basis function into robust parameter design, and especially introduced a new strategy that builds the dual response surface using the ensemble of surrogates, which can provide a more robust approximation model.
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Economic and economic-statistical designs of an control chart for two-unit series systems with condition-based maintenance
TL;DR: The economic and economic-statistical designs of an X¯ control chart for two-identical unit series systems with condition-based maintenance is studied and optimization models have been developed to find the optimal control chart parameters for minimizing the average maintenance costs.
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A new Bayesian approach to multi-response surface optimization integrating loss function with posterior probability
TL;DR: The proposed approach not only measures the reliability of an acceptable optimization result, but also incorporates expected loss and bias and robustness into a uniform framework of Bayesian modeling and optimization.
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Availability and optimal maintenance policy for systems degrading in dynamic environments
TL;DR: A model is developed for systems degrading in dynamic environments subject to several imperfect maintenance actions before each replacement, in which the degradation of the system within different environments is governed by different stochastic processes.
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An interval approach to robust design with parameter uncertainty
TL;DR: In this article, an interval robust design approach that takes parameter uncertainties into account through the confidence regions for these unknown parameters is presented, where the worst and best cases of mean squared error are both adopted to build an optimisation approach.