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Zhang et al. [43] use matrix norm-based regression models for robust face recognition.
This paper presents models for analyzing the propagation of dimensional variation in multi-stage compliant assembly systems and the use of such models for robust design and adaptive control of assembly quality.
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Golpîra et al. (2017) developed a bi-level programming model for robust design of a green supply chain network.
In this paper, by using pairwise metric learning, we present a novel appearance model for robust visual tracking.
The application of suggested model for robust layout to cellular layouts has given better results compared to the robust cellular layout model of literature.
A statistical model calibration method, used to improve the accuracy of the simulation model for robust optimization, was proposed and implemented, based on the uncertainty data.
The monitoring programme as described provides a model for robust, cost-effective evaluation of the efficacy of policy instruments for feedback into the adaptive management cycle.
We present in this paper a new model for robust combinatorial optimization with cost uncertainty that generalizes the classical budgeted uncertainty set.
We consider a model for robust network design in telecommunications, in which we minimize the cost of the maximum mismatch between supply and demand.
Other approaches for designing surveillance have been developed in the past; for instance (Bajardi et al. 2012) developed a model for robust outbreak cluster detection based on network features.
Because microscopic examination is qualitative, we also used B. subtilis as a baseline model for robust motility.
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