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There are a few explanations why the connection between the predictive genes and relapse associated mechanism is not necessarily immediate: First, the predictors were designed to include relatively small number of genes while the actual mechanisms may include dozens of signaling pathways.
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However, none of these predictors was designed to classify patients based on their likelihood of developing a late recurrent disease, as the vast majority of recurrent cases for the development and testing of these predictors had recurrence within five years after initial treatment.
Feedback control algorithm based on PI controller and that based on Smith predictor were designed and tested in a hot strip mill respectively.
In order to address this issue, a predictor is designed.
To handle the delays and data losses simultaneously, a predictor is designed for each subsystem estimator.
The predictor is designed using Lyapunov-based methods, by numerical solution of a linear matrix inequality.
Then, an adaptive predictor is designed based on the observer and is employed to predict non-measurable states.
With the non-persistent measurement data, a linear predictor is designed to provide the reference signal in the adaptive filter.
Our new method classifies image patches into several classes, for each class, a class-specific predictor is designed.
With the obtained nominal model, uncertainty bound and the robust criterion derived, a robust Smith predictor is designed.
As part of the networked controller, a state predictor is designed to compensate the time delay in the feedback channel.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com