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Moreover, the proposed monitoring model is applied to a real condition process of HGU.
By doing so, the fault diagnosis performance of the monitoring model can be improved online.
It is necessary to adopt an adaptive and realizable landscape structure monitoring model for ecosystem management.
The collected data of the new faults are then used to update the monitoring model.
The adequacy of the human monitoring model is partially confrrmed experimentally.
Further, we propose a novel video quality monitoring model by estimating the spatiotemporal complexity of video content.
Correspondingly, the respective subjective dataset for training and evaluating the planning model and the monitoring model should be built differently.
This method builds a knowledge-data-integrated sparse (KDIS) monitoring model by integrating process data with fundamental process knowledge.
Compared to ICA PCA and PCA monitoring model, the proposed model has superior performance in fault detection.
Moreover, this monitoring model can help to evaluate the effectiveness of salinity control and management action plans.
Second, we propose a novel quality monitoring model, which has low complexity and fully utilizes the video spatiotemporal complexity estimation at packet layer.
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