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RGB imaging and neural networks unveil seasonal oil and phenol variations for olive crop quality assessment

RGB imaging and neural networks unveil seasonal oil and phenol variations for olive crop quality assessment
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Giuseppe Montanaro , Propagation Neural Networks , Propagation Neural Network , Plant Phenomics , Back Propagation Neural Network , Phenotyping Key Fruit Quality Traits , Olive Using , Back Propagation Neural Networks ,

"A new model predictive control approach integrating physical and data-" by Zhihao Zhang, Xinlei Zhou et al.

District heating (DH) substations play a crucial role in ensuring the efficient and effective distribution of thermal energy necessary to provide space heating for buildings. However, optimizing their operation for energy savings while still ensuring indoor comfort poses significant challenges due to the complex dynamics of building demand and the inertia of building envelopes. To address these challenges, this study introduces a novel model predictive control (MPC) approach that combines a reduced-order physical model with a machine learning-based data-driven model to jointly optimize the operation parameters of a DH substation. In this approach, a reduced-order physical model is first used to capture essential operational principles and energy behaviors of the DH substations and generate candidate solutions for the control of the DH substations. Then, a data-driven model is constructed by integrating a Long Short-Term Memory model and a Back-propagation Neural Network, leveraging his ....

Neural Network , Long Short Term Memory , Back Propagation Neural Network , District Heating System , Machine Learning , Model Predictive Control , Physical Modelling ,