Economic model predictive controlSubspace identificationRotational moldingPolymer processingThe present manuscript addresses the problem of economically achieving a user specified set of product qualities in an industrial complex batch process, illustrated through a lab-scale uni-axial rotational molding (also ...
The supervision sublayer is based on the data-driven predictive model, according to the set optimization objectives, using the optimization control technology developed in this paper (such as the MPC, the DPC-RT, the DPC-LSBoost, and the TDNN), to form the control strategy, such as ensuring...
A crucial aspect in implementing data-driven process control is "What should we learn from the data?". In general, the data-driven control method can be categorized into two main approaches: Learning the model and learning the value. To assist in selecting the more suitable approach, this ...
The aforementioned refracturing design and optimizations are mainly based on high-fidelity model simulations and thus hinder their field-scale applications. The intensive computation-cost related to the hydraulic fracturing simulations has motivated many data-driven modeling and optimization methods recently....
driven operant conditioning and its regulation mechanism by the cerebellum are analysed for the first time. Proposition logic is applied to transfer the constraint satisfaction problem into a propositional satisfiability problem while an undirected graph is utilised to model design space. Inspired by the...
(2016). The Rapid Adoption of Data-Driven Decision-Making. American Economic Review, 106(5), 133–139. https://doi.org/10.1257/aer.p20161016 Article Google Scholar Brynjolfsson, E., & Yang, S. (1996). Information Technology and Productivity: A Review of the Literature. Advances in ...
Weights & Biases Effective MLOps: Model Development - Free Course and Certification for building an end-to-end machine using W&B Python for Data Science by Scaler - This course is designed to empower beginners with the essential skills to excel in today's data-driven world. The comprehensive...
Expertise: Machine learning, Data-driven models, Structured and unstructured datasets, Computational toxicology, Nanoinformatics David Cruz Ortiz Instituto Politécnico Nacional, MexicoExpertise: Rehabilitation robotics, Biomedical Engineering, Biomedical instrumentation, Biosignal processing, Biomechanics Uroš Cv...
Next, we not only examine how data-driven algorithms have been exploited to tackle the main challenges present in this area, but also point to promising future investigations both from theoretical and from practical viewpoints. Rule based control, reinforcement learning, model predictive control (MPC...
Review Building energy flexibility Data-driven Machine learning Model predictive control (MPC) Smart grid 1. Introduction 1.1. The importance of building energy flexibility in the smart grid Renewable energy sources such as wind and solar are intrinsically variable by nature and this creates a stabilit...