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 ...
RETRACTED: Data-driven model predictive control for real-time planned lead time optimization in a reconfigurable flow line 来自 dx.doi.org 喜欢 0 阅读量: 2 作者:W Chen,HF Rahman,H Liu,M Fang 摘要: This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://...
Machine learning driven smart electric power systems: Current trends and new perspectives 2020, Applied Energy Show abstract Day-ahead building-level load forecasts using deep learning vs. traditional time-series techniques 2019, Applied Energy Show abstract Data-driven model predictive control using rando...
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 ...
Our research offers managerial implications by providing valuable tools for navigating the vastly uncharted territory of data-driven business models in the automotive industry. Thereby, our taxonomy goes beyond technical or economic considerations, offering a differentiated view of business model design in ...
Expertise: Computational environmental science, Environmental sociology, Urban socio-economic-environmental system, Behavioral science Zongguo Wen Tsinghua University, ChinaExpertise: Recycling of solid waste, Industrial ecology, Carbon dioxide reduction, Plastic pollution control, Environmental big data Christiane...
It uses averaging to improve the predictive accuracy and control overfitting. Python Code Example from sklearn.ensemble import RandomForestClassifier # Sample data X = [[1], [2], [3], [4], [5]] y = [0, 0, 1, 1, 1] # Train a Random Forest classifier model = RandomForest...
In this paper, a data-driven approach for the development of a daily steam load model is presented. Data-mining algorithms are used to select significant parameters used to develop models. A neural network (NN) ensemble with five MLPs (multi-layer perceptrons) performed best among all data-...
Censoring was performed in order to test the models’ predictive power. Results of fitting in each of the 57 health zones can be seen in SI2. Model Y typically remained relatively consistent in its projections across the 3 fitting and projection rounds. In contrast, during the initial round ...