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Full-Text Articles in Engineering
Methodology For Simulation And Analysis Of Complex Adaptive Supply Network Structure And Dynamics Using Information Theory, Joshua V. Rodewald, John M. Colombi, Kyle F. Oyama, Alan W. Johnson
Methodology For Simulation And Analysis Of Complex Adaptive Supply Network Structure And Dynamics Using Information Theory, Joshua V. Rodewald, John M. Colombi, Kyle F. Oyama, Alan W. Johnson
Faculty Publications
Supply networks existing today in many industries can behave as complex adaptive systems making them more difficult to analyze and assess. Being able to fully understand both the complex static and dynamic structures of a complex adaptive supply network (CASN) are key to being able to make more informed management decisions and prioritize resources and production throughout the network. Previous efforts to model and analyze CASN have been impeded by the complex, dynamic nature of the systems. However, drawing from other complex adaptive systems sciences, information theory provides a model-free methodology removing many of those barriers, especially concerning complex network …
Short-Term Building Energy Model Recommendation System: A Meta-Learning Approach, Can Cui, Teresa Wu, Mengqi Hu, Jeffery D. Weir, Xiwang Li
Short-Term Building Energy Model Recommendation System: A Meta-Learning Approach, Can Cui, Teresa Wu, Mengqi Hu, Jeffery D. Weir, Xiwang Li
Faculty Publications
High-fidelity and computationally efficient energy forecasting models for building systems are needed to ensure optimal automatic operation, reduce energy consumption, and improve the building’s resilience capability to power disturbances. Various models have been developed to forecast building energy consumption. However, given buildings have different characteristics and operating conditions, model performance varies. Existing research has mainly taken a trial-and-error approach by developing multiple models and identifying the best performer for a specific building, or presumed one universal model form which is applied on different building cases. To the best of our knowledge, there does not exist a generalized system framework which …
A Recommendation System For Meta-Modeling: A Meta-Learning Based Approach, Can Cui, Mengqi Hu, Jeffery D. Weir, Teresa Wu
A Recommendation System For Meta-Modeling: A Meta-Learning Based Approach, Can Cui, Mengqi Hu, Jeffery D. Weir, Teresa Wu
Faculty Publications
Various meta-modeling techniques have been developed to replace computationally expensive simulation models. The performance of these meta-modeling techniques on different models is varied which makes existing model selection/recommendation approaches (e.g., trial-and-error, ensemble) problematic. To address these research gaps, we propose a general meta-modeling recommendation system using meta-learning which can automate the meta-modeling recommendation process by intelligently adapting the learning bias to problem characterizations. The proposed intelligent recommendation system includes four modules: (1) problem module, (2) meta-feature module which includes a comprehensive set of meta-features to characterize the geometrical properties of problems, (3) meta-learner module which compares the performance of instance-based …