Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 20 for “"uncertainty modeling"”.
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Uncertainty modeling for structural control analysis and synthesis
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 1996.
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Uncertainty Modeling of Wind Power Generation for Power System Planning and Stability Study
… stability and reliability. In addition, the uncertainty and variability of wind power generation (WPG) forces power utilities to retain higher levels of spinning reserves (SRs) to maintain power balance in the system. While necessary to ensure grid reliability, the utilization of those …
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Mu-synthesis controller design for temperature control of a solar steam gasifier with uncertainty modeling and robust analysis
… model for controller synthesis. Multiplicative uncertainty was used to create an uncertainty model to quantify and account for the dissimilarities between the high fidelity model and the linear model. Performance specifications on temperature error and maximum control effort were chosen to …
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Modeling Considerations for the Long-Term Generation and Transmission Expansion Power System Planning Problem
… is not mature. This work inspects: load uncertainty modeling; sequential (GEP then TEP) versus unified (GTEP) models; and analyzes the impact on the methodologies achieved near-optimal plan. A sensitivity simulation on the original system and final, upgraded system is performed.
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Uncertainty Quantification in Deep Learning Models of G-Computation for Outcome Prediction under Dynamic Treatment Regimes
… density estimates do not take into account uncertainty about model parameter estimates. These uncertainty estimates are necessary for establishing confidence intervals around the effect estimation, enabling a robust assessment of whether the effects of two treatment options exhibit …
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Probabilistic and Statistical Learning Models for Error Modeling and Uncertainty Quantification
Simulations and modeling of large-scale systems are vital to understanding real world phenomena. However, even advanced numerical models can only approximate the true physics. The discrepancy between model results and nature can be attributed to different sources of uncertainty including the …
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Calibrating Expert Assessments of Advanced Aerospace Technology Adoption Impact
… to assist in the task of quantifying parameter uncertainty for proposed new aerospace vehicles. From previous work, it has been shown that experts in the field of aerospace systems design and development can provide valuable input into the sizing and conceptual design of future space launch …
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Creative destruction in multi-source marketplaces : exploring factors influencing success or failure in multi-sided marketplaces
… literature review; dynamic simulations and uncertainty modeling were used to assess the level of influence of these factors. Simulation experiments for Facebook and Twitter were conducted and compared to historical adoption and financial data of both platforms, along with a hypothetical case …
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Development of an Aggregation Methodology for Risk Analysis in Aerospace Conceptual Vehicle Design
… not easily quantified and have a high degree of uncertainty associated with their values. Decision-makers must rely on expert assessments of the uncertainty associated with the design variables to evaluate the risk level of a conceptual design. Since multiple experts are often queried for their …
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Distribution network development planning with quality of supply (QOS) costing
… establish expensive networks due to cost risk. Uncertainty modeling approaches based on fuzzy logic are proposed as the solution for analysis of uncertain conditions where very limited information is available. Costs in distribution lines are usually due to capital investment and operating …
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A framework for space systems architecting under stakeholder objectives ambiguity
… architecting, multivariate statistical analysis, uncertainty modeling, economics, management science and social science research. It allows decision-makers to visualize an architectural synthesis of aerospace systems, understanding adverse impacts of ambiguity, and supporting negotiations among …
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Hard Instances, Improved Algorithms and New Interdiction Models for Robust Optimization
… effective across all possible realizations of an uncertainty set, making the choice of this set a crucial factor in both the complexity and practical applicability of robust models. A key challenge in this field is striking a balance between computational tractability and solution quality, …
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Machine Learning Aided Decision Making and Adaptive Stochastic Control in a Hierarchical Interactive Smart Grid
… important topics: (1) load prediction and uncertainty modeling, (2) demand response (DR), (3) stochastic tracking control of the conventional generation in the presence of DER's (both renewable energy and plug-in hybrid electric vehicle (PHEV)) and (4) machine learning aided decision making …
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Generalized Empirical Bayes: Theory, Methodology, and Applications
… DS}(G, m)$ that allows exploratory Bayesian modeling. However, at a practical level, major practical advantages of our proposal are: (i) computational ease (it does not require Markov chain Monte Carlo (MCMC), variational methods, or any other sophisticated computational techniques); (ii) …
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On the analysis of stochastic optimization and variational inequality problems
Uncertainty has a tremendous impact on decision making. The more connected we get, it seems, the more sources of uncertainty we unfold. For example, uncertainty in the parameters of price and cost functions in power, transportation, communication and financial systems have stemmed from the way …
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A unified computational framework for modeling health policy adoption in complex real-world environments
… and persistent challenge in predictive modeling of real-world environments: the need to systematically capture the complex, uncertain, and socio-cultural dimensions of human decision-making. Traditional approaches often treat behavior as static, rule-based, homogeneous input and overlook …
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Developing physics-informed machine learning models for complex engineering systems design
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-08-01
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Assessing the Performance of HSPF When Using the High Water Table Subroutine to Simulate Hydrology in a Low-Gradient Watershed
Modeling ground-water hydrology is critical in low-gradient, high water table watersheds where ground-water is the dominant contribution to streamflow. The Hydrological Simulation Program-FORTRAN (HSPF) model has two different subroutines available to simulate ground water, the traditional …