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Simulation-Based Analytics for Fabrication Quality-Associated Decision Support
Automated, data-driven quality management systems, which facilitate the ...
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Automated Abstraction of Operation Processes from Unstructured Text for Simulation Modeling
Abstraction of operation processes is a fundamental step for simulation ...
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Data-driven simulation for general purpose multibody dynamics using deep neural networks
In this paper, a machine learning-based simulation framework of general-...
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BDNNSurv: Bayesian deep neural networks for survival analysis using pseudo values
There has been increasing interest in modeling survival data using deep ...
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Enhanced Welding Operator Quality Performance Measurement: Work Experience-Integrated Bayesian Prior Determination
Measurement of operator quality performance has been challenging in the ...
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A Bayesian-Based Approach for Public Sentiment Modeling
Public sentiment is a direct public-centric indicator for the success of...
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Hierarchical Cloth Simulation using Deep Neural Networks
Fast and reliable physically-based simulation techniques are essential f...
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Enhanced Input Modeling for Construction Simulation using Bayesian Deep Neural Networks
This paper aims to propose a novel deep learning-integrated framework for deriving reliable simulation input models through incorporating multi-source information. The framework sources and extracts multisource data generated from construction operations, which provides rich information for input modeling. The framework implements Bayesian deep neural networks to facilitate the purpose of incorporating richer information in input modeling. A case study on road paving operation is performed to test the feasibility and applicability of the proposed framework. Overall, this research enhances input modeling by deriving detailed input models, thereby, augmenting the decision-making processes in construction operations. This research also sheds lights on prompting data-driven simulation through incorporating machine learning techniques.
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