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Complexity Analysis Approach for Prefabricated Construction Products Using Uncertain Data Clustering
This paper proposes an uncertain data clustering approach to quantitativ...
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Compressive Measurement Designs for Estimating Structured Signals in Structured Clutter: A Bayesian Experimental Design Approach
This work considers an estimation task in compressive sensing, where the...
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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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Enhanced Input Modeling for Construction Simulation using Bayesian Deep Neural Networks
This paper aims to propose a novel deep learning-integrated framework fo...
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Data-driven Risk Management for Requirements Engineering: An Automated Approach based on Bayesian Networks
Requirements Engineering (RE) is a means to reduce the risk of deliverin...
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Bayesian latent Gaussian graphical models for mixed data with marginal prior information
Associations between variables of mixed types are of interest in a varie...
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Model based learning for accelerated, limited-view 3D photoacoustic tomography
Recent advances in deep learning for tomographic reconstructions have sh...
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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 construction fabrication industry. Among various causes, the learning effect is a significant factor, which needs to be incorporated in achieving a reliable operator quality performance analysis. This research aims to enhance a previously developed operator quality performance measurement approach by incorporating the learning effect (i.e., work experience). To achieve this goal, the Plateau learning model is selected to quantitatively represent the relationship between quality performance and work experience through a beta-binomial regression approach. Based on this relationship, an informative prior determination approach, which incorporates operator work experience information, is developed to enhance the previous Bayesian-based operator quality performance measurement. Academically, this research provides a systematic approach to derive Bayesian informative priors through integrating multi-source information. Practically, the proposed approach reliably measures operator quality performance in fabrication quality control processes.
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