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"An intelligent decision support system for warranty claims forecasting" by Ali Nikseresht, Sajjad Shokouhyar et al.

This work develops a novel approach based on Machine Learning (ML)-assisted Quality Function Deployment (QFD) to sift the gold from the stone. It includes Time-Varying Filter-based Empirical Mode Decomposition (TVF-EMD), Deep Ensemble Random Vector Functional Link (DE-RVFL), and a Bayesian optimization algorithm for optimizing the shaped DE-RVFLTVF-EMD hyperparameters. This approach makes it possible for the proposed methods to be dynamic enough to deal with the data's volatility, complexit...
Machine Learning Quality Function Deployment Deep Ensemble Random Vector Functional Link Root Mean Square Error Deep Learning Quality Function Deployment
Source: uow.edu.au

"Online distortion simulation using generative machine learning models:" by Haochen Mu, Fengyang He et al.

In the era of Industry 4.0 and smart manufacturing, Wire Arc Additive Manufacturing (WAAM) stands at the forefront, driving a paradigm shift towards automated, digitalized production. However, online simulation remains a technical barrier toward building a Digital Twin (DT) for metallic AM due to the prolonged computing time of numerical simulations and limitations in accuracy of current data-driven models. This study addresses these issues by introducing an adaptive online simulation model for ...
Generative Adversarial Network Artificial Neural Networks Recurrent Neural Network Wire Arc Additive Manufacturing Digital Twin Vector Quantized Variational Autoencoder
Source: uow.edu.au

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Sensors | Free Full-Text | Enhancement Methods of Hydropower Unit Monitoring Data Quality Based on the Hierarchical Density-Based Spatial Clustering of Applications with a Noise–Wasserstein Slim Generative Adversarial Imputation Network with a Gradient Penalty

In order to solve low-quality problems such as data anomalies and missing data in the condition monitoring data of hydropower units, this paper proposes a monitoring data quality enhancement method based on HDBSCAN-WSGAIN-GP, which improves the quality and usability of the condition monitoring data of hydropower units by combining the advantages of density clustering and a generative adversarial network. First, the monitoring data are grouped according to the density level by the HDBSCAN cluster...
Generative Adversarial Networks Gans Wasserstein Generative Adversarial Networks Wgans Slim Generative Adversarial Imputation Network Generative Adversarial Imputation Network Wasserstein Generative Adversarial Imputation Network Wasserstein Generative Adversarial Networks
Source: mdpi.com

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