An Imbalanced Data Handling Framework for Industrial Big Data Using a Gaussian Process Regression-Based Generative Adversarial Network

An Imbalanced Data Handling Framework for Industrial Big Data Using a Gaussian Process Regression-Based Generative Adversarial Network

The developments in the fields of industrial Internet of Things (IIoT) and big data technologies have made it possible to collect a lot of meaningful industrial process and quality-based data. The gathered data are analyzed using contemporary statistical methods and machine learning techniques. Then...

Full description

Bibliographic Details
Main Authors:	Eunseo Oh, Hyunsoo Lee
Format:	Article
Language:	English
Published:	MDPI AG 2020-04-01
Series:	Symmetry
Subjects:	missing data generation industrial big data air pressure system generative adversarial network Gaussian process regression
Online Access:	https://www.mdpi.com/2073-8994/12/4/669

Similar Items

Generative adversarial networks for imputing missing data for big data clinical research
by: Weinan Dong, et al.
Published: (2021-04-01)

Adversarial Learning on Incomplete and Imbalanced Medical Data for Robust Survival Prediction of Liver Transplant Patients
by: Ehsan Hallaji, et al.
Published: (2021-01-01)

ATCS: Auto-Tuning Configurations of Big Data Frameworks Based on Generative Adversarial Nets
by: Mingyu Li, et al.
Published: (2020-01-01)

IGANI: Iterative Generative Adversarial Networks for Imputation With Application to Traffic Data
by: Amir Kazemi, et al.
Published: (2021-01-01)

Synthesizing Individual Consumers′ Credit Historical Data Using Generative Adversarial Networks
by: Nari Park, et al.
Published: (2021-01-01)

Data Fusion Generative Adversarial Network for Multi-Class Imbalanced Fault Diagnosis of Rotating Machinery
by: Qianjun Liu, et al.
Published: (2020-01-01)

A Reconstruction Method for Missing Data in Power System Measurement Based on LSGAN
by: Changgang Wang, et al.
Published: (2021-03-01)

The Regression Learning of the Imbalanced and Big Data by the Online Mixture Model for the Mach Number Forecasting
by: Xiao-Jun Wang, et al.
Published: (2019-01-01)

Imbalanced Fault Diagnosis of Rolling Bearing Using Data Synthesis Based on Multi-Resolution Fusion Generative Adversarial Networks
by: Du, J., et al.
Published: (2022)

Generative Adversarial Network for Class-Conditional Data Augmentation
by: Jeongmin Lee, et al.
Published: (2020-11-01)

Seismic Data Augmentation Based on Conditional Generative Adversarial Networks
by: Yuanming Li, et al.
Published: (2020-11-01)

Generating Synthetic Missing Data: A Review by Missing Mechanism
by: Miriam Seoane Santos, et al.
Published: (2019-01-01)

Wasserstein Generative Adversarial Networks Based Data Augmentation for Radar Data Analysis
by: Hansoo Lee, et al.
Published: (2020-02-01)

Imbalanced Fault Diagnosis of Rolling Bearing Using Enhanced Generative Adversarial Networks
by: Hongliang Zhang, et al.
Published: (2020-01-01)

INVESTIGATING MACHINE LEARNING ALGORITHMS WITH IMBALANCED BIG DATA

Data Augmentation for Imbalanced HRRP Recognition Using Deep Convolutional Generative Adversarial Network
by: Yiheng Song, et al.
Published: (2020-01-01)

Generative Oversampling Method for Imbalanced Data on Bearing Fault Detection and Diagnosis
by: Sungho Suh, et al.
Published: (2019-02-01)

The wild bootstrap resampling in regression imputation algorithm with a Gaussian Mixture Model
by: Mat Jasin, A., et al.
Published: (2018)

Generative Adversarial Network Synthesis of Hyperspectral Vegetation Data
by: Andrew Hennessy, et al.
Published: (2021-06-01)

Time-series Generative Adversarial Networks for Telecommunications Data Augmentation
by: Dimyati, Hamid
Published: (2021)

Data Augmentation Using Generative Adversarial Network for Automatic Machine Fault Detection Based on Vibration Signals
by: Van Bui, et al.
Published: (2021-03-01)

Data Augmentation for X-Ray Prohibited Item Images Using Generative Adversarial Networks
by: Jinfeng Yang, et al.
Published: (2019-01-01)

Effects of Transfer Learning on Data Augmentation with Generative Adversarial Networks
by: Berglöf, Olle, et al.
Published: (2019)

Evaluation of big data frameworks for analysis of smart grids
by: Mohammad Hasan Ansari, et al.
Published: (2019-12-01)

Data Augmentation Computing Model Based on Generative Adversarial Network
by: Yu Weng, et al.
Published: (2019-01-01)

Traffic Accident Data Generation Based on Improved Generative Adversarial Networks
by: Zhijun Chen, et al.
Published: (2021-08-01)

Dual Autoencoders Generative Adversarial Network for Imbalanced Classification Problem
by: Ensen Wu, et al.
Published: (2020-01-01)

ProEGAN-MS: A Progressive Growing Generative Adversarial Networks for Electrocardiogram Generation
by: Haixu Yang, et al.
Published: (2021-01-01)

Study of Restrained Network Structures for Wasserstein Generative Adversarial Networks (WGANs) on Numeric Data Augmentation
by: Wei Wang, et al.
Published: (2020-01-01)

Versatile Auxiliary Classification and Regression With Generative Adversarial Networks
by: Shabab Bazrafkan, et al.
Published: (2021-01-01)

Background Augmentation Generative Adversarial Networks (BAGANs): Effective Data Generation Based on GAN-Augmented 3D Synthesizing
by: Yan Ma, et al.
Published: (2018-12-01)

Generating Energy Data for Machine Learning with Recurrent Generative Adversarial Networks
by: Mohammad Navid Fekri, et al.
Published: (2019-12-01)

Big Data in Biodiversity Science: A Framework for Engagement
by: Tendai Musvuugwa, et al.
Published: (2021-08-01)

A Conditional Generative adversarial Network for energy use in multiple buildings using scarce data
by: Gaby Baasch, et al.
Published: (2021-09-01)

Big Data Solutions for Efficient Operation of Microgrids
by: Simona Vasilica Oprea, et al.
Published: (2019-01-01)

Local Data Debiasing for Fairness Based on Generative Adversarial Training
by: Ulrich Aïvodji, et al.
Published: (2021-03-01)

Semi-Supervised Adversarial Variational Autoencoder
by: Ryad Zemouri
Published: (2020-09-01)

Regression analysis of big count data via a-optimal subsampling
by: Zhao, Xiaofeng
Published: (2018)

Synthetic demand data generation for individual electricity consumers: Generative Adversarial Networks (GANs)
by: Korn, R., et al.
Published: (2022)

Generative adversarial network based data augmentation to improve cervical cell classification model
by: Suxiang Yu, et al.
Published: (2021-04-01)

Cannot write session to /tmp/vufind_sessions/sess_792idft3dgjkp50pibjpf18ovp