Generating Synthetic Fermentation Data of Shindari, a Traditional Jeju Beverage, Using Multiple Imputation Ensemble and Generative Adversarial Networks

Fermentation is an age-old technique used to preserve food by restoring proper microbial balance. Boiled barley and nuruk are fermented for a short period to produce Shindari, a traditional beverage for the people of Jeju, South Korea. Shindari has been proven to be a drink of multiple health benefits if fermented for an optimal period. It is necessary to predict the ideal fermentation time required by each microbial community to keep the advantages of the microorganisms produced by the fermentation process in Shindari intact and to eliminate contamination. Prediction through machine learning requires past data but the process of obtaining fermentation data of Shindari is time consuming, expensive, and not easily available. Therefore, there is a need to generate synthetic fermentation data to explore various benefits of the drink and to reduce any risk from overfermentation. In this paper, we propose a model that takes incomplete tabular fermentation data of Shindari as input and uses multiple imputation ensemble (MIE) and generative adversarial networks (GAN) to generate synthetic fermentation data that can be later used for prediction and microbial spoilage control. For multiple imputation, we used multivariate imputation by chained equations and random forest imputation, and ensembling was done using the bagging and stacking method. For generating synthetic data, we remodeled the tabular GAN with skip connections and adapted the architecture of Wasserstein GAN with gradient penalty. We compared the performance of our model with other imputation and ensemble models using various evaluation metrics and visual representations. Our GAN model could overcome the mode collapse problem and converged at a faster rate than existing GAN models for synthetic data generation. Experiment results show that our proposed model executes with less error, is more accurate, and generates significantly better synthetic fermentation data compared to other models.

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Synthetic data generation based on local-foreground generative adversarial networks for surface defect detection

Journal of Electronic Imaging ◽

10.1117/1.jei.29.1.013016 ◽

2020 ◽

Vol 29 (01) ◽

pp. 1

Author(s):

Bo Li ◽

Xue Yuan ◽

Minghan Shi

Keyword(s):

Defect Detection ◽

Surface Defect ◽

Synthetic Data ◽

Generative Adversarial Networks ◽

Data Generation ◽

Adversarial Networks ◽

Synthetic Data Generation ◽

Surface Defect Detection

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Supporting Database Constraints in Synthetic Data Generation based on Generative Adversarial Networks

Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data ◽

10.1145/3318464.3384414 ◽

2020 ◽

Author(s):

Wanxin Li

Keyword(s):

Synthetic Data ◽

Generative Adversarial Networks ◽

Data Generation ◽

Adversarial Networks ◽

Synthetic Data Generation ◽

Database Constraints

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Improving quality prediction in radial-axial ring rolling using a semi-supervised approach and generative adversarial networks for synthetic data generation

Production Engineering ◽

10.1007/s11740-021-01075-x ◽

2021 ◽

Author(s):

Simon Fahle ◽

Thomas Glaser ◽

Andreas Kneißler ◽

Bernd Kuhlenkötter

Keyword(s):

Machine Learning ◽

Synthetic Data ◽

Ring Rolling ◽

Supervised Machine Learning ◽

Generative Adversarial Networks ◽

Quality Prediction ◽

Data Generation ◽

Adversarial Networks ◽

Synthetic Data Generation ◽

Axial Ring

AbstractAs artificial intelligence and especially machine learning gained a lot of attention during the last few years, methods and models have been improving and are becoming easily applicable. This possibility was used to develop a quality prediction system using supervised machine learning methods in form of time series classification models to predict ovality in radial-axial ring rolling. Different preprocessing steps and model implementations have been used to improve quality prediction. A semi-supervised approach is used to improve the prediction and analyze, to what extend it can improve current research in machine learning for quality prediciton. Moreover, first research steps are taken towards a synthetic data generation within the radial-axial ring rolling domain using generative adversarial networks.

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Intrusion detection of railway clearance from infrared images using generative adversarial networks

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-192141 ◽

2020 ◽

pp. 1-13

Author(s):

Yundong Li ◽

Yi Liu ◽

Han Dong ◽

Wei Hu ◽

Chen Lin

Keyword(s):

Intrusion Detection ◽

Synthetic Data ◽

Generative Adversarial Networks ◽

Generation Model ◽

Single Shot ◽

Data Generation ◽

Infrared Images ◽

Adversarial Networks ◽

Training Samples ◽

Rgb Images

The intrusion detection of railway clearance is crucial for avoiding railway accidents caused by the invasion of abnormal objects, such as pedestrians, falling rocks, and animals. However, detecting intrusions using deep learning methods from infrared images captured at night remains a challenging task because of the lack of sufficient training samples. To address this issue, a transfer strategy that migrates daytime RGB images to the nighttime style of infrared images is proposed in this study. The proposed method consists of two stages. In the first stage, a data generation model is trained on the basis of generative adversarial networks using RGB images and a small number of infrared images, and then, synthetic samples are generated using a well-trained model. In the second stage, a single shot multibox detector (SSD) model is trained using synthetic data and utilized to detect abnormal objects from infrared images at nighttime. To validate the effectiveness of the proposed method, two groups of experiments, namely, railway and non-railway scenes, are conducted. Experimental results demonstrate the effectiveness of the proposed method, and an improvement of 17.8% is achieved for object detection at nighttime.

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Conditional Deep 3D-Convolutional Generative Adversarial Nets for RGB-D Generation

Mathematical Problems in Engineering ◽

10.1155/2021/8358314 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Richa Sharma ◽

Manoj Sharma ◽

Ankit Shukla ◽

Santanu Chaudhury

Keyword(s):

Object Tracking ◽

Action Recognition ◽

Synthetic Data ◽

Depth Map ◽

Generative Adversarial Networks ◽

Data Generation ◽

Adversarial Networks ◽

Proposed Model ◽

Spatio Temporal ◽

Class Labels

Generation of synthetic data is a challenging task. There are only a few significant works on RGB video generation and no pertinent works on RGB-D data generation. In the present work, we focus our attention on synthesizing RGB-D data which can further be used as dataset for various applications like object tracking, gesture recognition, and action recognition. This paper has put forward a proposal for a novel architecture that uses conditional deep 3D-convolutional generative adversarial networks to synthesize RGB-D data by exploiting 3D spatio-temporal convolutional framework. The proposed architecture can be used to generate virtually unlimited data. In this work, we have presented the architecture to generate RGB-D data conditioned on class labels. In the architecture, two parallel paths were used, one to generate RGB data and the second to synthesize depth map. The output from the two parallel paths is combined to generate RGB-D data. The proposed model is used for video generation at 30 fps (frames per second). The frame referred here is an RGB-D with the spatial resolution of 512 × 512.

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Generation of Synthetic Data with Conditional Generative Adversarial Networks

Logic Journal of IGPL ◽

10.1093/jigpal/jzaa059 ◽

2020 ◽

Author(s):

Belén Vega-Márquez ◽

Cristina Rubio-Escudero ◽

Isabel Nepomuceno-Chamorro

Keyword(s):

Research Work ◽

Synthetic Data ◽

Original Data ◽

Classification Problem ◽

Generative Adversarial Networks ◽

Data Generation ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Adversarial Networks ◽

Original Dataset

Abstract The generation of synthetic data is becoming a fundamental task in the daily life of any organization due to the new protection data laws that are emerging. Because of the rise in the use of Artificial Intelligence, one of the most recent proposals to address this problem is the use of Generative Adversarial Networks (GANs). These types of networks have demonstrated a great capacity to create synthetic data with very good performance. The goal of synthetic data generation is to create data that will perform similarly to the original dataset for many analysis tasks, such as classification. The problem of GANs is that in a classification problem, GANs do not take class labels into account when generating new data, it is treated as any other attribute. This research work has focused on the creation of new synthetic data from datasets with different characteristics with a Conditional Generative Adversarial Network (CGAN). CGANs are an extension of GANs where the class label is taken into account when the new data is generated. The performance of our results has been measured in two different ways: firstly, by comparing the results obtained with classification algorithms, both in the original datasets and in the data generated; secondly, by checking that the correlation between the original data and those generated is minimal.

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