Synthesizing time-series wound prognosis factors from electronic medical records using generative adversarial networks

The spontaneous combustion of residual coals in the mined-out area tends to cause an explosion, which is one kind of severe thermodynamic compound disaster of coal mines and leads to serious losses to people's lives and production safety. The prediction and early warning of coal mine thermodynamic disasters are mainly determined by the changes of the index gas concentration pattern in coal mine mined-out areas collected continuously. The time series anomaly pattern detection method is mainly used to reach the state change of gas concentration pattern. The change of gas concentration follows a certain rule as time changes. A great change in the gas concentration indicates the possibility of coal spontaneous combustion and other disasters. To emphasize the features of collected maker gas and overcome the low anomaly detection accuracy caused by the inadequate learning of the normal mode, this paper adopted a method of anomaly detection for time series with difference rate sample entropy and generative adversarial networks. Because the difference rate entropy feature of abnormal data was much larger than that of normal mode, this paper improved the calculation method of the abnormal score by giving different weights to the detection points to enhance the detection rate. To verify the effectiveness of the proposed method, this paper employed simulation models of the mined-out area and adopted coal samples from Dafosi Coal Mine to carry out experiments. Preliminary testing was performed using monitoring data from a coal mine. The experiment compared the entropy results of different time series with the detection results of generative adversarial networks and automatic encoders and showed that the method proposed in this paper had relatively high detection accuracy.

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Evaluation of Generative Adversarial Networks for Time Series Data

10.1109/ijcnn52387.2021.9534373 ◽

2021 ◽

Author(s):

Hiba Arnout ◽

Johanna Bronner ◽

Thomas Runkler

Keyword(s):

Time Series ◽

Time Series Data ◽

Series Data ◽

Generative Adversarial Networks ◽

Adversarial Networks

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EC-GAN: Inferring Brain Effective Connectivity via Generative Adversarial Networks

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i04.5921 ◽

2020 ◽

Vol 34 (04) ◽

pp. 4852-4859

Author(s):

Jinduo Liu ◽

Junzhong Ji ◽

Guangxu Xun ◽

Liuyi Yao ◽

Mengdi Huai ◽

...

Keyword(s):

Time Series ◽

Structural Equation ◽

Structural Equation Models ◽

Effective Connectivity ◽

Small Sample ◽

Fmri Data ◽

Generative Adversarial Networks ◽

The Real ◽

Adversarial Networks ◽

Fmri Time Series

Inferring effective connectivity between different brain regions from functional magnetic resonance imaging (fMRI) data is an important advanced study in neuroinformatics in recent years. However, current methods have limited usage in effective connectivity studies due to the high noise and small sample size of fMRI data. In this paper, we propose a novel framework for inferring effective connectivity based on generative adversarial networks (GAN), named as EC-GAN. The proposed framework EC-GAN infers effective connectivity via an adversarial process, in which we simultaneously train two models: a generator and a discriminator. The generator consists of a set of effective connectivity generators based on structural equation models which can generate the fMRI time series of each brain region via effective connectivity. Meanwhile, the discriminator is employed to distinguish between the joint distributions of the real and generated fMRI time series. Experimental results on simulated data show that EC-GAN can better infer effective connectivity compared to other state-of-the-art methods. The real-world experiments indicate that EC-GAN can provide a new and reliable perspective analyzing the effective connectivity of fMRI data.

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