A Deep Variational Autoencoder Based Inverse Method for Active Energy Consumption of Mining Plants and Ball Grinding Circuit Investigation

Tsagaan, Munkherdene; Batdemberel, G.; Renchin, Seddorj; Khurlee, Uugantuya; Ichin, Oyunjargal

doi:10.1007/s40684-021-00380-1

Cited by 1 publication

(1 citation statement)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where KL is the Kullback-Leibler (KL) divergence loss between N(µ, σ) and N(0, I) (along with this formulation, we refer [42,43]). KL measures how the distribution of N(µ, σ) associated with data y is different to the normal distribution, defined as:…”

Section: Vae For Embedded Generative Methodsmentioning

confidence: 99%

Embedded Generative Air Pollution Model with Variational Autoencoder and Environmental Factor Effect in Ulaanbaatar City

Tsagaan

Sereeter³

et al. 2021

Atmosphere

Self Cite

View full text Add to dashboard Cite

Air pollution is one of the most pressing modern-day issues in cities around the world. However, most cities have adopted air quality measurement devices that only measure the past pollution levels without paying attention to the influencing factors. To obtain preliminary pollution information with regard to environmental factors, we developed a variational autoencoder and feedforward neural network-based embedded generative model to examine the relationship between air quality and the effects of environmental factors. In the model, actual SO2, NO2, PM2.5, PM10, and CO measurements from 2016 to 2020 were used, which were assembled from 15 differently located ground monitoring stations in Ulaanbaatar city. A wide range of weather and fuel measurements were used as the data for the influencing factors, and were collected over the same period as the air pollution data were recorded. The prediction results concerned all measurement stations, and the results were visualized as a spatial–temporal distribution of pollution and the performance of individual stations. A cross-validated R2 was used to estimate the entire pollution distribution through the regions as SO2: 0.81, PM2.5: 0.76, PM10: 0.89, and CO: 0.83. Pearson’s chi-squared tests were used for assessing each measurement station, and the contingency tables represent a high correlation between the actual and model results. The model can be applied to perform specific analysis of the interdependencies between pollution and environmental factors, and the performance of the model improves with long-range data.

show abstract

Section: Vae For Embedded Generative Methodsmentioning

confidence: 99%