An overview of artificial intelligence in subway indoor air quality prediction and control

J Wang, CK Yoo, H Liu - Process Safety and Environmental Protection, 2023 - Elsevier
In the last few years, subways have rapidly spread in many countries and have replaced
different modes of commuting in some important areas. Despite the fact that passengers only …

A decision-aid system for subway microenvironment health risk intervention based on backpropagation neural network and permutation feature importance method

Q Chen, P Mao, S Zhu, X Xu, H Feng - Building and Environment, 2024 - Elsevier
Subway has become an important commute mode in daily life. However, subway
microenvironment poses a threat to people's health and there is growing concern about its …

A hybrid extreme learning machine and deep belief network framework for sludge bulking monitoring in a dynamic wastewater treatment process

U Safder, J Loy-Benitez, HT Nguyen, CK Yoo - Journal of Water Process …, 2022 - Elsevier
In biological wastewater treatment plants (WWTPs), sludge thickening is a common problem
with major economic and environmental effects. Monitoring the sludge volume index (SVI) is …

Dynamic slow feature analysis and random forest for subway indoor air quality modeling

K Zhang, J Yang, J Sha, H Liu - Building and Environment, 2022 - Elsevier
The indoor air quality of a metro station is closely related to the health of passengers. In
order to monitor the concentration of particulate matter in the air more stably and reduce the …

Enhancing the sustainable management of fine particulate matter-related health risks at subway stations through sequential forecast and gated probabilistic …

S Tariq, J Loy-Benitez, CK Yoo - Building and Environment, 2023 - Elsevier
The alarming rise in passenger traffic within underground subway stations has prompted
concerns about indoor air quality in these enclosed spaces. Implementing early warning …

Deep-AI soft sensor for sustainable health risk monitoring and control of fine particulate matter at sensor devoid underground spaces: a zero-shot transfer learning …

S Tariq, J Loy-Benitez, KJ Nam, SY Kim, MJ Kim… - … and Underground Space …, 2023 - Elsevier
Underground building spaces such as metro stations have been widely adopted in densely
populated metropolitan cities to combat high traffic congestion. To ensure a sustainable …

AI-driven ventilation control policy proximal optimization coupled with dynamic-informed real-time model calibration for healthy and sustainable indoor PM2. 5 …

CH Jeong, SK Heo, TY Woo, SY Kim, CK Yoo - Energy and Buildings, 2024 - Elsevier
Indoor air quality (IAQ) is an important factor for determining quality of life and urban
sustainability. In underground subway stations, improving IAQ through ventilation systems …

Machine learning-based estimation of gaseous and particulate emissions using internally observable vehicle operating parameters

J Seo, Y Lim, J Han, S Park - Urban climate, 2023 - Elsevier
Measuring vehicular emissions is crucial for emission management and air quality control.
However, conventional measurement equipment is costly and requires continuous …

Towards an awareness of time series anomaly detection models' adversarial vulnerability

S Tariq, BM Le, SS Woo - Proceedings of the 31st ACM International …, 2022 - dl.acm.org
Time series anomaly detection is extensively studied in statistics, economics, and computer
science. Over the years, numerous methods have been proposed for time series anomaly …

[HTML][HTML] Time Series Forecasting for Energy Management: Neural Circuit Policies (NCPs) vs. Long Short-Term Memory (LSTM) Networks

G Palma, ESJ Chengalipunath, A Rizzo - Electronics, 2024 - mdpi.com
This paper investigates the effectiveness of Neural Circuit Policies (NCPs) compared to
Long Short-Term Memory (LSTM) networks in forecasting time series data for energy …