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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 …
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
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 …
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
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 …
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 …
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 …
The alarming rise in passenger traffic within underground subway stations has prompted
concerns about indoor air quality in these enclosed spaces. Implementing early warning …
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 …
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 …
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 …
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 …
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
Measuring vehicular emissions is crucial for emission management and air quality control.
However, conventional measurement equipment is costly and requires continuous …
However, conventional measurement equipment is costly and requires continuous …
Towards an awareness of time series anomaly detection models' adversarial vulnerability
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 …
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 …
Long Short-Term Memory (LSTM) networks in forecasting time series data for energy …