Sustainable subway indoor air quality monitoring and fault-tolerant ventilation control using a sparse autoencoder-driven sensor self-validation

Loy-Benitez, Jorge; Li, Qian; Nam, Ki Jeon; Yoo, ChangKyoo

doi:10.1016/j.scs.2019.101847

Cited by 44 publications

(15 citation statements)

References 65 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, the progress of society has led to the development of China's transportation industry, such as the development of China's metro rail transportation field is in a better situation, and the station has a high flow of people. In this development context, to create a safe travel environment for the public, for metro transportation operations, it is necessary to focus on the operation, monitoring, and maintenance management of station equipment, timely understanding of equipment safety conditions, the discovery of faults, and effective handling of problems, to ensure the safe operation of metro transportation [1][2][3]. With the continuous progress of rail transportation technology in China, the development of rail transportation operating vehicles from steam locomotives to internal combustion locomotives and electric locomotive traction is particularly notable.…”

Section: Introductionmentioning

confidence: 99%

Design of Faster R-CNN-Based Fault Detection Method for Subway Vehicles

Yao

2022

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

A substantial amount of maintenance and fault data is not properly utilized in the daily maintenance of pantographs in urban metro cars. Pantograph fault analysis can begin with three factors: the external environment, internal flaws, and joint behavior. Based on the analysis of pantograph fault types, corresponding measures are proposed in terms of pantograph fault handling and maintenance strategies, in order to provide safety guarantee for the safe and effective realization of rail transit vehicle speed-up and also provide reference for the maintenance and overhaul of pantographs. For the problem of planned maintenance no longer meeting current pantograph maintenance requirements, a defect diagnosis system based on a combination of faster R-CNN neural networks is presented. The pantograph image features are extracted by introducing an alternative to the original feature extraction module that can extract deep-level image features and achieve feature reuse, and the data transformation operations such as image rotation and enhancement are used to expand the sample set in the experiment to enhance the detection effect. The simulation results demonstrate that the diagnosis procedure is quick and accurate.

show abstract

Section: Introductionmentioning

confidence: 99%

Design of Faster R-CNN-Based Fault Detection Method for Subway Vehicles

Yao

2022

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

show abstract

“…It is crucial to work on the development of IAQ monitoring systems with low power consumption requirements [ 110 ]. The upcoming researchers should integrate monitoring systems with ventilation control mechanisms [ 43 ]. It is possible to influence the air exchange rate within buildings automatically depending upon the IAQ levels [ 111 ].…”

Section: Discussionmentioning

confidence: 99%

“…Two [ 26 , 27 ] were excluded following EC4 as they were review papers or theoretical analysis (EC3, EC4). Furthermore, 20 studies [ 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 ] were removed due to missing information about the design methodology and the type of sensors used for measuring IAQ parameters (IC4, EC3). In addition, five more studies [ 48 , 49 , 50 , 51 , 52 ] were excluded as they were focused on thermal comfort parameters only or had no relevant details about IAQ sensors (IC2, IC3, EC3).…”

Section: Methodsmentioning

confidence: 99%

Indoor Air Quality Monitoring Systems Based on Internet of Things: A Systematic Review

Saini

Dutta

Marques

2020

IJERPH

129

View full text Add to dashboard Cite

Indoor air quality has been a matter of concern for the international scientific community. Public health experts, environmental governances, and industry experts are working to improve the overall health, comfort, and well-being of building occupants. Repeated exposure to pollutants in indoor environments is reported as one of the potential causes of several chronic health problems such as lung cancer, cardiovascular disease, and respiratory infections. Moreover, smart cities projects are promoting the use of real-time monitoring systems to detect unfavorable scenarios for enhanced living environments. The main objective of this work is to present a systematic review of the current state of the art on indoor air quality monitoring systems based on the Internet of Things. The document highlights design aspects for monitoring systems, including sensor types, microcontrollers, architecture, and connectivity along with implementation issues of the studies published in the previous five years (2015–2020). The main contribution of this paper is to present the synthesis of existing research, knowledge gaps, associated challenges, and future recommendations. The results show that 70%, 65%, and 27.5% of studies focused on monitoring thermal comfort parameters, CO2, and PM levels, respectively. Additionally, there are 37.5% and 35% of systems based on Arduino and Raspberry Pi controllers. Only 22.5% of studies followed the calibration approach before system implementation, and 72.5% of systems claim energy efficiency.

show abstract

“…Social networks are related to data generated from social media platforms. The data shows the customer schedule on the subject of trends and the interests of customers [1,2]. Mobile devices have become the largest business-building community because they allow users to update their profile, learn about the latest promotions, and track location by simply connecting to wireless signals [3].…”

Section: Smac (Social Mobile Analytics and Cloudmentioning

confidence: 99%