YongSu Kim

Predictive monitoring and diagnosis of periodic air pollution in a subway station

The purpose of this study was to develop a predictive monitoring and diagnosis system for the air pollutants in a subway system using a lifting technique with a multiway principal component analysis (MPCA) which monitors the periodic patterns of the air pollutants and diagnoses the sources of the contamination. The basic purpose of this lifting technique was to capture the multivariate and periodic characteristics of all of the indoor air samples collected during each day. These characteristics could then be used to improve the handling of strong periodic fluctuations in the air quality environment in subway systems and will allow important changes in the indoor air quality to be quickly detected. The predictive monitoring approach was applied to a real indoor air quality dataset collected by telemonitoring systems (TMS) that indicated some periodic variations in the air pollutants and multivariate relationships between the measured variables. Two monitoring models--global and seasonal--were developed to study climate change in Korea. The proposed predictive monitoring method using the lifted model resulted in fewer false alarms and missed faults due to non-stationary behavior than that were experienced with the conventional methods. This method could be used to identify the contributions of various pollution sources.

Statistical Evaluation of Indoor Air Quality Changes after Installation of the PSD System in Seoul’s Metro

The purpose of this study was to evaluate changes in the concentration of air pollutants in the indoor environments, which could be caused by seasonal changes or changes in operating conditions of subway metro stations. In fact, there are many different types of pollution that can cause contamination in subway stations, and changes in operating conditions can also lead to changes in the indoor air quality (IAQ). Therefore, in order to establish a proper management of IAQ, it would be necessary to evaluate the changes in IAQ according to the changes in conditions. To do this, the present study used a multivariate analysis of variance (MANOVA). The results of testing the hypothesis proved that two groups, divided by the condition of a platform screen door (PSD) system, could differ statistically. Furthermore, those multidimensional differences were caused by installation of a PSD system. When applied to a real-time tele-monitoring system, MANOVA could clearly identify the daily and weekly variations of IAQ in the subway station, as well as the PSD system’s condition. Accordingly, this method could be useful for developing a multivariate system to statistically evaluate the experimental IAQ results in order to optimise operating conditions in a subway metro station to improve IAQ, and to minimise adverse health effects on passengers by exposure to harmful substances.

A systematic approach to data-driven modeling and soft sensing in a full-scale plant

The well-known mathematical modeling and neural networks (NNs) methods have limitations to incorporate the key process characteristics at the wastewater treatment plants (WWTPs) which are complex, non-stationary, temporal correlation, and nonlinear systems. In this study, a systematic methodology of NNs modeling which can be efficiently included in the key modeling information of the WWTPs is performed by selecting the temporal effect of the hydraulics based on multi-way principal components analysis (MPCA). The proposed method is applied for modeling wastewater quality of a full-scale plant, which is a Daewoo nutrient removal (DNR) process. Through the experimental results in a full-scale plant, the efficiency of the proposed method is evaluated and the prediction capability is highly improved by the inclusion of the hydraulics term due to the optimized structure of neural networks.

Analysis and prediction of indoor air pollutants in a subway station using a new key variable selection method

A new key variable selection and prediction model of IAQ that can select key variables governing indoor air quality (IAQ), such as PM10, CO2, CO, VOCs and formaldehyde, are suggested in this paper. The essential problem of the prediction model is the question of which of the original variables are the most important for predicting IAQ. The next issue is determining the number of key variables that should be ranked. A new index of discriminant importance in the projection (DIP) of Fisher’s linear discriminant (FLD) is suggested for selecting key variables of the prediction models with multiple linear regression (MLR) and partial least squares (PLS), as well as for ranking the importance of input measurement variables on IAQ prediction. The prediction models were applied to a real IAQ dataset from telemonitoring data (TMS) in a metro system. The prediction results of the model using all variables were compared with the results of the model using only key variables of DIP. It shows that the use of our new variable selection method cannot only reduce computational effort, but will also enhance the prediction performances of the models.