• Journal of the Korean Solar Energy Society
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• 제37권2호
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• pp.23-33
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• 2017

Accurate prediction of stochastic behavior of occupants is a well known problem for improving prediction performance of building energy use. Many researchers have been tried various sensors that have information on the status of occupant such as $CO_2$ sensor, infrared motion detector, RFID etc. to predict occupants, while others have been developed some algorithm to find occupancy probability with those sensors or some indirect monitoring data such as energy consumption in spaces. In this research, various sensor data and energy consumption data are utilized for decision tree algorithms (C4.5 & CART) for estimation of sub-hourly occupancy status. Although the experiment is limited by space (private room) and period (cooling season), the prediction result shows good agreement of above 95% accuracy when energy consumption data are used instead of measured $CO_2$ value. This result indicates potential of IoT data for awareness of indoor environmental status.

• Journal of Korean Society for Atmospheric Environment
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• 제10권4호
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• pp.252-259
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• 1994

A ground station has been established at Kosan, Cheju Island, since January of 1992 for the monitoring of background air Pollutant levels in Korea. Anthropogenic pollutant sources and meteorological conditions of Kosan were surveyed. Concentrations of SO$_2$, NO, NO$_{y}$ and $O_3$, were measured and analyzed for the period of February through December, 1992. The annual means of NO and SO$_2$, levels were very low in comparison to other urban's levels and similiar to other country's background levels. The annual mean of $O_3$, level was higher than urban's but comparable to other coastal region's. The NO concentration showed a distinct seasonal and diurnal variations. Summer peak was detected in the monthly means of NO and smooth peak around noon was found in the annual means of hourly data. Diurnal variation of the SO$_2$ concentration was barely detected but a slice increase in winter was detected. The $O_3$, concentration data, however, showed seasonal and diurnal variations similar to the urban's.an's.

• Journal of Korea Society of Industrial Information Systems
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• 제18권3호
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• pp.35-46
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• 2013

In order to develop an accurate analytical model for domestic electricity demand forecasting, we propose a prediction method of the electric power demand pattern by combining SMO classification techniques and a dimension reduction conceptualized subspace clustering techniques suitable for high-dimensional data cluster analysis. In terms of electricity demand pattern prediction, hourly electricity load patterns and the demographic and geographic characteristics can be analyzed by integrating the wireless load monitoring data as well as sub-regional unit of census information. There are composed of a total of 18 characteristics clusters in the prediction result for the sub-regional demand pattern by using census information and power load of Seoul metropolitan area. The power demand pattern prediction accuracy was approximately 85%.

• International Journal of Contents
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• 제15권2호
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• pp.75-78
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• 2019

The Integration technique of combining the measurement method with the fine precision of the sensor collecting the satellite-based information to determine the displacement space is available to a variety of diagnostic information. The measurement method by a GNSS with the sensors is needed since there will always be occasional occurrence of natural disasters caused by various environmental factors and the surroundings. Such attempts carried out nationally by distributed torsional displacement of the terrain and facilities. The combination of the various positioning analysis of mm-class for the facility of main area observed is required constantly in real time information of the USN/IoT Smart sensors and should be able to utilize such information as a precisely fine positioning information for the precisely fine displacement of the semi-permanent main facilities. In this study, for the installation of the receiving system, the USN/IoT base line positioning are easily accessible for the target bridges. Transmitting hourly from the received data is also executed in real time using the wireless Wi-Fi/Bluetooth bridges and related facilities to automatically process a fine position displacement. The results obtained from this method can be analyzed by real-time monitoring for a large structure or facilities for disaster prevention.

• Proceedings of the Korea Water Resources Association Conference
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• 한국수자원학회 2022년도 학술발표회
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• pp.134-134
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• 2022

Streamflow forecasting plays a crucial role in water resource control, especially in highly urbanized areas that are very vulnerable to flooding during heavy rainfall event. In addition to providing the accurate prediction, the evaluation of effects and importance of the input predictors can contribute to water manager. Recently, machine learning techniques have applied their advantages for modeling complex and nonlinear hydrological processes. However, the techniques have not considered properly the importance and uncertainty of the predictor variables. To address these concerns, we applied the GA-BART, that integrates a genetic algorithm (GA) with the Bayesian additive regression tree (BART) model for hourly streamflow forecasting and analyzing input predictors. The Jungrang urban basin was selected as a case study and a database was established based on 39 heavy rainfall events during 2003 and 2020 from the rain gauges and monitoring stations. For the goal of this study, we used a combination of inputs that included the areal rainfall of the subbasins at current time step and previous time steps and water level and streamflow of the stations at time step for multistep-ahead streamflow predictions. An analysis of multiple datasets including different input predictors was performed to define the optimal set for streamflow forecasting. In addition, the GA-BART model could reasonably determine the relative importance of the input variables. The assessment might help water resource managers improve the accuracy of forecasts and early flood warnings in the basin.

• Journal of Korean Society for Atmospheric Environment
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• 제25권5호
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• pp.459-471
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• 2009

Air Monitoring Network(11 urban stations) is operated to measure ambient air quality in Daegu city. The urban air monitoring stations include 6 in residence area, 3 in industrial area, 1 in commercial area, and 1 in green area. In this study, hourly data (2006. 1. 1~2008. 12. 31) of $PM_{10}$ were measured at 11 urban air monitoring stations. $PM_{10}$ mean concentrations were high in fall and winter because of low wind speed and many haze days. The number of exceeding the daily standard of $PM_{10}$ in industrial area was approximately twice as many as that in residence area. $PM_{10}$ concentrations and visibility were influenced significantly by wind speed. Wind speed and visibility were below 1.8 m/s and 10 km, respectively when $PM_{10}$ concentrations were over $120{\mu}g/m^3$. $PM_{10}$ concentrations were high when haze was observed. The mean concentrations of $PM_{10}$ were $104{\pm}41.3{\mu}g/m^3$, $63{\pm}35.1{\mu}g/m^3$, and $49{\pm}26.9{\mu}g/m^3$, respectively when haze, mist and clear were observed.

• Asian Journal of Atmospheric Environment
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• 제11권2호
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• pp.114-121
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• 2017

Real-time monitoring of hourly concentrations of atmospheric Radon-222 ($^{222}Rn$, radon) and some gaseous pollutants ($SO_2$, CO, $O_3$) was performed throughout 2013-2014 at Gosan station of Jeju Island, one of the cleanest regions in Korea, in order to characterize their background levels and temporal variation trend. The hourly mean concentrations of radon and three gaseous pollutants ($SO_2$, CO, $O_3$) over the study period were $2216{\pm}1100mBq/m^3$, $0.6{\pm}0.7ppb$, $211.6{\pm}102.0ppb$, and $43.0{\pm}17.0ppb$, respectively. The seasonal order of radon concentrations was as fall ($2644mBq/m^3$)$${\sim_\sim}$$winter ($2612mBq/m^3$)>spring ($2022mBq/m^3$)>summer ($1666mBq/m^3$). The concentrations of $SO_2$ and CO showed similar patterns with those of radon as high in winter and low in summer, whereas the $O_3$ concentrations had a bit different trend. Based on cluster analyses of air mass back trajectories, the air mass frequencies originating from Chinese continent, North Pacific Ocean, and the Korean Peninsula routes were 30, 18, and 52%, respectively. When the air masses were moved from Chinese continent to Jeju Island, the concentrations of radon and gaseous pollutants ($SO_2$, CO, $O_3$) were relatively high: $2584mBq/m^3$, 0.76 ppb, 225.8 ppb, and 46.4 ppb. On the other hand, when the air masses were moved from North Pacific Ocean, their concentrations were much low as $1282mBq/m^3$, 0.24 ppb, 166.1 ppb, and 32.5 ppb, respectively.

• Journal of Korean Society for Atmospheric Environment
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• 제25권6호
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• pp.550-561
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• 2009

This study is to investigate the air pollution characteristics of an industrialized midsize west-coastal city by comparing air quality to a neighboring inland city. The hourly averaged data of $O_3$, $SO_2$, $NO_2$, CO, and $PM_{10}$ measured from continuous air quality monitoring sites in Gunsan (coastal) and Jeonju (inland) were analyzed. The data set covers the period from 2004 to 2006. The annual average concentrations of the air pollutants in two cities were compared in their abundances and temporal trends as well. $O_3$ and $SO_2$ in Gunsan were relatively higher than those in Jeonju, while vice versa in case of $NO_2$ and $PM_{10}$. It seems that heavy automobile emissions from Jeonju mainly bring on higher $NO_2$ and $PM_{10}$ than those in Gunsan on annual base. $NO_2$ concentrations in both cities showed bimodal diurnal variations with peaks in the morning and in the late evening. These peaks correspond to the coupled effects of rush hour traffic and meteorological conditions (i.e., variation of mixing height and dispersion conditions). Maximum hourly averages of $NO_2$ ranged from 18 ppb to 28 ppb at Jeonju, and from 12 ppb to 20 ppb at Gunsan. $O_3$ showed typical diurnal variation with a maximum in the afternoon between 14:00 and 16:00 LST. Diurnal variations of CO and $PM_{10}$ were similar to $NO_2$ while $SO_2$ was similar to $O_3$. Seasonal variations of $PM_{10}$ in both cities indicated that their concentrations during spring season were significantly high. Asian dust storms occur frequently during spring and seem to affect increase in $PM_{10}$. High $O_3$ and $PM_{10}$ days were selected from both cities. The analyses based on the HYSPLIT trajectory model during the high $O_3$ and $PM_{10}$ showed these episodes (six cases) were mostly coincident with Asian dust storm originated from northern China and Mongolia. However, these high air pollution episodes in the west coastal cities may not only be caused by the Asian dust but also affected by other air pollutants transported from China accompanying the Asian dust.

• Journal of Korean Society for Atmospheric Environment
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• 제34권4호
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• pp.542-553
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• 2018

Since many previous studies reported the health effect of air pollution and indicated traffic as a major pollution source, significant policy efforts have been made to control traffic to reduce air pollution. However, there have been few studies that evaluated such policy implementation. In Seoul, Korea, the exclusive median bus lane system was implemented in 2004, and the metropolitan government applied air pollution reduction policies such as conversion of diesel buses to compressed natural gas buses and installation of emission control devices. This paper aimed to investigate the impact of the exclusive median bus lane system on air pollution reduction. Using hourly concentrations of particulate matter ($PM_{10}$) and nitrogen dioxide ($NO_2$) measured at 131 regulatory monitoring sites in Seoul and Gyeonggi-do for 2001-2014, we calculated annual and daily average concentrations at each site. We assessed the impact of the policy using differences-in-differences analysis by annual and daily average models after adjusting for geographic and/or meteorological variables. This method divides population into treatment and control groups with and without policy application, and compares the difference between the two time periods before and after the policy implementation in the treatment group with the difference in the control group. We classified all monitoring sites into treatment and control groups using two definitions: 1) Seoul vs. Gyeonggi-do; 2) within vs. outside 300 meters from the median bus lane. Pre- and post-policy periods were defined as 2001-2005 and 2006-2014, and 2004 and 2014 in the annual and daily models, respectively. The decrease in $PM_{10}$ concentrations between the two periods across monitoring sites in the treatment group was larger by $1.73-5.88{\mu}g/m^3$ than in the control group. $NO_2$ also showed the decrease without statistical significance. Our findings suggest that an efficient public transport policy combined with pollution abatement policies can contribute to reduction in air pollution.

• Journal of Environmental Health Sciences
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• 제40권3호
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• pp.204-214
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• 2014

Objectives: Vehicular emissions are one of the main sources of air pollution in urban areas. Correlation analysis was conducted between air pollutants and traffic volume in order to identify causes of air pollution in Gwangju. Methods: Using traffic volumes and air quality monitoring data from 2002 to 2012 from nine stations (seven urban areas, two roadside areas), especially at three sites where traffic volumes were high, the correlation coefficients were obtained between air pollutants as PM-10 (particulate matter), $NO_2$, $SO_2$, CO and $O_3$ at the stations and traffic volumes near the air monitoring stations. Results: Due to traffic volume and distance between the station and the traffic road, concentrations of pollutants at roadside areas were higher than at urban areas, with the exception of $O_3$. The concentration of $O_3$ showed statistically significance with those of other gas materials as $NO_2$, $SO_2$, and CO in winter (p<0.001) and spring (p<0.05). During the period of October 7 to 20, 2012, excluding periods of yellow dust, smog and rainy season, the ratio of $NO/(NO+NO_2)$ showed the highest value 0.57 and 0.40 at Unam and Chipyeong of two roadside stations, followed by 0.35 at Nongseong with vehicular effects. The correlation coefficient between traffic volume and $O_3$, CO, $NO_2$ became higher when the data on mist and haze days were excluded, than when all hourly data were used in that period, at the three sites of Unam, Chipyeong, and Nongseong. Conclusions: Air quality showed a considerable effect from vehicles at roadside areas compared to in urban areas. Air pollutant diminishment strategies need to be aggressively adopted in order to protect atmospheric environment.