• 한국멀티미디어학회논문지
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• 제20권8호
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• pp.1208-1215
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• 2017

Recently, people's attention and worries about fine particulate matter have been increasing. Due to the construction and maintenance costs, there are insufficient air quality monitoring stations. As a result, people have limited information about the concentration of fine particulate matter, depending on the location. Studies have been undertaken to estimate the fine particle concentrations in areas without a measurement station. Yet there are limitations in that the estimate cannot take account of other factors that affect the concentration of fine particle. In order to solve these problems, we propose a framework for estimating the concentration of fine particulate matter of a specific area using meteorological data and traffic data. Since there are more grids without a monitor station than grids with a monitor station, we used a domain adversarial neural network based on the domain adaptation method. The features extracted from meteorological data and traffic data are learned in the network, and the air quality index of the corresponding area is then predicted by the generated model. Experimental results demonstrate that the proposed method performs better as the number of source data increases than the method using conditional random fields.

• 한국대기환경학회지
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• 제29권4호
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• pp.447-458
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• 2013

The present operating system for the atmospheric monitoring network in the city of Seoul, Korea, has been established since the late 90s by the Korean Ministry of Environment (KMOE). In this research, it was evaluated by the multi-statistical approaches through combinations of time series analysis, correlation matrix, and multiple cluster analysis. Finally, road traffic including resuspended materials can be one of the main sources of particulate matter in the atmosphere. Based on its importance, it will be significant challenges in quantitative evaluation of its contribution to airborne concentrations. The future directions for their amendments such as a new management plan for the source of road dust (including car emissions) were devised and proposed based on the statistical judgements derived in this research.

• 한국항행학회논문지
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• 제17권6호
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• pp.672-678
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• 2013

본 논문에서는 최근 정보통신 기술의 발전으로 인해 IT와 전통산업 간의 융합 기술을 적용한 응용서비스가 대두되고 이를 이용한 유해가스 감지 응용서비스로 자동 및 원격으로 항공기내환경을 모니터링하여 제어하고 유지 관리할 수 있는 무선 센서 네트워크기반의 유해가스환경 감지시스템에 대해 활발하게 논의되고 있다. 본 논문에서는 유해가스를 측정하여 모니터링 장치로 전송하는 무선 센서 네트워크센서노드, 센서노드로부터 수집한 데이터를 취합하고 디스플레이 및 서버로 환경정보를 전송하는 모니터링 관리장치와 또한 원격 모니터링을 위한 PC/스마트폰 기반 사용자 운용프로그램으로 구성하여, 항공기내에서 발생하는 유해가스 가스를 측정하고, 모니터링하는 무선 센서 네트워크기반의 실시간 모니터링 시스템을 설계하고 구현한다.

• 한국대기환경학회지
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• 제26권6호
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• pp.666-682
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• 2010

The forecasting system for Today's and Tomorrow's PM10 was developed based on the statistical model and the forecasting was performed at 9 AM to predict Today's 24 hour average PM10 concentration and at 5 PM to predict Tomorrow's 24 hour average PM10. The Today's forecasting model was operated based on measured air quality and meteorological data while Tomorrow's model was run by monitored data as well as the meteorological data calculated from the weather forecasting model such as MM5 (Mesoscale Meteorological Model version 5). The observed air quality data at ambient air quality monitoring stations as well as measured and forecasted meteorological data were reviewed to find the relationship with target PM10 concentrations by the regression analysis. The PM concentration, wind speed, precipitation rate, mixing height and dew-point deficit temperature were major variables to determine the level of PM10 and the wind direction at 500 hpa height was also a good indicator to identify the influence of long-range transport from other countries. The neural network, regression model, and decision tree method were used as the forecasting models to predict the class of a comprehensive air quality index and the final forecasting index was determined by the most frequent index among the three model's predicted indexes. The accuracy, false alarm rate, and probability of detection in Tomorrow's model were 72.4%, 0.0%, and 42.9% while those in Today's model were 80.8%, 12.5%, and 77.8%, respectively. The statistical model had the limitation to predict the rapid changing PM10 concentration by long-range transport from the outside of Korea and in this case the chemical transport model would be an alternative method.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2010년도 추계학술대회
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• pp.389-392
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• 2010

특수한 작업장이외의 주택실내나 일반 사무실 등에서의 공기오염 문제는 인간이 주거시설에 거주한 이례로 계속되어온 문제로서 그 대책이 시급하다. 쉽게 외부의 신선한 공기와 충분한 양의 환기로서 해결이 가능하나 사람이 계속해서 관리해 주기에는 불편한 상황이다. 네트워크상에서 센서 네트워크를 이용한다면 쉽게 관리 및 제어가 가능하다. 유비쿼터스 센서 네트워크는 부착된 태그와 센서로부터 사물 및 환경 정보를 감지, 저장, 가공, 전달하여 인간 생활에 폭넓게 활용되며, 환경오염에 대해서도 사람이 직접 측정 및 모니터링 하기 힘든 지역에 설치되어 활용되고 있다. 다른 네트워크에 비해 초소형, 저전력, 저비용으로 쉽게 구성 가능하기 때문에 꾸준히 연구되어지고 있으며 환경과 IT의 전략적 융합을 통한 그린 IT의 네트워크 접목 또한 중요한 연구분야로 조명되고 있다. 본 논문에서는 네트워크 기반하에 실내 공기질 관리를 구성하고 관리 및 제어하여 활용 방안에 대하여 제시한다.

• International Journal of Computer Science & Network Security
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• 제24권3호
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• pp.196-200
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• 2024

Weather forecasting is considered to be of utmost important among various important sectors such as flood management and hydro-electricity generation. Although there are various numerical methods for weather forecasting but majority of them are reported to be Mechanistic computationally demanding due to their complexities. Therefore, it is necessary to develop and build models for accurately predicting the weather conditions which are faster as well as efficient in comparison to the prevalent meteorological models. The study has been undertaken to forecast various atmospheric parameters in the city of Bangalore using Naïve Bayes algorithms. The individual parameters analyzed in the study consisted of wind speed (WS), wind direction (WD), relative humidity (RH), solar radiation (SR), black carbon (BC), radiative forcing (RF), air temperature (AT), bar pressure (BP), PM₁₀ and PM_2.5 of the Bangalore city collected from Air Quality Monitoring Station for a period of 5 years from January 2015 to May 2019. The study concluded that Naive Bayes is an easy and efficient classifier that is centered on Bayes theorem, is quite efficient in forecasting the various air pollution parameters of the city of Bangalore.

• 전기학회논문지
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• 제65권12호
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• pp.2075-2083
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• 2016

The real time IAQ (Indoor Air Quality) management is very important for large buildings and underground facilities such as subways because poor IAQ is immediately harmful to human health. Such IAQ management requires monitoring, prediction and control in an integrated and real time manner. In this paper, we present three PM10 hourly prediction models for such realtime IAQ management as both Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) models. Both MLR and ANN models show good performances between 0.76 and 0.88 with respect to R (correlation coefficient) between the measured and predicted values, but the MLR models outperform the corresponding ANN models with respect to RMSE (root mean square error).

• 한국대기환경학회지
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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.

• 한국환경과학회:학술대회논문집
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• 한국환경과학회 2003년도 International Symposium on Clean Environment
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• pp.73-78
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• 2003

In this research, we implement Realtime Air Diffusion Prediction System which is a parallel Fortran model running on distributed-memory parallel computers. The system is designed for air diffusion simulations with four-dimensional data assimilation. For regional air quality forecasting a series of dynamic downscaling technique is adopted using the NCAR/Penn. State MM5 model which is an atmospheric model. The realtime initial data have been provided daily from the KMA (Korean Meteorological Administration) global spectral model output. It takes huge resources of computation to get 24 hour air quality forecast with this four step dynamic downscaling (27km, 9km, 3km, and lkm). Parallel implementation of the realtime system is imperative to achieve increased throughput since the realtime system have to be performed which correct timing behavior and the sequential code requires a large amount of CPU time for typical simulations. The parallel system uses MPI (Message Passing Interface), a standard library to support high-level routines for message passing. We validate the parallel model by comparing it with the sequential model. For realtime running, we implement a cluster computer which is a distributed-memory parallel computer that links high-performance PCs with high-speed interconnection networks. We use 32 2-CPU nodes and a Myrinet network for the cluster. Since cluster computers more cost effective than conventional distributed parallel computers, we can build a dedicated realtime computer. The system also includes web based Gill (Graphic User Interface) for convenient system management and performance monitoring so that end-users can restart the system easily when the system faults. Performance of the parallel model is analyzed by comparing its execution time with the sequential model, and by calculating communication overhead and load imbalance, which are common problems in parallel processing. Performance analysis is carried out on our cluster which has 32 2-CPU nodes.

• 대한원격탐사학회지
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• 제36권4호
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• pp.637-643
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• 2020

In this study, the information from environmental impact statements was converted into spatial data because environmental data from development sites are collected during the environmental impact assessment (EIA) process. Spatiotemporal big data were built from environmental spatial data for each environmental medium for 2,235 development sites during 2007-2018, available from public data portals. Comparing air-quality monitoring stations, 33,863 measurement points were constructed, which is approximately 75 times more measurement points than that 452 in Air Korea's real-time measurement network. Here, spatiotemporal big data from 2,677,260 EIAs were constructed. In the future, such data might be used not only for EIAs but also for various spatial plans.