Air Pollution Prediction Using Long Short-Term Memory (LSTM) and Deep Autoencoder (DAE) Models

Title : Air Pollution Prediction Using Long Short-Term Memory (LSTM) and Deep Autoencoder (DAE) Models
Journal : Sustainability
Authors : Thanongsak Xayasouk, HwaMin Lee, Giyeol Lee
These authors contributed equally to this work : Thanongsak Xayasouk, HwaMin Lee
Corresponding author : Giyeol Lee
DOI : https://doi.org/10.3390/su12062570

Abstract
Many countries worldwide have poor air quality due to the emission of particulate matter (i.e., PM10 and PM2.5), which has led to concerns about human health impacts in urban areas. In this study, we developed models to predict fine PM concentrations using long short-term memory (LSTM) and deep autoencoder (DAE) methods, and compared the model results in terms of root mean square error (RMSE). We applied the models to hourly air quality data from 25 stations in Seoul, South Korea, for the period from 1 January 2015, to 31 December 2018. Fine PM concentrations were predicted for the 10 days following this period, at an optimal learning rate of 0.01 for 100 epochs with batch sizes of 32 for LSTM model, and DAEs model performed best with batch size 64. The proposed models effectively predicted fine PM concentrations, with the LSTM model showing slightly better performance. With our forecasting model, it is possible to give reliable fine dust prediction information for the area where the user is located.

Acknowledgments
This research was supported by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2020-2015-0-00403)supervised by the IITP(Institute for Information &communications Technology Planning &Evaluation) and by Soonchunhyang Research Fund.

CNN and RNN models for predicting particulate matter in South Korea

Title : CNN and RNN models for predicting particulate matter in South Korea
Published in : The 11th International Conference on Internet (ICONI 2019)
Author : Guang Yang, Thanongsak Xayasouk, HwaMin Lee
Corresponding author : HwaMin Lee
Location : Lotte Hotels & Resorts Hanoi, Hanoi, Vietnam

Abstract
Particulate matter proved to have severe effects on human health. To implement a real-time predicting system for is indispensable for every country face air pollution problem. Based on the previous study, predicting models for local areas are implemented. In this paper, a convolutional network, a recurrent neural network and a hybrid model combines the convolutional layers with the recurrent layers apply to predict future one weeks PM10 and PM2.5 concentration in seven urban cities in South Korea. The main contribution in this paper is: proposed three predicting models, make a comparison of three different models for predicting the concentration of particulate matter, all models are proved capable to obtain reliable predicting results. Experiments show GRU archive best performance for most cities both for PM10 and PM2.5, CNN models speed up the training process with least time consumption. The hybrid model performance stable with less time for training.

Acknowledgments
This research supported by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2019-2015-0-00403) supervised by the IITP(Institute for Information & communications Technology Planning & Evaluation) and Basic Science Research Program through the National Research Foundation of Korea (NRF-2017R1A2B4010570).

실종자 웹 탐색 시스템

제목 : 크롤링을 이용한 자동매칭 게임톡 웹 서비스
저자 : 천태희, 채민수, 양광, 사야속 타농싹, 이화민
게재지 : 2019년 한국정보처리학회 추계학술발표대회 논문집
교신저자 : 이화민
장소 : 제주도, 제주대학교
DOI : https://doi.org/10.3745/PKIPS.y2019m10a.66

초록
실종자는 계속 늘어나는 추세이다. 경찰에서의 실종자 찾는 인력이 부족한 상황이다. 특히 청소년 실종과 달리 성인 실종의 경우 위치정보 파악과 같은 지원을 받기 힘들다. 또한 성인 실종의 경우 단순 가출의 경우가 많아 범죄 가능성이 없을 경우 실종자 탐색에 있어 후순위로 밀린다. 그에 따라 본 논문은 실종자 웹 탐색 시스템을 개발하여 실종자 가족은 실종자를 등록할 수 있게 제공해주며, 실종자정보를 손쉽게 공유할 수 있도록 하였다. 또한 목격자는 실종자를 목격했을 경우 제보하기를 통해서 목격된 정보를 추가할 수 있도록 하였다. 목격자가 제보를 할 경우에는 해당 실종자 가족에게 연락을 가도록 구현하였다.

Acknowledgments
본 연구는 과학기술정보통신부 및 정보통신기기획평가원의 대학ICT연구센터육성지원사업의 연구결과로 수행되었음(IITP-2019-2015-0-00403 & IITP-2019-2014-1-00720)

A hybrid CNN-RNN model for particulate matter forecasting in Seoul

Title : A hybrid CNN-RNN model for particulate matter forecasting in Seoul
Published in : The 14th Asia Pacific International Conference on Information Science and Technology (APIC-IST 2019)
Author : Guang Yang, Thanongsak Xayasouk, HwaMin Lee
Corresponding author : HwaMin Lee
Location : Grand Gongda JianGuo Hotel, Beijing, China

Abstract
Epidemiological research shows that short-term exposure to air pollution especially particulate matter can damage human health. A reliable predicting model with high accuracy is required. Contribute to building real-time forecasting and alarm system. Previous papers focus on improving accuracy by optimizing a single statistical predicting method. In this paper, a hybrid deep learning model is proposed to predict the future day’s particulate matter concentration in Seoul, South Korea. The proposed model combined RNN with a pre-trained ConvNet to forecast future PM2.5 and PM10 concentrations. CNN used to extract the hidden features and used RNN consisted of the gated recurrent unit to make a prediction. With combine the meteorological data, the proposed model can achieve satisfying accuracy and hold less time to consume compared with the bare RNN network.

Acknowledgments
This research supported by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program (IITP-2019-2015-0-00403) supervised by the IITP(Institute for Information & communications Technology Planning & Evaluation) and Basic Science Research Program through the National Research Foundation of Korea (NRF-2017R1A2B4010570).

Fine Dust Predicting using Recurrent Neural Network with GRU

Title : Fine Dust Predicting using Recurrent Neural Network with GRU
Journal : International Journal of Innovative Technology and Exploring Engineering
Authors : Thanongsak Xayasouk, Guang Yang, HwaMin Lee
Corresponding author : HwaMin Lee

Abstract
The particulate matter especially PM2.5 can cause respiratory, cardiovascular and nervous system damage as many studies prove. The monitoring and forecasting system are highly required. This paper proposed a predicting model to forecast PM10 and PM2.5 concentrations in Seoul, South Korea. The proposed model combines the recurrent neural network with GRU. The proposed model can extract the hidden patterns in the long sequence data as RNN’s feature. The proposed model proved they could make satisfying particulate matter concentration in the urban area. The prediction results are reliable even for future 20 days. Meteorological data also contribute to higher predicting results as auxiliary data for the neural network. In further work, we will try to evaluate the model’s universality with more urban cities. Additionally, try to combine other deep learning methods to improve accuracy and reduce time-consuming for prediction.

Acknowledgments
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (NRF-2017R1A2B4010570) and Soonchunhyang University Research Fund.

Particulate Matter Prediction Using LSTM and GRU

Title : Particulate Matter Prediction Using LSTM and GRU
Published in : The 2019 World Congress on Information Technology Applications and Services (World IT Congress 2019)
Author : Guang Yang, Thanongsak Xayasouk, HwaMin Lee
Corresponding author : HwaMin Lee
Location : Jeju National University, Jeju, Korea

Abstract
Particulate matters (PM) proved can cause cardiovascular, respiratory and nervous system damage, especially PM2.5. Many types of research contributed to making high accuracy prediction of PM concentrations. Machine learning as one of a powerful tool for prediction, have many types of research make a prediction with machine learning models. In this paper, we implemented a prediction system to predict several future days PM10 and PM2.5 concentration with a recurrent neural network (RNN). Consisted of LSTM and GRU units. We predicted several main areas of South Korea and the experiment shows the proposed models can give more than 10 days prediction with high accuracy.

Acknowledgments
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (NRF-2017R1A2B4010570).

Stacked Autoencoders Model for Fine Particulate Matter (PM10, PM2.5) Prediction

Title : Stacked Autoencoders Model for Fine Particulate Matter (PM10, PM2.5) Prediction
Published in : The 10th International Conference on Internet (ICONI 2018)
Author : Thanongsak Xayasouk, Guang Yang, HwaMin Lee
Corresponding author : HwaMin Lee
Location : Sokha Phnom Penh Hotel, Phnom Penh, Cambodia

Abstract
Nowadays, Fine Particulate matter (PM10, PM2.5) is very harmful to peoples’ health assuming that we know the information about the concentration of fine particulate matter which dangerous for people, it was able to prevent harmful of the fine particulate matter immediately. Deep learning is a type of machine learning method has drawn a lot of academic and industrial interest. This paper presents a deep learning modeling approach, we mostly use the concentrations of particulate matter (PM10, PM2.5) data and meteorological elements in South Korea. We propose a prediction system based on stacked autoencoder model for learning and training data our data. The experimental results, based on Fine Particulate matter and meteorological data, show that the proposed method can achieve great performance in terms of prediction accuracy.

Acknowledgments
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning(NRF-2017R1A2B4010570) and the Ministry of Science and ICT, Korea, under the ITRC(Information Technology Research Center) support program (IITP-2018-2014-0-00720) supervised by the IITP(Institute for Information & communications Technology Promotion).

Docker를 이용한 에너지 효율이 높은 로드 밸런서 설계

제목 : Docker를 이용한 에너지 효율이 높은 로드 밸런서 설계
저자 : 채민수, 타농싹, 양광, 이화민
게재지 : 2018년 한국정보처리학회 추계학술발표대회 논문집
교신저자 : 이화민
장소 : 부산, 부경대학교
DOI : https://doi.org/10.3745/PKIPS.y2018m10a.43

초록
전 세계적으로 클라우드 서버의 가상화 기술이 중요해졌다. VM에서 사용되는 데이터 특징을 기준으로 에너지를 효율적으로 사용하기 위한 연구가 지속적으로 연구되고 있다. 그러나 접속자가 적거나, 특정 시간에만 접속이 되는 경우에도 웹 서버를 작동하기 위하여 VM를 실행되고 있다. 그에 따라 VM 자원 낭비가 되고 있다. 로드 밸런서를 통해 접속 요청이 없는 웹 서버는 동작시키지 않음으로써 자원 효율을 높이고자 한다. 그에 따라 본 논문에서는 에너지 효율이 높은 로드 밸런서를 설계하였다.

Acknowledgments
본 연구는 과학기술정보통신부 및 정보통신기술진흥센터의 대학ICT연구센터육성 지원사업의 연구결과로 수행되었음(IITP-2018-2014-1-00720 & IITP–2017-2015-0-00403).

AIR POLLUTION PREDICTION SYSTEM USING DEEP LEARNING

Title : AIR POLLUTION PREDICTION SYSTEM USING DEEP LEARNING
Journal : WIT Transactions on Ecology and the Environment
Authors : THANONGSAK XAYASOUK, HWAMIN LEE
Corresponding author : HwaMin Lee
DOI : http://doi.org/10.2495/AIR180071

Abstract
One of the most influential factors on human health is air pollution, such as the concentration of PM10 and PM2.5 is a damage to a human. Despite the growing interest in air pollution in Korea, it is difficult to obtain accurate information due to the lack of air pollution measuring stations at the place where the user is located. Deep learning is a type of machine learning method has drawn a lot of academic and industrial interest. In this paper, we proposed a deep learning approach for the air pollution prediction in South Korea. We use Stacked Autoencoders model for learning and training data. The experiment results show the performance of the air pollution prediction system and model that proposed.

Acknowledgments
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (NRF-2017R1A2B4010570).

Fine Dust Prediction System based on Deep Learning

Title : Fine Dust Prediction System based on Deep Learning
Published in : The 13th Asia Pacific International Conference on Information Science and Technology (APIC-IST 2018)
Author : Thanongsak Xayasouk, Hee-Woo Park, Hwamin Lee
Corresponding author : HwaMin Lee
Location : Intercontinental Nha Trang Hotel, NhaTrang, Vietnam

Abstract
Currently, Fine dust is very harmful to peoples’ health assuming that we know the information about the concentration of fine dust which dangerous for people, it was able to prevent harmful of fine dust immediately. In this paper, we proposed a deep learning approach used the air pollution data and meteorological data in South Korea for fine dust prediction and used stacked autoencoder model for learning and training data. The experiment results show the performance of the fine dust prediction system and model that proposed.

Acknowledgments
This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT & Future Planning(NRF-2017R1A2B4010570) and the Ministry of Science and ICT, Korea, under the ITRC(Information Technology Research Center) support program (IITP-2018-2014-1-00720) supervised by the IITP(Institute for Information & communications Technology Promotion).