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).

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).

Particulate Matter forecasting using RNN with LSTM

Title : Particulate Matter forecasting using RNN with LSTM
Published in : International Research Conference on Innovation, Technology and Sustainability (IRCITS 2019)
Author : Guang Yang, Minsu Chae, HwaMin Lee
Corresponding author : HwaMin Lee
Location : Century Park Hotel, Manila, Philippines

Abstract
The particulate matter especially PM2.5 can cause respiratory, cardiovascular and nervous system damage as many studies prove. With the increasing concentration of PM in many countries, the prediction of PM became an important issue. In this paper, we tried to create ann recurrent neural network (RNN) with long short-term memory(LSTM) to predict the concentrations of PM2.5 and PM10 7 days in advance in several cities and areas in South Korea.

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).

Resource-efficient web application framework using Docker

Title : Resource-efficient web application framework using Docker
Published in : International Research Conference on Innovation, Technology and Sustainability (IRCITS 2019)
Author : Minsu Chae, Guang Yang, HwaMin Lee
Corresponding author : HwaMin Lee
Location : Century Park Hotel, Manila, Philippines

Abstract
Cloud server virtualization technology has become important because of the spread of smart devices, the use of big data, and the proliferation of the Internet of things around the world. Use cloud computing to allocate and deploy IT resources. In case of Docker, performance degradation occurs by using API when monitoring resources. Therefore, in this paper, we propose a framework to manage the container resources according to the user to reduce the load on the Docker API request in the web application.

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-2018-2015-0-00403) supervised by the IITP(Institute for Information & communications Technology Promotion).

Serverless Framework for Efficient Resource Management in Docker Environment

Title : Serverless Framework for Efficient Resource Management in Docker Environment
Published in : The 10th International Conference on Computer Science and its Applications (CSA 2018)
Author : Sangwook Han , Minsu Chae , HwaMin Lee
Corresponding author : HwaMin Lee
Location : Double Tree by Hilton Hotel, Kuala Lumpur, Malaysia

Abstract
APIs provided by the Docker is executed through the container engine. Thus, it is a reality that the speed of creation and deletion of containers or requests for information is very slow. The load is even worse when many containers sending API requests at once. In this paper, we propose a method to apply module related to resource management in a container to reduce the load on API request generated in serverless environment. And we propose a new framework that manages resources of several containers or multiple Docker serves.

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-2017-2015-0-00403) supervised by the IITP(Institute for Information & communications Technology Promotion).

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).

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).

Cloud based Voice Navigation Services for the Visually Impaired

Title : Cloud based Voice Navigation Services for the Visually Impaired
Published in : The 2nd International Conference on Interdisciplinary Research on Computer Science, Psychology, and Education (ICICPE’ 2018)
Author : Minsu Chae, DaeWon Lee, HwaMin Lee
Corresponding author : HwaMin Lee
Location : Aonang Villa Resort, Krabi, Thailand

Abstract
The number of visually impaired people is increasing worldwide. The most famous of the walking support system is the white cane. However, the white cane helps to detect obstacles, but most of the information necessary for walking is obtained by visual information. In this paper, we implemented cloud based voice navigation services for the visually impaired. Our smart cane supports directions guide that was obtained using the previous coordinates and the current coordinates. It is also important for the visually impaired to arrive safely. Each navigate score was set differently according to each coordinate situation. We calculated the route with the shortest and safety using cloud computing.

Acknowledgments
This research was supported by Next-Generation Information Computing Development Program and Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT & Future Planning (NRF-2017M3C4 A7083417 & NRF-2017R1A2B4010570).

Driving Habits Analysis Using OBD-II Data Clustering

Title : Driving Habits Analysis Using OBD-II Data Clustering
Published in : The 1st International Conference on ICT for Smart Health (ICT4sHealth 2017)
Author : Fei Hao, Sangwook Han, Minsu Chae, HwaMin Lee
Corresponding author : HwaMin Lee
Location : Sokha Phnom Penh Hotel, Phnom Penh, Cambodia

Abstract
There are various systems and applications that use OBDII to check the status of the vehicle using data obtained from the vehicle. However, OBD products that are available in the market are mainly used for vehicle driving information and vehicle diagnosis services. This paper research a method to provide useful information to analyze driver ‘s driving habits using vehicle data. The RPM information extracted from the vehicle is classified into three types using the K-means algorithm, and the directions for how to use it for driving habit analysis are presented. It is very important to inform the driver of driving habits using vehicle data. It is possible not only to prevent a car accident but also to lower the probability of failure of the car and to improve the fuel efficiency.

Acknowledgments
This research was supported by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2017-2014-0-00720 & IITP-2017-2015-0-00403) supervised by the IITP(Institute for Information & communications Technology Promotion)

Performance Comparison between GPU and CPU in CNN Learning Process

Title : Performance Comparison between GPU and CPU in CNN Learning Process
Published in : The 12th KIPS International Conference on Ubiquitous Information Technologies and Applications (CUTE 2017)
Author : EunKwang Jeon, JungYeon Seo, HwaMin Lee
Corresponding author : HwaMin Lee
Location : Providence University, Taichung City, Taiwan

Abstract
The initial deep neural networks took a long time to learn and it was generally impractical to apply them to other areas. However, recent advances in computing performance and the ability to collect big data have re-emerged depth neural networks. GPU is used in the learning process of the GPU to reduce the learning time of the neural network. Using CUDA provided by NVIDIA, GPU enables quick learning. We used the GPU and CPU to perform the learning process in the CNN algorithm and confirmed the learning performance. As a result, the GPU used in the experiment was about 28 times faster than the CPU.

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-2017-2015-0-00403) supervised by the IITP(Institute for Information & communications Technology Promotion).