• Proceedings of the Korean Geotechical Society Conference
• /
• 2003.03a
• /
• pp.233-240
• /
• 2003

A GIS-based tunnelling risk management system (GIS-TURIMS) has been developed in this study The developed system uses ArcView 8.2 as a basic platform and the built-in interface(VBA) has been used to perform first-order simplified analyses for prediction of tunnelling-induced ground movements and building damage assessment. The main emphasis in this study was to develop a working framework that can be used in the perspective of tunnelling risk management. The developed system is capable of carrying out computationally intensive first-order analyses for ground movement prediction as well as building/utilities damage assessment with fully taking advantage of the GIS technologies. This paper describes the concept and details of the GIS-TURIMS development and implementation.

• Proceedings of the Korean Geotechical Society Conference
• /
• 2002.10a
• /
• pp.311-318
• /
• 2002

A GIS-based tunnelling risk management system (GIS-TURIMS) has been developed in this study, The developed system uses ArcView 8.2 as a basic platform and the built-in interface (VBA) has been used to perform first-order simplified analyses for prediction of tunnelling-induced ground movements and building damage assessment. The main emphasis in this study was to develop a working framework that can be used in the perspective of tunnelling risk management. The developed system is capable of carrying out computationally intensive analyses for ground movement prediction as well as building/utilities damage assessment with fully taking advantage of the GIS technologies. This paper describes the concept and details of the GIS-TURIMS development and implementation

• Electronics and Telecommunications Trends
• /
• v.35 no.1
• /
• pp.80-88
• /
• 2020

This paper describes the development trends and service provision examples of disaster occurrence and spread prediction technology for various disasters such as tsunamis, floods, and fires. In terms of fires, we introduce the WIFIRE system, which predicts the spread of large forest fires in the United States, and the Metro21: Smart Cities Institute project, which predicts the risk of building fires. This paper describes the development trends in tsunami prediction technology in the United States and Japan using artificial intelligence (AI) to predict the occurrence and size of tsunamis that cause great damage to coastal cities in Japan, Indonesia, and the United States. In addition, it introduces the NOAA big data platform built for natural disaster prediction, considering that the use of big data is very important for AI-based disaster prediction. In addition, Google's flood forecasting system, domestic and overseas earthquake early warning system development, and service delivery cases will be introduced.

• Journal of the Korean Geotechnical Society
• /
• v.20 no.1
• /
• pp.49-59
• /
• 2004

A GIS-based tunnelling risk management system(GIS-TURIMS) was developed as a product of this study, The developed system uses ArcView 8.1 as a basic platform and the built-in interface(VBA) has been used to perform first-order simplified analyses for the prediction of tunnelling-induced ground movements and building damage assessment. The main emphasis of this study was to develop a working framework that can be used in the perspective of tunnelling risk management. The developed system is capable of carrying out cornputationally intensive analyses for ground movement prediction as well as buildings/utilities damage assessment taking full advantage of the GIS technologies. This paper describes the concept and details of the GIS-TURIMS development and implementation.

• Journal of Digital Contents Society
• /
• v.19 no.4
• /
• pp.771-777
• /
• 2018

This study proposes a big data based risk analysis framework to analyze more comprehensive disaster risk and vulnerability. We introduce a distributed and parallel framework that allows large volumes of data to be processed in a short time by using open-source disaster risk assessment tool. A performance analysis of the proposed system presents that it achieves a more faster processing time than that of the existing system and it will be possible to respond promptly to precise prediction and contribute to providing guideline to disaster countermeasures. Proposed system is able to support accurate risk prediction and mitigate severe damage, therefore will be crucial to giving decision makers or experts to prepare for emergency or disaster situation, and minimizing large scale damage to a region.

• Journal of the Korea Society of Computer and Information
• /
• v.27 no.12
• /
• pp.267-275
• /
• 2022

This paper proposed a Deep Neural Network Model system utilizing Keras for predicting air pollution risk of the cardiovascular disease through the effect of each component of air on the harmful virus using past air information, with analyzing 18,000 data sets of the Seoul Open Data Plaza. By experiments, the model performed tasks with higher accuracy when using methods of sigmoid, binary_crossentropy, adam, and accuracy through 3 hidden layers with each 8 nodes, resulting in 88.92% accuracy. It is meaningful in that any respiratory disease can utilize the risk prediction system if there are data on the effects of each component of air pollution and fine dust on oil-borne diseases. It can be further developed to provide useful information to companies that produce masks and air purification products.

• Journal of Internet Computing and Services
• /
• v.21 no.2
• /
• pp.27-37
• /
• 2020

Recently, big data is rapidly emerging as a core technology in the 4th industrial revolution. Further, the utilization and the demand of drones are continuously increasing with the development of the 4th industrial revolution. However, as the drones usage increases, the risk of drones falling increases. Drones always have a risk of being able to fall easily even with small problems due to its simple structure. In this paper, in order to predict the risk of drone fall and to prevent the fall, ESC (Electronic Speed Control) is attached integrally with the drone's driving motor and the acceleration sensor is stored to collect the vibration data in real time. By processing and monitoring the data in real time and analyzing the data through big data obtained in such a situation using a Fast Fourier Transform (FFT) algorithm, we proposed a prediction system that minimizes the risk of drone fall by analyzing big data collected from drones.

• Journal of Integrative Natural Science
• /
• v.4 no.1
• /
• pp.31-37
• /
• 2011

Various algorithms and system development are being required to support the advanced decision making of navigation information support system because of a serious loss of lives and property accidents by officer's error like as carelessness and decision faults. Much of researchers have introduced the techniques about the systems, but they hardly consider environmental factors. In this paper, We collect the context information in order to assess the risk, which is considered the various factor of the sailing ship, then extract the features of knowledge context, which is to apply the weight of correlation coefficients among data in context information. We decide the risk after the extract features through the classification and prediction of context information, and compare the value accuracy of proposed method in order to compare efficiency of the weighted value with the non-weighted value. As a result of experience, we know that the method of weight properties effectively reflect the marine environment because the weight accurate better than the non-weighted.

• Journal of Korea Multimedia Society
• /
• v.23 no.7
• /
• pp.870-882
• /
• 2020

It is that failure of equipment at the factory site causes personal injury and property damage. We are required a real-time monitoring and risk forecasting techniques to prevent for equipment failure. In this paper, we proposed a 3-phase smart plug and real-time monitoring system that can be used in factories, and collected environmental information and power information using a smart plug to analyze the data. In order to analyze the correlation between the risk situation and the collected data, we predicted the risk situation using Linear Regression, SVM, and ANN algorithms. As a result, the SVM and ANN algorithms obtained high predictive accuracy and developed a mobile app that could use it to check the risk forecast results.

• Journal of Korea Water Resources Association
• /
• v.40 no.6 s.179
• /
• pp.447-458
• /
• 2007

In this study, the coastal urban flood prediction and warning system based on HEC-RAS and SWMM were investigated to evaluate a watershed of On-Cheon stream in Busan which has characteristics of costal area cased by flooding of coastal urban areas. The basis of this study is a selection of various geological data from the numerical map that is a watershed of On-Cheon stream and computation of hydrologic GIS data. Thiessen method was used for analyzing of rainfall on the On-Cheon stream and 6th regression equation, which is Huff's Type II was time-distribution of rainfall. To evaluate the deployment of flood prediction and warning system, risk depth was used on the 3 selected areas. To find the threshold runoff for hydraulic analysis of stream, HEC-RAS was used and flood depth and threshold runoff was considered with the effect of tidal water level. To estimate urban flash flood trigger rainfall, PCSWMM 2002 was introduced for hydrologic analysis. Consequently, not only were the criteria of coastal urban flood prediction and warning system decided on the watershed of On-Cheon stream, but also the deployment flow charts of flood prediction and warning system and operation system was evaluated. This study indicates the criteria of flood prediction and warning system on the coastal areas and modeling methods with application of ArcView GIS, HEC-RAS and SWMM on the basin. For the future, flood prediction and warning system should be considered and developed to various basin cases to reduce natural flood disasters in coastal urban area.