• Earthquakes and Structures
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• v.22 no.1
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• pp.53-64
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• 2022

This study proposes an intelligent semi-active isolation system combining a variable-stiffness control device and ground motion characteristic prediction. To determine the optimal control parameter in real-time, a genetic algorithm (GA)-fuzzy control law was developed in this study. Data on various types of ground motions were collected, and the ground motion characteristics were quantified to derive a near-fault (NF) characteristic ratio by employing an on-site earthquake early warning system. On the basis of the peak ground acceleration (PGA) and the derived NF ratio, a fuzzy inference system (FIS) was developed. The control parameters were optimized using a GA. To support continuity under near-fault and far-field ground motions, the optimal control parameter was linked with the predicted PGA and NF ratio through the FIS. The GA-fuzzy law was then compared with other control laws to verify its effectiveness. The results revealed that the GA-fuzzy control law could reliably predict different ground motion characteristics for real-time control because of the high sensitivity of its control parameter to the ground motion characteristics. Even under near-fault and far-field ground motions, the GA-fuzzy control law outperformed the FPEEA control law in terms of controlling the isolation layer displacement and the superstructure acceleration.

• Proceedings of the KSRS Conference
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• v.1
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• pp.29-33
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• 2006

The potential impact of past Caribbean tsunamis generated by earthquakes and/or massive submarine slides/slumps, as well as the tsunamigenic potential and population distribution within the Intra-Americas Sea (IAS) was examined to help define the optimal location for coastal sea level gauges intended to serve as elements of a regional tsunami warning system. The goal of this study was to identify the minimum number of sea level gauge locations to aid in tsunami detection and provide the most warning time to the largest number of people. We identified 12 initial, prioritized locations for coastal sea level gauge installation. Our study area approximately encompasses $7^{\circ}N$, $59^{\circ}W$ to $36^{\circ}N$, $98^{\circ}$ W. The results of this systematic approach to assess priority locations for coastal sea level gauges will assist in developing a tsunami warning system (TWS) for the IAS by the National Oceanic and Atmospheric Administration (NOAA) and the Intergovernmental Oceanographic Commission's Regional Sub-Commission for the Caribbean and Adjacent Regions (IOCARIBE-GOOS).

• Economic and Environmental Geology
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• v.56 no.1
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• pp.65-73
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• 2023

As one of the most vulnerable countries to floods, there should be an increased necessity for accurate and reliable flood forecasting in Indonesia. Therefore, a new prediction model using a machine learning algorithm is proposed to provide daily flood prediction in Indonesia. Data crawling was conducted to obtain daily rainfall, streamflow, land cover, and flood data from 2008 to 2021. The model was built using a Random Forest (RF) algorithm for classification to predict future floods by inputting three days of rainfall rate, forest ratio, and stream flow. The accuracy, specificity, precision, recall, and F1-score on the test dataset using the RF algorithm are approximately 94.93%, 68.24%, 94.34%, 99.97%, and 97.08%, respectively. Moreover, the AUC (Area Under the Curve) of the ROC (Receiver Operating Characteristics) curve results in 71%. The objective of this research is providing a model that predicts flood events accurately in Indonesian regions 3 months prior the day of flood. As a trial, we used the month of June 2022 and the model predicted the flood events accurately. The result of prediction is then published to the website as a warning system as a form of flood mitigation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.1
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• pp.801-806
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• 2015

The incidence of flood damage by global climate change has increased recently. Because of the increased frequency of flooding in Korea, the technology of flood prediction and prevalence has developed mainly for large river watersheds. On the other hand, there is a limit on predicting flooding through the most present flood forecasting systems because local floods in small watersheds rise quite quickly with little or no advance warning. Therefore, this study estimated the flood warning rainfall using a flood forecasting model at the two alarm trigger points in the Suamcheon basin, which is an urban basin with backwater effects. The flood warning rainfall was estimated to be 25.4mm/120min ~ 78.8mm/120min for the low water alarm, and 68.5mm/120min ~ 140.7mm/120min for the high water alarm. The frequency of the flood warning rainfall is 3-years for the low water alarm, and 80-years for the high water alarm. The results of this analysis are expected to provide a basic database in forecasting local floods in urban watersheds. Nevertheless, more tests and implementations using a large number of watersheds will be needed for a practical flood warning or alert system in the future.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.463-467
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• 2015

Mega-shock means a sporadic event such as the earning shock, which occurred by sudden market changes, and it can cause serious problems of profit loss of international construction projects. Therefore, the early response and prevention by analyzing and predicting the Mega-shock is critical for successful project delivery. This research is preliminary study to develop a prediction model that supports market condition analysis and Mega-shock forecasting. To avoid disadvantages of classic statistical approaches that assume the market factors are linear and independent and thus have limitations to explain complex interrelationship among a range of international market factors, the research team explored the Fractal Theory that can explain self-similarity and recursiveness of construction market changes. The research first found out correlation of the major market factors by statistically analyzing time-series data. The research then conducted a base of the Fractal analysis to distinguish features of fractal from data. The outcome will have potential to contribute to building up a foundation of the early shock warning system for the strategic international project management.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.5
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• pp.473-478
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• 2013

In this paper, we develop the Heavy Rain Advisory Decision Model based on intelligent neuro-fuzzy algorithm RBFNNs by using KLAPS(Korea Local Analysis and Prediction System) Reanalysis data. the prediction ability of existing heavy rainfall forecasting systems is usually affected by the processing techniques of meteorological data. In this study, we introduce the heavy rain forecast method using the pre-processing techniques of meteorological data are in order to improve these drawbacks of conventional system. The pre-processing techniques of meteorological data are designed by using point conversion, cumulative precipitation generation, time series data processing and heavy rain warning extraction methods based on KLAPS data. Finally, the proposed system forecasts cumulative rainfall for six hours after future t(t=1,2,3) hours and offers information to determine heavy rain advisory. The essential parameters of the proposed model such as polynomial order, the number of rules, and fuzzification coefficient are optimized by means of Differential Evolution.

• Atmosphere
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• v.30 no.2
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• pp.195-207
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• 2020

This study aims to introduce the structure of the impact-based heat health warning system on 165 counties in South Korea developed by the National Institute of Meteorological Sciences. This system was developed using the daily maximum perceived temperature (PTmax), which is a human physiology-based thermal comfort index, and the Local ENSemble prediction system for the probability forecasts. Also, A risk matrix proposed by the World Meteorological Organization was employed for the impact-based forecasts of this system. The threshold value of the risk matrix was separately set depending on regions. In this system, the risk level was issued as four levels (GREEN, YELLOW, ORANGE, RED) for first, second, and third forecast lead-day (LD1, LD2, and LD3). The daily risk level issued by the system was evaluated using emergency heat-related patients obtained at six cities, including Seoul, Incheon, Daejeon, Gwangju, Daegu, and Busan, for LD1 to LD3. The high-risks level occurred more consistently in the shorter lead time (LD3 → LD1) and the performance (r_s) was increased from 0.42 (LD3) to 0.45 (LD1) in all cities. Especially, it showed good performance (r_s = 0.51) in July and August, when heat stress is highest in South Korea. From an impact-based forecasting perspective, PTmax is one of the most suitable temperature indicators for issuing the health risk warnings by heat in South Korea.

• International Journal of Aeronautical and Space Sciences
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• v.14 no.1
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• pp.91-98
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• 2013

The rigorous requirements of modern spacecraft missions necessitate a precise attitude determination strategy. This paper mainly researches that, based on three space-borne attitude sensors: 3-axis rate gyros, 3-antenna GPS receiver and star-sensor. To obtain global attitude estimation after an information fusion process, a feedback-involved Federated Kalman Filter (FKF), consisting of two subsystem Kalman filters (Gyros/GPS and Gyros/Star-sensor), is established. In these filters, the state equation is implemented according to the spacecraft's kinematic attitude model, while the residual error models of GPS and star-sensor observed attitude are utilized, to establish two observation equations, respectively. Taking the sensors' different update rates into account, these two subsystem filters are conducted under a variable step size state prediction method. To improve the fault tolerant capacity of the attitude determination system, this paper designs malfunction warning factors, based on the principle of ${\chi}^2$ residual verification. Mathematical simulation indicates that the information fusion strategy overwhelms the disadvantages of each sensor, acquiring global attitude estimation with precision at a 2-arcsecs level. Although a subsystem encounters malfunction, FKF still reaches precise and stable accuracy. In this process, malfunction warning factors advice malfunctions correctly and effectively.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2002.09a
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• pp.19-27
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• 2002

The four agencies in Korea - KMA, KIGAM, KEPRI, and KINS - have been operating their own seismic network for many years. In this study we have developed an integrated seismic system named KISS (Korea Integrated Seismic System), which is very similar to LISS (Live Internet Seismic Server) of Albuquerque Seismological Laboratory. Through KISS we could share all the earthquake data observed by those organizations in near real time. This research result will lead to provide the opportunity to use all seismic information of the earthquakes around Korean peninsula. And KISS will make us enable to do systematic researches, such as study on focal mechanisms of earthquakes around Korean peninsula, seismic design, earthquake prediction, etc. KISS will be used in developing an Early Earthquake Warning System like TriNet in Southern California, USA so as to minimize seismic hazard.

• Journal of Biomedical Engineering Research
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• v.42 no.4
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• pp.175-185
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• 2021

Purpose: Vital sign are used to help assess the general physical health of a person, give clues to possible diseases, and show progress toward recovery. Researchers are using vital sign data and AI(artificial intelligence) to manage a variety of diseases and predict mortality. In order to analyze vital sign data using AI, it is important to select and extract vital sign data suitable for research purposes. Methods: We developed a method to visualize vital sign and early warning scores by processing retrospective vital sign data collected from EMR(electronic medical records) and patient monitoring devices. The vital sign data used for development were obtained using the open EMR big data MIMIC-III and the wearable patient monitoring device(CareTaker). Data processing and visualization were developed using Python. We used the development results with machine learning to process the prediction of mortality in ICU patients. Results: We calculated NEWS(National Early Warning Score) to understand the patient's condition. Vital sign data with different measurement times and frequencies were sampled at equal time intervals, and missing data were interpolated to reconstruct data. The normal and abnormal states of vital sign were visualized as color-coded graphs. Mortality prediction result with processed data and machine learning was AUC of 0.892. Conclusion: This visualization method will help researchers to easily understand a patient's vital sign status over time and extract the necessary data.