• Journal of the Korean GEO-environmental Society
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• v.20 no.5
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• pp.5-12
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• 2019

On November 15, 2017, a unpredictable liquefaction damage was occurred at the $M_L=5.4$ Pohang earthquake and after, many researches have been conducted in Korea. In Korea, where there were no cases of earthquake damage, it has been extremely neglectable in preparing earthquake risk maps and building earthquake systems that corresponded to prevention and preparation. Since it is almost impossible to observe signs and symptoms of drought, floods, and typhoons in advance, it is very effective to predict the impacts and magnitudes of seismic events. In this study, 14,040 borehole data were collected in the metropolitan area and liquefaction evaluation was performed using the amplification factor. Based on this data, liquefaction hazard maps were prepared for ground accelerations of 0.06 g, 0.14 g, 0.22 g, and 0.30 g, including 200years return period to 4,800years return period. Also, the correlation analysis between the earthquake acceleration and LPI was carried out to draw a real-time predictable liquefaction hazard map. As a result, 707 correlation equations in every cells in GIS map were proposed. Finally, the simulation for liquefaction risk mapping against artificial earthquake was performed in the metropolitan area using the proposed correlation equations.

• The Journal of Bigdata
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• v.6 no.1
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• pp.23-35
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• 2021

In the era of the 4th industrial revolution, smart factories have received great attention, where production and manufacturing technology and ICT converge. With the development of IoT technology and big data, automation of production systems has become possible. In the advanced manufacturing industry, production systems are subject to unscheduled performance degradation and downtime, and there is a demand to reduce safety risks by detecting and reparing potential errors as soon as possible. This study designs a model based on supervised and unsupervised learning for detecting anomalies. The accuracy of XGBoost, LightGBM, and CNN models was compared as a supervised learning analysis method. Through the evaluation index based on the confusion matrix, it was confirmed that LightGBM is most predictive (97%). In addition, as an unsupervised learning analysis method, MD, AE, and LSTM-AE models were constructed. Comparing three unsupervised learning analysis methods, the LSTM-AE model detected 75% of anomalies and showed the best performance. This study aims to contribute to the advancement of the smart factory by combining supervised and unsupervised learning techniques to accurately diagnose equipment failures and predict when abnormal situations occur, thereby laying the foundation for preemptive responses to abnormal situations. do.

• 한국공간정보시스템학회:학술대회논문집
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• 2007.06a
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• pp.273-279
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• 2007

환경분야에서 사용되는 대부분의 자료는 공간상 모든 위치에 그 값이 존재하나 모든 지점에서 자료를 획득하는 것이 불가능하므로 몇 개의 대표 지점에서 필요로 하는 자료를 수집한 후 이를 미관측 지역까지 확장하여 사용하게 된다. 관측된 자료를 이용하여 미관측 지점의 값을 예측하는 과정에는 공간보간 기법이 사용되는데, 본 논문에서는 지역경향면 모델, IDW, RBF, 크리깅 등의 공간보간 기법을 서울시의 미세먼지(PM10) 연평균 농도 공간보간에 적용하고 그 정확성을 살펴보았다. 정확성 평가를 위하여 예측값의 범위, RMSE, 평균오차 등을 살펴보았으며 이로부터 크리깅, RBF 기법의 예측 정확도가 높은 것으로 분석되었다.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.4
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• pp.174-183
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• 2021

This study aims to suggest a method to provide a real-time noise map based on cloud using Amazon AWS. Acquiring environmental noise information, an Android app was developed to collect data on noise level, location, and measurement time of campus in Inha Technical College as a study area. Noise measurement information is transmitted to the AWS Cloud and managed, and the noise information collected through Amazon Quick Site is displayed in charts and maps. Finally, a web-based noise contour map and the results mapped to buildings were visualized with a Google map for users to search for the current environmental noise distribution. The real-time noise map presented as a result of this study is expected to be helpful for noise status and reduction policies.

• Journal of the Korean Association of Geographic Information Studies
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• v.11 no.1
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• pp.105-115
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• 2008

In this study, we wished to forecast the damage district by tsunami's occurrence. For this, we analyzed tsunami that can happen in our country's neighborhood coast using past data, and established tsunami's scenario by imagination with analysis result. we created a 3D topographical model about study area and analyzed an inundation area by achieving simulation by scenario. Also, we produced an imaginary inundation map by overlaying the simulation results on digital map. This study result might be utilized as infra-technology for operation of tsunami's forecast/alarm system and establishment of disaster prevention policy.

• Journal of Korean Society for Geospatial Information Science
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• v.2 no.2 s.4
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• pp.43-53
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• 1994

GSIS(geo-spatial information system), particularly when utilized in hazard management decision, is one of hazard analysis tool. Data of GSIS input from digitizing or scanning of map or aerial photos. This paper focuses upon the hazard prediction in GSIS and RS analysis to assess map, aerialphotos, satellite imagery and soil map. This study found computation of hazard area analysis. the results is formed as raster data model of quadtree. Authors knew more accurate results of overlay. This paper shows building up integrated data base as well as search of hazard area in aerial photographs.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.17 no.11
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• pp.1064-1068
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• 2007

Around metropolitan landfill area, specific vehicles such as garbage carrying trucks make noise problems and residents near landfill area organized to protest. However, it is difficult to distinguish the effect of noise of specific vehicles (ex: garbage trucks). In this study, noise map and CRTN were used to assess the noise from specific vehicles. Noise levels, which were predicted by using measured parameters such as traffic flow, traffic speed, composition of traffic for 1 year, were compared with measured results of noise level.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.6
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• pp.11-19
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• 2018

Recently, many companies offer various web-based map that is based on GIS(Geographic Information System) information. Google Map, Open street, Bing Map, Naver Map, Daum Map, Vwolrd Map, etc are the few examples of such system. In this paper, we propose a method to visualize ocean forecasting model data considering the flow diagram of tidal current, streamline expression algorithm, and user convenience by using vector field data information that is currently being served. It is confirmed that the proposed method of the flow diagram of tidal current, and stream line expression algorithm is faster than that of conventional ocean prediction model data by more than 2 times.

• Journal of Agricultural Extension & Community Development
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• v.19 no.2
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• pp.301-320
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• 2012

The study uses a seasonal ARIMA model to forecast the number of tourists of Yeongdeok in an uni-variable time series. The monthly data for time series were collected ranging from 2006 to 2011 with some variation between on-season and off-season tourists in Yeongdeok county. A total of 72 observations were used for data analysis. The forecast multiplicative seasonal ARIMA(1,0,0)$(0,1,1)_{12}$ model was found the most appropriate one. Results showed that the number of tourists was 10,974 thousands in 2012 and 13,465 thousands in 2013, It was suggested that the grasping forecast model is very important in respect of how experts in tourism development in Yeongdeok county, policy makers or planners would establish strategies to allocate service in Yeongdeok tourist destination and provide tourism facilities efficiently.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2006.11a
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• pp.25-29
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• 2006

Noise map presents new alternatives of noise reduction counter measure and becomes important tool for making environmental policy. Many input factors such as road conditions, number of vehicles, speeds of vehicles are used for noise prediction of the noise map. However, results of noise prediction make difference depending on the values of the input factors. In this study, effect of measurement method for vehicle speeds all the prediction results of the noise map.