• Journal of Convergence for Information Technology
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• v.11 no.7
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• pp.7-13
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• 2021

Recently, due to the many features and advantages of cloud computing, cloud service is being introduced to countless industries around the world at an unbelievably rapid pace. With the rapid increase in the introduction of multi-cloud based services, security vulnerabilities are increasing, and the risk of data leakage from cloud computing services are also expected to increase. Therefore, this study will propose an AWS Well-Architected based security architecture configuration method such as AWS standard security architecture, AWS shared security architecture model that can be applied for personal information security including cost effective of cloud services for better security in AWS cloud service. The AWS security architecture proposed in this study are expected to help many businesses and institutions that are hoping to establish a safe and reliable AWS cloud system.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.3
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• pp.182-189
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• 2016

For high accuracy of forecast using numerical weather prediction models, we need to get weather observation data that are large and high dense. Korea Meteorological Administration (KMA) mantains Automatic Weather Stations (AWSs) to get weather observation data, but their installation and maintenance costs are high. Mini-AWS is a very compact automatic weather station that can measure and record temperature, humidity, and pressure. In contrast to AWS, costs of Mini-AWS's installation and maintenance are low. It also has a little space restraints for installing. So it is easier than AWS to install mini-AWS on places where we want to get weather observation data. But we cannot use the data observed from Mini-AWSs directly, because it can be affected by surrounding. In this paper, we suggest a correcting method for using pressure data observed from Mini-AWS as weather observation data. We carried out preconditioning process on pressure data from Mini-AWS. Then they were corrected by using machine learning methods with the aim of adjusting to pressure data of the AWS closest to them. Our experimental results showed that corrected pressure data are in regulation and our correcting method using SVR showed very good performance.

• Korean Journal of Remote Sensing
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• v.17 no.1
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• pp.15-31
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• 2001

The relationship between GMS-5 IR1 brightness temperature (CTT:cloud top temperature) and AWS (automatic weather station) rainfall is investigated on a heavy rain event over the mid-western part of Korea for August 5-6, 1998. It is found that a temporal variability of the heavy rain can be described in detail y the time series of rain area and rain rates over the study area that are calculated from AWS accumulated rainfalls for 15 minutes. A time period of 0030-0430 LST 6 August 1998 is chosen in the time series as a heavy rain period which has relatively small rain area (20~25%) and very strong rain rates(6~9 mm/15 min.) with a good time continuity. In the heavy rain period, CTT of a point and AWS 15-minute rainfall beneath that point are compared. From the comparison, AWS rainfalls are shown to be not closely correlated with CTT. In the range of CTT lower than -5$0^{\circ}C$ where most AWS with rain are distributed, the probability of rain is at most about 30%. However, when the satellite images are shifted by 2~3 pixels southward and 3 pixels westward for the geometric correction of images, AWS rainfalls are shown to be statistically correlated with CTT (correlation coefficient:-0.46). Most AWS with rain are distributed in the much lower CTT range(lower than -58$^{\circ}C$), but there is still not much change in the rain probability. Even though a temporal change of CTT is taken into account, the rain probability amount to at most 50~55% in the same range.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.94-94
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• 2011

최근에 산악지역에서의 국지성 강우에 의한 사고 발생이 증가하고 있고, 2009년에는 북한의 무단 댐방류로 인해 인명피해가 발생함에 따라서 산악이나 북한 지역과 같은 지역의 모니터링이 필요하게 되었으며, 강수량의 기후학적 분포의 특성과 같은 장기적인 강수량 정보가 필요하게 되었다. 레이더는 넓은 영역에 대해서 시 공간적으로 고해상도의 자료를 제공할 수 있기 때문에 국지 규모의 단시간 강수량 정보를 제공하는데 유용하다. 국립기상연구소(National Institute of Meteorological Research; NIMR)는 기존의 층운형 Z-R 관계식(Z=$200R^{1.6}$, Marshall-Palmer, 1948)을 이용한 레이더 강우강도 산출에서 과소추정 문제를 개선하기 위해 레이더-AWS 강우강도(Radar-AWS Rain rate; RAR) 산출 시스템을 개발하여 현재 운영하고 있다. RAR 산출 알고리즘은 각 레이더에 대해서 레이더 강우강도와 지상 AWS 우량계 자료를 비교하여 실시간으로 Z-R 관계식을 산출하여, 레이더 반사도를 강우강도로 변환하고, 이를 합성하여 한반도 영역에 대해서 강우강도 정보를 제공한다. 2010년에는 RAR 자료와 지상 AWS 우량계 자료를 이용하여 레이더-AWS 누적강수량을 산출하는 시스템을 구축하였으며, 현재 시험운영 중에 있다. 본 연구에서는 레이더-AWS 누적강수량의 정확도를 평가하기 위해서 2009년에 대해 레이더-AWS 누적강수량 자료와 지상 AWS 누적강수량 자료에 대해 RMSE, Bias 등의 통계값을 산출하였으며, 북한 지역에 대한 적용가능성을 분석하기 위해서 레이더 관측 반경 내의 북한 지역의 GTS 지점 자료를 이용하여 사례 분석하였다. 본 연구는 레이더 자료를 이용한 지상 관측 공백지역의 강수량에 대한 모니터링을 통하여 이러한 지역의 사고에 대비할 수 있고, 기후학적인 강수량 정보 제공 및 향후 유역별 레이더 면적강수지도 시험판 개발을 통하여 수문 기상 분야에 적용하여 효과적인 물관리에 기여할 수 있을 것으로 사료된다.

• Korean Journal of Remote Sensing
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• v.31 no.2
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• pp.65-75
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• 2015

In this study, the observation environment of surface wind at an automatic weather station (AWS 288) located at Naei-dong, Mirang-si was analyzed using a computational fluid dynamics (CFD) model and geographic information system (GIS). The 16 cases with different inflow directions were considered before and after construction of an apartment complex around the AWS 288. For three inflow directions (south-south-westerly, south-south-easterly, and north-north-westerly), flow characteristics around the AWS 288 were investigated in detail, focusing on the changes in wind speed and direction at the AWS location. There was marked difference in wind speed between before and after construction of the apartment complex in the south-south-westerly case. In the south-south-easterly and north-north-westerly cases which were frequently observed at the AWS 288, the construction of the apartment complex had no marked influence on the observation of surface wind.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.6
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• pp.7-12
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• 2022

This paper introduces the implementation of the AWS IoT service and MQTT based smart home system. The smart home system implemented in this study can monitor temperature and humidity, and can manually adjust the air conditioner heating, and can check the visitors with the camera and remotely control the door lock. The implemented smart home system controls door locks, heating and air conditioners using Arduino, and manages the collected data and control information using the AWS IoT service. In this study, the Android app has been developed to allow users to control IoT devices remotely, and the MQTT protocol was used for data communication and control between the app and the AWS IoT server and Arduino. The implemented smart home system has been implemented based on AWS IoT service, which has scalability to add sensors and devices.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.6
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• pp.177-186
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• 2020

Recent advances in deep learning technology have improved image recognition performance in the field of computer vision, and serverless computing is emerging as the next generation cloud computing technology for event-based cloud application development and services. Attempts to use deep learning and serverless computing technology to increase the number of real-world image recognition services are increasing. Therefore, this paper describes how to develop an efficient deep learning based image recognition service system using serverless computing technology. The proposed system suggests a method that can serve large neural network model to users at low cost by using AWS Lambda Server based on serverless computing. We also show that we can effectively build a serverless computing system that uses a large neural network model by addressing the shortcomings of AWS Lambda Server, cold start time and capacity limitation. Through experiments, we confirmed that the proposed system, using AWS Lambda Serverless Computing technology, is efficient for servicing large neural network models by solving processing time and capacity limitations as well as cost reduction.

• Journal of Korea Water Resources Association
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• v.45 no.8
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• pp.743-753
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• 2012

AWS data can be used effectively to understand the rainfall characteristics in Korea. In spite of this advantage, AWS data have been used restrictively in flood control analysis and the study on water use analysis such as water balance assessment is very insufficient. In this study, AWS data are used to analyze spatial rainfall characteristics quantitatively and water balance assessment is performed based on AWS data. Water balance assessment is carried out from year 2002 to year 2010 considering water supply networks in Korea. The analysis shows that year 2009 is the driest year during 9 years (2002~2010) and the regions with low level water supply reliability are concentrated in the west coast of Jeonnam and the upper region of the Nakdong River. As a result, the regions that have a lack of available water resources such as the coastal and insular areas are vulnerable to droughts. Therefore, regional water supply and management plans are urgently needed. Additionally, AWS data, which consider rainfall characteristics of the coastal and insular areas, can be useful in water balance assessment.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.11a
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• pp.1061-1062
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• 2010

• Atmosphere
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• v.27 no.1
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• pp.67-77
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• 2017

In this study, we analyzed the characteristics of flow around the Daeyeon automatic weather station (AWS 942) and established formulas estimating inflow wind speeds at a computational fluid dynamics (CFD) model domain for the area around Pukyong national university using a computational fluid dynamics (CFD) model. Simulated wind directions at the AWS 942 were quite similar to those of inflows, but, simulated wind speeds at the AWS 942 decreased compared to inflow wind speeds except for the northerly case. The decrease in simulated wind speed at the AWS 942 resulted from the buildings around the AWS 942. In most cases, the AWS 942 was included within the wake region behind the buildings. Wind speeds at the inflow boundaries of the CFD model domain were estimated by comparing simulated wind speeds at the AWS 942 and inflow boundaries and systematically increasing inflow wind speeds from $1m\;s^{-1}$ to $17m\;s^{-1}$ with an increment of $2m\;s^{-1}$ at the reference height for 16 inflow directions. For each inflow direction, calculated wind speeds at the AWS 942 were fitted as the third order functions of the inflow wind speed by using the Marquardt-Levenberg least square method. Estimated inflow wind speeds by the established formulas were compared to wind speeds observed at 12 coastal AWSs near the AWS 942. The results showed that the estimated wind speeds fell within the inter quartile range of wind speeds observed at 12 coastal AWSs during the nighttime and were in close proximity to the upper whiskers during the daytime (12~15 h).