• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.140-145
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• 2022

In an advanced modern society, among air pollutants caused by urban industrialization and public transportation, fine dust flows into indoors from the outdoors. The fine dust meter used indoors provides limited information and measures the pollution level differently, so there is a problem that users cannot monitor and monitor the data they want. To solve this problem, in this paper, indoor air quality data fine dust and ultra-fine dust (PM1.0, PM2.5, PM10), VOC (Volatile Organic Compounds) and PIR (Passive Infrared Sensor) are used to measure fine dust. and a monitoring system were designed and implemented. We propose a fine dust meter and monitoring system that is installed in a designated area to measure fine dust in real time, collects, stores, and visualizes data through App Engine of Google Cloud Platform and provides it to users.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.04a
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• pp.10-13
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• 2015

Google App Engine(GAE)은 플랫폼 서비스 형태(Platform as a Service, PaaS)의 클라우드 인프라이며 GAE를 기반으로 웹어플리케이션을 제작할 수 있도록 다양한 개발 도구를 제공해 준다. 본 논문에서는 Python 및 Go를 이용하여 GAE 상에서 구현한 클라우드 기반의 web application들의 성능을 비교하고자 한다. 각 web application의 주요 기능은 회원가입, 로그인, 채팅 등으로 구성되어 있고 특히, 회원목록이나 채팅 데이터를 처리하기 위하여 GAE에서 제공하는 Google Datastore를 사용하였다. 성능비교를 위하여 Python2.5, Python 2.7 및 Go를 사용하여 통일한 기능의 web application을 구현하였으며 각각의 메뉴에 대하여 서버 로직의 실행과 장고 (Django) 스타일의 HTML 템플릿을 렌더링하는데 걸리는 시간을 구하고 이를 비교 분석하였다.

• The Bulletin of The Korean Astronomical Society
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• v.43 no.2
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• pp.36.3-37
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• 2018

By utilizing large-scale graph analytic tools in the modern Big Data platform, Apache Spark, we investigate the topological structures of five different multiverses produced by cosmological n-body simulations with various cosmological initial conditions: (1) one standard universe, (2) two different dark energy states, and (3) two different dark matter densities. For the Big Data calculations, we use a custom build of stand-alone Spark cluster at KIAS and Dataproc Compute Engine in Google Cloud Platform with the sample sizes ranging from 7 millions to 200 millions. Among many graph statistics, we find that three simple graph measurements, denoted by (1) $n_\k$, (2) $\tau_\Delta$, and (3) $n_{S\ge5}$, can efficiently discern different topology in discrete point distributions. We denote this set of three graph diagnostics by kT5+. These kT5+ statistics provide a quick look of various orders of n-points correlation functions in a computationally cheap way: (1) $n = 2$ by $n_k$, (2) $n = 3$ by $\tau_\Delta$, and (3) $n \ge 5$ by $n_{S\ge5}$.

• Korean Journal of Remote Sensing
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• v.39 no.3
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• pp.311-323
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• 2023

Oil spill accidents can cause various environmental issues, so it is important to quickly assess the extent and changes in the area and location of the spilled oil. In the case of oil spill detection using satellite imagery, it is possible to detect a wide range of oil spill areas by utilizing the information collected from various sensors equipped on the satellite. Previous studies have analyzed the reflectance of oil at specific wavelengths and have developed an oil spill index using bands within the specific wavelength ranges. When analyzing multiple images before and after an oil spill for monitoring purposes, a significant amount of time and computing resources are consumed due to the large volume of data. By utilizing Google Earth Engine, which allows for the analysis of large volumes of satellite imagery through a web browser, it is possible to efficiently detect oil spills. In this study, we evaluated the applicability of four types of oil spill indices in the area of various land cover using Sentinel-2 MultiSpectral Instrument data and the cloud-based Google Earth Engine platform. We assessed the separability of oil spill areas by comparing the index values for different land covers. The results of this study demonstrated the efficient utilization of Google Earth Engine in oil spill detection research and indicated that the use of oil spill index B ((B3+B4)/B2) and oil spill index C (R: B3/B2, G: (B3+B4)/B2, B: (B6+B7)/B5) can contribute to effective oil spill monitoring in other regions with complex land covers.

• Journal of Platform Technology
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• v.6 no.1
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• pp.17-23
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• 2018

Taxi's queue for entering into taxi stand makes traffic jam and congestion around taxi stand. If we make that taxi waits in another place around taxi stand, and can approach to taxi stand when it gets in its turn, these traffic jam and congestion around taxi stand can be reduced. In this paper, we propose entry sequence operating system for taxi stand to reduce traffic jam and congestion around taxi stand. In this system, taxi driver can request his sequence number, and the system issues sequence number to driver. When it is time to approach to taxi stand due to issued sequence number, the proposed system notifies to taxi driver via taxi terminal. Taxi getting the proposed service can wait in another place around taxi stand, and can approach to taxi stand after receiving notify for approaching. Therefore, the proposed system in this paper can reduce traffic jam and congestion around taxi stand because it can reduce or get rid of taxi's queue around taxi stand. We implement the taxi stand approach sequence management system proposed in this paper for taxi stand installed in Chungju-Si, Chungbuk. We use Google Cloud service and Android platform for implementing.

• Journal of Internet Computing and Services
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• v.23 no.5
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• pp.69-78
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• 2022

As the use of Internet of Things (IoT) devices has expanded, digital forensics coverage of the National Police Agency has expanded to smart home areas. Accordingly, most of the existing studies conducted to acquire smart home platform data were mainly conducted to analyze local data of mobile devices and analyze network perspectives. However, meaningful data for evidence analysis is mainly stored on cloud storage on smart home platforms. Therefore, in this paper, we study how to acquire stored in the cloud in a Hey Home Air environment by extracting accessToken of user accounts through a cookie database of browsers such as Microsoft Edge, Google Chrome, Mozilia Firefox, and Opera, which are recorded on a PC when users use the Hey Home app-based "Hey Home Square" service. In this paper, the it was configured with smart temperature and humidity sensors, smart door sensors, and smart motion sensors, and artifacts such as temperature and humidity data by date and place, device list used, and motion detection records were collected. Information such as temperature and humidity at the time of the incident can be seen from the results of the artifact analysis and can be used in the forensic investigation process. In addition, the cloud data acquisition method using OpenAPI proposed in this paper excludes the possibility of modulation during the data collection process and uses the API method, so it follows the principle of integrity and reproducibility, which are the principles of digital forensics.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.2
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• pp.670-686
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• 2017

MapReduce (MRV1), a popular programming model, proposed by Google, has been well used to process large datasets in Hadoop, an open source cloud platform. Its new version MapReduce 2.0 (MRV2) developed along with the emerging of Yarn has achieved obvious improvement over MRV1. However, MRV2 suffers from long finishing time on certain types of jobs. Speculative Execution (SE) has been presented as an approach to the problem above by backing up those delayed jobs from low-performance machines to higher ones. In this paper, an adaptive SE strategy (ASE) is presented in Hadoop-2.6.0. Experiment results have depicted that the ASE duplicates tasks according to real-time resources usage among work nodes in a cloud. In addition, the performance of MRV2 is largely improved using the ASE strategy on job execution time and resource consumption, whether in a multi-job environment.

• IEMEK Journal of Embedded Systems and Applications
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• v.6 no.4
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• pp.249-254
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• 2011

A user desktop service can be made available on internet or local area network with the help of virtualization and cloud technologies. The service is usually called a virtual desktop or a desktop cloud. However, a user interface is limited to I/O capabilities of a user's mobile terminal. In order to enhance a user interface on a remote virtual desktop, it is important to connect full-featured I/O devices which are founded locally. Our previous work called SoD (System-on- Demand) has proposed a technique to associate local full-featured I/O devices with a remote virtual desktop in Xen. On the technique, it is required to install a SoD client agent in a user's mobile terminal for connecting a remote virtual desktop. In this paper, we propose a new framework called Web-SoD that does not require any explicit installation to make SoD service available. The SoD client agent is provided by the web technology so that the agent can be installed transparently, and the platform independency is also achieved. Due to insufficient network socket performance of current web technologies, we extend Native Client (NaCl) proposed by Google to support a network functionality by modifying a NaCl library and a service runtime. With conducted experiment, we show that the network extension supports a full socket functionality over the compromised overhead on the web environment.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.871-874
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• 2013

기존 PC에서 발생되는 악성코드가 안드로이드 모바일 플랫폼에서 스미싱 어플리케이션으로 급증하고 있다. 스마트 폰 사용자는 SMS에 의해 악성코드를 설치하게 되며, 악성코드가 소액결제 서비스 인증번호를 가로채어 C&C 서버 등으로 송신함으로써 30 만원 이내의 금전적 손해를 일으키게 된다. 본 논문에서는 GCM(Google Cloud Messaging)과 MDM(Mobile Device Management)을 이용하여 사용자의 스마트 폰에서 동작하고 있는 악성 어플리케이션을 탐지하고, 악성 행위를 통제시키며 사용자로부터 직접 어플리케이션을 삭제하길 권하는 시스템을 설계하여 제안하고자 한다.

• Journal of Korea Society of Industrial Information Systems
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• v.24 no.6
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• pp.43-53
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• 2019

In recent years, there have been many cases of damage to indoor air hazardous materials, and major damage due to the lack of quick action. In this regard, the system is intended to establish for sending push messages to the user's mobile when the concentration of hazardous substances is exceeded. This system extracts data with IoT system such as Arduino and Raspberry Pi and then constructs database through MongoDB and MySQL in cloud computing system. The database is imported through the application server using NodeJS and sent to the application for visualization. Also, when receiving signals about a dangerous situation in IoT system, push message is sent using Google FCM library. Mobile application is developed using Android Web view, and page to enter Web view is developed using HTML5 (HTML, Javascript CSS). The application of this system enables real-time monitoring of indoor air-dangerous substances. In addition, real-time information on indoor/outdoor detection location and concentration can be sent to the user's mobile in case of a risk situation, which can be expected to help the user respond quickly.