• International Journal of Advanced Culture Technology
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• v.9 no.3
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• pp.315-320
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• 2021

Currently, cloud computing is being used as a technology that greatly changes the IT field. For many businesses, many cloud services are available in the form of custom, reliable, and cost-effective web applications. Most cloud service providers provide functions such as IoT, machine learning, AI services, blockchain, AR & VR, mobile services, and containers in addition to basic cloud services that support the scalability of processors, memory, and storage. In this paper, we will look at the most used cloud service providers and compare the services provided by the cloud service providers.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.5
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• pp.664-671
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• 2020

A Smart Building, one that uses automated processes to control its operations, refers in this study to one that uses both Internet of Things (IoT) devices and cloud services software. Cloud service providers (e.g. Amazon, Google, and Microsoft) have recently providedIoT cloud platform application services on IoT devices. According to Postscapes, there are now 152 IoT cloud platforms. Choosing one for a smart building is challenging. We selected Microsoft Azure IoT Hub and Amazon's AWS (Amazon Web Services) IoT. The two platforms were evaluated and selected from a smart building perspective. Each prototype was evaluated on two different IoTplatforms, assuming a typical smart building scenario. The selection was based on information and experience gained from developing the prototype system using the IoT cloud platform. The assessment made in this evaluation may be used to select an IoTcloud platform for smart buildings in the future.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.07a
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• pp.129-130
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• 2020

본 논문에서는 청각장애인의 의사소통을 위한 안드로이드 애플리케이션 시스템 구현 결과를 보인다. 구글 클라우드 플랫폼(Google Cloud Platform)의 STT(Speech to Text) API를 이용하여 음성 인식을 통해 대화의 내용을 텍스트의 형태로 출력한다. 그리고 TTS(Text to Speech)를 이용한 음성 합성을 통해 텍스트를 음성으로 출력한다. 또한, 포그라운드 서비스(Service)에서 가속도계 센서(Accelerometer Sensor)를 이용하여 스마트폰을 2~3회 흔들었을 때 해당 애플리케이션을 실행할 수 있도록 하여 애플리케이션의 활용성을 높인 시스템을 개발하였다.

• Review of KIISC
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• v.24 no.6
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• pp.31-36
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• 2014

클라우드 컴퓨팅 서비스는 자원 공유와 가상화 기술 및 자원의 서비스화 등 기존 컴퓨팅 환경과 다른 특성으로 인해 클라우드 컴퓨팅 환경에 적합한 식별/접근제어 기술 및 보안 통제 사항이 요구된다. 그러므로 기존 컴퓨팅 자원을 클라우드 컴퓨팅 환경으로 변경하는 서비스 제공자나 클라우드 서비스로 이동하는 서비스 사용자는 특정한 보안 요건을 검토해야 한다. Cloud Security Alliance에서 배포한 Cloud Control Matrix와 ISO/IEC 27001을 비교 분석하여, 클라우드 컴퓨팅 환경에서 특별히 요구되는 식별 및 접근제어의 보안 통제 요건을 확인하였다. 또한, 주요 클라우드 컴퓨팅 서비스인 아마존의 AWS, 구글의 Google Cloud Platform과 VMware의 vCloud 서비스의 식별 및 접근제어 기술을 조사하였다. 이를 기반으로 클라우드 컴퓨팅 환경의 식별 및 접근제어 기술에서 필요한 보안 요건을 확인하였다.

• The Journal of Bigdata
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• v.2 no.2
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• pp.9-16
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• 2017

Global Platform Solution Company (aka Amazon, Google, MS, IBM) who has cloud platform, are driving AI and Big Data service on their cloud platform. It will dramatically change Enterprise business value chain and infrastructures in Supply Chain Management, Enterprise Resource Planning in Customer relationship Management. Enterprise are focusing the channel with customers and Business Partners and also changing their infrastructures to platform by integrating data. It will be Digital Transformation for decision support. AI and Deep learning technology are rapidly combined to their data driven platform, which supports mobile, social and big data. The collaboration of platform service with business partner and the customer will generate new ecosystem market and it will be the new way of enterprise revolution as a part of the 4th industrial revolution.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.07a
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• pp.243-244
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• 2019

본 논문에서는지속 가능한 IoT Platform을 개발 하였다. 개발된 IoT(Internet of Things) Platform은 센서를 제어하는 특정 시스템과의 통신을 통한 제어 및 데이터 전달에 용이하고, 제한된 통신 환경 및 낮은 전력에서도 지속적인 작동이 가능하여 가용성(Availability)과 확장성(Extensibility)이 뛰어나다. 본 논문에서는 지속 가능한 IoT Platform의 테스트를 위해 클라우드 컴퓨팅 플랫폼인 AWS EC2(Amazon Elastic Compute Cloud, EC2)에 구축하였으며, DataBase 서버로는 오픈 소스 관계형 데이터베이스 관리 시스템인 MariaDB를 선정하였으며, 센서를 제어하는 특정 시스템인 스마트 미러 시스템(Smart Mirror System)과 미세먼지 제어 시스템(Air Quality Control System)에 기존의 Google IoT Platform에서 사용되는 MQTT Protocol(Message Queuing Telemetry Transport Protocol)와 지속 가능한 IoT Platform를 위해 개발된 TCP/IP Protocol를 사용하여 비교했다. 개발된 IoT Platform은 UTM(Unmanned Aircraft System Traffic Management)으로 확장할 계획이다.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.40-42
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• 2011

Smart hand-held devices like iPhone, iPad, Andriod and other mobile-OS machines are becoming a well known part of our daily lives. Utilization of these devices has gone beyond the expectations of their inventors. Evolution of Apple's iOS from a mobile phone Operating System to a wholesome platform for Portable Gaming is an adequate proof. Using these smart devices people are downloading applications from numerous online App Stores. Utilizing remote storage facilities and confining themselves to computing power far below than an entry level laptop, netbooks have emerged. Google's idea of Chrome OS coupled with Google's AppEngine is an eye-opener for researchers and developers. Keeping all these industry-proven innovations in mind we are proposing a Context-Driven Cloud-Oriented Application Architecture for smart devices. This architecture enables our smart devices to behave smarter by utilizing very less of local resources.

• Proceedings of the Korean Society of Computer Information Conference
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• 2015.01a
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• pp.155-158
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• 2015

클라우드 컴퓨팅 기술의 발전과 맞물려서 이를 위한 많은 플랫폼들이 제안되고 있다. Amazon이나 Google 등의 세계적인 기업들을 이미 자신들 만의 플랫폼을 구축하여 안정적으로 서비스를 제공하고 있으며 오픈소스 커뮤니티들이 주도 하는 오픈 플랫폼들도 속속 등장하여 발전을 거듭하고 있다. 본 논문에서는 가장 대표적이고 널리 사용되는 오픈소스 기반의 클라우드 컴퓨팅 플랫폼들을 분석하고 그 기능들을 서로 비교해서 사용자가 자신의 요구사항에 가장 적합한 플랫폼을 선택할 수 있게 한다.

• Korean Journal of Remote Sensing
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• v.38 no.6_3
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• pp.1761-1775
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• 2022

Due to unpredictable climate change, the frequency of occurrence of water-related disasters and the scale of damage are also continuously increasing. In terms of disaster management, it is essential to identify the damaged area in a wide area and monitor for mid-term and long-term forecasting. In the field of water disasters, research on remote sensing technology using Synthetic Aperture Radar (SAR) satellite images for wide-area monitoring is being actively conducted. Time-series analysis for monitoring requires a complex preprocessing process that collects a large amount of images and considers the noisy radar characteristics, and for this, a considerable amount of time is required. With the recent development of cloud computing technology, many platforms capable of performing spatiotemporal analysis using satellite big data have been proposed. Google Earth Engine (GEE)is a representative platform that provides about 600 satellite data for free and enables semi real time space time analysis based on the analysis preparation data of satellite images. Therefore, in this study, immediate water disaster damage detection and mid to long term time series observation studies were conducted using GEE. Through the Otsu technique, which is mainly used for change detection, changes in river width and flood area due to river flooding were confirmed, centered on the torrential rains that occurred in 2020. In addition, in terms of disaster management, the change trend of the time series waterbody from 2018 to 2022 was confirmed. The short processing time through javascript based coding, and the strength of spatiotemporal analysis and result expression, are expected to enable use in the field of water disasters. In addition, it is expected that the field of application will be expanded through connection with various satellite bigdata in the future.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.179-184
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• 2021

In our study we performed a sentiments analysis from the images. For this purpose, we used 153 images that contain: people, animals, buildings, landscapes, cakes and objects that we divided into two categories: images that suggesting a positive or a negative emotion. In order to classify the images using the two categories, we created two models. The SAI-G model was created with Google's AutoML Vision service. The SAI-C model was created on the Clarifai platform. The data were labeled in a preprocessing stage, and for the SAI-C model we created the concepts POSITIVE (POZITIV) AND NEGATIVE (NEGATIV). In order to evaluate the performances of the two models, we used a series of evaluation metrics such as: Precision, Recall, ROC (Receiver Operating Characteristic) curve, Precision-Recall curve, Confusion Matrix, Accuracy Score and Average precision. Precision and Recall for the SAI-G model is 0.875, at a confidence threshold of 0.5, while for the SAI-C model we obtained much lower scores, respectively Precision = 0.727 and Recall = 0.571 for the same confidence threshold. The results indicate a lower classification performance of the SAI-C model compared to the SAI-G model. The exception is the value of Precision for the POSITIVE concept, which is 1,000.