• International Journal of Naval Architecture and Ocean Engineering
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• v.11 no.1
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• pp.353-363
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• 2019

This paper presents a comprehensive simulation and assessment of gas dispersion above sea from a subsea release using a Computational Fluid Dynamics (CFD) approach. A 3D CFD model is established to evaluate the behavior of flammable gas above sea, and a jack-up drilling platform is included to illustrate the effect of flammable gas cloud on surface vessels. The simulations include a matrix of scenarios for different surface release rates, distances between surface gas pool and offshore platform, and wind speeds. Based on the established model, the development process of flammable gas cloud above sea is predicted, and the dangerous area generated on offshore platform is assessed. Additionally, the effect of some critical factors on flammable gas dispersion behavior is analyzed. The simulations produce some useful outputs including the detailed parameters of flammable gas cloud and the dangerous area on offshore platform, which are expected to give an educational reference for conducting a prior risk assessment and contingency planning.

• Spatial Information Research
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• v.22 no.5
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• pp.1-8
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• 2014

Globally, mobile, cloud computing or big data are the recent marketable key terms. These trend technologies or paradigm in the ICT (Information Communication Technology) fields exert large influence on the most application fields including geo-spatial applications. Among them, cloud computing, though the early stage in Korea now, plays a important role as a platform for other trend technologies uses. Especially, mobile cloud, an integrated platform with mobile device and cloud computing can be considered as a good solution to overcome well known limitations of mobile applications and to provide more information processing functionalities to mobile users. This work is a case study to design and implement the mobile application system for geo-spatial image filtering processing operated on mobile cloud platform built using OpenStack and various open sources. Filtering processing is carried out using R environment, recently being recognized as one of big data analysis technologies. This approach is expected to be an element linking geo-spatial information for new service model development and the geo-spatial analysis service development using R.

• Cartoon and Animation Studies
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• s.48
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• pp.199-215
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• 2017

Recently, a drone is used for the general purpose application although the drone was builtfor the military purpose. A drone is actively used for the creation of contents, and an image acquisition. In this paper, we develop a 3D library module platform using 3D mesh model data, which is generated by a drone image and its point cloud. First, a lot of 2D image data are taken by a drone, and a point cloud data is generated from 2D drone images. A 3D mesh data is acquired from point cloud data. Then, we develop a service library platform using a transformed 3D data for multi-purpose uses. Our platform with 3D data can minimize the cost and time of contents creation for special effects during the production of a movie, drama, or documentary. Our platform can contribute the creation of experts for the digital contents production in the field of a realistic media, a special image, and exhibitions.

• Journal of Korean Neurosurgical Society
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• v.66 no.5
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• pp.488-493
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• 2023

We aimed to develop a cloud-based telemedicine platform for patients with intracerebral hemorrhage (ICH) at local hospitals in rural and underserved areas in Gangwon-do using artificial intelligence and non-face-to-face collaboration treatment technology. This is a prospective and multi-center development project in which neurosurgeons from four university hospitals in Gangwon-do will participate. Information technology experts will verify and improve the performance of the cloud-based telemedicine collaboration platform while treating ICH patients in the actual medical field. Problems identified will be resolved, and the function, performance, security, and safety of the telemedicine platform will be checked through an accredited certification authority. The project will be carried out over 4 years and consists of two phases. The first phase will be from April 2022 to December 2023, and the second phase will be from April 2024 to December 2025. The platform will be developed by dividing the work of the neurosurgeons and information technology experts by setting the order of items through mutual feedback. This article provides information on a project to develop a cloud-based telemedicine platform for acute ICH patients in Gangwon-do.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.4
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• pp.775-783
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• 2017

As interest in cloud computing grows, the number of users using cloud computing services is increasing. However, cloud computing technology has been steadily challenged by security concerns. Therefore, various security breaches are springing up to enhance the system security for cloud services users. In particular, research on detection of malicious VM (Virtual Machine) is actively underway through the introspecting virtual machines on the cloud platform. However, memory analysis technology is not used as a monitoring tool in the environments where multiple virtual machines are run on a single server platform due to obstructive monitoring overhead. As a remedy to the challenging issue, we proposes a computationally efficient instance memory introspection scheme to minimize the overhead that occurs in memory dump and monitor it through a partial memory monitoring based on the well-defined kernel memory map library.

• 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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• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.9
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• pp.2113-2120
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• 2013

As a smart mobile device is gaining popularity as a tool to utilize internet services, the demand on the cloud service (which eliminates the spatial and/or temporal restrictions in enjoying the services) is increasing. This trend also has an effect on the style of performing business tasks so that the concept of 'smart-work' (e.g. doing business tasks in anywhere and anytime) is being spread over the industries. The facilities for the smart-work is, however, focused on providing the tools designed to supporting business tasks such as co-working in writing documents, video conference, searching data via smart (mobile) devices neglecting the industries' needs for the infrastructure by which they can create their own business applications and mobile cloud services at a low (or moderate) price. The paper proposes a mobile cloud service platform, which provides an SDK for developing business applications and offers the applications to smart mobile devices as a PaaS (Platform as a Service).

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.6
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• pp.517-524
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• 2017

The objective of this research is to improve the efficiency of data collection from many machine components on smart factory floors using IoT(Internet of things) techniques and cloud platform, and to make it easy to update outdated diagnostic schemes through online deployment methods from cloud resources. The short-term analysis is implemented by a micro-controller, and it includes machine-learning algorithms for inferring snapshot information of the machine components. For long-term analysis, time-series and high-dimension data are used for root cause analysis by combining a cloud platform and multivariate analysis techniques. The diagnostic results are visualized in a web-based display dashboard for an unconstrained user access. The implementation is demonstrated to identify its performance in data acquisition and analysis for rotating machinery.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.3
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• pp.974-992
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• 2021

Recently, the healthcare field has undergone rapid changes owing to the accumulation of health big data and the development of machine learning. Data mining research in the field of healthcare has different characteristics from those of other data analyses, such as the structural complexity of the medical data, requirement for medical expertise, and security of personal medical information. Various methods have been implemented to address these issues, including the machine learning model and cloud platform. However, the machine learning model presents the problem of opaque result interpretation, and the cloud platform requires more in-depth research on security and efficiency. To address these issues, this paper presents a recent technology for Internet-of-Things-based (IoT-based) health big data processing. We present a cloud-based IoT health platform and health big data processing technology that reduces the medical data management costs and enhances safety. We also present a data mining technology for health-risk prediction, which is the core of healthcare. Finally, we propose a study using explainable artificial intelligence that enhances the reliability and transparency of the decision-making system, which is called the black box model owing to its lack of transparency.