• Proceedings of the Korea Information Processing Society Conference
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• 2007.05a
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• pp.525-528
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• 2007

기존의 웹서비스가 정적이고 수동적인데 반해 최근의 웹 서비스는 점차 동적이고 능동적으로 변화하고 있다. 이러한 웹서비스 변화의 흐름을 잘 반영하는 것이 웹 2.0이다. 웹 2.0에서 대부분의 정보는 사용자에 의해 생산되고, 사용자가 붙인 태그(tag)에 의해 분류되어진다. 그러나 현재 태그에 관한 서비스 및 연구들은 태깅(tagging) 방법에 대한 연구를 비롯해 이를 표현하기 위한 tag cloud에 초점이 맞춰져 진행됨에 따라, 다양한 태그 정보자원 간의 체계와 연결 관계인 지식체계를 제공하지 못하고 있다. 이에 본 논문에서는 체계화된 지식표현을 위해 웹상에 편재되어 있는 학습 관련 리소스(resources) 및 태그들를 수집한다. 이를 사용자가 요청한 검색 키워드와 연관성이 있는 태그 정보들을 맵핑 및 클러스터링하여 최적화된 표현 형식인 토픽 맵(topic map)화하기 위한 시스템을 제안하며, 이 중 토픽 맵 생성을 위한 초기 연구 단계로서, 연관 태그들 간의 맵핑 및 클러스터링을 위한 알고리즘 제시를 중심으로 소개한다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.9
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• pp.316-325
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• 2018

In this paper, to integrate distributed IT resources and manage human resource efficiently as purpose of cost reduction, infrastructure of wind turbine monitoring system have been designed and developed on the basis of SaaS cloud. This infrastructure hierarchize data according to related task and services. Softwares to monitor conditions via the infrastructure are also developed. Softwares are made up of DB design, field measurement, data transmission and monitoring programs. The infrastructure is able to monitor conditions from SCADA data and additional sensors. Total time delay from field measurement to monitoring is defined by modeling of step-wise time delay in condition monitoring algorithms. Since vibration data are acquired by measurements of high resolution, the delay is unavoidable and it is essential information for application of O&M program. Monitoring target is gearbox in wind turbine of MW-class and it is operating for 10 years, which means that accurate monitoring is essential for its efficient O&M in the future. The infrastructure is in operation to deal with the gearbox conditions with high resolution of 50 TB data capacity, annually.

• Journal of Internet Computing and Services
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• v.19 no.3
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• pp.57-66
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• 2018

EDISON ( EDucation-research Integration through Simulation On the Net) is a web simulation service based on cloud compu-ting. EDISON provides that web service and provide analysis result to users through pre-built infrastructure and various calcu-lation nodes computational science engineering problems that are difficult or impossible to analysis as user's personal resources to users. As a result, a simulation execution environment is provided in a web portal environment so that EDISON can be ac-cessed regardless of a user's device or operating system to perform computational science engineering analysis simulation. The purpose of this research is to design and construct a workbench based real - time dynamic service to provide an integrat-ed user interface to the EDSION system, which is a computational science engineering simulation and analysis platform, which is currently provided to users. We also devised a workbench-based user simulation service environment configuration. That has a user interface that is similar to the computational science engineering simulation software environment used locally. It can configure a dynamic web environment such as various analyzers, preprocessors, and simulation software. In order to provide these web services, the service required by the user is configured in portlet units, and as a result, the simulation service using the workbench is constructed.

• KIISE Transactions on Computing Practices
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• v.21 no.1
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• pp.13-18
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• 2015

Computational science simulations have been used to enable discovery in a broad spectrum of application areas, these simulations show irregular demanding characteristics of computing resources from time to time. The adoption of virtualized high performance cloud, rather than CPU-centric computing platform (such as supercomputers), is gaining interest of interests mainly due to its ease-of-use, multi-tenancy and flexibility. Basically, provisioning a virtual cluster, which consists of a lot of virtual machines, in a real-time has a critical impact on the successful deployment of the virtualized HPC clouds for computational science simulations. However, the cost of concurrently creating many virtual machines in constructing a virtual cluster can be as much as two orders of magnitude worse than expected. One of the main factors in this bottleneck is the time spent to create the virtual images for the virtual machines. In this paper, we propose a novel technique to minimize the creation time of virtual machine images and improve I/O performance of the provisioned virtual clusters. We also confirm that our proposed technique outperforms the conventional ones using various sets of experiments.

• Telecommunications review
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• v.24 no.5
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• pp.677-691
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• 2014

Connectivity is becoming more important keywords recently. For example, many devices are going to be connected to the internet. It is usually called as the IoT(internet of things). Many IoT devices can be evolved as a part of giant system of the world wide web. It is a great opportunity for us, because many new services can have emerged through this paradigm. In this paper, we propose a device virtualization framework for smart home service. The proposed framework connects the many home appliances devices and the internet using a dynamic protocol conversion. After our protocol conversion for device virtualization, our framework provides a RESTful API to access the resources of device through the internet. Therefore, the proposed framework can provide a variety of services, so it also can be developed into the ecosystem for smart home service. The current framework version only supports UPnP enabled devices of the home, but it can easily be extended to many other home middleware solutions. To verify the feasibility of the framework, we have implemented several service scenarios.

• Korean Journal of Remote Sensing
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• v.37 no.5_3
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• pp.1405-1423
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• 2021

Precipitation is one of the main factors that affect water and energy cycles, and its estimation plays a very important role in securing water resources and timely responding to water disasters. Satellite-based quantitative precipitation estimation (QPE) has the advantage of covering large areas at high spatiotemporal resolution. In this study, machine learning-based rainfall intensity models were developed using Himawari-8 Advanced Himawari Imager (AHI) water vapor channel (6.7 ㎛), infrared channel (10.8 ㎛), and weather radar Column Max (CMAX) composite data based on random forest (RF). The target variables were weather radar reflectivity (dBZ) and rainfall intensity (mm/hr) converted by the Z-R relationship. The results showed that the model which learned CMAX reflectivity produced the Critical Success Index (CSI) of 0.34 and the Mean-Absolute-Error (MAE) of 4.82 mm/hr. When compared to the GeoKompsat-2 and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN)-Cloud Classification System (CCS) rainfall intensity products, the accuracies improved by 21.73% and 10.81% for CSI, and 31.33% and 23.49% for MAE, respectively. The spatial distribution of the estimated rainfall intensity was much more similar to the radar data than the existing products.

• Journal of Convergence for Information Technology
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• v.9 no.3
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• pp.140-145
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• 2019

With the development of IT technology, many changes are taking place in the health service environment over the past. However, even if medical technology is converged with IT technology, the problem of medical costs and management of health services are still one of the things that needs to be addressed. In this paper, we propose a model for hospitals that have established the IoT system to efficiently analyze and manage the personal information of users who receive medical services. The proposed model aims to efficiently check and manage users' medical information through an in-house IoT system. The proposed model can be used in a variety of heterogeneous cloud environments, and users' medical information can be managed efficiently and quickly without additional human and physical resources. In particular, because users' medical information collected in the proposed model is stored on servers through the IoT gateway, medical staff can analyze users' medical information accurately regardless of time and place. As a result of performance evaluation, the proposed model achieved 19.6% improvement in the efficiency of health care services for occupational health care staff over traditional medical system models that did not use the IoT system, and 22.1% improvement in post-health care for users who received medical services. In addition, the burden on medical staff was 17.6 percent lower on average than the existing medical system models.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.12
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• pp.291-306
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• 2020

Nowadays, Data-Network-AI (DNA)-based intelligent services and applications have become a reality to provide a new dimension of services that improve the quality of life and productivity of businesses. Artificial intelligence (AI) can enhance the value of IoT data (data collected by IoT devices). The internet of things (IoT) promotes the learning and intelligence capability of AI. To extract insights from massive volume IoT data in real-time using deep learning, processing capability needs to happen in the IoT end devices where data is generated. However, deep learning requires a significant number of computational resources that may not be available at the IoT end devices. Such problems have been addressed by transporting bulks of data from the IoT end devices to the cloud datacenters for processing. But transferring IoT big data to the cloud incurs prohibitively high transmission delay and privacy issues which are a major concern. Edge computing, where distributed computing nodes are placed close to the IoT end devices, is a viable solution to meet the high computation and low-latency requirements and to preserve the privacy of users. This paper provides a comprehensive review of the current state of leveraging deep learning within edge computing to unleash the potential of IoT big data generated from IoT end devices. We believe that the revision will have a contribution to the development of DNA-based intelligent services and applications. It describes the different distributed training and inference architectures of deep learning models across multiple nodes of the edge computing platform. It also provides the different privacy-preserving approaches of deep learning on the edge computing environment and the various application domains where deep learning on the network edge can be useful. Finally, it discusses open issues and challenges leveraging deep learning within edge computing.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.554-557
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• 2016

Recently, supercomputer has been increasingly adopted as a computing environment for scientific simulation as well as education, healthcare and national defence. Especially, supercomputing system with heterogeneous computing resources is gaining resurgence of interest as a next-generation problem solving environment, allowing theoretical and/or experimental research in various fields to be free of time and spatial limits. However, traditional supercomputing services have only been handled through a simple form of command-line based console, which leads to the critical limit of accessibility and usability of heterogeneous computing resources. To address this problem, in this paper, we provide the design and implementation of web-based HPC (High Performance Computing) job management framework for computational scientific simulation. The proposed framework has highly extensible design principles, providing the abstraction interfaces of job scheduler (as well as bundle scheduler plug-ins for LoadLeveler, Sun Grid Engine, OpenPBS scheduler) in order to easily incorporate the broad spectrum of heterogeneous computing resources such as cluster, computing cloud and grid. We also present the detailed specification of HTTP standard based RESTful endpoints, which manage simulation job's life-cycles such as job creation, submission, control and status monitoring, etc., enabling various 3rd-party applications to be newly created on top of the proposed framework.

• 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.