• 국제학술발표논문집
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• The 6th International Conference on Construction Engineering and Project Management
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• pp.349-352
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• 2015

Size of building has a direct relationship with building cost, energy use and space maintenance cost. Therefore, minimizing building size during a project development is of paramount importance against such wastes. However, incautious reduction of building size may result in crowded space, and therefore harms the functionality despite the fact that building is supposed to satisfactorily support users' activity. A well-balanced design solution is, therefore, needed at an optimum level that minimizes building size in tandem with providing sufficient space to maintain functionality. For such design, architects and engineers need to be informed accurate and reliable space-use information. We present in this paper a conceptual framework of an agent-based space-use prediction simulation system that provides individual level space-use information over time in a building in consideration of project specific user information and activity schedules, space preference, ad beavioural rules. The information will accordingly assist architects and engineers to optimize space of the building as appropriate.

• Journal of Construction Engineering and Project Management
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• 제5권4호
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• pp.12-15
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• 2015

Size of building has a direct relationship with building cost, energy use and space maintenance cost. Therefore, minimizing building size during a project development is of paramount importance against such wastes. However, incautious reduction of building size may result in crowded space, and therefore harms the functionality despite the fact that building is supposed to satisfactorily support users' activity. A well-balanced design solution is, therefore, needed at an optimum level that minimizes building size in tandem with providing sufficient space to maintain functionality. For such design, architects and engineers need to be informed accurate and reliable space-use information. We present in this paper a conceptual framework of an agent-based space-use prediction simulation system that provides individual level space-use information over time in a building in consideration of project specific user information and activity schedules, space preference, ad beavioural rules. The information will accordingly assist architects and engineers to optimize space of the building as appropriate.

• 국제학술발표논문집
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• The 6th International Conference on Construction Engineering and Project Management
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• pp.299-303
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• 2015

Flexible space is an adaptable space that has been increasingly used in many office and academic buildings as it increases the use of the space available and reduces the unnecessary building area. However, the architectural, engineering and construction (AEC) industry lacks a formalized method that helps architects predict and update the space utilization of flexible space during the project development, as such prediction aims to maximize the use of the building space available without exceeding the target utilization policy. Consequently, current manual utilization prediction results in lower accuracy level and limits the maximized use of the flexible space, which has multiple space-use types that affect the prediction of utilization. To address this problem, we identified eight space-use type differentiators (SUTDs) based on the literature review and observations and discussed the use of them in automated space-use analysis (SUA), which can predict the utilization of flexible space via a computer program. This research builds on SUA and contributes to flexible space planning by providing a means of a more comprehensive and accurate SUA.

• Journal of Astronomy and Space Sciences
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• 제33권4호
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• pp.279-285
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• 2016

The space radiation dose over air routes including polar routes should be carefully considered, especially when space weather shows sudden disturbances such as coronal mass ejections (CMEs), flares, and accompanying solar energetic particle events. We recently established a heliocentric potential (HCP) prediction model for real-time operation of the CARI-6 and CARI-6M programs. Specifically, the HCP value is used as a critical input value in the CARI-6/6M programs, which estimate the aviation route dose based on the effective dose rate. The CARI-6/6M approach is the most widely used technique, and the programs can be obtained from the U.S. Federal Aviation Administration (FAA). However, HCP values are given at a one month delay on the FAA official webpage, which makes it difficult to obtain real-time information on the aviation route dose. In order to overcome this critical limitation regarding the time delay for space weather customers, we developed a HCP prediction model based on sunspot number variations (Hwang et al. 2015). In this paper, we focus on improvements to our HCP prediction model and update it with neutron monitoring data. We found that the most accurate method to derive the HCP value involves (1) real-time daily sunspot assessments, (2) predictions of the daily HCP by our prediction algorithm, and (3) calculations of the resultant daily effective dose rate. Additionally, we also derived the HCP prediction algorithm in this paper by using ground neutron counts. With the compensation stemming from the use of ground neutron count data, the newly developed HCP prediction model was improved.

• 항공우주기술
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• 제5권2호
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• pp.227-239
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• 2006

신뢰성 예측은 설계안 중 하나를 선택하거나, 적용되는 부품의 품질 수준, 부하경감(derating) 정도, 기존의 입증기술 또는 최신기술의 채택여부 등과 같은 사항을 설계시 결정하는 데에 합리적인 근거를 제공할 수 있으므로, 우주 시스템의 개발과정에 있어서 필수적인 사항으로 인식되고 있다. 신뢰성의 정량적인 표현을 위해서는 수치적인 정보와 그 정보를 생성하기 위한 기법의 정확성이 확보되어야 하며, 설계에 대한 평가를 돕고, 신뢰성 요구조건 할당, 시정조치 우선순위 결정 등의 기초자료로 적용하기 위하여 개념설계 초기 단계에서부터 수행되어야 한다. 대상품의 설계, 환경 요구조건, 운용환경 데이터, 고장률 데이터 또는 운용 프로파일의 변경이 발생할 때마다 업데이트되어야 한다. 본 논문에서는 우주시스템 개발과정에서 수행되는 신뢰성 예측을 위한 세부절차, 정량적 데이터의 도출 및 적용기법 등에 대하여 검토하였다.

• Journal of Astronomy and Space Sciences
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• 제26권2호
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• pp.267-278
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• 2009

Automation of the key flight dynamics operations for the geostationary orbit satellite mission is analyzed and designed. The automation includes satellite orbit determination, orbit prediction, event prediction, and fuel accounting. An object-oriented analysis and design methodology is used for design of the automation system. Automation scenarios are investigated first and then the scenarios are allocated to use cases. Sequences of the use cases are diagramed. Then software components and graphical user interfaces are designed for automation. The automation will be applied to the Communication, Ocean, and Meteorology Satellite (COMS) flight dynamics system for daily routine operations.

• 천문학회보
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• 제44권1호
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• pp.82.3-82.3
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• 2019

In this study, we generate future full disk magnetograms in 12, 24, 36 and 48 hours advance from SDO/HMI images using deep learning. To perform this generation, we apply the convolutional generative adversarial network (cGAN) algorithm to a series of SDO/HMI magnetograms. We use SDO/HMI data from 2011 to 2016 for training four models. The models make AI-generated images for 2017 HMI data and compare them with the actual HMI magnetograms for evaluation. The AI-generated images by each model are very similar to the actual images. The average correlation coefficient between the two images for about 600 data sets are about 0.85 for four models. We are examining hundreds of active regions for more detail comparison. In the future we will use pix2pix HD and video2video translation networks for image prediction.

• Journal of Astronomy and Space Sciences
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• 제37권4호
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• pp.229-236
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• 2020

There has been increasing necessity of more precise prediction and measurements of aviation radiation in Korea. For our air crew and passengers' radiation safety, we develop our own radiation prediction model of KREAM. In this paper, we validate the KREAM model based on comparison with Liulin observations. During early three months of this year, we perform total 25 experiments to measure aviation radiation exposure using Liulin-6K in commercial flights. We found that KREAM's result is very well consistent with Liulin observation in general. NAIRAS shows mostly higher results than Liulin observation, while CARI-6M shows generally lower results than the observations. The percent error of KREAM compared with Liulin observation is 10.95%. In contrast, the error for NAIRAS is 43.38% and 22.03% for CARI-6M. We found that the increase of the altitude might cause sudden increase in radiation exposure, especially for the polar route. As more comprehensive and complete analysis is required to validate KREAM's reliability to use for the public service, we plan to expand these radiation measurements with Liulin and Tissue Equivalent Proportional Counter (TEPC) in the near future.

• 천문학회지
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• 제45권2호
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• pp.31-38
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• 2012

In this study we apply Support Vector Machine (SVM) to the prediction of geo-effective halo coronal mass ejections (CMEs). The SVM, which is one of machine learning algorithms, is used for the purpose of classification and regression analysis. We use halo and partial halo CMEs from January 1996 to April 2010 in the SOHO/LASCO CME Catalog for training and prediction. And we also use their associated X-ray flare classes to identify front-side halo CMEs (stronger than B1 class), and the Dst index to determine geo-effective halo CMEs (stronger than -50 nT). The combinations of the speed and the angular width of CMEs, and their associated X-ray classes are used for input features of the SVM. We make an attempt to find the best model by using cross-validation which is processed by changing kernel functions of the SVM and their parameters. As a result we obtain statistical parameters for the best model by using the speed of CME and its associated X-ray flare class as input features of the SVM: Accuracy=0.66, PODy=0.76, PODn=0.49, FAR=0.72, Bias=1.06, CSI=0.59, TSS=0.25. The performance of the statistical parameters by applying the SVM is much better than those from the simple classifications based on constant classifiers.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권11호
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• pp.3913-3934
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• 2021

Real-time prediction of Web service of quality (QoS) provides more convenience for web services in cloud environment, but real-time QoS prediction faces severe challenges, especially under the cold-start situation. Existing literatures of real-time QoS predicting ignore that the QoS of a user/service is related to the QoS of other users/services. For example, users/services belonging to the same group of category will have similar QoS values. All of the methods ignore the group relationship because of the complexity of the model. Based on this, we propose a real-time Matrix Factorization based Clustering model (MFC), which uses category information as a new regularization term of the loss function. Specifically, in order to meet the real-time characteristic of the real-time prediction model, and to minimize the complexity of the model, we first map the QoS values of a large number of users/services to a lower-dimensional space by the PCA method, and then use the K-means algorithm calculates user/service category information, and use the average result to obtain a stable final clustering result. Extensive experiments on real-word datasets demonstrate that MFC outperforms other state-of-the-art prediction algorithms.