• Journal of the Korean Operations Research and Management Science Society
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• v.28 no.3
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• pp.135-148
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• 2003

In this paper we introduce the Internet-based purchase support systems using data mining and case-based reasoning (CBR). Internet Business activity that involves the end user is undergoing a significant revolution. The ability to track users browsing behavior has brought the vendor and end customer's closer than ever before. It is now possible for a vendor to personalize his product message for individual customers at massive scale. Most of former researchers, in this research arena, used data mining techniques to pursue the customer's future behavior and to improve the frequency of repurchase. The area of data mining can be defined as efficiently discovering association rules from large collections of data. However, the basic association rule-based data mining technique was not flexible. If there were no inference rules to track the customer's future behavior, association rule-based data mining systems may not present more information. To resolve this problem, we combined association rule-based data mining with CBR mechanism. CBR is used in reasoning for customer's preference searching and training through the cases. Data mining and CBR-based hybrid purchase support mechanism can reflect both association rule-based logical inference and case-based information reuse. A Web-log data gathered in the real-world Internet shopping mall is given to illustrate the quality of the proposed systems.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.637-641
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• 2007

Airborne LIDAR (Light Detection and Ranging) technology has reached a degree of the required accuracy in mapping professions, and advanced LIDAR systems are becoming increasingly common in the various fields of application. LiDAR data constitute an excellent source of information for reconstructing the Earth's surface due to capability of rapid and dense 3D spatial data acquisition with high accuracy. However, organizing the LIDAR data and extracting information from the data are difficult tasks because LIDAR data are composed of randomly distributed point clouds and do not provide sufficient semantic information. The main reason for this difficulty in processing LIDAR data is that the data provide only irregularly spaced point coordinates without topological and relational information among the points. This study introduces an efficient and robust method for automatic extraction of building footprints using airborne LIDAR data. The proposed method separates ground and non-ground data based on the histogram analysis and then rearranges the building boundary points using convex hull algorithm to extract building footprints. The method was implemented to LIDAR data of the heavily built-up area. Experimental results showed the feasibility and efficiency of the proposed method for automatic producing building layers of the large scale digital maps and 3D building reconstruction.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.545-548
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• 2016

Big data is all data generated in the digital environment which has a variety of large and a short life cycle. The amount and type of data are becoming more and more produced on a larger scale, as a smart phone and the internet are popular, and consequently it has been converted into time for users to take advantage and extract only the valuable and useful data from the generated big data. Big data can also be applied to the medical industry and health sectors. It has created the synergy to be fused with ICT such as IoT, smart healthcare, and so on. However, there will be challenges like data security in order securely to use a meaningful and useful vast amounts of data. In this study, we analyze the future prospects of the healthcare, applications and issues of medical big data, and the expected challenges.

• Nuclear Engineering and Technology
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• v.54 no.5
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• pp.1686-1697
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• 2022

As a part of probabilistic risk (or safety) assessment (PRA or PSA) of nuclear power plants (NPPs), the primary role of human reliability analysis (HRA) is to provide credible estimations of the human error probabilities (HEPs) of safety-critical tasks. Accordingly, HRA community has emphasized the accumulation of HRA data to support HRA practitioners for many decades. To this end, it is critical to resolve practical problems including (but not limited to): (1) how to collect HRA data from available information sources, and (2) how to inform HRA practitioners with the collected HRA data. In this regard, the U.S. Nuclear Regulatory Commission (NRC) and Korea Atomic Energy Research Institute (KAERI) independently initiated two large projects to accumulate HRA data by using full-scale simulators (i.e., simulator data). In terms of resolving the first practical problem, the NRC and KAERI developed two dedicated HRA data collection systems, SACADA (Scenario Authoring, Characterization, And Debriefing Application) and HuREX (Human Reliability data EXtraction), respectively. In addition, to inform HRA practitioners, the NRC and KAERI proposed several ideas to extract useful information from simulator data. This paper is the first of two papers to discuss the technical underpinnings of the development of the SACADA and HuREX systems.

• Journal of Distribution Science
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• v.21 no.1
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• pp.53-63
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• 2023

Purpose: Big Data analytics (BDA) has been recognized to improve firm performance because it can efficiently manage and process large-scale, wide variety, and complex data structures. This study examines the determinants of Big Data analytics adoption toward marketing and financial performance of global logistic companies in Thailand. The research framework is adopted from the technology-organization-environment (TOE) model, including technological factors (relative advantages), organizational factors (technological infrastructure and absorptive capability), environmental factors (industry competition and government support), Big Data analytics adoption, marketing performance, and financial performance. Research design, data, and methodology: A quantitative method is applied by distributing the survey to 450 employees at the manager's level and above. The sampling methods include judgmental, stratified random, and convenience sampling. The data were analyzed by Confirmatory Factor Analysis (CFA) and Structural Equation Model (SEM). Results: The results showed that all factors significantly influence Big Data analytics adoption, except technological infrastructure. In addition, Big Data analytics adoption significantly influences marketing and financial performance. Conversely, marketing performance has no significant influence on financial performance. Conclusions: The findings of this study can contribute to the strategic improvement of firm performance through Big Data analytics adoption in the logistics, distribution, and supply chain industries.

• The Bulletin of The Korean Astronomical Society
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• v.40 no.1
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• pp.48.1-48.1
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• 2015

We investigate the role of small-scale and large-scale environments in triggering nuclear activity of the local galaxies using a volume-limited sample with $M_r$ < -19.5 and 0.02 < z < 0.0685 from the Sloan Digital Sky Survey Data Release 7. To fix the mass of the supermassive black hole in its host galaxy, we limit the central velocity dispersion of the sample galaxies. The active galactic nuclei (AGN) host sample is composed of Type II AGNs identified with flux ratios of narrow emission lines with S/N > 6. In this study, we find that the AGN fraction of late-type host galaxies are commonly larger than of early type galaxies. The AGN fraction of host galaxy with late-type nearest neighbor starts to increase as the host galaxy approaches the virial radius of the nearest neighbor (about a few hundred kpc scale). Our result may support the idea that the hydrodynamic interaction with the nearest neighbor plays an important role in triggering the nuclear activity of galaxy. The early-type galaxies in high density regions show decline of AGN activity compared to ones in lower density regions, whereas the direction of the environmental dependence of AGN activity for late-type galaxies is rather opposite. We also find that the environmental dependence of star formation rate is analogous to one of AGN activity except in the high density region.

• The Bulletin of The Korean Astronomical Society
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• v.38 no.2
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• pp.70.2-70.2
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• 2013

We present collections of optical photometry for a pre-main sequence star HBC 722. It showed large amplitude optical outburst (${\Delta}V=4.7$ mag) in 2010 and classified as a FU Orionis type object. We have been observing HBC 722 from 2011 April to 2013 May, using Camera for QUasars in EArly uNiverse (CQUEAN) attached to the 2.1 m Otto Struve telescope at the McDonald Observatory. Time-series monitoring data (minute-scale interval) were obtained in SDSS r, i and z bands to see short-scale behaviors as well as trace the long-term brightness changes after the eruption in 2010. Interestingly, it started to brighten from 2011 early summer and became brighter than the first outburst peak in our 2013 May observation. We expect that the recovering phase would result from re-increase of disk accretion rate, might attribute to distinctive short-scale color features. In this presentation, we report long- and short-timescale optical behaviors of HBC 722 in the post-outburst phase.

• 한국신재생에너지학회:학술대회논문집
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• 2005.06a
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• pp.403-407
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• 2005

Objective of the research is to demonstrate solar thermal space and ground heating system which is integrated to a greenhouse culture facility for reducing heating cost, increasing the value of product by environment control, and developing advanced culture technology by deploying solar thermal system. Field test for the demonstration was carried out in horticulture complex in Jeju Island. Medium scale solar hot water system was installed in a ground heating culture facility. Reliability and economic aspect of the system which was operated complementary with thermal storage and solar hot water generation were analyzed by investigating collector efficiency, operation performance, and control features. Short term day test on element performance and Long term test of the whole system were carried out. Optimum operating condition and its characteristics were closely investigated by changing the control condition based on the temperature difference which is the most important operating parameter. For establishing more reliable and optimal design data regarding system scale and operation condition, continuous operation and monitoring on the system need to be further carried out. However, it is expected that, in high-insolation areas where large-scale ground storage is adaptable, solar system demonstrated in the research could be economically competitive and promisingly disseminate over various application areas.

• New & Renewable Energy
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• v.1 no.2 s.2
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• pp.53-59
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• 2005

Objective of the research is to demonstrate solar thermal space and ground heating system which is integrated to a green-house culture facility for reducing healing cost, Increasing the value of product by environment control, and developing advanced culture technology by deploying solar thermal system. Field test for the demonstration was carried out in horticulture complex In Jeju Island. Medium scale solar hot water system was installed in a ground heating culture facility. Reliability and economic aspect of the system which was operated complementary with thermal storage and solar hot water generation were analyzed by investigating collector efficiency, operation performance, and control features. Short term day test on element performance and Long term test of the whole system were carried out. Optimum operating condition and its characteristics were closely Investigated by changing the control condition based on the temperature difference which Is the most important operating parameter For establishing more reliable and optimal design data regarding system scale and operation condition, continuous operation and monitoring on the system need to be further carried out. However, It is expected that, in high-insolation areas where large-scale ground storage is adaptable, solar system demonstrated in the research could be economically competitive and promisingly disseminate over various application areas.

• Proceedings of the KSRS Conference
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• v.2
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• pp.631-634
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• 2006

Three main important sources for establishing GIS are the orthomap in scale 1:5 000 with Ground Sampling Distance of 0,5m; DEM/DTM data with height error of ${\pm}$1,0m and topographic map in scale 1: 10 000. The new era with Very High Resolution Satellite (VHRS) images as IKONOS, QuickBird, EROS, OrbView and other ones having Ground Sampling Distance (GSD) even lower than 1m has been in potential for producing orthomap in large scale 1:5 000, to update existing maps, to compile general-purpose or thematic maps and for GIS. The accuracy of orthomap generated from VHRS image affects strongly on GIS reliability. Nevertheless, orthomap accuracy taken from VHRS image is at first dependent on chosen sensor geometrical models. This paper presents, at fist, theoretical basic of the Rational Polynomial Coefficient (RPC) model installed in the commercial ImageStation Systems, realized for orthorectifying VHRS images. The RPC model of VHRS image is a replacement camera mode that represents the indirect relation between terrain and its image acquired on the flight orbit. At the end of this paper the practical accuracies of IKONOS and QuickBird image orthorectified by RPC model on Canadian PCI Geomatica System have been presented. They are important indication for practical application of producing digital orthomaps.