• Proceedings of the Korean Operations and Management Science Society Conference
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• 2001.10a
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• pp.265-268
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• 2001

As growing the electronic commerce there are significant changes in the products/services catalog into the on-line environment. Advertent of e-catalog business opportunity for their own product/services enlarges the market volume and there are diverse methods for the presentation of its product/services. A method for the presentation of product/services features one uses identification and classification system. This study constructs a classification system and database layout for the product/services classification system as a part of e-catalog system. We consider the specific method for the GDAS-based dataset and UNSPSC classification system in the distribution industry.

• Journal of the Korean Society for Library and Information Science
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• v.53 no.2
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• pp.247-266
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• 2019

The purpose of this study is to propose a W3C standard, DCAT, to manage and service dataset that is becoming increasingly important as new knowledge information resources. To do this, we first analyzed the class and properties of the four core classes of DCAT. In addition, I modeled and presented a system that can manage and service various data sets based on DCAT in digital library. The system is divided into source data, data set management, linked data connection, and user service. Especially, the DCAT mapping function is suggested in dataset management. This feature can ensure interoperability of various datasets.

• The Bulletin of The Korean Astronomical Society
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• v.37 no.1
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• pp.58.1-58.1
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• 2012

We present a comprehensive re-analysis of stellar photometric variability in the field of open cluster M37, using our new high-precision light curves. This dataset provides a rare opportunity to explore different types of variability between short (-minutes) and long (-one month) time-scales. To investigate the variability properties of -30,000 objects, we developed new algorithms for detecting periodic, aperiodic, and sporadic variability in their light curves. About 7.5% (2,284) of the total sample exhibits convincing variations that are induced by flares, pulsations, eclipses, starspots and, in some cases, unknown causes. The benefits of our new photometry and analysis package are evident. The discovery rate of new variables is increased by 63% in comparison with the existing catalog of variables, and 51 previously identified variables were found to be false positives resulting from time-dependent systematic effects. Based on extended and improved catalog of variables, we will review the basic properties (e.g., periodicity, amplitude, type) of the variability and how different they are for different spectral types and for cluster memberships.

• The Journal of Engineering Geology
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• v.32 no.3
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• pp.377-388
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• 2022

Recently, several studies have utilized machine learning to efficiently and accurately analyze seismic data that are exponentially increasing. In this study, we expand earthquake information such as occurrence time, hypocentral location, and magnitude to produce a dataset for applying to machine learning, reducing the dimension of the expended data into dominant features through principal component analysis. The dimensional extended data comprises statistics of the earthquake information from the Global Centroid Moment Tensor catalog containing 36,699 seismic events. We perform data preprocessing using standard and max-min scaling and extract dominant features with principal components analysis from the scaled dataset. The scaling methods significantly reduced the deviation of feature values caused by different units. Among them, the standard scaling method transforms the median of each feature with a smaller deviation than other scaling methods. The six principal components extracted from the non-scaled dataset explain 99% of the original data. The sixteen principal components from the datasets, which are applied with standardization or max-min scaling, reconstruct 98% of the original datasets. These results indicate that more principal components are needed to preserve original data information with even distributed feature values. We propose a data processing method for efficient and accurate machine learning model to analyze the relationship between seismic data and seismic behavior.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2010.04a
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• pp.363-365
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• 2010

In this study, based on numerical map GIS-T Dataset build and by using ArcGIS Network Analysis emergency vehicle's reach time were analyzed. AutoCad using 1: 50,000 based on roads and hospitals of numerical map were creating a Polyline and Point and Network Dataset made using ArcCatalog. ArcGIS Analysis setting the interval for the period reached 3 minutes, 5 minutes, 15 minutes was set and then U-Turn was set to not allow because U-turn takes a long time to calculate and does not happen often on the real road. Intersection of the passage of time, considering that the emergency vehicles were set to 3 seconds. To expand by taking advantage of this facility on Vulnerable area will be used as base material. If we focus on analyzing the emergency activity to convert little data, To prepare for disaster and disaster will be able to use the materials.

• Genomics & Informatics
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• v.12 no.4
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• pp.165-170
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• 2014

Genome-wide association (GWA) studies have found many important genetic variants that affect various traits. Since these studies are useful to investigate untyped but causal variants using linkage disequilibrium (LD), it would be useful to explore the haplotypes of single-nucleotide polymorphisms (SNPs) within the same LD block of significant associations based on high-density variants from population references. Here, we tried to make a haplotype catalog affecting body mass index (BMI) through an integrative analysis of previously published whole-genome next-generation sequencing (NGS) data of 7 representative Korean individuals and previously known Korean GWA signals. We selected 435 SNPs that were significantly associated with BMI from the GWA analysis and searched 53 LD ranges nearby those SNPs. With the NGS data, the haplotypes were phased within the LDs. A total of 44 possible haplotype blocks for Korean BMI were cataloged. Although the current result constitutes little data, this study provides new insights that may help to identify important haplotypes for traits and low variants nearby significant SNPs. Furthermore, we can build a more comprehensive catalog as a larger dataset becomes available.

• Journal of Information Processing Systems
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• v.19 no.4
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• pp.498-512
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• 2023

The traditional newspaper industry faces the opportunities and challenges of industry transformation and integration with new media. Consequently, the catalogs of newspapers and magazines are also updated. In this study, necessary information on catalogs was obtained and used to analyze the overall development trend of the newspaper industry. A word frequency analysis was then performed on the introduction and product categories of the catalogs, and the content and types of newspapers and magazines were examined. Furthermore, related factors such as price, number of pages, publishing frequency, and best-selling status were analyzed; the correlation among factors affecting best-selling status was also explored. Subsequently, each element and a combination of elements were used to generate a dataset, build three classification models, and analyze the accuracy of predictions of whether newspapers sold well under other circumstances. The experimental results showed that price is the most critical factor affecting the best-selling status of newspapers and magazines. Publishing frequency and the number of pages were also found to be significant indicators that impact people's subscription choices. Finally, a competitive strategy regarding content, price, quality, and positioning was developed.

• Journal of Astronomy and Space Sciences
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• v.41 no.3
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• pp.149-158
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• 2024

This study developed a machine learning-based methodology to classify gravitational wave (GW) signals from black hol-eneutron star (BH-NS) mergers by combining convolutional neural network (CNN) with conditional information for feature extraction. The model was trained and validated on a dataset of simulated GW signals injected to Gaussian noise to mimic real world signals. We considered all three types of merger: binary black hole (BBH), binary neutron star (BNS) and neutron starblack hole (NSBH). We achieved up to 96% correct classification of GW signals sources. Incorporating our novel conditional information approach improved classification accuracy by 10% compared to standard time series training. Additionally, to show the effectiveness of our method, we tested the model with real GW data from the Gravitational Wave Transient Catalog (GWTC-3) and successfully classified ~90% of signals. These results are an important step towards low-latency real-time GW detection.

• Journal of the Korean Society for Library and Information Science
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• v.56 no.4
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• pp.5-28
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• 2022

In this study, LD construction cases of overseas libraries were analyzed with focus on published datasets, reused vocabulary, and interlinked external datasets, and based on the analysis results, basic data on LD construction plans of domestic libraries were obtained. As a result of the analysis of 21 library cases, overseas libraries have established a faithful authority LD and conducted new services using published LDs. To this end, overseas libraries collaborated with other libraries and cultural institutions within the region, within the country, and nationally under the leadership of the library, and based on this cooperation, a specialized dataset was published. Overseas libraries used Schema.org to increase the visibility of published LDs, and used BIBFRAME for subdivision of description to define various entities and build LDs based on the defined entities. Overseas libraries have utilized various defined entities to link related information, display results, browse, and download in bulk. Overseas libraries were interested in the continuous up-to-date of interlinked external datasets, and directly utilized external data to reinforce catalog information. In this study, based on the derived implications, points to be considered when issuing LDs by domestic libraries were proposed. The research results can be used as basic data when future domestic libraries plan LD services or upgrade existing services.