• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2008.10a
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• pp.122-125
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• 2008

Even though many researchers studied the foot shape and dimension, those applications lacked. The purpose of this study was to develop insole pattern of elderly women according to footprint. Discrepancy in the classification criteria among of foot parameters complicates attempts for elderly women classification of foot sole. To develop a footprint-based classification technique for the classification of foot sole types by allowing simultaneous use of several parameters. Foot sole data from static standing footprints were recorded from 48 elderly women. The factors of footprint shape were determined. Cluster analysis was applied to obtain individual foot sole classifications. The classification model of foot insole is proposed for a classification of footprint in elderly women. An application of ANOVA, Duncan's analysis, frequency analysis, factor analysis, and cluster analysis have been made to footprint data. In order to make clear foot sole characteristics, the factors of footprint shape have been discussed. The results are as follows. The factors of footprint shape have been classified into four types: foot length, sole slope, outside sole slope, and foot width. The types of foot sole shape have been classified into four types: longed, shortened, outside sloped, and toes sloped.

• Korean Journal of Geomatics
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• v.1 no.1
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• pp.61-67
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• 2001

This study aims to develop the Extended Urban Land Information System (EULIS) which can support the sustainable urban management. Although the existing Urban Land Use Information system (ULUIS) that aids the micro-level land use information is a good means for the understanding of urban spatial structure and district-level planning and management (such as urban design, redevelopment planning and district-level transportation planning, etc.), it has some limitations in supplying the information for sustainable urban management, such as environmental and traffic analysis, urban infrastructure's carrying capacity analysis, etc. The EULIS is designed to efficiently supply the information for sustainable urban management. For the successful construction of EULIS, the followings have to be considered. 1) the integration of topographic maps which contain the building's footprints and cadastral maps which contain the parcel's boundary, 2) the integration of EULIS and FM (Facility Management) system for the full utilization of information about capacity analysis of infrastructure, 3) the construction of standardized georeferencing system and spatial unit for the combined use of environment and traffic census data. This study shows 1) why EULIS is needed for the sustainable urban management and which elements are needed for the system,2) the E-R data model for the EULIS, 3) the strategies for the construction of EULIS and 4) the conclusion.

• Composites Research
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• v.26 no.3
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• pp.195-200
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• 2013

This study aims to detecting and analyzing the defects of damaged carbon fiber reinforced composites after impacts, particularly focusing on barely visible impact damages. The impact test was progressed by a drop-weight machine and applied to introduce simulated damages on laminated composites used in aircrafts. Various nondestructive testing (NDT) techniques were applied to identify the defects on the specimens with different levels of impact energies. Based on the measurements data, the levels of the barely visible impacts, and the applicability and effectiveness of the detection methods were discussed. Generally, the results demonstrated that their inner damages contained bigger footprints than those on the surfaces. However, when the damage energy was low, it was found that the inner damage size could be smaller than those appeared on the surfaces.

• The Bulletin of The Korean Astronomical Society
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• v.39 no.1
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• pp.43.1-43.1
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• 2014

It has been suggested that only the most luminous AGNs ($L{\geq}$ [10] $^{45}L_{\odot}$ ) are triggered by galaxy mergers, while less luminous AGNs (L~ [10] $^{43}L_{\odot}$) are driven by other internal processes. Lack of merging features in low luminosity AGN host galaxies has been a main argument against the idea of merger triggering of low luminosity AGNs, but merging, especially a rather minor one, might still have played an important role in low luminosity AGNs since minor merging features in low luminosity are more difficult to identify than major merging features. Using SNUCAM on the 1.5m telescope at Madanak observatory, we obtained deep images of NGC 7743 which is a barred spiral galaxy classified as a Seyfert 2 AGN with a low bolometric luminosity of $5{\times}$ [10] $^{42}L_{\odot}$. Surprisingly, we newly discovered merging features around the galaxy, which indicate past merging activity on the galaxy. This example indicates the merging fraction of low luminosity AGNs may be much higher than previously thought, hinting the importance of galaxy merger even in low luminosity AGN.

• ETRI Journal
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• v.33 no.4
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• pp.537-546
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• 2011

This paper introduces an automated method for building height recovery through the integration of high-resolution satellite images and digital vector maps. A cross-correlation matching method along the vertical line locus on the Ikonos images was deployed to recover building heights. The rational function models composed of rational polynomial coefficients were utilized to create a stereopair of the epipolar resampled Ikonos images. Building footprints from the digital maps were used for locating the vertical guideline along the building edges. The digital terrain model (DTM) was generated from the contour layer in the digital maps. The terrain height derived from the DTM at each foot of the buildings was used as the starting location for image matching. At a preset incremental value of height along the vertical guidelines derived from vertical line loci, an evaluation process that is based on the cross-correlation matching of the images was carried out to test if the top of the building has reached where maximum correlation occurs. The accuracy of the reconstructed buildings was evaluated by the comparison with manually digitized 3D building data derived from aerial photographs.

• BMB Reports
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• v.46 no.7
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• pp.346-351
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• 2013

Although there have been many studies of native Korean cattle, Hanwoo, there have been no selective sweep studies in these animals. This study was performed to characterize genetic variation and identify selective signatures. We sequenced the genomes of 12 cattle, and identified 15125420 SNPs, 1768114 INDELs, and 3445 CNVs. The SNPs, INDELs, and CNVs were similarly distributed throughout the genome, and highly variable regions were shown to contain the BoLA family and GPR180, which are related to adaptive immunity. We also identified the domestication footprints of the Hanwoo population by searching for selective sweep signatures, which revealed the RCN2 gene related to BPV resistance. The results of this study may contribute to genetic improvement of the Hanwoo population in Korea.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2004.05b
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• pp.47-53
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• 2004

The paper shows that the combination of the hardware, NI PCI 6110E board and the software, Fourier and continuous wavelet transform(CWT) can be used to implement for extracting the important features of the real-time signal. The results confirmed that CWT produces the fast computation enough for the application of the real-time signal processing except the negligible time delay. In denoising case, because of the lack of translation invariance of wavelet basis, traditional wavelet thresholding leads to pseudo-Gibbs phenomena in the vicinity of discontinuities of signal. In this paper, in order to reduce the pseudo-Gibbs phenomena, wavelet coefficients are threshold and reconstruction algorithm is implement through shift-invariant gibbs free denoising algorithm based on wavelet transform footprint. The proposed algorithm can potentially be extended to more general signals like piecewise smooth signals and represents an effective solution to problems like signal denoising.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.1
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• pp.158-166
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• 2019

In pursuing carbon emission reduction efforts, companies have focused for the most part on reducing emissions due to the more efficient equipment and facilities. However they overlook a significant source of carbon emissions, one that is driven by operational policies. Currently companies are looking for solutions to reduce carbon emissions associated with their operations. Operational adjustments, such as modifications in order quantities could an effective way in reducing carbon emissions in the supply chain. Also, Cap-and-Trade mechanism is generally accepted as on of the most effective market-based mechanism to reduce carbon emissions. In this paper, we investigate a supply chain with single manufacturer and multiple retailers multi-product inventory model under the cap-and-trade system incorporating the carbon emissions caused by transportation and warehousing activities. Also, we provide an iterative solution algorithm and derive the common order interval and the number of intervals for each product. We show by numerical example that the inventory model incorporating cap & trade mechanism can reduce total cost and carbon emissions compared to the classical inventory model. Using the numerical examples, we also investigates different carbon price on the performance of the inventory model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2069-2085
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• 2021

A Social Networking Service (SNS) platform provides digital footprints to discover users' interests and track the social diffusion of product adoptions. How to identify a small set of seed users in a SNS who is potential to adopt a new promoting product with high probability, is a key question in social networks. Existing works approached this as a social influence maximization problem. However, these approaches relied heavily on text information for topic modeling and neglected the impact of seed users' relation in the model. To this end, in this paper, we first develop a general product adoption function integrating both users' interest and social influence, where the user interest model relies on historical user behavior and the seed users' evaluations without any text information. Accordingly, we formulate a product adoption maximization problem and prove NP-hardness of this problem. We then design an efficient algorithm to solve this problem. We further devise a method to automatically learn the parameter in the proposed adoption function from users' past behaviors. Finally, experimental results show the soundness of our proposed adoption decision function and the effectiveness of the proposed seed selection method for product adoption maximization.

• Journal of Internet Computing and Services
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• v.21 no.6
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• pp.33-39
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• 2020

The paper is to build recommendation systems leveraging Deep Learning and Big Data platform, Spark to predict item ratings of the Amazon e-commerce site. Recommendation system in e-commerce has become extremely popular in recent years and it is very important for both customers and sellers in daily life. It means providing the users with products and services they are interested in. Therecommendation systems need users' previous shopping activities and digital footprints to make best recommendation purpose for next item shopping. We developed the recommendation models in Amazon AWS Cloud services to predict the users' ratings for the items with the massive data set of Amazon customer reviews. We also present Big Data architecture to afford the large scale data set for storing and computation. And, we adopted deep learning for machine learning community as it is known that it has higher accuracy for the massive data set. In the end, a comparative conclusion in terms of the accuracy as well as the performance is illustrated with the Deep Learning architecture with Spark ML and the traditional Big Data architecture, Spark ML alone.