• Journal of Fashion Business
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• v.18 no.4
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• pp.80-96
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• 2014

The purpose of this study is to consider the expression of costume through the review of cinema costumes and to provide the model of cooperation between fashion and movie industry by analysis of collaboration with fashion brands in the movie . The subjects are the 1974 film directed by Jack Clayton and the 2013 version by Baz Luhrmann. Cinema Fashion was studied by analyzing the costumes of the two main characters, Gatsby and Daisy, in each scene. Gatsby's costume appeared as a model of traditional American classic suit, sensitive G-G look that symbolizes social success and traditional casual style that reflects upper-class life style. Daisy's costume expressed pastel toned luxury flapper look, oriental art deco style, and prestigious jewelry representing high class. The collaborations with fashion brands were carried out with Ralph Lauren and Cartier in 1974 film, and Brooks Brothers, Prada, and Tiffany in 2013. The value of prestige brands that matched the images of the movie was utilized, but marketing strategies for the promotion of fashion goods were not enough in 1974 version. On the other hand, in 2013 film, the effects of collaboration of the movie and fashion brands were forecasted sufficiently and marketing campaigns for promotion were performed in a various ways. The characteristics of collaborations were as follows: (1) the usage of prestige brands value, (2) collections planning and promotion using the stories of a movie, (3) the usage of multidirectional digital media, and (4) multi-dimensional promotion using entertainment factors. In collaborations with the movie, fashion brands could make cooperative relationship to produce the positive effects for promotion and prestige image strategies and draw attention of the people to the movie and fashion.

• The Bulletin of The Korean Astronomical Society
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• v.40 no.1
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• pp.67.4-68
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• 2015

We present a multi-wavelength observational study of a low-mass star-forming region, L1251-C, with observational results at wavelengths from the near-infrared to the millimeter. Spitzer Space Telescope observations confirmed that IRAS 22343+7501 is a small group of protostellar objects. The extended emission to east-west direction with its intensity peak at the center of L1251A has been detected at 350 and 850 mm with the CSO and JCMT telescopes, tracing dense envelope materials around L1251A. The single-dish data from the KVN and TRAO telescopes show inconsistencies between the intensity peaks of several molecular line emission and that of the continuum emission, suggesting complex distributions of molecular abundances around L1251A. The SMA interferometer data, however, show intensity peaks of CO 2-1 and $^{13}CO$ 2-1 located at the position of IRS 1, which is both the brightest source in IRAC image and the weakest source in the 1.3 mm dust continuum map. IRS 1 is the strongest candidate for being the driving source of a newly detected the compact CO 2-1 outflow. Over the whole region ($14^{\prime}{\times}14^{\prime}$) of L125l-C, 3 Class I and 16 Class II sources have been detected, including three YSOs in L1251A. A comparison with the average projected distance among 19 YSOs in L1251-C and that among 3 YSOs in L1251A suggests L1251-C is an example of low-mass cluster formation, where protostellar objects are forming in a small group.

• Journal of the Korea Society of Computer and Information
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• v.26 no.11
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• pp.173-182
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• 2021

In this paper, we analyzed driver's and passenger's motions that cause driver's distraction, and recognized 10 driver's behaviors related to mobile phones. First, distraction-inducing behaviors were classified into environments and factors, and related recent papers were analyzed. Based on the analyzed papers, 10 driver's behaviors related to cell phones, which are the main causes of distraction, were recognized. The experiment was conducted based on about 100,000 image data. Features were extracted through SURF and tested with three models (CNN, ResNet-101, and improved ResNet-101). The improved ResNet-101 model reduced training and validation errors by 8.2 times and 44.6 times compared to CNN, and the average precision and f1-score were maintained at a high level of 0.98. In addition, using CAM (class activation maps), it was reviewed whether the deep learning model used the cell phone object and location as the decisive cause when judging the driver's distraction behavior.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.131-131
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• 2017

Though the Platycodon grandiflorum, has a broad range of pharmacologic properties, but the mechanisms underlying these effects remain unclear. In order to profile proteins from the nodal segment, callus, root and shoot, high throughput proteome approach was executed in the present study. Two-dimensional gels stained with CBB, a total of 84 differential expressed proteins were confirmed out of 839 protein spots using image analysis by Progenesis SameSpot software. Out of total differential expressed spots, 58 differential expressed protein spots (${\geq}2-fold$) were analyzed using MASCOT search engine according to the similarity of sequences with previously characterized proteins along with the UniProt database. Out of 58 differential expressed protein, 32 protein spots were up-regulated such as ribulose-1,5-bisphosphate carboxylase, endoplasmic oxidoreductin-1, heat stress transcription factor A3, RNA pseudourine synthase 4, cysteine proteinase, GntR family transcriptional regulator, E3 xyloglucan 6-xylosyltransferase, while 26 differential protein spots were down-regulated such as L-ascorbate oxidase precursor, late embryogenesis abundant protein D-34, putative SCO1 protein, oxygen-evolving enhancer protein 3. However, the frequency distribution of identified proteins using iProClass databases, and assignment by function based on gene ontology revealed that the identified proteins from the explants were mainly associated with the nucleic acid binding (17%), transferase activity (14%) and ion binding (12%). Taken together, the protein profile may provide insight clues for better understanding the characteristics of proteins and its metabolic activities in various explants of this essential medicinal plant P. grandiflorum.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.132-132
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• 2017

Plants, including Platycodon grandiflorum have been used globally across varied cultures as a safe natural source of medicines. From time immemorial, humans have relied on plants that could meet their basic necessities such as food, shelter, fuel and health. This study was executed to profile proteins from the hormone induced diploid and tetraploid roots using high throughput proteome approach. Two dimensional gels stained with CBB, a total of 64 differential expressed proteins were identified from the diploid root using image analysis by Progenesis SameSpot software. Out of total differential expressed spots, 20 differential expressed protein spots ( ${\geq}1.5-fold$) were analyzed using LTQ-FTICR MS whereas a total of 13 protein spots were up regulated and 7 protein spots were down-regulated. However, in the case of tetraploid root, a total of 78 differential expressed proteins were identified from tetraploid root of which a total of 28 differential expressed protein spots (${\geq}1.5-fold$) were analyzed by mass spectrometry whereas a total of 16 protein spots were up regulated and a total of 12 protein spots were down-regulated. However, proteins identified using iProClass databases revealed that the identified proteins from the explants were mainly associated with the nucleic acid binding, oxidoreductase activity, transporter activity and isomers activity. The exclusive protein profile may provide insight clues for better understanding the characteristics of protein function and its metabolic activity that can help for the development of the nutritional and breeding aspects of this economically important medicinal plant.

• Korean Journal of Remote Sensing
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• v.38 no.6_4
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• pp.1925-1934
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• 2022

Monitoring nuclear activity for inaccessible areas using remote sensing technology is essential for nuclear non-proliferation. In recent years, deep learning has been actively used to detect nuclear-activity-related small objects. However, high-resolution satellite imagery containing small objects can result in class imbalance. As a result, there is a performance degradation problem in detecting small objects. Therefore, this study aims to improve detection accuracy by analyzing the effect of the ratio of small objects related to nuclear activity in the input data for the performance of the deep learning model. To this end, six case datasets with different ratios of small object pixels were generated and a U-Net model was trained for each case. Following that, each trained model was evaluated quantitatively and qualitatively using a test dataset containing various types of small object classes. The results of this study confirm that when the ratio of object pixels in the input image is adjusted, small objects related to nuclear activity can be detected efficiently. This study suggests that the performance of deep learning can be improved by adjusting the object pixel ratio of input data in the training dataset.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.60 no.1
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• pp.97-106
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• 2015

Platycodon grandiflorum, commonly known as Doraji in Korea, has a wide range of pharmacologic properties, such as reducing adiposity and hyperlipidemia, and antiatherosclerotic effects. However, the mechanisms underlying these effects remain unclear. In order to profile proteins from the nodal segment, callus, root and shoot, high throughput proteome approach was executed in the present study. Two dimensional gels stained with CBB, a total of 84 differential expressed proteins were confirmed out of 839 protein spots using image analysis by Progenesis SameSpot software. Out of total differential expressed spots, 58 differential expressed protein spots (${\geq}$ 2-fold) were analyzed using MASCOT search engine according to the similarity of sequences with previously characterized proteins along with the UniProt database. Out of 58 differential expressed protein, 32 protein spots were up-regulated such as ribulose-1,5-bisphosphate carboxylase, endoplasmic oxidoreductin-1, heat stress transcription factor A3, RNA pseudourine synthase 4, cysteine proteinase, GntR family transcriptional regulator, E3 xyloglucan 6-xylosyltransferase, while 26 differential protein spots were down-regulated such as L-ascorbate oxidase precursor, late embryogenesis abundant protein D-34, putative SCO1 protein, oxygen-evolving enhancer protein 3. However, frequency distribution of identified proteins using iProClass databases, and assignment by function based on gene ontology revealed that the identified proteins from the explants were mainly associated with the nucleic acid binding (17%), transferase activity (14%) and ion binding (12%). In that way, the exclusive protein profile may provide insight clues for better understanding the characteristics of proteins and metabolic activity in various explants of the economically important medicinal plant Platycodon grandiflorum.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.12
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• pp.113-123
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• 2012

Recently, various attempts have been made to apply Intelligent Transportation System under various environments and conditions. Consequently, an accurate license plate recognition regardless of vehicle location and viewing angle is required. In this paper, we propose a novel license plate recognition system which exploits a) the format of license plates to remove false candidates of license plates and to extract characters in license plates and b) the characteristics of Hangul for accurate character recognition. In order to eliminate false candidates of license plates, the proposed method first aligns the candidates of license plates horizontally, and compares the position and the shape of objects in each candidate with the prior information of license plates provided by Korean Ministry of Construction & Transportation. The prior information such as aspect ratio, background color, projection image is also used to extract characters in license plates accurately applying an improved local binarization considering luminance variation of license plates. In case of recognizing Hangul in license plates, they are initially grouped according to their shape similarity. Then a super-class method, a hierarchical analysis based on key feature points is applied to recognize Hangul accurately. The proposed method was verified with high recognition rate regardless of background image, which eventually proves that the proposed LPR system has high performance regardless of the vehicle location or viewing angle.

• Journal of the Korean Association of Geographic Information Studies
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• v.4 no.1
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• pp.57-66
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• 2001

This study used four theoretical models, such as two-point linear model, linear regression model, quadratic regression model and cubic regression model which are presented from The Ministry of Science and Technology, for extraction of urban surface temperature from Landsat TM band 6 image. Through correlation and regression analysis between result of four models and AWS(automatic weather station) observation data, this study could verify spatial distribution characteristic of urban surface temperature using GIS spatial analysis method. The result of analysis for surface temperature by landcover showed that the urban and the barren land belonged to the highest surface temperature class. And there was also -0.85 correlation in the result of correlation analysis between surface temperature and NDVI. In this result, the meteorological environmental characteristics wuld be regarded as one of the important factor in urban planning.

• Journal of Korean Society for Geospatial Information Science
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• v.22 no.1
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• pp.71-79
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• 2014

Most researches have created the impervious surface map by using low-spatial-resolution satellite imagery and are inefficient to generate the object-based impervious map with a broad area. In this study, segment-based impervious surface mapping algorithm is proposed using the RapidEye satellite imagery in order to map impervious area. At first, additional bands are generated by using TOA reflectance conversion RapidEye data. And then, shadow and water class are extracted using training data of converted reflectance image. Object-based impervious surface can be generated by spectral mixture analysis based on land cover map of Ministry of Environment with medium scale, in the case of other classes except shadow and water classes. The experiment shows that result by our method represents high classification accuracy compared to reference data, quantitatively.