• Journal of the Korean Society of Costume
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• v.56 no.7 s.107
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• pp.142-151
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• 2006

Bikini, a brief two-piece bathing suit revealing the wearer's navel, was first introduced in Paris, in 1946. However, it was not until the late 1950s that Americans were ready to adopt bikinis. Therefore, I focused on the following research questions to understand the popularization process of bikinis in the United States, from 1946 to the 1960s: 1. Why were Americans initially hesitant to adopt the bikini? 2. What were the factors that influenced the popularization of the bikini among Americans in the late 1950s? Primary sources including Bazaar. Madmoiselle, Vogue, The New York Times, and Life were reviewed. I referred to secondary sources on the history of fashion and American popular culture to interpret primary sources. According to the primary sources, Americans were hesitant to adopt the bikini, partly due to the excessive demand on the wearer's figure. However, the conservative social atmosphere during Cold War would not accept immorality and obscenity which would threaten America's future. Therefore, the campaigns against the sex industry, which developed prominently after WWII, predominated American society during the 1950s. Under this atmosphere, a small number of pictures and articles on bikinis appeared in the primary sources. Bikinis were only found in advertisements including sun lotions and hair removers. However, American society had to accept the change in sexual mores by the end of the 1930s. Body-revealing fashions including miniskirts, hot pants, and see-through material reflected the change in social convention. By the end of the 1950s, the number of pictures and articles on bikinis also began to increase in the primary sources. More Americans adopted bikinis with the increasing number of private pools and European trips. The vogue of sun-tanning and movies featuring bikinis further contributed to their popularity in the late 1930s and into the 1960s.

• Journal of Fashion Business
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• v.15 no.4
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• pp.55-66
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• 2011

The purpose of this study is to evaluate the differences of the visual image on variations in the shoulder strap and the length of the bikini swimsuit. Nine samples were examined: 3 variations of the shoulder strap and 3 variations of the pants length. Data have been obtained from 90 fashion design majors and analyzed using Factor Analysis, Anova, Scheffe's Test and the MCA method. The results of this study are as follows: 1) The visual image, according to changes in the shoulder strap and length of the bikini swimsuit, was composed of boldness, matureness and attraction factors. Boldness was the most important factor in the bikini swimsuit. 2) The visual images according to changes in the shoulder strap of the bikini swimsuits appeared the most (1) plain and simple image, (2) decent and neat image, (3) a wanted-not-to-dress and a natural image in the two shoulder straps, (1) unique and complicated image, (2) a lively and sexy image in the one shoulder strap and an unnatural but a wanted-to-dress image in the strapless. 3) The visual images according to changes in length of the bikini swimsuits appeared the most (1) unique and complicated image, (2) a lively and sexy image in high cut, but plain and simple image in low cut. 4) The number of shoulder straps and length do interact with each other in boldness factor: One shoulder strap and high cut of the bikini swimsuit has the most unique and complicated image. However two shoulder straps and regular cut of that has the most plain and simple image. 5) The result of matureness and attraction factors using the MCA, length affects more than the number of shoulder straps in the visual images of the bikini swimsuit.

• Korea Petroleum Association Journal
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• no.6 s.160
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• pp.97-99
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• 1994

이 자료는 일본 국제경제연구소의 히타나카 비키 차석연구원이 일본경제신문에 발표한 논문을 옮긴 것이다. <편집자 주>

• Proceedings of the Korea Information Processing Society Conference
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• 2012.04a
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• pp.406-409
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• 2012

'나꼼수 비키니 시위'에 대 긍정적(지지), 부정적(비판) 의견을 담은 트위터 데이터를, 단어의 출현에 주목하여 Naïve Bayes (NB)와 Support Vector Machine (SVM)을 적용하여 자동분류 한 결과, NB가 75.98%로, 73.65%인 SVM 보다 약간 더 나은 성능을 보였다. 본 실험을 통해, 기계학습을 이용한 대중의견(opinion) 자동분류 시스템을 실용화할 때의 고려사항에 대해 살펴 본다.

• Journal of the Korean Data and Information Science Society
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• v.26 no.5
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• pp.1167-1173
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• 2015

The development of information and communication technology like SNS, blogs, and bulletin boards, was provided a variety of places where you can express your thoughts and comments and allowing Big Data to grow, many people reveal the opinion of the social issues in SNS such as Twitter. In this study, we would like to pre-built sentimental dictionary about social issues and conduct a sentimental analysis with structured dictionary, to gather opinions on social issues that are created on twitter. The data that I used is "bikini", "nakkomsu" including tweet. As the result of analysis, precision is 61% and F1- score is 74%. This study expect to suggest the standard of dictionary construction allowing you to classify positive/negative opinion on specific social issues.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.658-660
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• 2021

The work within this study develops an algorithm based on the structural similarity index to assess numerically between reconstructed images with a reference image to separate the homogeneity and heterogeneity for diffuse optical tomography. Global geometry and region of interest assessment have been measured to yield the similarity. The results indicate that the mean of structural similarity index shows potential performance to distinguish between visible and invisible inclusion inside the model. Therefore, the structural similarity index may promise to assist the image assessment for evaluating breast structural information.

• Journal of Digital Convergence
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• v.14 no.10
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• pp.431-438
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• 2016

This study focused on the Korean-wave content consumed in the global market and analyzed success/failure factors of services through business model analysis. It aims to offer an effective new media content platform model. ViKi, Drama Fever, Maaduu.com which are representative global OTT were researched on management strategy by case analysis. The success of the global OTT platform is organized into three factors, target customer coverage, revenue model and community activation. Clear and wide coverage of target customer is important to determine the value of the service. Also, revenue model based on the pay service and community for Korean-wave fandom are essential to make good performance in new media platform business.

• Journal of Digital Convergence
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• v.17 no.11
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• pp.523-530
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• 2019

This study examines how global fandom for Korean dramas is formed and maintained by examining the reason and purpose of voluntarily making the subtitles of Korean drama through in-depth email interviews with foreign subtitle producers(fan subber) working on a video site called Viki.com. The research focused on Viki's fan community, which has grown into the most influential Korean Wave platform. Collective intelligence expresses in the fan community produces more than professional results and they are acting as consumers, cultural producers, and second creators of K-content. In order to continue the spread of K-content, it needs to pay more attention to the long-term strategy of global fandom combined with the fan community activities of the new media platform and network effects.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.399-401
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• 2022

A definitive diagnosis to classify the breast malignancy status may be achieved by microscopic analysis using surgical open biopsy. However, this procedure requires experts in the specializing of histopathological image analysis directing to time-consuming and high cost. To overcome these issues, deep learning is considered practically efficient to categorize breast cancer into benign and malignant from histopathological images in order to assist pathologists. This study presents a pre-trained convolutional neural network model architecture with a 100% fine-tuning scheme and Adagrad optimizer to classify the breast cancer histopathological images into benign and malignant using a 40× magnification BreaKHis dataset. The pre-trained architecture was constructed using the InceptionResNetV2 model to generate a modified InceptionResNetV2 by substituting the last layer with dense and dropout layers. The results by demonstrating training loss of 0.25%, training accuracy of 99.96%, validation loss of 3.10%, validation accuracy of 99.41%, test loss of 8.46%, and test accuracy of 98.75% indicated that the modified InceptionResNetV2 model is reliable to predict the breast malignancy type from histopathological images. Future works are necessary to focus on k-fold cross-validation, optimizer, model, hyperparameter optimization, and classification on 100×, 200×, and 400× magnification.