• Journal of the Korean Society of Costume
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• v.64 no.7
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• pp.128-142
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• 2014

In the new media age, the importance of personal style is highlighted, as the fashion recipients independently create their own images by transforming and recombining the fashion information gathered from the fashion blogs - the most representative form of social networks. The study aims to identify the types and styles of imaginary ego-images represented on the personal fashion blogs as a new space of self-expression, based on Lacan's gaze; the imaginary of the unconscious world and the ego-concept. According to literature search, the imaginary ego-image is classified as narcissism, regression, identification, and virtuality. In the case study, Narcissism is represented mostly as bloggers' satisfaction and beliefs about their fashion styles. The degeneration represents childhood images including a mother, as well as retro and vintage images that recreate the fashions of bygone eras - such as medieval, $19^{th}$ or 20th century fashion. Identification is the connection with the various areas of culture and art, especially movies and music. Virtuality represents hypothetical situations of mythical, fairy tale-like, surreal, or dreamlike atmospheres and hypothetical bodies that appear removed, disassembled, or crooked. The imaginary ego-images emerged on the personal fashion blogs are also classified into specific style depending on the attributes of the ego images-such as kidult style, retro style, ethnic style, and surreal style.

• Proceedings of the Korea Contents Association Conference
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• 2007.11a
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• pp.386-390
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• 2007

In this paper we present an Ontology Instance Management Tool. OntoManager is a user-friendly interactive ontology Instance management tool with webpage annotation tool and an image annotation tool. It supports the user with the task of creating and maintaining ontology-based OWL-markup, creating of OWL-instances, attributes and relationships. It include an ontology browser for the exploration of the ontology and instances and a HTML browser that will display the annotated parts of the text. And OntoManager is an image annotation tool that allows users to markup regions of an image with respect to concepts in an ontology. It provides the functionality to import images, ontologies, instance bases, perform markup, and export the resulting annotations to disk or the Web.

• Journal of the Korean Institute of Landscape Architecture
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• v.25 no.4
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• pp.1-17
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• 1998

Most of the tourist resort facilities in our country cannot be used in the winter season, and only a few spa resorts and sky resorts are available in the winter. To ameliorate this problem, various types of winter resort facilities have been constructed since 1970s and the massive development of winter resort facilities changed the resort market from a seller's market to a buyer's market. There has been however,few researches on marketing strategies for winter resorts, and there is a growing need for a rational method to maximize tourists' satisfaction and developers'profit at the same time. This research aims to develop a positining strategy to engance the marketability of winter resorts by classifying the resort market with the self-image types of users, and by analyzing the structure of the market, users' preferences, and locational factors. A survey was conducted with cases of Yong-Pyung resort, Mu-Ju resort, Alps resort, Bears resort, Back-Am spa resort, Su-An-Bo spa resort, and I-Chon spa resort. A list of questions in five categories -- similarity, characteristics, preferences, self-image, and personal characteristics of the respondents -- was constructed and tested. Among the 750 copies of questionnaire distributed, 700 were returned by only 378 were analyzed after screening missing or reckless answers. The statistical analysis of the data were conducted using techniques of correlation analysis, frequency analysis, factor analysis. Factor analysis and cluster analysis were used to group the cluster of self-image and a discriminant analysis were used to confirm this classification. The demographical characteristics were identified by frequency analysis, and resorts attributes were analyzed by oneway ANOVA analysis. Multidimensional scaling methods such as KYST, PROFIT, and PREFMAP were used for the positioning strategy.

• Journal of Science of Art and Design
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• v.8
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• pp.267-296
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• 2005

Typography, as the median for communication, has expanded its roles according the attributes of the media where it Is used, and the change of media follwing the change of times is presenting new directions to the field of typography. The attempts of 20C experimental typography prioritize visual formativeness, free typography and language started to appear on printed matters and different kinds of prints put pep in magazines. Thus, experiments in the aspects of effective delivery of inpormation that letters have and aesthetic side of shapes of letter are continuing. Today with the appearance of multimedia, development of visual colture and rapid development of digital technology, the range of experimental typography has expanded even wider and therefore, different kinds of expressions became possible. As seen above, unlike the existing static printed media that carries linguistic information only, letters in the multimedia environment is shown in the movie titles, TV or web not only as a basic visual media such as type and image, but as a dynamic and complex factor that contains additional information of motion and sound factor. This study will attempt to find the historic context of focus of moment from the kinetic art and define the moving letters as kinetic typography. Therefore, this paper will have an understanding on kinetic typography's background, concept and characteristics following the change of communication environment in the multimedia era and tried to study the basic theories of kinetic typography and the information delivery and imagery function of letters. Also, this paper attempted to carry out a study on whether kinetic typography is delivering information smoothly from the aspect of communication through the image role as delivering information by studying major works of artists who have influenced kinetic typography. Based on this study, I would like to suggest new direction for effective delivery of information and value of use.

• Journal of the Korean Society of Clothing and Textiles
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• v.40 no.4
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• pp.716-732
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• 2016

Focusing on the communication effects of fast fashion visual merchandising (VM), this paper examines the effects of a fast fashion store's VM attributes on consumer's perceptions of store image towards newness and prestige that influence the time spent at stores as well as the frequency of visits. This study was conducted by collecting data online using males and females in their twenties to forties; subsequently, a total of 382 samples were analyzed. The VM communication effect model utilized in fast fashion stores was developed and tested on structural equation modeling. The findings of the study were as follows. First, the show window presentation and ancillary facilities of VM elements have a positive effect on the perception of newness, while merchandise display, layout, and signage have a positive influence on the perception of prestige. Therefore, the VM elements in the fast fashion stores that affect the perception of newness and prestige are unique. Second, the perceptions of newness and prestige have a positive impact on time spent in fast fashion stores; however, only the perception of store's newness has a significant effect on the frequency of visits. Third, show window presentation and facilities are VM elements that directly influence the time spent and frequency of visits. Finally, we confirm that store image partially mediate between VM elements and shopping behavior at a fast fashion store.

• The Journal of the Korea Contents Association
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• v.15 no.11
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• pp.1-9
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• 2015

Emotion recognition and regular controls are concentrated interest in computer studies to emotional changes. Thus, the quantified by objective assessment methods are essential for application of color sensibility computing situations. In this paper, it is applied to a digital color image emotion emotional computing calculations numbered recognized as one representation. Emotional computing research approach consists of a color attribute to the image recognition focused sensibility and emotional attributes of color is the color, brightness and saturation separated by. Computes the sensitivity weighted according to the score and the percentage increase or decrease in the sensitivity property tone applied to emotional expression. Sensitivity calculation is free-degree (X), and calculates the tension (Y-axis). And free-level (X-axis) coordinate of emotion, which is located the intersection of the tension (Y-axis) as a sensitivity point. The emotional effect of the Russell coordinates are utilizing the core (Core Affect). Tue numbers represent the size and sensitivity in the emotional relationship between emotional point location and quantified by computing the color sensibility.

• Journal of the Korean Society of Marine Environment & Safety
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• v.25 no.4
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• pp.476-481
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• 2019

Early detection of fire is an important measure for minimizing the loss of life and property damage. However, fire and smoke need to be simultaneously detected. In this context, numerous studies have been conducted on image-based fire detection. Conventional fire detection methods are compute-intensive and comprise several algorithms for extracting the flame and smoke characteristics. Hence, deep learning algorithms and convolution neural networks can be alternatively employed for fire detection. In this study, recorded image data of fire in a ship engine room were analyzed. The flame and smoke characteristics were extracted from the outer box, and the YOLO (You Only Look Once) convolutional neural network algorithm was subsequently employed for learning and testing. Experimental results were evaluated with respect to three attributes, namely detection rate, error rate, and accuracy. The respective values of detection rate, error rate, and accuracy are found to be 0.994, 0.011, and 0.998 for the flame, 0.978, 0.021, and 0.978 for the smoke, and the calculation time is found to be 0.009 s.

• The Research Journal of the Costume Culture
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• v.30 no.3
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• pp.381-402
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• 2022

This study aimed to identify sub-dimensions of the authenticity and fictionality of a brand story and analyze the effects of authenticity and fictionality on customer-based brand equity. Data were obtained from a group of 213 males and females in their 20s and 30s living in Korea using an online survey institute. Results showed that the authenticity and fictionality of a brand story are composed of reality, excitement, exaggeration, fictional symbolism, influence, sincerity, relativeness, mysteriousness, and unreality. Of these, sincerity, excitement, reality, influence, and mysteriousness had significant effects on brand imagery; sincerity particularly exerted a relatively more substantial influence on brand imagery. Also, influence, mysteriousness, excitement, and relativeness impacted performance positively, and exaggeration impacted performance negatively. This indicated that a well-constructed brand story with authenticity and fictionality had a positive impact on the brand image. Excitement, mysteriousness, reality, relativeness, sincerity, and influence of a brand story had significant effects on brand judgement. In contrast, only excitement and influence positively impacted brand feelings, and unreality had a negative impact on feelings. The exciting and influential brand story impacted brand attitude. Also, brand image and attitude positively impacted sharing and purchase intention, while brand performance did not affect recommendation intention. These findings contribute to identifying a brand story's attributes, authenticity, and fictionality and provide insights for marketers on creating brand stories to increase brand image and attitude and to build customer-based brand equity.

• Journal of the Korean Society of Clothing and Textiles
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• v.46 no.1
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• pp.165-181
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• 2022

The fashion industry is creating innovative business models using artificial intelligence. To efficiently utilize artificial intelligence (AI), fashion data must be classified. Until now, such data have been classified focusing only on the objective properties of fashion products. Their subjective attributes, such as fashion brand sensibilities, are holistic and heuristic intuitions created by a combination of design elements. This study aims to improve the performance of collaborative filtering in the fashion industry by extracting fashion brand sensibility using computer vision technology. The image data set of fashion brand sensibility consists of high-end fashion brand photos that share sensibilities and communicate well in fashion. About 26,000 fashion photos of 11 high-end fashion brand sensibility labels have been collected from the 16FW to 21SS runway and 50 years of US Vogue magazines beginning from 1971. We use EfficientNet-B1 to establish the main architecture and fine-tune the network with ImageNet-ILSVRC. After training fashion brand sensibilities through deep learning, the proposed model achieved an F-1 score of 74% on accuracy tests. Furthermore, as a result of comparing AI machine and human experts, the proposed model is expected to be expanded to mass fashion brands.

• International Journal of Computer Science & Network Security
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• v.23 no.12
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• pp.151-160
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• 2023

Breast cancer is the most dangerous and deadly form of cancer. Initial detection of breast cancer can significantly improve treatment effectiveness. The second most common cancer among Indian women in rural areas. Early detection of symptoms and signs is the most important technique to effectively treat breast cancer, as it enhances the odds of receiving an earlier, more specialist care. As a result, it has the possible to significantly improve survival odds by delaying or entirely eliminating cancer. Mammography is a high-resolution radiography technique that is an important factor in avoiding and diagnosing cancer at an early stage. Automatic segmentation of the breast part using Mammography pictures can help reduce the area available for cancer search while also saving time and effort compared to manual segmentation. Autoencoder-like convolutional and deconvolutional neural networks (CN-DCNN) were utilised in previous studies to automatically segment the breast area in Mammography pictures. We present Automatic SegmenAN, a unique end-to-end adversarial neural network for the job of medical image segmentation, in this paper. Because image segmentation necessitates extensive, pixel-level labelling, a standard GAN's discriminator's single scalar real/fake output may be inefficient in providing steady and appropriate gradient feedback to the networks. Instead of utilising a fully convolutional neural network as the segmentor, we suggested a new adversarial critic network with a multi-scale L1 loss function to force the critic and segmentor to learn both global and local attributes that collect long- and short-range spatial relations among pixels. We demonstrate that an Automatic SegmenAN perspective is more up to date and reliable for segmentation tasks than the state-of-the-art U-net segmentation technique.