• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.40 no.2
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• pp.34-43
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• 2022

This study compared the characteristics of cultural heritage in China and Vietnam, which have developed in the relationship of mutual geopolitical and cultural influence in history, and the following conclusions were made. First, the definition of cultural heritage in China and Vietnam has similar meanings in both countries. In the case of cultural heritage classification, both countries introduced the legal concept of intangible cultural heritage through UNESCO, and have similarities in terms of intangible cultural heritage. Second, while China has separate laws for managing tangible and intangible cultural heritages, Vietnam integrally manages the two types of cultural heritages under a single law. Vietnam has a slower introduction of the concept of cultural heritage than China, but it shows high integration in terms of system. Third, cultural heritages in both China and Vietnam are graded, which is applied differently depending on the type of heritage. The designation method has a similarity in which the two countries have a vertical structure and pass through steps. By restoring the value of heritage and complementing integrity through such a step-by-step review, balanced development across the country is being sought through tourism to enjoy heritage and create economic effects. Fourth, it was confirmed that the cultural heritage management organization has a central government management agency in both countries, but in China, the authority of local governments is higher than that of Vietnam. In addition, unlike Vietnam, where tangible and intangible cultural heritage are managed by an integrated institution, China had a separate institution in charge of intangible cultural heritage. Fifth, China is establishing a conservation management policy focusing on sustainability that harmonizes the protection and utilization of heritage. Vietnam is making efforts to integrate the contents and spirit of the agreement into laws, programs, and projects related to cultural heritage, especially intangible heritage and economic and social as a whole. However, it is still dependent on the influence of international organizations. Sixth, China and Vietnam are now paying attention to intangible heritage recently introduced, breaking away from the cultural heritage protection policy centered on tangible heritage. In addition, they aim to unite the people through cultural heritage and achieve the nation's unified policy goals. The two countries need to use intangible heritage as an efficient means of preserving local communities or regions. A cultural heritage preservation network should be established for each subject that can integrate the components of intangible heritage into one unit to lay the foundation for the enjoyment of the people. This study has limitations as a research stage comparing the cultural heritage system and preservation management status in China and Vietnam, and the characteristic comparison of cultural heritage policies by type remains a future research task.

• Journal of Industrial Convergence
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• v.22 no.2
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• pp.73-83
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• 2024

The purpose of this study is to diagnose various problems arising around social enterprises in the sport field from the perspective of the organization and derive necessary tasks and implications. In order to achieve the purpose of the study, the study was largely divided into three stages, and the results were derived. First, the main status and characteristics of social enterprises in the sport field were examined. The current status was analyzed focusing on aspects such as background and origin, legislation and policy, organizational goals, organizational structure and procedures, and organizational characteristics. Social enterprises in the sport sector were in their early stages, and the government's social enterprise policy goal tended to focus on increasing the number of social enterprises in a short period of time through financial input. In addition, it was found that most individual companies rely on government subsidy support due to insufficient profit generation capacity. In the second stage, we focused on the situational factors that affect the functional performance of social enterprises in the sport field. As a result of reviewing the value, ideology, technology, and history of the organization, which are situational factors, it was derived that when certified as a social enterprise in the sport field and supported by the central government or local governments, political control is strong to some extent and exposure to the market is not severe. In the last third step, tasks and implications were derived to form an appropriate organization for social enterprises in the sport field. After the social enterprise ecosystem in the sport sector has been established to some extent, it is necessary to gradually move from the current "government-type" organization to the "national enterprise" organization. This is true in light of the government's limited financial level, not in the short term, but in order for the organization of social enterprises in the sports sector to survive in the long term.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.1-19
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• 2018

Large amount of data is now available for research and business sectors to extract knowledge from it. This data can be in the form of unstructured data such as audio, text, and image data and can be analyzed by deep learning methodology. Deep learning is now widely used for various estimation, classification, and prediction problems. Especially, fashion business adopts deep learning techniques for apparel recognition, apparel search and retrieval engine, and automatic product recommendation. The core model of these applications is the image classification using Convolutional Neural Networks (CNN). CNN is made up of neurons which learn parameters such as weights while inputs come through and reach outputs. CNN has layer structure which is best suited for image classification as it is comprised of convolutional layer for generating feature maps, pooling layer for reducing the dimensionality of feature maps, and fully-connected layer for classifying the extracted features. However, most of the classification models have been trained using online product image, which is taken under controlled situation such as apparel image itself or professional model wearing apparel. This image may not be an effective way to train the classification model considering the situation when one might want to classify street fashion image or walking image, which is taken in uncontrolled situation and involves people's movement and unexpected pose. Therefore, we propose to train the model with runway apparel image dataset which captures mobility. This will allow the classification model to be trained with far more variable data and enhance the adaptation with diverse query image. To achieve both convergence and generalization of the model, we apply Transfer Learning on our training network. As Transfer Learning in CNN is composed of pre-training and fine-tuning stages, we divide the training step into two. First, we pre-train our architecture with large-scale dataset, ImageNet dataset, which consists of 1.2 million images with 1000 categories including animals, plants, activities, materials, instrumentations, scenes, and foods. We use GoogLeNet for our main architecture as it has achieved great accuracy with efficiency in ImageNet Large Scale Visual Recognition Challenge (ILSVRC). Second, we fine-tune the network with our own runway image dataset. For the runway image dataset, we could not find any previously and publicly made dataset, so we collect the dataset from Google Image Search attaining 2426 images of 32 major fashion brands including Anna Molinari, Balenciaga, Balmain, Brioni, Burberry, Celine, Chanel, Chloe, Christian Dior, Cividini, Dolce and Gabbana, Emilio Pucci, Ermenegildo, Fendi, Giuliana Teso, Gucci, Issey Miyake, Kenzo, Leonard, Louis Vuitton, Marc Jacobs, Marni, Max Mara, Missoni, Moschino, Ralph Lauren, Roberto Cavalli, Sonia Rykiel, Stella McCartney, Valentino, Versace, and Yve Saint Laurent. We perform 10-folded experiments to consider the random generation of training data, and our proposed model has achieved accuracy of 67.2% on final test. Our research suggests several advantages over previous related studies as to our best knowledge, there haven't been any previous studies which trained the network for apparel image classification based on runway image dataset. We suggest the idea of training model with image capturing all the possible postures, which is denoted as mobility, by using our own runway apparel image dataset. Moreover, by applying Transfer Learning and using checkpoint and parameters provided by Tensorflow Slim, we could save time spent on training the classification model as taking 6 minutes per experiment to train the classifier. This model can be used in many business applications where the query image can be runway image, product image, or street fashion image. To be specific, runway query image can be used for mobile application service during fashion week to facilitate brand search, street style query image can be classified during fashion editorial task to classify and label the brand or style, and website query image can be processed by e-commerce multi-complex service providing item information or recommending similar item.