• 한국노년학
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• v.30 no.3
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• pp.831-842
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• 2010

BACKGROUND: Sarcopenic obesity(SO), a condition of the reduction in muscle mass paired with an increased fat mass has been paid attention because of its association with disability in later life. A few evidence, however, has reported the association with these factors. PURPOSE: To explore the association among SO, physical function and fitness in older women. METHOD: 257 older women(age of 74) were recruited from Y city and 7 physical functions and 4 fitness tests were measured. Participants were classified into one of four groups based on their body fat and muscle mass: Normal group (GR-A), high fat(GR-B), sarcopenia(GR-C), and sarcopenic obese(GR-D). GLMand LSD-test were conducted with SPSS 12.0. RESULTS: Chair stand, arm-curl, back-scratch, 2-min steps of GR-A was higher than GR-C and GR-D(p<.05). One-leg stand of GR-A was higher than GR-D(p<.01) and of GR-C was higher than GR-D(p<.01).8ft-TUG of GR-D was lower than GR-A(p<.01). Grip strength, knee extension of GR-A was higher than that of GR-C and GR-D(p<.01) and knee flexion of GR-A was also higher than that of GR-C and GR-D(p<.01). CONCLUSION: Based on our findings, we conclude that SO is significantly associated with lower physical function and fitness in older Korean women, which alarm the risk of frailty induced by SO.

• Asia Marketing Journal
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• v.15 no.1
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• pp.1-22
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• 2013

This case focuses on Shanghai Tang, the first truly Chinese luxury brand that appeals to both Westerners and, more recently, to Chinese consumers worldwide. A visionary and wealthy businessman Sir David Tang created this company from scratch in 1994 in Hong Kong. Its story, spanned over almost two decades, has been fascinating. It went from what best a Chinese brand could be in the eyes of Westerners who love the Chinese culture, to a nearly-bankrupted company in 1998, before being acquired by Richemont, the second largest luxury group in the world. Since then, its turnaround has been spectacular with a growing appeal among Chinese luxury consumers who represent the core segment of the luxury industry today. The main objective of this case study is to formally examine how Shanghai Tang overcame its downfall and re-emerged as one the very few well- known Chinese luxury brands. More specifically, this case highlights the ways with which Shanghai Tang made a transitional change from a brand for Westerners who love the Chinese culture, to a brand for both, Westerners who love the Chinese culture and Chinese who love luxury. A close examination reveals that Shanghai Tang has followed the brand identity concept that consists of two major components: functional and emotional. The functional component for developing a luxury brand concerns all product characteristics that will make a product 'luxurious' in the eyes of the consumer, such as premium quality of cachemire from Mongolia, Chinese silk, lacquer, finest leather, porcelain, and jade in the case of Shanghai Tang. The emotional component consists of non-functional symbolic meanings of a brand. The symbolic meaning marks the major difference between a premium and a luxury brand. In the case of Shanghai Tang, its symbolic meaning refers to the Chinese culture and the brand aims to represent the best of Chinese traditions and establish itself as "the ambassador of modern Chinese style". It touches the Chinese heritage and emotions. Shanghai Tang has reinvented the modern Chinese chic by drawing back to the stylish decadence of Shanghai in the 1930s, which was then called the "Paris of the East", and this is where the brand finds inspiration to create its own myth. Once the functional and emotional components assured, Shanghai Tang has gone through a four-stage development to become the first global Chinese luxury brand: introduction, deepening, expansion, and revitalization. Introduction: David Tang discovered a market gap and had a vision to launch the first Chinese luxury brand to the world. The key success drivers for the introduction and management of a Chinese luxury brand are a solid brand identity and, above all, a creative mind, an inspired person. This was David Tang then, and this is now Raphael Le Masne de Chermont, the current Executive Chairman. Shanghai Tang combines Chinese and Western elements, which it finds to be the most sustainable platform for drawing consumers. Deepening: A major objective of the next phase is to become recognized as a luxury brand and a fashion or design authority. For this purpose, Shanghai Tang has cooperated with other well-regarded luxury and lifestyle brands such as Puma and Swarovski. It also expanded its product lines from high-end custom-made garments to music CDs and restaurant. Expansion: After the opening of his first store in Hong Kong in 1994, David Tang went on to open his second store in New York City three years later. However this New York retail operation was a financial disaster. Barely nineteen months after the opening, the store was shut down and quietly relocated to a cheaper location of Madison Avenue. Despite this failure, Shanghai Tang products found numerous followers especially among Western tourists and became "souvenir-like" must-haves. However, despite its strong brand DNA, the brand did not generate enough repeated sales and over the years the company cumulated heavy debts and became unprofitable. Revitalizing: After its purchase by Richemont in 1998, Le Masne de Chermont was appointed to lead the company, reposition the brand and undertake some major strategic changes such as revising the "Shanghai Tang" designs to appeal not only to Westerners but also to Chinese consumers, and to open new stores around the world. Since then, Shanghai Tang has become synonymous to a modern Chinese luxury lifestyle brand.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.205-225
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• 2018

Convolutional Neural Network (ConvNet) is one class of the powerful Deep Neural Network that can analyze and learn hierarchies of visual features. Originally, first neural network (Neocognitron) was introduced in the 80s. At that time, the neural network was not broadly used in both industry and academic field by cause of large-scale dataset shortage and low computational power. However, after a few decades later in 2012, Krizhevsky made a breakthrough on ILSVRC-12 visual recognition competition using Convolutional Neural Network. That breakthrough revived people interest in the neural network. The success of Convolutional Neural Network is achieved with two main factors. First of them is the emergence of advanced hardware (GPUs) for sufficient parallel computation. Second is the availability of large-scale datasets such as ImageNet (ILSVRC) dataset for training. Unfortunately, many new domains are bottlenecked by these factors. For most domains, it is difficult and requires lots of effort to gather large-scale dataset to train a ConvNet. Moreover, even if we have a large-scale dataset, training ConvNet from scratch is required expensive resource and time-consuming. These two obstacles can be solved by using transfer learning. Transfer learning is a method for transferring the knowledge from a source domain to new domain. There are two major Transfer learning cases. First one is ConvNet as fixed feature extractor, and the second one is Fine-tune the ConvNet on a new dataset. In the first case, using pre-trained ConvNet (such as on ImageNet) to compute feed-forward activations of the image into the ConvNet and extract activation features from specific layers. In the second case, replacing and retraining the ConvNet classifier on the new dataset, then fine-tune the weights of the pre-trained network with the backpropagation. In this paper, we focus on using multiple ConvNet layers as a fixed feature extractor only. However, applying features with high dimensional complexity that is directly extracted from multiple ConvNet layers is still a challenging problem. We observe that features extracted from multiple ConvNet layers address the different characteristics of the image which means better representation could be obtained by finding the optimal combination of multiple ConvNet layers. Based on that observation, we propose to employ multiple ConvNet layer representations for transfer learning instead of a single ConvNet layer representation. Overall, our primary pipeline has three steps. Firstly, images from target task are given as input to ConvNet, then that image will be feed-forwarded into pre-trained AlexNet, and the activation features from three fully connected convolutional layers are extracted. Secondly, activation features of three ConvNet layers are concatenated to obtain multiple ConvNet layers representation because it will gain more information about an image. When three fully connected layer features concatenated, the occurring image representation would have 9192 (4096+4096+1000) dimension features. However, features extracted from multiple ConvNet layers are redundant and noisy since they are extracted from the same ConvNet. Thus, a third step, we will use Principal Component Analysis (PCA) to select salient features before the training phase. When salient features are obtained, the classifier can classify image more accurately, and the performance of transfer learning can be improved. To evaluate proposed method, experiments are conducted in three standard datasets (Caltech-256, VOC07, and SUN397) to compare multiple ConvNet layer representations against single ConvNet layer representation by using PCA for feature selection and dimension reduction. Our experiments demonstrated the importance of feature selection for multiple ConvNet layer representation. Moreover, our proposed approach achieved 75.6% accuracy compared to 73.9% accuracy achieved by FC7 layer on the Caltech-256 dataset, 73.1% accuracy compared to 69.2% accuracy achieved by FC8 layer on the VOC07 dataset, 52.2% accuracy compared to 48.7% accuracy achieved by FC7 layer on the SUN397 dataset. We also showed that our proposed approach achieved superior performance, 2.8%, 2.1% and 3.1% accuracy improvement on Caltech-256, VOC07, and SUN397 dataset respectively compare to existing work.