• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.40 no.1
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• pp.26-42
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• 2022

The purpose of this study is to examine and understand the area and the original outer boundaries of Hamchunwon(含春苑), which was the outer royal garden of Gyeonghuigung Palace, which existed before the site of the Russian legation. The results of the study are as follows. First, examining the 3 types of drawings prepared for securing the Russian legation's site and constructing a new building, it was confirmed that two low peaks, which appear to be the original terrain of Hamchunwon, existed in the north and south directions inside the site. According to the initial plan of the of the legation's site, it appears that the entrance of the legation building is connected to the Saemunan-ro in the northwest. However, according to the report made at the time when the Russian temporary minister Veber purchased the legation's site, it was recorded that the site already had a narrow entrance and a dirt road in place, and hence, it was connected to Saemunan-ro. This fact makes it possible to learn that the line of movement for officials and the original gate were located to the northwest of the site planned as the entrance of the legation building towards Hamchunwon. Second, the site was created by cutting the top of the high hill at the time of the construction of the legation building, and as a result, a two tiered staircase typed terrace was built. The ground on which the main building and the secretary's building, etc., were erected was made by cutting the highest peak and solidifying it flat, and a large quantity of soil was used for grading. In the case of the northern area of the main building, the traces of leveling the terrain by cutting the mountains are apparent, and an observation typed garden with a walking path and pavilion was formed by utilizing the physical environment equipped with an easy view. This may be considered as a use which is consistent with the topographical conditions of creating an outer royal garden to block the civilian views on a high terrain overlooking the palace. Third, Hamchunwon's fences were partially exposed in the photos from the 1880s through the 1890s, which demonstrate the spatial changes made around the US, UK, and the Russian legations. As a result of the photo analysis performed, Hamchunwon occupies the northern area of the Russian legation's site, and it is estimated that the north, west, and east walls of the legation resembled those of Hamchunwon. The area to the south of the Russian legation was originally a place made available for civilian houses, and it was possible to examine the circumstances of purchasing dozens of civilian houses and farmlands according to various materials. Fourth, Hamchunwon, which was formed as the outer royal garden of Gyeongdeokgung Palace of Lord Gwanghaegun, lost its sense of place as an outer royal garden when the entire building of Gyeonghuigung Palace was torn down and used as a construction members during the reconstruction of Gyeongbokgung Palace, and faded away as the site was sold to Russia around 1885. The area where Hamchunwon used to be located transformed into a core space of the Russian legation where the main building and garden were located after the construction of the new building. Hence, Hamchunwon, which was limited to the northern area of the Russian legation, does not carry the temporal and spatial context with Gyeongungung Palace and Seonwonjeon which were constructed after 1897, and it is determined that the view of Seonwonjeon as Baehoorim or Baegyeongrim is not valid.

• Journal of dental hygiene science
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• v.10 no.1
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• pp.17-24
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• 2010

The purpose of this study was to provide the basic data for effective intervention of oral health behaviors strategy and to compare the actual conditions about hypertension and diabetes case management of the elderly at the hall for the aged and the D senior's college. The research method was a questionnaire including hypertension and diabetes case management of the elderly and the subjects were 174 of the elderly(65 age over) at the hall for the aged(100) and the senior's college(74). The results of this study were as follows; 1. Hypertension 1)The incidence of hypertension of elderly at the hall for the aged and the senior's college were 32.2%. 2)83.9% of the hypertension cases were initially diagnosed during hospital examination(p < 0.05). 3)Regular blood pressure checks were performed more than one time monthly on 76.8% of the cases(p < 0.05). 4)Blood pressure control was well controlled on 75%(p < 0.05). 5)85.7% of the elderly at the hall for the aged took hypertension drugs daily and 42.9% of the elderly at the senior's college took no drug alternatively(p < 0.05). 2. Diabetes 1)The incidence of the diabetes of elderly at the hall for the aged and the senior's college were 14.4%. 2)80.0% of the diabetes cases were initially diagnosed during hospital examination(p < 0.05). 3)64.0% of the cases did not have blood sugar measuring instrument(p < 0.05). 4. In the quality of life, the thinking of no difficulty in walking and no anxiety/depression was more presented on the elderly at the senior's college than those at the hall for the aged(p < 0.05). 5. The subjective health condition scores were higher on the elderly at the senior's college than those at the hall for the aged(p < 0.05).

• 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.