A study on Perfume case (향(香) 집에 관한연구)
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- Journal of the Korean Society of Costume
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- v.33
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- pp.117-142
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- 1997
This study is on perfume case that is one of a great number of ornaments which was designed by out ancestors. We had developed the tradition and the history of perfume case bag fan-weight etc. These have the same function as the present perfume. case. There were basket-shaped perfume cases which were the smellest one among the personal girdle ornaments in the silla era. The various patterned perfume case were made of gold metal coral platinum or green jadeite etc. In the single-crop trinkets a embroidered perfume bag with the gold and silver thread whose forms perfectly match their functions appeared during the Yi Dynasty. There was also a perfume bag which was one of the daily necessities. A precious "jul perfume" was carried by noble women. A fan-weight perfume bag was attached to the fan to emphasize the intrinsic beauty of utility and function. It is necessary to know the function of prefume case. As perfume case is weared on the clothes it was given more decorative effects as well as the function of medicinal amulet with a sweet smell. Therefore it is very important for us to study perfume case that has various function as an ornament. So The purpose of this study is to investigate the practical and decorative side of perfume case with the general examines of perfume finding how to practice use through our life. The results were as follows; 1. The first use of perfume is perfume through smoke which is for ceremony of religion It removes human body odor by degrees and spreads a sweet odor. Also the materials for making perfume of early age is aromatic plants which will be used flour-made flowers stems. As the materials for making perfume use is increasing today we can invent alcohol perfume today 2. Our country the custom of perfume-used is wide. Among them men's perfume-used was very special phenomenon. For example men were wearing perfume bag in the Silla era. Because perfume represented wealth and noble in those days. They shew off social position personality through perfume-used. 3. One of early religion ceremony article there was the perfume. And perfume case was means for containing perfume. Gradually the perfume case was used widly as increasing needs of perfume in human life. 4. In the middle period of 'Koryo' Dynasty perfume cases had a close relationship with clothes but after Mongolian has been attacked 'Koryo' there were changes in wearing clothes therefore the position of perfume cases were transfered to coat string that was the origin of decoration style that they began. That is to say the perfume case has been influenced the position of perfume case shapes with changing of fashion. 5. The perfume case has been made manifest various function as an ornament. In the practical side First medical-perfume in perfume case has been played an important role in first-aid medicine in critical condition. Second it was amulet for self protection. That is the shape pattern color materials perfume of the perfume case was represented the amuletive nature. Third it was used as substitute article of perfume. Modern women use liquid-perfume as our ancestors used perfume case bag or jul perfume As started above. Also In the decorative side the perfume case has a beautiful formative arts by itself as well as a close relationship with clothes. That well as a close relationship with clothes. That is when the perfume case is worn on the clothes costume is showed aesthetices. That is the materials shapes color pattern of the perfume case we can see the visual beauty also the materials colors embroidered pattern knots tassel that are used the perfume case are increased the decorative beauty of costume. Sixth the symbol in pattern of the pattern case is shown ancetor's wealth and rank health longevity immortality many-born-boy in those days. Today the perfume case is not used with changing of costume by degrees, Accordingly I hope that the result of this study is an influened in devlopment of the perfume case design with matching the modern fashion.
Selecting high-quality information that meets the interests and needs of users among the overflowing contents is becoming more important as the generation continues. In the flood of information, efforts to reflect the intention of the user in the search result better are being tried, rather than recognizing the information request as a simple string. Also, large IT companies such as Google and Microsoft focus on developing knowledge-based technologies including search engines which provide users with satisfaction and convenience. Especially, the finance is one of the fields expected to have the usefulness and potential of text data analysis because it's constantly generating new information, and the earlier the information is, the more valuable it is. Automatic knowledge extraction can be effective in areas where information flow is vast, such as financial sector, and new information continues to emerge. However, there are several practical difficulties faced by automatic knowledge extraction. First, there are difficulties in making corpus from different fields with same algorithm, and it is difficult to extract good quality triple. Second, it becomes more difficult to produce labeled text data by people if the extent and scope of knowledge increases and patterns are constantly updated. Third, performance evaluation is difficult due to the characteristics of unsupervised learning. Finally, problem definition for automatic knowledge extraction is not easy because of ambiguous conceptual characteristics of knowledge. So, in order to overcome limits described above and improve the semantic performance of stock-related information searching, this study attempts to extract the knowledge entity by using neural tensor network and evaluate the performance of them. Different from other references, the purpose of this study is to extract knowledge entity which is related to individual stock items. Various but relatively simple data processing methods are applied in the presented model to solve the problems of previous researches and to enhance the effectiveness of the model. From these processes, this study has the following three significances. First, A practical and simple automatic knowledge extraction method that can be applied. Second, the possibility of performance evaluation is presented through simple problem definition. Finally, the expressiveness of the knowledge increased by generating input data on a sentence basis without complex morphological analysis. The results of the empirical analysis and objective performance evaluation method are also presented. The empirical study to confirm the usefulness of the presented model, experts' reports about individual 30 stocks which are top 30 items based on frequency of publication from May 30, 2017 to May 21, 2018 are used. the total number of reports are 5,600, and 3,074 reports, which accounts about 55% of the total, is designated as a training set, and other 45% of reports are designated as a testing set. Before constructing the model, all reports of a training set are classified by stocks, and their entities are extracted using named entity recognition tool which is the KKMA. for each stocks, top 100 entities based on appearance frequency are selected, and become vectorized using one-hot encoding. After that, by using neural tensor network, the same number of score functions as stocks are trained. Thus, if a new entity from a testing set appears, we can try to calculate the score by putting it into every single score function, and the stock of the function with the highest score is predicted as the related item with the entity. To evaluate presented models, we confirm prediction power and determining whether the score functions are well constructed by calculating hit ratio for all reports of testing set. As a result of the empirical study, the presented model shows 69.3% hit accuracy for testing set which consists of 2,526 reports. this hit ratio is meaningfully high despite of some constraints for conducting research. Looking at the prediction performance of the model for each stocks, only 3 stocks, which are LG ELECTRONICS, KiaMtr, and Mando, show extremely low performance than average. this result maybe due to the interference effect with other similar items and generation of new knowledge. In this paper, we propose a methodology to find out key entities or their combinations which are necessary to search related information in accordance with the user's investment intention. Graph data is generated by using only the named entity recognition tool and applied to the neural tensor network without learning corpus or word vectors for the field. From the empirical test, we confirm the effectiveness of the presented model as described above. However, there also exist some limits and things to complement. Representatively, the phenomenon that the model performance is especially bad for only some stocks shows the need for further researches. Finally, through the empirical study, we confirmed that the learning method presented in this study can be used for the purpose of matching the new text information semantically with the related stocks.