• Korean Journal of Heritage: History & Science
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• v.56 no.2
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• pp.136-146
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• 2023

Biological materials, such as stuffed specimens, can release various acids or volatiles. There has been no research carried out on the emission characteristics of organic compounds generated from the preservatives used in taxidermy specimens or associated manufacturing materials and methods. Therefore, in order to identify the organic compounds generated from taxidermy specimens, a degradation experiment was conducted on specimens for each material and for storage specimens. To produce Ogye chicken specimens, naphthalene and borax were used as preservatives, and planer sawdust, newspaper, and polystyrene foam were used as the core body materials. The deterioration experiment was conducted for 2 weeks in a high-temperature environment(50℃) and a high-humidity environment (95%), with an Ogye chicken specimen (year 2015) kept in an animal storage facility. Results indicated that the concentration of organic compounds generated by the specimen in the high-temperature environment tended to be greater than that in the high-humidity environment. The preservatives benzene, toluene, xylene, and p-dichlorobenzene were detected in the specimens using naphthalene, confirming that naphthalene is a major organic compound release factor, and the specimens that used sawdust, newspaper, and polystyrene foam also exhibited organic compounds. This appears to have been due to degradation of the material. In addition, ammonia was detected in the specimens for each material due to decay. In particular, the specimens using borax at high temperature were subject to approximately 9 times higher rates of ammonia-related deterioration than the specimens using naphthalene. These results can be considered to result from the prevention of biological damage through insecticidal effects by accelerating the sublimation of naphthalene in a high-temperature environment. Naphthalene is a potentially carcinogenic substance, and when used as a preservative, proper use management is required. Taxidermy specimens can release various organic compounds depending on the manufacturing techniques used, so a systematic preservation management plan is required that depends on conditions such as the applicable manufacturing materials and preservatives.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.149-169
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• 2020

"The Urban Renewal New Deal project", one of the government's major national projects, is about developing underdeveloped areas by investing 50 trillion won in 100 locations on the first year and 500 over the next four years. This project is drawing keen attention from the media and local governments. However, the project model which fails to reflect the original characteristics of the area as it divides project area into five categories: "Our Neighborhood Restoration, Housing Maintenance Support Type, General Neighborhood Type, Central Urban Type, and Economic Base Type," According to keywords for successful urban regeneration in Korea, "resident participation," "regional specialization," "ministerial cooperation" and "public-private cooperation", when local governments propose urban regeneration projects to the government, they can see that it is most important to accurately understand the characteristics of the city and push ahead with the projects in a way that suits the characteristics of the city with the help of local residents and private companies. In addition, considering the gentrification problem, which is one of the side effects of urban regeneration projects, it is important to select and implement urban regeneration types suitable for the characteristics of the area. In order to supplement the limitations of the 'Urban Regeneration New Deal Project' methodology, this study aims to propose a system that recommends urban regeneration types suitable for urban regeneration sites by utilizing various machine learning algorithms, referring to the urban regeneration types of the '2025 Seoul Metropolitan Government Urban Regeneration Strategy Plan' promoted based on regional characteristics. There are four types of urban regeneration in Seoul: "Low-use Low-Level Development, Abandonment, Deteriorated Housing, and Specialization of Historical and Cultural Resources" (Shon and Park, 2017). In order to identify regional characteristics, approximately 100,000 text data were collected for 22 regions where the project was carried out for a total of four types of urban regeneration. Using the collected data, we drew key keywords for each region according to the type of urban regeneration and conducted topic modeling to explore whether there were differences between types. As a result, it was confirmed that a number of topics related to real estate and economy appeared in old residential areas, and in the case of declining and underdeveloped areas, topics reflecting the characteristics of areas where industrial activities were active in the past appeared. In the case of the historical and cultural resource area, since it is an area that contains traces of the past, many keywords related to the government appeared. Therefore, it was possible to confirm political topics and cultural topics resulting from various events. Finally, in the case of low-use and under-developed areas, many topics on real estate and accessibility are emerging, so accessibility is good. It mainly had the characteristics of a region where development is planned or is likely to be developed. Furthermore, a model was implemented that proposes urban regeneration types tailored to regional characteristics for regions other than Seoul. Machine learning technology was used to implement the model, and training data and test data were randomly extracted at an 8:2 ratio and used. In order to compare the performance between various models, the input variables are set in two ways: Count Vector and TF-IDF Vector, and as Classifier, there are 5 types of SVM (Support Vector Machine), Decision Tree, Random Forest, Logistic Regression, and Gradient Boosting. By applying it, performance comparison for a total of 10 models was conducted. The model with the highest performance was the Gradient Boosting method using TF-IDF Vector input data, and the accuracy was 97%. Therefore, the recommendation system proposed in this study is expected to recommend urban regeneration types based on the regional characteristics of new business sites in the process of carrying out urban regeneration projects."