• Journal of the Korean Institute of Landscape Architecture
• /
• v.51 no.2
• /
• pp.12-27
• /
• 2023

In this study, among the construction-type parks in Gangdong-gu, targeting parks with high environmental and ecological value located on the urban green axis, a plan was prepared for the ecological renewal of urban parks, and a design that applied to them was proposed. The renewal target site was selected by analyzing the general condition of Gangdong-gu and urban parks, the land use and green area ratio, park green area, and the green axis of Gangdong-gu. Gangdong-gu has 54 parks, including 2 neighborhood parks and 52 children's parks. In the first stage of the current status review, 17 parks were extracted through locational value analysis, such as location and adjacency to the natural axis and green axis. In the second stage, eight parks were selected among the first-stage extraction parks based on the ratio of green spaces and open spaces within each park service area. In the third stage, two of the second stage extraction parks were selected based on whether the legal standard of the park area was met, and in the fourth stage, one of the third stage extraction parks was selected through an aging survey of the park. As for the urban ecological status of the renewal target site, the status of land use in the aspect of entropy reduction, the status of soil cover in the aspect of water circulation, and the status of planting structure in the aspect of biodiversity were investigated. As for the status of the three renewal sites, the green area was insufficient at 18.3-45.3%, and the facility area was 54.7%-81.7%, which was judged to have low urban temperature reduction effects. The impervious pavement area accounted for 34.5% to 48.9% of the park area, accounting for most of the facility area, and it was judged that the water circulation function was insufficient. The planting structure consisted of a single layer and a double layer structure, and although the tree layer was good, the lower vegetation was poor, and there was no planting site of edible plants or large hardwood trees, so the biodiversity was low. After the ecological renewal design of Seonrin Children's Park, Dangmal Children's Park, and Saemmul Children's Park, which were selected as the renewal targets in this study, the ecological area ratio of each park increased by 1.4 to 3 times than before the renewal. If the urban parks located on the urban green axis are examined from the perspective of the urban ecosystem and renewed ecologically, it is judged that the expected effect will be high in reducing entropy, improving water circulation, and laying the foundation for biodiversity in terms of the urban ecosystem.

• Journal of IKEEE
• /
• v.27 no.1
• /
• pp.116-121
• /
• 2023

In this paper, we develop a deep learning structure for a complex microbial incubator that applies deep learning prediction result information. The proposed complex microbial incubator consists of pre-processing of complex microbial data, conversion of complex microbial data structure, design of deep learning network, learning of the designed deep learning network, and GUI development applied to the prototype. In the complex microbial data preprocessing, one-hot encoding is performed on the amount of molasses, nutrients, plant extract, salt, etc. required for microbial culture, and the maximum-minimum normalization method for the pH concentration measured as a result of the culture and the number of microbial cells to preprocess the data. In the complex microbial data structure conversion, the preprocessed data is converted into a graph structure by connecting the water temperature and the number of microbial cells, and then expressed as an adjacency matrix and attribute information to be used as input data for a deep learning network. In deep learning network design, complex microbial data is learned by designing a graph convolutional network specialized for graph structures. The designed deep learning network uses a cosine loss function to proceed with learning in the direction of minimizing the error that occurs during learning. GUI development applied to the prototype shows the target pH concentration (3.8 or less) and the number of cells (10⁸ or more) of complex microorganisms in an order suitable for culturing according to the water temperature selected by the user. In order to evaluate the performance of the proposed microbial incubator, the results of experiments conducted by authorized testing institutes showed that the average pH was 3.7 and the number of cells of complex microorganisms was 1.7 × 10⁸. Therefore, the effectiveness of the deep learning structure for the complex microbial incubator applying the deep learning prediction result information proposed in this paper was proven.