• Korean Journal of Environment and Ecology
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• v.37 no.2
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• pp.163-178
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• 2023

This study was conducted to understand the vegetation characteristics of Cheongwansan Provincial Park through the analysis of the plant community structure and to build data necessary for the continuous management and protection of Cheongwansan Provincial Park. The TWINSPAN and DCS analyses of the plant community structure of 63 survey districts in Cheongwansan Provincial Park identified eight colonies, including Cryptomeria japonica Community (I), Chamaecyparis obtusa-Pinus densiflora Commuity (II), P. rigida-P. densiflora Community (III), mixed coniferous and broad-leaved Community (IV), P. densiflora Community (V), deciduous broad-leaved such as Quercus spp. Community (VI), Q. mongolica-P. densiflora Community (VII) and P. thunbergii Community (VIII). The colonies can be grouped into afforestation communities (I, II, and III) dominated by C. obtusa, C. japonica, and P. rigida and natural forest communities (IV, V, VI, VII, and VIII) dominated by native species. Although Cheongwansan Provincial Park is a provincial park area that can represent natural ecosystems and landscapes, the rate of artificial forests is higher than that of other provincial parks. Most of the artificial forest communities are expected to maintain their current state, but since native species such as Machilus thunbergii, Neolitsea sericea, and deciduous broad-leaved, which are warm-temperate trees introduced through surrounding natural forests, appear in the lower layer, it is determined that it is possible to induce succession to natural forests suitable for climatic characteristics through management, and monitoring for continuous management is also necessary. Deciduous broad-leaved such as Quercus spp. Copete with P. densiflora in most natural forest communities. The vegetation series in the warm-temperate region of Korea appears to be in the early stages, and it is believed that the succession to Q. serrata or Q. mongolica, which appears next to coniferous in the series, is in progress. However, M. thunbergii and N. sericea, which appear in the middle stage of the succession in the warm-temperate region, have started to appear, and since Jangheung-gun belongs to the warm-temperate region considering the climate characteristics, the eventual succession to the warm-temperate forests dominated by evergreen broad-leaved is also expected. In this study, we built vegetation data from Cheongwansan Provincial Park, which lacks research on vegetation. However, since vegetation research in Cheongwansan Provincial Park is still insufficient, it is believed that further research should be continuously conducted to establish forest vegetation data and observe vegetation changes.

• Proceedings of the Korean Society of Crop Science Conference
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• 2023.04a
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• pp.7-7
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• 2023

Currently, the main technologies of various fourth industries are big data, the Internet of Things, artificial intelligence, blockchain, mixed reality (MR), and drones. In particular, "digital twin," which has recently become a global technological trend, is a concept of a virtual model that is expressed equally in physical objects and computers. By creating and simulating a Digital twin of software-virtualized assets instead of real physical assets, accurate information about the characteristics of real farming (current state, agricultural productivity, agricultural work scenarios, etc.) can be obtained. This study aims to streamline agricultural work through automatic water management, remote growth forecasting, drone control, and pest forecasting through the operation of an integrated control system by constructing digital twin data on the main production area of the nojinot industry and designing and building a smart farm complex. In addition, it aims to distribute digital environmental control agriculture in Korea that can reduce labor and improve crop productivity by minimizing environmental load through the use of appropriate amounts of fertilizers and pesticides through big data analysis. These open-field agricultural technologies can reduce labor through digital farming and cultivation management, optimize water use and prevent soil pollution in preparation for climate change, and quantitative growth management of open-field crops by securing digital data for the national cultivation environment. It is also a way to directly implement carbon-neutral RED++ activities by improving agricultural productivity. The analysis and prediction of growth status through the acquisition of the acquired high-precision and high-definition image-based crop growth data are very effective in digital farming work management. The Southern Crop Department of the National Institute of Food Science conducted research and development on various types of open-field agricultural smart farms such as underground point and underground drainage. In particular, from this year, commercialization is underway in earnest through the establishment of smart farm facilities and technology distribution for agricultural technology complexes across the country. In this study, we would like to describe the case of establishing the agricultural field that combines digital twin technology and open-field agricultural smart farm technology and future utilization plans.

• The Korean Journal of Nuclear Medicine
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• v.38 no.6
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• pp.486-491
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• 2004

Purpose: factor analysis and independent component analysis (ICA) has been used for handling dynamic image sequences. Theoretical advantages of a newly suggested ICA method, ensemble ICA, leaded us to consider applying this method to the analysis of dynamic myocardial $H_2^{15}O$ PET data. In this study, we quantified patients' blood flow using the ensemble ICA method. Materials and Methods: Twenty subjects underwent $H_2^{15}O$ PET scans using ECAT EXACT 47 scanner and myocardial perfusion SPECT using Vertex scanner. After transmission scanning, dynamic emission scans were initiated simultaneously with the injection of $555{\sim}740$ MBq $H_2^{15}O$. Hidden independent components can be extracted from the observed mixed data (PET image) by means of ICA algorithms. Ensemble learning is a variational Bayesian method that provides an analytical approximation to the parameter posterior using a tractable distribution. Variational approximation forms a lower bound on the ensemble likelihood and the maximization of the lower bound is achieved through minimizing the Kullback-Leibler divergence between the true posterior and the variational posterior. In this study, posterior pdf was approximated by a rectified Gaussian distribution to incorporate non-negativity constraint, which is suitable to dynamic images in nuclear medicine. Blood flow was measured in 9 regions - apex, four areas in mid wall, and four areas in base wall. Myocardial perfusion SPECT score and angiography results were compared with the regional blood flow. Results: Major cardiac components were separated successfully by the ensemble ICA method and blood flow could be estimated in 15 among 20 patients. Mean myocardial blood flow was $1.2{\pm}0.40$ ml/min/g in rest, $1.85{\pm}1.12$ ml/min/g in stress state. Blood flow values obtained by an operator in two different occasion were highly correlated (r=0.99). In myocardium component image, the image contrast between left ventricle and myocardium was 1:2.7 in average. Perfusion reserve was significantly different between the regions with and without stenosis detected by the coronary angiography (P<0.01). In 66 segment with stenosis confirmed by angiography, the segments with reversible perfusion decrease in perfusion SPECT showed lower perfusion reserve values in $H_2^{15}O$ PET. Conclusions: Myocardial blood flow could be estimated using an ICA method with ensemble learning. We suggest that the ensemble ICA incorporating non-negative constraint is a feasible method to handle dynamic image sequence obtained by the nuclear medicine techniques.