• Journal of KIISE:Software and Applications
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• v.32 no.8
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• pp.730-741
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• 2005

Layered Architecture is a kind of nodule decomposition techniques, which decomposes a program by generality This paper proposes a ]aver based method for recovering layered architecture from object-oriented program and checking conformance against architectural document. To specify the rules for layered style in object-oriented program, we define a partially ordered set on modules by module use relationship and module layer relationship by module override relationship. The meaning of module layer relationship is explained with an example from design patterns. Steps to recover layered architecture from program are described and a metamodel for the recovery is proposed. Architecture recovery is performed on source codes from open-source software project, and the implication of parts that do not conform to its architectural document is discussed. As a result of checking, it is pointed out that, although the parts are considered allowable exceptions of layered architecture, their modifications should be controlled carefully.

• Korean Journal of Remote Sensing
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• v.35 no.2
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• pp.229-239
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• 2019

All vegetation colonies have layered structure. This layer is called 'forest vertical structure.' Nowadays it is considered as an important indicator to estimate forest's vital condition, diversity and environmental effect of forest. So forest vertical structure should be surveyed. However, vertical structure is a kind of inner structure, so forest surveys are generally conducted through field surveys, a traditional forest inventory method which costs plenty of time and budget. Therefore, in this study, we propose a useful method to classify the vertical structure of forests using remote sensing aerial photographs and machine learning capable of mass data mining in order to reduce time and budget for forest vertical structure investigation. We classified it as SVM (Support Vector Machine) using RGB airborne photos and LiDAR (Light Detection and Ranging) DSM (Digital Surface Model) DTM (Digital Terrain Model). Accuracy based on pixel count is 66.22% when compared to field survey results. It is concluded that classification accuracy of layer classification is relatively high for single-layer and multi-layer classification, but it was concluded that it is difficult in multi-layer classification. The results of this study are expected to further develop the field of machine learning research on vegetation structure by collecting various vegetation data and image data in the future.

• Korean Journal of Environment and Ecology
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• v.33 no.5
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• pp.587-595
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• 2019

This study analyzed the influence of the environmental factor of each vegetation type in an urban, mountainous park (Tangeumdae Park in Chungju) on air ion. The measuring points were divided according to the tree species, diameter at breast height, crown density, and layered structure, and the meteorological factors and the air ion were measured. The results of the measurement showed the average generation of positive ions of $610.90{\pm}50.27ea/cm^3$, the average generation of negative ions of $723.58{\pm}64.25ea/cm^3$, and the air ion index of $1.19{\pm}0.10$. The results of the analysis, according to the vegetation type, are as follows. Firstly, the air ion varied according to the species, the chest diameter at breast height, and the layered structure, and was analyzed to be statistically significant. Secondly, the air ion and the vegetation type showed a positive correlation with the species, diameter at breast height, crown density, and layered structure. The cation showed a negative correlation with the species, diameter at breast height, and the crown density, and the anion showed a positive correlation with the species, the diameter at breast height, crown density, and layered structure. Thirdly, the ion index in ridges had a higher correlation with the vegetation type than the meteorological factors. In detail, the correlation was higher in the species > layered structure > crown density > diameter at breast height. This study had the limitation of evaluating air ions in the ridge. Therefore, future studies on air ion should consider both terrain structure and vegetation type and analyze the seasonal changes and comparison.

• Korean Journal of Remote Sensing
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• v.35 no.3
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• pp.447-455
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• 2019

Since the forest type map in Korea has been mostly constructed every five years, the forest information from the map lacks up-to-date information. Forest research has been carried out by aerial photogrammetry and field surveys, and hence it took a lot of times and money. The vertical structure of forests is an important factor in evaluating forest diversity and environment. The vertical structure is essential information, but the observation of the vertical structure is not easy because the vertical structure indicates the internal structure of forests. In this study, the index map and texture map produced from KOMPSAT-3/3A/5 satellite images and the canopy information generated by the difference between DSM (Digital Surface Model) and DTM (Digital Terrain Model) were classified using the artificial neural network. The vertical structure of forests of single and multi-layer forests was classified to identify 81.59% of the final classification result.

• Proceedings of the Korean Society of Environment and Ecology Conference
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• 2009.10a
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• pp.155-157
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• 2009

• Korean Journal of Environment and Ecology
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• v.14 no.4
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• pp.238-251
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• 2001

본 연구는 계룡산국립공원 동학사 계곡의 식물군집구조를 파악하기 위하여 실시되었으며, 10cm$\times$10m(100$\m^2$) 조사구를 52개 설정하여 식생조사를 실시하였다. 52개 조사구는 TWINSPAN에 의한 classifi-cation 분석과 DCA ordination 분석을 통하여 소나무군집(16개 조사구), 밤나무군집(4개 조사구), 신갈나무군집(5개 조사구), 졸참나무군집(13개 조사구), 느티나무군집(4개 군집), 서어나무군집(10개 조사구)으로 나누어졌다. 6개 군집의 천이경향을 살펴보면 소나무군집, 신갈나무군집, 졸참나무군집,느티나무군집은 각 층위별 안정된 층위구조로 현상태를 유지할 것이며, 밤나무구닙은 참나무류를 거쳐 서어나무로의 천이가 진행될 것으로 판단되었고, 서어나무군집은 졸참나무와 굴참나무군집으로 퇴행천이될 것이다. 6개 군집의 Shannon의 종다양도지수는 1.2732~1.4699이었다.

• Proceedings of the Korean Society for Information Management Conference
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• 2004.08a
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• pp.209-212
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• 2004

이글은 인터넷기반 정보시스템에서 XML의 존재 층위를 나누고 각 층위에 적합한 XML과 RDB의 매핑 모델을 제안했다. 그리고 자료생성 층위에 주목하여 XML과 RDB의 매핑 모델을 설계하고 그 절차에 대해 논의했다. 이 연구에서 제안한 XML과 RDB 매핑 모델은 DTD 구조에 유연하며 플랫폼 독립적인 점에서 정보시스템 구축의 비용을 저렴하게 한다.

• Korean Journal of Environment and Ecology
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• v.26 no.5
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• pp.801-811
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• 2012

The purpose of this study is to find out how land cover and planting of an urban park influence temperature. Field research on the land cover and planting status was conducted for Bundang Central Park in Sungnam-si. 30 study plots in the site were selected to closely analyze land cover type and planting structure. The temperature was measured 10 times for each plot. Land coverage type, planting type, planting layer structure and green space area (the ratio of green coverage, GVZ) were chosen as factors impacting temperature and statistics were analyzed for the actual temperature measured. Analysis on how the land coverage type influences temperature showed that planting site had a low temperature and that grassland and paved land had a high temperature. When it comes to planting type, the temperature at the land planted with conifers and broad-leaved trees was low, while the temperature at grassland and paved land was high. With regard to planting layer structure, canopy and canopy-underplanting type showed low temperature, while grassland and paved land showed high temperature. An analysis on the relation between green space area and temperature found out that both ratio of green coverage and GVZ had a high level of negative correlation with the temperature measured. According to regression model of green space area and the temperature measured, for every 1% increase in the ratio of green coverage, temperature is expected to lower by $0.002^{\circ}C$. Also, for every $1m^3/m^2$ increase in GVZ, temperature is expected to go down by $0.122^{\circ}C$.

• Korean Journal of Remote Sensing
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• v.36 no.5_1
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• pp.653-666
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• 2020

Understanding the vegetation structure is important to manage forest resources for sustainable forest development. With the recent development of technology, it is possible to apply new technologies such as drones and deep learning to forests and use it to estimate the vegetation structure. In this study, the vegetation structure of Gongju, Samchuk, and Seoguipo area was identified by fusion of drone-optical images and LiDAR data using Artificial Neural Networks(ANNs) with the accuracy of 92.62% (Kappa value: 0.59), 91.57% (Kappa value: 0.53), and 86.00% (Kappa value: 0.63), respectively. The vegetation structure analysis technology using deep learning is expected to increase the performance of the model as the amount of information in the optical and LiDAR increases. In the future, if the model is developed with a high-complexity that can reflect various characteristics of vegetation and sufficient sampling, it would be a material that can be used as a reference data to Korea's policies and regulations by constructing a country-level vegetation structure map.

• Korean Journal of Environment and Ecology
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• v.26 no.5
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• pp.719-729
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• 2012

Baekdudan has 670km long within South Korea, so the study for detail vegetation characteristics is needed. In this study, we surveyed the plant community structure from Daetjae to Baekbongryung for the next restoration and management plan. We designated 40 quadrats ($10m{\times}10m(100m^2)$ for this study. As a result of TWINSPAN, plant community separated 8 different communities such as Abies koreana comm., Pinus densiflora comm., Quercus mongolica comm. and Larix kaempferi comm. etc. Abies koreana comm. and Pinus densiflora comm. which is mainly located in the mountain ridge and near rocks are needed avoidance from the competition with Quercus mongolica comm. The possibility of atrophy of these communities is to be high, the protection is needed. Species diversity index was between 0.8046~1.1283. Most communities have multi-layer structure and have the ecological value of protection.