• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.1
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• pp.1-18
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• 2019

This research measured and visualized the spatial dependency and the spatial heterogeneity of the small business in Cheonan-si, Asan-si with $100m{\times}100m$ grids based on global and local spatial autocorrelation. First, we confirmed positive spatial autocorrelation of small business in the research area using Moran's I Index, which is ESDA(Exploratory Spatial Data Analysis). And then, through Getis-Ord $GI{\ast}$, one kind of LISA(Local Indicators of Spatial Association), local patterns of spatial autocorrelation were visualized. These verified that Spatial Regression Model is valid for the location factor analysis on small business commercial buildings. Next, GWR(Geographically Weighted Regression) was used to analyze the spatial relations between the distribution of small business, hourly mobile traffic-based floating population, land use attributes index, residence, commercial building, road networks, and the node of traffic networks. Final six variables were applied and the accessibility to bus stops, afternoon time floating population, and evening time floating population were excluded due to multicollinearity. By this, we demonstrated that GWR is statistically improved compared to OLS. We visualized the spatial influence of the individual variables using the regression coefficients and local coefficients of determinant of the six variables. This research applied the measured population information in a practical way. Reflecting the dynamic information of the urban people using the commercial area. It is different from other studies that performed commercial analysis. Finally, this research has a differentiated advantage over the existing commercial area analysis in that it employed hourly changing commercial service population data and it applied spatial statistical models to micro spatial units. This research proposed new framework for the commercial analysis area analysis.

• Korean Chemical Engineering Research
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• v.50 no.4
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• pp.666-671
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• 2012

Gas hydrates are inclusion compounds formed when small-sized guest molecules are incorporated into the well defined cages made up of hydrogen bonded water molecules. Since large masses of natural gas hydrates exist in permafrost regions or beneath deep oceans, these naturally occurring gas hydrates in the earth containing mostly $CH_4$ are regarded as future energy resources. The heat of dissociation is one of the most important thermal properties in exploiting natural gas hydrates. The accurate and direct method to measure the dissociation enthalpies of gas hydrates is to use a calorimeter. In this study, the high pressure micro DSC (Differential Scanning Calorimeter) was used to measure the dissociation enthalpies of methane, ethane, and propane hydrates. The accuracy and repeatability of the data obtained from the DSC was confirmed by measuring the dissociation enthalpy of ice. The dissociation enthalpies of methane, ethane, and propane hydrates were found to be 54.2, 73.8, and 127.7 kJ/mol-gas, respectively. For each gas hydrate, at given pressures the dissociation temperatures which were obtained in the process of enthalpy measurement were compared with three-phase (hydrate (H) - liquid water (Lw) - vapor (V)) equilibrium data in the literature and found to be in good agreement with literature values.

• Economic and Environmental Geology
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• v.38 no.6 s.175
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• pp.607-622
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• 2005

This study was carried out in order to evaluate where the soil loss was mainly occurred, .and to verify how riverbed sediments in the tributaries of the Seomjin River were related to their source rocks distributed in Sunchang area. The study area including the Seomjin River with 4 tributaries of Kyeongcheon, Okgwacheon, Changjeong-cheon and Ipcheon was divided into 10 watershed. The RUSLE (Revised Universal Soil Loss Equation) was estimated for all the grids (10 m cells) in the corresponding watershed. The amount of soil loss per unit area was calculated as follows: dry fold (53,140.94 tons/ha/year), orchard (25,063.38 tons/ha/year), paddy field (6,506.7 tons/ha/year) and Idlest (6,074.36 tons/ha/year). The differences of soil loss per unit area appear to be depends on areas described earlier. Soil erosion hazard zones were generally distributed within dry fields. Several thematic maps such as land use maps, topographical maps and soil maps were used as a data to generate the RUSLE factors. The amount of soil loss, computed by using the RUSLE, showed that soil loss mainly occurred at the regions where possible source rocks were distributed along the stream. Based on the this study on soil loss and soil erosion hazard zone together with chondrite-normalized REE patterns that were previously analyzed in same study area, a closed relationship between riverbed sediments and possible source rocks is formed. Especially in the Okgwacheon that are widely distributed by various rocks, chondrite-normalized REE pattern derived from the riverbed sediments, source rock and soil is expected to have a closed relationship with the distribution of soil loss.

• Proceedings of the Korean Vacuum Society Conference
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• 2011.08a
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• pp.134-135
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• 2011

양자점은 전자와 양공을 3차원으로 속박 시키므로 기존의 bulk나 양자우물보다 양자점을 이용한 레이저 다이오드의 경우 낮은 문턱 전류, 높은 미분이득 및 온도 안전성의 장점이 있을 거라 기대되고 있다. 그러나, 양자점은 낮은 areal coverage 때문에 높은 속박효율을 얻지 못하고 있다. 이러한 양자점의 문제점을 해결하기 위해 양자점을 양자우물 안에 성장시켜 운반자들의 포획을 향상시키는 방법들이 연구되고 있다. 양자우물 안에 양자점을 넣으면 양자우물이 운반자들의 포획을 증가 시키고, 열적 방출도 억제하여 온도 안정성이 향상 되는 것으로 알려져 있다. 광통신 대역의 1.3 ${\mu}m$ 경우, GaAs계를 이용하여 InAs 양자점을 strained InGaAs 박막을 우물층으로 한 dot-in-a-well 구조의 연구는 몇몇 보고된 바 있다. 그러나 InP계를 사용하는 1.55 ${\mu}m$ 대역에서 dot-in-a-well구조의 연구는 아직 미미하다. 본 연구에서는 유기 금속 화학 증착법(metal organic chemical vapor deposition)을 이용하여 InP 기판 위에 InAs 양자점을 자발성장법으로 성장하였으며 dot-in-a-well 구조에서 우물층으로 1.35 ${\mu}m$ 파장의 $In_{0.69}Ga_{0.31}As_{0.67}P_{0.33}$ (1.35Q)를, 장벽층으로는 1.1 ${\mu}m$ 파장의 $In_{0.85}Ga_{0.15}As_{0.32}P_{0.68}$(1.1Q)를 사용하였다. 양자우물층과 장벽층은 모두 InP 기판과 격자가 일치하는 조건으로 성장하였다. III족 원료로는 trimethylindium (TMI)와 trimethylgalium (TMGa)을 사용하였으며 V족 원료 가스로는 $PH_3$ 100%, $AsH_3$ 100%를, carrier gas로는 $H_2$를 사용하였다. InP buffer층의 성장 온도는 640$^{\circ}C$이며 양자점 성장 온도는 520$^{\circ}C$이다. 양자점 형성은 원자력간 현미경(Atomic force microscopy)를 이용하여 확인하였으며, 박막의 결정성은 쌍결정 회절분석(Double crystal x-ray deffractometry)를 이용하여 확인하였다. 확인된 성장 조건을 이용하여 양자점 시료를 성장하였으며 광여기분광법(Photoluminescence)을 이용하여 광특성을 분석하였다. Fig. 1은 dot in a barrier 와 dot-in-a-well 시료의 성장구조이다. Fig. 1(a)는 일반적인 dot-in-a-barrier 구조로 InP buffer층을 성장하고 1.1Q를 100 nm 성장한 후 양자점을 성장하였다. 그 후 1.1Q 100 nm와 InP 100 nm로 capping하였다. Fig. 1(b)는 dot-in-a-well 구조로 InP buffer층을 성장하고 1.1Q를 100 nm 성장 후 1.35Q 우물층을 4 nm 성장하였다. 그 위에 InAs 양자점을 성장하였다. 그 후에 1.35Q 우물층을 4 nm 성장하고 1.1Q 100 nm와 InP 100 nm로 capping하였다. Fig. 2는 dot-in-a-barrier 시료와 dot-in-a-well 시료의 상온 PL data이다. Dot-in-a-barrier 시료의 PL 파장은 1544 nm이며 반치폭은 79.70 meV이다. Dot-in-a-well 시료의 파장은 1546 nm이며 반치폭은 70.80 meV이다. 두 시료의 PL 파장 변화는 없으며, 반치폭은 dot-in-a-well 시료가 8.9 meV 감소하였다. Dot-in-a-well 시료의 PL peak 강도는 57% 증가하였으며 적분강도(integration intensity)는 45%가 증가하였다. PL 데이터에서 높은 에너지의 반치폭 변화는 없으며 낮은 에너지의 반치폭은 8 meV 감소하였다. 적분강도 증가에서 dot-in-a-well 구조가 dot-in-a-barrier 구조보다 전자-양공의 재결합이 증가한다는 것을 알 수 있으며, 반치폭 변화로부터 특히 높은 에너지를 갖는 작은 양자점에서의 재결합이 증가 된 것을 알 수 있다. 이는 양자우물이 장벽보다 전자-양공의 구속력을 증가시키기 때문에 양자점에 전자와 양공의 공급을 증가시키기 때문이다. 따라서 낮은 에너지를 가지는 양자점을 모두 채우고 높은 에너지를 가지는 양자점까지 채우게 되므로, 높은 에너지를 가지는 양자점에서의 전자-양공 재결합이 증가되었기 때문이다. 뿐만 아니라 파장 변화 없이 PL peak 강도와 적분강도가 증가하고 낮은 에너지 쪽의 반치폭이 감소한 것으로부터 에너지가 낮은 양자점보다는 에너지가 높은 양자점에서의 전자-양공 재결합율이 급증하였음을 알 수 있다. 우리는 이와 같은 연구에서 InP계를 이용해 1.55 ${\mu}m$에서도 dot in a well구조를 성장 하여 더 좋은 특성을 낼 수 있으며 앞으로 많은 연구가 필요할 것이라 생각한다.

• Journal of the Korean Chemical Society
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• v.66 no.2
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• pp.78-85
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• 2022

Natural white clay was treated with 6 M of H₂SO₄ and heated at 80℃ for 6 h under mechanical stirring and the resulting acid active clay was used as an adsorbent for the removal of Cs⁺ in water. The physicochemical changes of natural white clay and acid active clay were observed by X-ray Fluorescence Spectrometry (XRF), BET Surface Area Analyser and Energy Dispersive X-line Spectrometer (EDX). While activating natural white clay with acid, the part of Al₂O₃, CaO, MgO, SO₃ and Fe₂O₃ was dissolved firstly from the crystal lattice, which bring about the increase in the specific surface area and the pore volume as well as active sites. The specific surface area and the pore volume of acid active clay were roughly twice as high compared with natural white clay. The adsorption of Cs⁺ on acid active clay was increased rapidly within 1 min and reached equilibrium at 60 min. At 25 mg L^- of Cs⁺ concentration, 96.88% of adsorption capacity was accomplished by acid active clay. The adsorption data of Cs⁺ were fitted to the adsorption isotherm and kinetic models. It was found that Langmuir isotherm was described well to the adsorption behavior of Cs⁺ on acid active clay rather than Freundlich isotherm. For adsorption Cs⁺ on acid active clay, the Langmuir isotherm coefficients, Q, was found to be 10.52 mg g^-1. In acid active clay/water system, the pseudo-second-order kinetic model was more suitable for adsorption of Cs⁺ than the pseudo-first-order kinetic model owing to the higher correlation coefficient R² and the more proximity value of the experimental value q_e,exp and the calculated value q_e,cal. The overall results of study showed that acid active clay could be used as an efficient adsorbent for the removal of Cs⁺ from water.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1505-1514
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• 2022

Image matching is a crucial preprocessing step for effective utilization of multi-temporal and multi-sensor very high resolution (VHR) satellite images. Deep learning (DL) method which is attracting widespread interest has proven to be an efficient approach to measure the similarity between image pairs in quick and accurate manner by extracting complex and detailed features from satellite images. However, Image matching of VHR satellite images remains challenging due to limitations of DL models in which the results are depending on the quantity and quality of training dataset, as well as the difficulty of creating training dataset with VHR satellite images. Therefore, this study examines the feasibility of DL-based method in matching pair extraction which is the most time-consuming process during image registration. This paper also aims to analyze factors that affect the accuracy based on the configuration of training dataset, when developing training dataset from existing multi-sensor VHR image database with bias for DL-based image matching. For this purpose, the generated training dataset were composed of correct matching pairs and incorrect matching pairs by assigning true and false labels to image pairs extracted using a grid-based Scale Invariant Feature Transform (SIFT) algorithm for a total of 12 multi-temporal and multi-sensor VHR images. The Siamese convolutional neural network (SCNN), proposed for matching pair extraction on constructed training dataset, proceeds with model learning and measures similarities by passing two images in parallel to the two identical convolutional neural network structures. The results from this study confirm that data acquired from VHR satellite image database can be used as DL training dataset and indicate the potential to improve efficiency of the matching process by appropriate configuration of multi-sensor images. DL-based image matching techniques using multi-sensor VHR satellite images are expected to replace existing manual-based feature extraction methods based on its stable performance, thus further develop into an integrated DL-based image registration framework.

• Journal of Korea Water Resources Association
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• v.55 no.12
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• pp.1125-1135
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• 2022

Damages by heavy snow does not occur very often, but when it does, it causes damage to a wide area. To mitigate snow damage, it is necessary to know, in advance, the depth of snow that causes damage in each region. However, snow depths are measured at observatory locations, and it is difficult to understand the spatial distribution of snow depth that causes damage in a region. To understand the spatial distribution of snow depth, the point measurements are interpolated. However, estimating spatial distribution of snow depth is not easy when the number of measured snow depth is small and topographical characteristics such as altitude are not similar. To overcome this limit, satellite images such as Synthetic Aperture Radar (SAR) can be analyzed using Differential Interferometric SAR (DInSAR) method. DInSAR uses two different SAR images measured at two different times, and is generally used to track minor changes in topography. In this study, the spatial distribution of snow depth was estimated by DInSAR analysis using dual polarimetric IW mode C-band SAR data of Sentinel-1B satellite operated by the European Space Agency (ESA). In addition, snow depth was estimated using geostationary satellite Chollian-2 (GK-2A) to compare with the snow depth from DInSAR method. As a result, the accuracy of snow cover estimation in terms with grids was about 0.92% for DInSAR and about 0.71% for GK-2A, indicating high applicability of DInSAR method. Although there were cases of overestimation of the snow depth, sufficient information was provided for estimating the spatial distribution of the snow depth. And this will be helpful in understanding regional damage-causing snow depth.

• Journal of the Korean Association of Geographic Information Studies
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• v.27 no.2
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• pp.78-95
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• 2024

For the establishment and comparison of environmental plans across various domains, considering climate change and urban issues, it is crucial to build spatial data at the regional scale classified with consistent criteria. This study mapping the Local Climate Zone (LCZ) of Changwon City, where active climate and environmental research is being conducted, using the protocol suggested by the World Urban Database and Access Portal Tools (WUDAPT). Additionally, to address the fragmentation issue where some grids are classified with different climate characteristics despite being in regions with homogeneous climate traits, a filtering technique was applied, and the LCZ classification characteristics were compared according to the filtering radius. Using satellite images, ground reference data, and the supervised classification machine learning technique Random Forest, classification maps without filtering and with filtering radii of 1, 2, and 3 were produced, and their accuracies were compared. Furthermore, to compare the LCZ classification characteristics according to building types in urban areas, an urban form index used in GIS-based classification methodology was created and compared with the ranges suggested in previous studies. As a result, the overall accuracy was highest when the filtering radius was 1. When comparing the urban form index, the differences between LCZ types were minimal, and most satisfied the ranges of previous studies. However, the study identified a limitation in reflecting the height information of buildings, and it is believed that adding data to complement this would yield results with higher accuracy. The findings of this study can be used as reference material for creating fundamental spatial data for environmental research related to urban climates in South Korea.