• Journal of Ecology and Environment
• /
• v.39 no.1
• /
• pp.31-42
• /
• 2016

The object of this study was to determine long-term temporal and spatial patterns of nutrients (nitrogen and phosphorus), suspended solids, and chlorophyll (Chl) in Chungju Reservoir, based on the dataset of 1992 - 2013, and then to develop the empirical models of nutrient-Chl for predicting the eutrophication of the reservoir. Concentrations of total nitrogen (TN) and total phosphorus (TP) were largely affected by an intensity of Asian monsoon and the longitudinal structure of riverine (Rz), transition (Tz), and lacustrine zone (Lz). This system was nitrogen-rich system and phosphorus contents in the water were relatively low, implying a P-limiting system. Regression analysis for empirical model, however, showed that Chl had a weak linear relation with TP or TN, and this was mainly associated with turbid, and nutrient-rich inflows in the system. The weak relation was associated with non-algal light attenuation coefficients (Kna), which is inversely related water residence time. Thus, values of Chl had negative functional relation (R² = 0.25, p < 0.001) with nonalgal light attenuation. Thus, the low chlorophyll at a given TP indicated a light-limiting for phytoplankton growth and total suspended solids (TSS) was highly correlated (R² = 0.94, p < 0.001) with non-algal light attenuation. The relations of Trophic State Index (TSI) indicated that phosphorus limitation was weak [TSI (Chl) - TSI (TP) < 0; TSI (SD) - TSI (Chl) > 0] and the effects of zooplankton grazing were also minor [TSI (Chl) - TSI (TP) > 0; TSI (SD) - TSI (Chl) > 0].

• Journal of Korean Society for Geospatial Information Science
• /
• v.15 no.1 s.39
• /
• pp.61-66
• /
• 2007

The final object of this research is to construct general management system that can manage diverse facilities of city on the basis of ubiquitous technology. Defining data format of items in each management, type and code for the urban facilities, the researcher designed database of urban facilities through the process of establishing logic model and data modelling. By inputting designed database into RFID Tag of each urban facilities and using RFID Reader and PDA, the researcher developed system that can efficiently manage the basic attributes and information and management information of urban facilities. Applying the general management system for urban facilities constructed on the basis of ubiquitous, the researcher could check the information of target facilities and input revised data. Further, it was found that the characteristics of RFID Tag and GPS should improve the optimal hardware combination and PDA performance for the surrounding environmental influence and system performance.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.19 no.2
• /
• pp.137-146
• /
• 2001

Route selection needs the analysis function of GSIS to analyze and manipulate a lot of spatial information efficiently. Therefore, it needs the linkage of system requiring the knowledge and the experience of experts as a method that can estimate each quantitative route for an efficient route selection. In this study, the route selection model through construction and analysis procedure of position information using GSIS were presented, and route selection system linked with expert system was developed. This system is easy to be used and managed for presenting route alignment according to conditions as a graphic user interface environmental window system by applying three tiers based object-oriented method. Using GSIS, the various information required for route selections in database was constructed, the characteristics of subject area by executing three-dimensional terrain analysis was grasped effectively, and the control point through buffering, overlay and location operation was extracted. Three alternative routes between a beginning point and an end point inputted by route selection system were selected. Therefore, the applications of the route selection system are presented by applying this system to the real study area.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2015.05a
• /
• pp.626-626
• /
• 2015

본 연구에서는 하천공간정보의 웹서비스를 위해 SOAP(Simple Object Access Protocol) API 및 REST(Representation State Transfer) API로 제공하는 HydroG-OneFlow 웹서비스를 개발하였다. HydroG-OneFlow는 GML 기반의 서비스를 제공하며 GetBasin, GetGeoVariable 및 GetData 등의 기본서비스로 구성된다. GML은 GIS S/W의 벡터 GML 포맷과 공간정보 오픈플랫폼 서비스인 브이월드 데이터 API에서 제공하는 GML 포맷을 참고하여 하천공간 벡터정보를 제공할 수 있도록 GML을 구성하였다. GDM 공간 데이터에 대한 벡터정보 ML 수용 수준을 향상시킬 수 있도록 벡터구조의 점, 선, 면 정보에 대하여 GML의 PointPropertyType, CurvePropertyType, SurfacePropertyType을 도입하였다. 또한 일반적인 공간자료에서는 Multi 객체에 대한 지원도 필요하다. 현 GDM 데이터베이스에서도 OGC 표준의 MultiPoint, MultiLineString, MultiPolygon을 지원하고 있다. 이를 위하여 GML의 상응 요소인MultiPointPropertyType, MultiCurvePropertyType, MultiSurfacePropertyType을 하천공간정보 벡터 스키마에 도입하여 활용하였다. 클라이언트 서버 통신은 메시지 교환프로토콜인 SOAP을 사용하여 서버의 객체를 직접 호출하여 이루어진다. 서버는 서버의 제공 서비스를 WSDL(Web Service Description Language)를 통하여 게시하고 클라이언트는 이 기준(Criteria)을 참고하여 접근한다. GetData의 경우 Type(GRID or VECTOR), GDM(Geospatial Data Model) 여부(true or false), LayerName, BasinID, GenTime을 인자로 받아 GeoData에서 검색된 정보를 반환한다. SOAP버전은 1.1과 1.2를 지원하여 접근하는 클라이언트에서 선택할 수 있도록 개발하였다.

• International Journal of Computer Science & Network Security
• /
• v.22 no.4
• /
• pp.193-202
• /
• 2022

The classification of hyperspectral imagery (HSI) is essential in the surface of earth observation. Due to the continuous large number of bands, HSI data provide rich information about the object of study; however, it suffers from the curse of dimensionality. Dimensionality reduction is an essential aspect of Machine learning classification. The algorithms based on feature extraction can overcome the data dimensionality issue, thereby allowing the classifiers to utilize comprehensive models to reduce computational costs. This paper assesses and compares two HSI classification techniques. The first is based on the Joint Spatial-Spectral Stacked Autoencoder (JSSSA) method, the second is based on a shallow Artificial Neural Network (SNN), and the third is used the SVM model. The performance of the JSSSA technique is better than the SNN classification technique based on the overall accuracy and Kappa coefficient values. We observed that the JSSSA based method surpasses the SNN technique with an overall accuracy of 96.13% and Kappa coefficient value of 0.95. SNN also achieved a good accuracy of 92.40% and a Kappa coefficient value of 0.90, and SVM achieved an accuracy of 82.87%. The current study suggests that both JSSSA and SNN based techniques prove to be efficient methods for hyperspectral classification of snow features. This work classified the labeled/ground-truth datasets of snow in multiple classes. The labeled/ground-truth data can be valuable for applying deep neural networks such as CNN, hybrid CNN, RNN for glaciology, and snow-related hazard applications.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.24 no.4
• /
• pp.127-135
• /
• 2021

The purpose of this research is to present about a series of processes for 3D model generation from scan data of two types of Korean styled architectures, namely, a pavilion and a house, which were acquired with the terrestrial LiDAR and evaluate a 3D surveying method to document digitally the traditional buildings, cultural properties, archeological sites, etc. Since most ancient buildings and cultural assets which require digital documentation by the terrestrial laser scanner usually need to acquire data from multi-directions. Therefore this paper suggested a process of acquiring and integrating data from mult-stations around the object. Also we presented a way for reconstructing automatically at once both the interior and exterior surfaces of buildings from laser scan data.

• Journal of Internet of Things and Convergence
• /
• v.10 no.2
• /
• pp.25-41
• /
• 2024

For the scenario of open pit mining, at present, manual periodic verification is mainly carried out in China with the help of video surveillance, which requires continuous investment in labor cost and has poor timeliness. In order to solve this difficult problem of early warning and monitoring, this paper researches a spatialized algorithmic model and designs an early warning system for open-pit mine transboundary mining, which is realized by calculating the coordinate information of the mining and extracting equipments and comparing it with the layer coordinates of the approval range of the mines in real time, so as to realize the determination of the transboundary mining behavior of the mines. By taking the Pingxiang area of Jiangxi Province as the research object, after the field experiment, it shows that the system runs stably and reliably, and verifies that the target tracking accuracy of the system is high, which can effectively improve the early warning capability of the open-pit mines' overstepping the boundary, improve the timeliness and accuracy of mine supervision, and reduce the supervision cost.

• Journal of Korean Society for Geospatial Information Science
• /
• v.21 no.2
• /
• pp.27-34
• /
• 2013

Due to the increased interest in global warming, interest in forest resources aimed towards reducing greenhouse gases have subsequently increased. Thus far, data related to forest resources have been obtained, through the employment of aerial photographs or satellite images, by means of plotting. However, the use of imaging data is disadvantageous; merely, due to the fact that recorded measurements such as the height of trees, in dense forest areas, lack accuracy. Within such context, the authors of this study have presented a method of data processing in which an individual tree is isolated within forested areas through the use of LIDAR data and ortho-images. Such isolation resulted in the provision of more efficient and accurate data in regards to the height of trees. As for the data processing of LIDAR, the authors have generated a normalized digital surface model to extract tree points via local maxima filtering, and have additionally, with motives to extract forest areas, applied object oriented image classifications to the processing of data using ortho-images. The final tree point was then given a figure derived from the combination of LIDAR and ortho-images results. Based from an experiment conducted in the Yongin area, the authors have analyzed the merits and demerits of methods that either employ LIDAR data or ortho-images and have thereby obtained information of individual trees within forested areas by combining the two data; thus verifying the efficiency of the above presented method.

• Nutrition Research and Practice
• /
• v.12 no.3
• /
• pp.191-198
• /
• 2018

BACKGROUD/OBJECTIVES: Neuroinflammation plays critical role in neurodegenerative disorders, such as Alzheimer's disease (AD). We investigated the effect of three licorice varieties, Glycyrhiza uralensis, G. glabra, and Shinwongam (SW) on a mouse model of inflammation-induced memory and cognitive deficit. MATERIALS/METHODS: C57BL/6 mice were injected with lipopolysaccharide (LPS; 2.5 mg/kg, intraperitoneally) and orally administrated G. uralensis, G. glabra, and SW extract (150 mg/kg/day). SW, a new species of licorice in Korea, was combined with G. uralensis and G. glabra. Behavioral tests, including the T-maze, novel object recognition and Morris water maze, were carried out to assess learning and memory. In addition, the expressions of inflammation-related proteins in brain tissue were measured by western blotting. RESULTS: There was a significant decrease in spatial and objective recognition memory in LPS-induced cognitive impairment group, as measured by the T-maze and novel object recognition test; however, the administration of licorice ameliorated these deficits. In addition, licorice-treated groups exhibited improved learning and memory ability in the Morris water maze. Furthermore, LPS-injected mice had up-regulated pro-inflammatory proteins, such as inducible nitric oxide synthase (iNOS), cyclooxygenase-2, interleukin-6, via activation of toll like receptor 4 (TLR4) and nuclear factor-kappa B ($NF{\kappa}B$) pathways in the brain. However, these were attenuated by following administration of the three licorice varieties. Interestingly, the SW-administered group showed greater inhibition of iNOS and TLR4 when compared with the other licorice varieties. Furthermore, there was a significant increase in the expression of brain-derived neurotrophic factor (BDNF) in the brain of LPS-induced cognitively impaired mice that were administered licorice, with the greatest effect following SW treatment. CONCLUSIONS: The three licorice varieties ameliorated the inflammation-induced cognitive dysfunction by down-regulating inflammatory proteins and up-regulating BDNF. These results suggest that licorice, in particular SW, could be potential therapeutic agents against cognitive impairment.

• Korean Journal of Remote Sensing
• /
• v.38 no.6_1
• /
• pp.991-1005
• /
• 2022

Satellite imaging is an effective supplementary data source for detecting and verifying nuclear activity. It is also highly beneficial in regions with limited access and information, such as nuclear installations. Time series analysis, in particular, can identify the process of preparing for the conduction of a nuclear experiment, such as relocating equipment or changing facilities. Differences in the semantic segmentation findings of time series photos were employed in this work to detect changes in meaningful items connected to nuclear activity. Building, road, and small object datasets made of KOMPSAT 3/3A photos given by AIHub were used to train deep learning models such as U-Net, PSPNet, and Attention U-Net. To pick relevant models for targets, many model parameters were adjusted. The final change detection was carried out by including object information into the first change detection, which was obtained as the difference in semantic segmentation findings. The experiment findings demonstrated that the suggested approach could effectively identify altered pixels. Although the suggested approach is dependent on the accuracy of semantic segmentation findings, it is envisaged that as the dataset for the region of interest grows in the future, so will the relevant scope of the proposed method.