• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.8
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• pp.1005-1012
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• 2021

Recently, the use of various location-based services-based location information systems using maps on the web has been expanding, and there is a need for a monitoring system that can check power demand in real time as an alternative to energy saving. In this study, we developed a deep learning real-time virtual power demand prediction web system using open source-based mapping service to analyze and predict the characteristics of power demand data using deep learning. In particular, the proposed system uses the LSTM(Long Short-Term Memory) deep learning model to enable power demand and predictive analysis locally, and provides visualization of analyzed information. Future proposed systems will not only be utilized to identify and analyze the supply and demand and forecast status of energy by region, but also apply to other industrial energies.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.6
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• pp.381-392
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• 2021

In this study, we perform deep learning-based object detection analysis on eight types of buildings defined by the digital map topography standard code, leveraging images taken with UAV (Unmanned Aerial Vehicle). Image labeling was done for 509 images taken by UAVs and the YOLO (You Only Look Once) v5 model was applied to proceed with learning and inference. For experiments and analysis, data were analyzed by applying an open source-based analysis platform and algorithm, and as a result of the analysis, building objects were detected with a prediction probability of 88% to 98%. In addition, the learning method and model construction method necessary for the high accuracy of building object detection in the process of constructing and repetitive learning of training data were analyzed, and a method of applying the learned model to other images was sought. Through this study, a model in which high-efficiency deep neural networks and spatial information data are fused will be proposed, and the fusion of spatial information data and deep learning technology will provide a lot of help in improving the efficiency, analysis and prediction of spatial information data construction in the future.

• Journal of Advanced Technology Convergence
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• v.1 no.1
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• pp.1-6
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• 2022

Since 2015, the government has been making efforts to distribute Korean smart farms. However, the supply is limited to large-scale facility vegetable farms due to the limitations of technology and current cultivation research data. In addition, the efficiency and reliability compared to the introduction cost are low due to the simple application of IT technology that does not consider the crop growth and cultivation environment. Therefore, in this paper, data analysis services was performed based on public and external data. To this end, a data-based target smart farm system was designed that is suitable for the situation of farms growing in facilities. To this end, a farm risk information notification service was developed. In addition, light environment maps were provided for proper fertilization. Finally, a disease prediction model for each cultivation crop was designed using temperature and humidity information of facility farms. Through this, it was possible to implement a smart farm data service by linking and utilizing existing smart farm sensor data. In addition, economic efficiency and data reliability can be secured for data utilization.

• Journal of Advanced Navigation Technology
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• v.27 no.4
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• pp.488-494
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• 2023

In this paper, scenario-based urban flood prediction for the entire Jinju city was performed, and a simulation domain was constructed using G2D as a 2-dimensional urban flood analysis model. The domain configuration is DEM, and the land cover map is used to set the roughness coefficient for each grid. The input data of the model are water level, water depth and flow rate. In the simulation of the built G2D model, virtual rainfall (3 mm/10 min rainfall given to all grids for 5 hours) and virtual flow were applied. And, a GPU acceleration technique was applied to determine whether to run the flood analysis model in the target area. As a result of the simulation, it was confirmed that the high-resolution flood analysis time was significantly shortened and the flood depth for visual flood judgment could be created for each simulation time.

• Journal of the Korea Society of Computer and Information
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• v.14 no.11
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• pp.159-167
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• 2009

The vehicles play a significant role in modern people's life as economy grows. The development of car navigation system(CNS) provides various convenience because it shows the driver where they are and how to get to the destination from the point of source. However, the existing map-based CNS does not consider any environments such as traffic congestion. Given the same starting point and destination, the system always provides the same route and the required time. This paper proposes a path planning method with traffic prediction by applying historical driving information to the Fuzzy theory and Bayesian update. Fuzzy theory classifies the historical driving information into groups of leaving time and speed rate, and the traffic condition of each time zone is calculated by Bayesian update. An ellipse area including starting and destination points is restricted in order to reduce the calculation time. The accuracy and practicality of the proposed scheme are verified by several experiments and comparisons with real navigation.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• v.4 no.4
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• pp.159-176
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• 2023

The conservation of the raccoon dog (Nyctereutes procyonoides) in South Korea requires the protection and preservation of natural habitats while additionally ensuring coexistence with human activities. Applying habitat map modeling techniques provides information regarding the distributional patterns of raccoon dogs and assists in the development of future conservation strategies. The purpose of this study is to generate potential habitat distribution maps for the raccoon dog in South Korea using geospatial technology-based models. These models include the frequency ratio (FR) as a bivariate statistical approach, the group method of data handling (GMDH) as a machine learning algorithm, and convolutional neural network (CNN) and long short-term memory (LSTM) as deep learning algorithms. Moreover, the imperialist competitive algorithm (ICA) is used to fine-tune the hyperparameters of the machine learning and deep learning models. Moreover, there are 14 habitat characteristics used for developing the models: elevation, slope, valley depth, topographic wetness index, terrain roughness index, slope height, surface area, slope length and steepness factor (LS factor), normalized difference vegetation index, normalized difference water index, distance to drainage, distance to roads, drainage density, and morphometric features. The accuracy of prediction is evaluated using the area under the receiver operating characteristic curve. The results indicate comparable performances of all models. However, the CNN demonstrates superior capacity for prediction, achieving accuracies of 76.3% and 75.7% for the training and validation processes, respectively. The maps of potential habitat distribution are generated for five different levels of potentiality: very low, low, moderate, high, and very high.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.19 no.5
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• pp.492-501
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• 2007

Coastal disasters have become one of the most important issues in every coastal country. In Korea, coastal disasters such as storm surge, sea level rise and extreme weather have placed many coastal regions in danger of being exposed or damaged during subsequent storms and gradual shoreline retreat. A storm surge is an onshore gush of water associated with a tow pressure weather system, typically in typhoon season. However, it is very difficult to predict storm surge height and inundation due to the irregularity of the course and intensity of a typhoon. To provide a new scheme of typhoon damage prediction model, the scenario which changes the central pressure, the maximum wind radius, the track and the proceeding speed by corresponding previous typhoon database, was composed. The virtual typhoon scenario database was constructed with individual scenario simulation and evaluation, in which it extracted the result from the scenario database of information of the hereafter typhoon and information due to climate change. This virtual typhoon scenario database will apply damage prediction information about a typhoon. This study performed construction and analysis of the simulation system with the storm surge/coastal inundation model at Masan coastal areas, and applied method for predicting using the scenario of the storm surge.

• Journal of the Korean Association of Geographic Information Studies
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• v.9 no.2
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• pp.67-78
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• 2006

Local governments began to construct geographic information system to improve government productivity and performance. In support, central government organized a national commission for GIS. The master plan by NGIS has been the base for local government to participate in the construction of GIS at the local level in the under ground facilities management including water and sewers. The challenge faced by sewer facility managers includes controlling 'data accuracy'. The input for sewer data handling for efficient performance in local government requires accurate data. However data manipulation to get the 'good quality' data can be burdensome. Thus, the aim of this research is to provide the appropriate tool to guarantee the high quality of digital data in sewer facility management. It is helpful to pass the data examination by government as well as to insure confidence of decision and data analysis works in local government. In this research, error types of sewer data were classified and pointed the limitation of traditional examination methods. Thus this research suggested more improved method for finding and correcting errors in data input using sewer volume analysis and prediction model as immigrating sewer facility management work to Geographic Information System.

• Spatial Information Research
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• v.14 no.1 s.36
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• pp.129-150
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• 2006

Mineral potential mapping is an important procedure in mineral resource assessment. The purpose of this study is to analyze mineral potential using weight of evidence model and a Geographic Information System (GIS) environment to identify areas that have not been subjected to the same degree of exploration. For this, a variety of spatial geological data were compiled, evaluated and integrated to produce a map of potential mineral in the Gangreung area, Korea. for this, a spatial database considering mineral deposit, topographic, geologic, geophysical and geochemical data was constructed for the study area using a GIS. The used mineral deposits were non-metallic(Kaolin, Porcelainstone, Silicastone, Mica, Nephrite, Limestone and Pyrophyllite) deposits of sedimentary type. The factors relating to mineral deposits were the geological data such as lithology and fault structure, geochemical data, including the abundance of Al, As, Ba, Ca, Cd, Co, Cr, Cu, Fe, K, Li, Mg, Mn, Mo, Na, Ni, Pb, Si, Sr, V, Zn, $Cl^-,\;F^-,\;{PO_4}^{3-},\;{NO_2}^-,\;{NO_3}^-,\;SO_{42-}$, Eh, PH and conductivity and geophysical data, including the Bouguer and magnetic anomalies. These factors were used with weight of evidence model to analyze mineral potential. Probability models using the weight of evidence were applied to extract the relationship between mineral deposits and related factors, and the ratio were calculated. Then the potential indices were calculated by summation of the likelihood ratio and mineral potential maps were constructed from Geographic Information System (GIS). The mineral potential maps were then verified by comparison with the known mineral deposit areas. The result showed the 85.66% in prediction accuracy.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.37 no.1
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• pp.43-59
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• 2017

This study presents the prediction methodology of debris flow occurrence areas using the SINMAP model. Former studies used a single calibration region applying some of the soil test results to predict debris flow occurrence in SINMAP model, which couldn't subdivide the soil properties for the target areas. On the other hands, a multi-calibration region using a detailed soil map and soil strength parameters (c, ${\phi}$) for each soil series to make up for limitation of former studies is proposed. In this process, soils with soil erodibility factor (K) are classified into three types: 1) gravel and gravelly soil. 2) sand and sandy soil, and 3) silt and clay. In addition, T/R estimation method using mean elevation of target area instead of T/R method using actual occurrence time is suggested in this study. The suggested method is applied to Seobyeok-1 ri area, Bonghwa-gun where debris flow occurred. As a result of comparison between two T/R estimation method, both T/R estimations are almost equal. Therefore, the suggested methodologies in this study will contribute to set up the national-wide mitigation plan against debris flow occurrence.