• Journal of Cadastre & Land InformatiX
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• v.52 no.2
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• pp.17-34
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• 2022

With rapid advancements taking place in the Fourth Industrial Revolution, human-robot interactions have been garnering increasing attention. Robots are being actively adopted in building systems and facilities. In this study, we developed robot-friendly building certification indicators. Because these indicators were being developed for the first time, we focused only on commercial buildings. We conducted exploratory research using methodologies such as focus group interviews and the analytic hierarchy process. First, the concept of the robot-friendly building was defined through focus group interviews, and the requirements were categorized by the appropriateness of operating facilities and systems and the appropriateness of architectural and robot operating systems and networks. Next, the relative importance of the evaluation items (23 items in total) was calculated using the analytic hierarchy process. Their average score of the marks was 4.4, and the minimum and maximum were 2.0 and 11.3, respectively. This study is significant because we collected the basic data necessary to develop a one-of-its-kind evaluation system for certifying the robot-friendliness of buildings using scientific methods.

• Korean Journal of Remote Sensing
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• v.37 no.5_3
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• pp.1405-1423
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• 2021

Precipitation is one of the main factors that affect water and energy cycles, and its estimation plays a very important role in securing water resources and timely responding to water disasters. Satellite-based quantitative precipitation estimation (QPE) has the advantage of covering large areas at high spatiotemporal resolution. In this study, machine learning-based rainfall intensity models were developed using Himawari-8 Advanced Himawari Imager (AHI) water vapor channel (6.7 ㎛), infrared channel (10.8 ㎛), and weather radar Column Max (CMAX) composite data based on random forest (RF). The target variables were weather radar reflectivity (dBZ) and rainfall intensity (mm/hr) converted by the Z-R relationship. The results showed that the model which learned CMAX reflectivity produced the Critical Success Index (CSI) of 0.34 and the Mean-Absolute-Error (MAE) of 4.82 mm/hr. When compared to the GeoKompsat-2 and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN)-Cloud Classification System (CCS) rainfall intensity products, the accuracies improved by 21.73% and 10.81% for CSI, and 31.33% and 23.49% for MAE, respectively. The spatial distribution of the estimated rainfall intensity was much more similar to the radar data than the existing products.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 1995.12a
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• pp.9-21
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• 1995

A planning support system enhances our ability to use water capacity expansion as an urban growth management strategy. This paper reports the development of capacity expansion modeling of water supply as part of the continuing development of such a planning support system (PEGASUS: Planning Environment for Generation and Analysis of Spatial Urban Systems) to incorporate water supply, This system is designed from the understanding that land use and development drive the demand for infrastructure and infrastructure can have a significant influence on the ways in which land is developed and used. Capacity expansion Problems of water supply can be solved in two ways: 1) optimal control theory, and 2) mixed integer nonlinear programming (MINLP). Each method has its strengths and weaknesses. In this study the MINLP approach is used because of its strength of determining expansion sizing and timing simultaneously. A dynamic network optimization model and a water-distribution network analysis model can address the dynamic interdependence between water planning and land use planning. While the water-distribution network analysis model evaluates the performance of generated networks over time, the dynamic optimization model chooses alternatives to meet expanding water needs. In addition, the user and capacity expansion modeling-to-generate-alternatives (MGA) can generate alternatives. A cost benefit analysis module using a normalization technique helps in choosing the most economical among those alternatives. GIS provide a tool for estimating the volume of demanded water and showing results of the capacity expansion model.

• Journal of Cadastre & Land InformatiX
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• v.53 no.1
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• pp.83-105
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• 2023

A robot-friendly building certification system was established in 2022 to accommodate the growing number of service robots introduced into buildings. However, this system primarily targeted office buildings, with limitations in applying other functional architectures. To address this problem, we developed a certification model of a robot-friendly environment to extend the existing system to apartment complexes. Using focus group interviews and the analytic hierarchy process, we established 28 evaluating items categorized as (a) architecture and facility design, (b) networks and systems, (c) building operations management, and (d) support for robot activity and other services. These indicators were weighted based on their relative importance within and between categories, resulting in scores ranging from 1 to 18 points and a total of 176 points. According to evaluations with the 28 items, each apartment complex could be graded as "best," "excellent," or "general" based on its total achieved scores. This study is significant, as we present the world's first certification model of a robot-friendly environment for apartment complexes that considers human-robot interactions

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.234-237
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• 2007

Road networks are important geospatial databases for various GIS (Geographic Information System) applications. Road digital maps may contain geometric spatial errors due to human and scanning errors, but manually updating roads information is time consuming. In this paper, we developed a new road features updating methodology using from multispectral high-resolution satellite image and pre-existing vector map. The approach is based on initial seed point generation using line segment matching and a modified double snake algorithm. Firstly, we conducted line segment matching between the road vector data and the edges of image obtained by Canny operator. Then, the translated road data was used to initialize the seed points of the double snake model in order to refine the updating of road features. The double snake algorithm is composed of two open snake models which are evolving jointly to keep a parallel between them. In the proposed algorithm, a new energy term was added which behaved as a constraint. It forced the snake nodes not to be out of potential road pixels in multispectral image. The experiment was accomplished using a QuickBird pan-sharpened multispectral image and 1:5,000 digital road maps of Daejeon. We showed the feasibility of the approach by presenting results in this urban area.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.17 no.4
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• pp.339-348
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• 1999

The information & Communication industry including internet is rapidly developing and expanding, which integrates our living space and provides diversity. Internet provides users with an variety of real-time information through networking. Also the functions and services of Geographic Information Systems are on a changing trend providing services for various organizations and users dispersed in different networks. It is necessary to understand that GIS is available not only on a desktop and server, but in any place where the network is connected using the web. Recently, Internet GIS for the search and subscription of spatial informations through the internet is receiving an active research field. So. this study aims to apply the Gas Disaster Management to the internet and develop the internet GIS techniques which make an effective utilization of GIS functions using MapObjects IMS, SDE, and Oracle.

• Journal of Electrical Engineering and Technology
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• v.13 no.1
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• pp.38-50
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• 2018

Spatio-temporal load forecasting (STLF) is a foundation for building the prediction-based power map, which could be a useful tool for the visualization and tendency assessment of urban energy application. Constructing one point-forecasting model for each electricity cell in the geographic space is possible; however, it is unadvisable and insufficient, considering the aggregation features of electricity cells and uncertainties in input variables. This paper presents a new STLF method, with a data-driven framework consisting of 3 subroutines: multi-level clustering of cells considering their aggregation features, load regression for each category of cells based on SLS-SVRNs (sparse least squares support vector regression networks), and interval forecasting of spatio-temporal load with sampled blind number. Take some area in Pudong, Shanghai as the region of study. Results of multi-level clustering show that electricity cells in the same category are clustered in geographic space to some extent, which reveals the spatial aggregation feature of cells. For cellular load regression, a comparison has been made with 3 other forecasting methods, indicating the higher accuracy of the proposed method in point-forecasting of spatio-temporal load. Furthermore, results of interval load forecasting demonstrate that the proposed prediction-interval construction method can effectively convey the uncertainties in input variables.

• Journal of the Korean association of regional geographers
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• v.21 no.1
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• pp.21-38
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• 2015

This study attempts to estimate the complementarity between 21 cities in the Korean Southeastern Region using data on the network time distance and the volume of flow between the cities. Four types of flows recognized are people, commodities, information and finance. The first two types of flows are thought to be made on the transportation network while the last two are on the communication network. For the purpose of the study, the expected volumes of flows between cities are first estimated using the gravity-based regression and doubly-constrained entropy maximization models. These baseline volumes are then subtracted from the observed volumes of flows (of people and commodities) or the estimated volumes of flows (of information and finance) in order to identify positive differences or complementarities. The result shows that these four types of complementarity flows form distinctive urban networks in terms of spatial pattern and urban hierarchy. This suggests that more customized strategies to different types of complementarity are recommended to properly address the issues related to network infrastructure provision in the pursuit of the network city model in the region.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.3
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• pp.149-156
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• 2021

This study collected and analyzed case studies related with the use of spatial information in North Korea to prioritize construction and required supporting processes in order to propose a short-term and long-term road map for the establishment of spatial information in North Korea. Recent research cases related to the prioritization of spatial information development were analyzed, and priority for the construction of infrastructure was derived based on the interconnectedness and relationship of national infrastructure. Due to the inaccessibility and remoteness of North Korea, all of the five studies determined priorities according to questionnaires and consulting of refugees and knowledgeable figures by expert groups. In summary, priority was given to unarmed and transborder areas, major cities, special zones, and development zones, while in terms of facilities, priority was given to power communication, railroads, water and sewage architectural buildings, roads, and dams. In the case of prioritizing the establishment of national infrastructure for the unified Korean Peninsula, the development of major areas, ports, and the related city-level spots to develop a line that promotes the sharing of routes such as transportation networks, water, and energy, thus leading to a scenario involving the development of cotton at an urban and national level.

• Journal of the Korean association of regional geographers
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• v.22 no.1
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• pp.1-24
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• 2016

This paper is to consider trends and issues of regional research in major sub-fields of human geography in Korea, following the previous one which dealt with contexts and general trends of new regional geography in Korea since the 2000s. They include historical and cultural geography on place and landscape, economic geography on industrial districts or agglomerated regions (i.e. clusters) and urban (and social) geography on urban networks and differentiation. Even though researchers in sub-fields have used different terms and concepts to identify region, they are in common to relate specificities of region to general processes such as (de)modernization, (de)industrialization, and globalization, to understand region as social and discursive constitution as well as substantive reality, and to give more attention to socio-spatial networks and relationality than territoriality of regions. These common points seem to reflect the emerging trend of new regional geography, and to get rid of existing traditional concept of region. It is suggested that major tasks for conceptualization of region in future research are to overcome dichotomy of speciality and generality, of substantive reality and discursive constitution, and of territoriality and relationality, and that important issues for empirical research on region include regional synthesis from new perspectives, uneven regional development as relational process in and between regions, and producing practice for alternative regions.