• Journal of the Korea Society of Computer and Information
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• v.17 no.6
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• pp.173-182
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• 2012

A recent advance in smart phones is increasing utilization of location information. Existing positioning system was using GPS location for positioning. However, the GPS cannot be used indoors, if GPS location has an incorrectly problem. In order to solve indoor positioning problems of indoor location-based positioning techniques have been investigated. There are a variety of techniques based on indoor positioning techniques like as RFID, UWB, WLAN, etc. But WLAN location positioning techniques take advantage the bond in real life. WLAN indoor positioning techniques have a two kind of method that is centroid and fingerprint method. Among them, the fingerprint technique is commonly used because of the high accuracy. In order to use fingerprinting techniques make a WLAN signal map building that is need to lot of resource. In this paper, we try to solve this problem in an Indoor environment for WLAN-based fingerprint of a virtual building technique, which is proposed. Proposed technique is classified Cell environment in existed Indoor environment, all of fingerprint points are shown virtual grid map in each Cell. Its method can make fingerprint grid map very quickly using estimate virtual signal value. Also built signal value can take different value depending of the real estimate value. To solve this problem using a calibration technique for the Splite-tree is proposed. Through calibration technique that improves the accuracy for short period of time. It also is improved overall accuracy using predicted value of around position in cell.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.123-123
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• 2020

The present study is aimed to correcting radar-based mean areal precipitation forecasts to improve urban flood predictions and uncertainty analysis of water levels contributed at each stage in the process. For this reason, a long short-term memory (LSTM) network is used to reproduce three-hour mean areal precipitation (MAP) forecasts from the quantitative precipitation forecasts (QPFs) of the McGill Algorithm for Precipitation nowcasting by Lagrangian Extrapolation (MAPLE). The Gangnam urban catchment located in Seoul, South Korea, was selected as a case study for the purpose. A database was established based on 24 heavy rainfall events, 22 grid points from the MAPLE system and the observed MAP values estimated from five ground rain gauges of KMA Automatic Weather System. The corrected MAP forecasts were input into the developed coupled 1D/2D model to predict water levels and relevant inundation areas. The results indicate the viability of the proposed framework for generating three-hour MAP forecasts and urban flooding predictions. For the analysis uncertainty contributions of the source related to the process, the Bayesian Markov Chain Monte Carlo (MCMC) using delayed rejection and adaptive metropolis algorithm is applied. For this purpose, the uncertainty contributions of the stages such as QPE input, QPF MAP source LSTM-corrected source, and MAP input and the coupled model is discussed.

• Journal of Korean Society for Geospatial Information Science
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• v.19 no.4
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• pp.71-80
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• 2011

A simple and an improved methods for the assessment of flood damage were used in previous studies, and the Multi-Dimensional Flood Damage Assessment (MD-FDA) has been applied since 2004 in Korea. This study evaluated flood damage of dam downstream using considering MD-FDA method based on GIS data. Firstly, flood water level with FLDWAV (Flood Wave routing) model was input into cross section layer based on enforcement drainage algorithm, water depth grid data were created through spatial calculation with DEM data. The value of asset of building and agricultural land according to local government was evaluated using building layer from digital map and agricultural land map from landcover map. Also, itemized flood damage was calculated by unit price to building shape, evaluated value of housewares to urban type, unit cost to crop, tangible and inventory asset of company connected with building, agricultural land, flooding depth layer. Flood damage in rainfall frequency of 200 year showed 1.19, 1.30 and 1.96 times to flood damage in rainfall frequency of 100 year, 50 year and 10 year respectively by flood damage analysis.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.5
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• pp.407-414
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• 2020

SVM (Support vector machine) analysis was performed after applying a deep learning technique based on an Inception-based model (GoogLeNet). The accuracy of automatic image classification was analyzed using an SVM with multiple virtual grid sizes. Six classes were selected from a standard land cover map. Cars were added as a separate item to increase the classification accuracy of roads. The virtual grid size was 2-5 m for natural areas, 5-10 m for traffic areas, and 10-15 m for building areas, based on the size of items and the resolution of input images. The results demonstrate that automatic classification accuracy can be increased by adopting an integrated approach that utilizes weighted virtual grid sizes for different classes.

• Proceedings of the KIEE Conference
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• 2003.11b
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• pp.23-26
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• 2003

The vector field histogram(VFH) uses a two-dimensional Cartesian histogram grid as a world model. The VFH method subsequently employs a two-stage data-reduction process in order to compute the desired control commands for the vehicle. In the first stage the histogram grid is reduced to a one dimensional polar histogram that is constructed around the robot's momentary location. Each sector in the polar histogram contains a value representing the polar obstacle density in that direction. In the second stage, the algorithm selects the most suitable sector from among all polar histogram sectors with a low polar obstacle density, and the steering of the robot is aligned with that direction. We applied this algorithm to our four-legged robot.

• Journal of the Korea Society of Computer and Information
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• v.15 no.1
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• pp.61-69
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• 2010

An autonomous robot must have a global workspace map in order to cover the complete workspace. However, most previous coverage algorithms assume that they have a grid workspace map that is to be covered before running the task. For this reason, most coverage algorithms can not be applied to complete coverage tasks in unknown environments. An autonomous robot has to build a workspace map by itself for complete coverage in unknown environments. Thus, we propose a new DmaxCoverage algorithm that allows a robot to carry out a complete coverage task in unknown environments. This algorithm integrates a SLAM algorithm for simultaneous workspace map building. Experimentally, we verify that DmaxCoverage algorithm is more efficient than previous algorithms.

• Proceedings of the KIEE Conference
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• 2000.11d
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• pp.750-753
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• 2000

본 논문에서는 미지의 환경에서 이동 로봇이 주행 중 얻어진 격자지도(grid map)상의 장애물 정보를 이용하여 이동 로봇 주변환경을 직선선분으로 표현한다. 격자지도의 장애물 정보는 초음파 센서를 이용하여 얻어지므로 이동로봇과 인접한 장애물 정보만을 얻게된다. 얻어진 격자 정보를 호프변환을 이용하여 직선선분을 구축하고 이를 이전에 얻어진 직선선분과 결합하여 전체지도를 완성해 간다. 논의된 방법은 모의실험을 통하여 증명하였다.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.3080-3082
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• 1999

본 논문에서는 실내환경에서 얻어진 격자 지도(grid map)상의 장애물 정보의 표현을 간략화 시키는 방법에 대하여 논하였다. 여기서 기본이 되는 격자 지도의 장애물 정보는 초음파 센서를 이용하여 얻어졌다고 가정하였다. 그리고 생성된 장애물 정보를 직선 성분으로 간단히 표현하여 저장될 정보의 양을 줄이는 변환 방법들인 체인 코드(chain code)와 호프 변환(Hough transform)에 대하여 논하였다. 그리고, 논의된 방법의 유효성을 증명하기 위하여 컴퓨터 시뮬레이션에 의한 결과를 제시하였으며, 단점들에 대한 앞으로의 연구 방향을 제시 하였다.

• Korean Journal of Remote Sensing
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• v.39 no.5_2
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• pp.771-783
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• 2023

This study aims to compare the performance of each machine learning model for preparing a grid-based disaster risk map related to flooding in Jung-gu, Ulsan, for Typhoon Chaba which occurred in 2016. Dynamic data such as rainfall and river height, and static data such as building, population, and land cover data were used to conduct a risk analysis of flooding disasters. The data were constructed as 10 m-sized grid data based on the national point number, and a sample dataset was constructed using the risk value calculated for each grid as a dependent variable and the value of five influencing factors as an independent variable. The total number of sample datasets is 15,910, and the training, verification, and test datasets are randomly extracted at a 6:2:2 ratio to build a machine-learning model. Machine learning used random forest (RF), support vector machine (SVM), and k-nearest neighbor (KNN) techniques, and prediction accuracy by the model was found to be excellent in the order of SVM (91.05%), RF (83.08%), and KNN (76.52%). As a result of deriving the priority of influencing factors through the RF model, it was confirmed that rainfall and river water levels greatly influenced the risk.

• Journal of Korean Society of Forest Science
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• v.90 no.4
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• pp.535-542
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• 2001

The objective of this study is to develop a spatial analysis modeling technique to evaluate the functional suitability of forest lands for land slide prevention. The functional suitability is classified into 3 categories of high, medium and low according to the potential of land slide on forest lands. The potential of land slide hazards is estimated using the measurements of 7 major site factors : slope, bed rock, soil depth, shape of slope, forest type and D.B.H. class of trees. The analytic hierarchical process is applied to determining the relative weight of site factors in estimating the potential of land slides. The spatial analysis modeling starts building base layers for the 7 major site factors by $25m{\times}25m$ grid analysis or TIN analysis, reclassifies them and produces new layers containing standardized attribute values, needed in estimating land slide potential. To these attributes, applied is the weight for the corresponding site factor to build the suitability classification map by map algebra analysis. Then, finally, cell-grouping operations convert the suitability classification map to the land unit function map. The whole procedures of the spatial analysis modeling are presented in this paper.