• 한국지하수토양환경학회:학술대회논문집
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• 한국지하수토양환경학회 2001년도 추계학술발표회
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• pp.155-159
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• 2001

A study on multivariate statistical classification of ground water hydrographs was conducted. The vast data of national ground water monitoring network (78 sites of alluvium) were used. 6 factors were selected to classify the ground water level change. Factor analysis was proved to be useful tool for classifying vast hydrogeological data.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2010년도 춘계학술대회 논문집
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• pp.755-756
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• 2010

• 한국측량학회:학술대회논문집
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• 한국측량학회 2006년도 춘계학술발표회 논문집
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• pp.469-473
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• 2006

In general, a classification process between ground data and non-ground data, which include building objects, is required prior to producing a DEM for a certain surface reconstruction from LiDAR data in which the DEM can be produced from the ground data, and certain objects like buildings can be reconstructed using non-ground data. Thus, an exact classification between ground and non-ground data from LiDAR data is the most important factor in the ground reconstruction process using LiDAR data. In particular, building objects can be largely used as digital maps, orthophotos, and urban planning regarding the object in the ground and become an essential to providing three dimensional information for certain urban areas. In this study, an entropy theory, which has been used as a standard of disorder or uncertainty for data used in the information theory, is used to apply a more objective and generalized method in the recognition and segmentation of buildings from raw LiDAR data. In particular, a method that directly uses the raw LiDAR data, which is a type of point shape vector data, without any changes, to a type of normal lattices was proposed, and the existing algorithm that segments LiDAR data into ground and non-ground data as a binarization manner was improved. In addition, this study proposes a generalized building extraction method that excludes precedent information for buildings and topographies and subsidiary materials, which have different data sources.

• 제어로봇시스템학회논문지
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• 제15권4호
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• pp.372-378
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• 2009

For effective mobility control of unmanned ground vehicles in outdoor off-road environments, terrain cover classification technology using passive sensors is vital. This paper presents a novel method far terrain classification based on color and texture information of off-road images. It uses a neural network classifier and wavelet features. We exploit the wavelet mean and energy features extracted from multi-channel wavelet transformed images and also utilize the terrain class spatial coordinates of images to include additional features. By comparing the classification performance according to applied features, the experimental results show that the proposed algorithm has a promising result and potential possibilities for autonomous navigation.

• 대한공간정보학회지
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• 제18권2호
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• pp.29-33
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• 2010

항공레이저측량을 통한 지형 분류작업은 분류 정확도의 확보와 세밀한 지형 표현의 두 목표를 동시에 만족해야 한다. 이 두 목표를 달성하기 위한 자동분류 처리에 연구로서 노이즈가 많은 지형분류 결과로부터 필터링을 통한 품질향상 연구가 다수 있었으나 한국과 같이 삼림이 울창하고 지표면 투과율이 낮은 환경에서의 항공레이저측량 결과 적용 시 관목 및 교목 하층이 지면으로 분류되는 오류가 많았다. 이에 본 연구는 정확도가 높고 점밀도가 낮은 1차 지형분류 결과를 기반으로 아직 지형으로 등록되지 않은 LiDAR 지형 분류 후보 점군들로부터 세밀 지형 표현에 필요한 점들을 추출하는 기법으로 점분류 처리절차를 개선하였다. 주변 지형 포인트의 가중치를 부여하여 경사 (gradient) 계산을 통해 미추출 LiDAR 점군들로부터 지형 표현 점들을 분류하는 본 알고리즘은 특히 능선부분의 사라진 특징을 찾아내거나 무너진 논둑을 복원하는 등 최소의 점들로 중요한 지형 요소점(terrain model key points)을 놓치지 않고 세밀하게 표현하는데 효과적이다. 이 알고리즘을 통해 추출한 점들과 1차 지형분류 결과를 결합하여 지형분류최적화 방법을 제안하였다.

• 대한원격탐사학회지
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• 제26권6호
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• pp.705-719
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• 2010

We investigated the speckle filtering effect in supervised classification of the C-band polarimetric Ground Based SAR image data. Wishart classification method was used for the supervised classification of the polarimetric GB-SAR image data and total of 6 kinds of speckle filters were applied before supervised classification, which are boxcar, Gaussian, Lopez, IDAN, the refined Lee, and the refined Lee sigma filters. For each filters, we changed the filtering kernel size from $3{\times}3$ to $9{\times}9$ to investigate the filtering size effect also. The refined Lee filter with the kernel size of bigger than $5{\times}5$ showed the best result for the Wishart supervised classification of polarimetric GB-SAR image data. The result also showed that the type of trees could be discriminated by Wishart supervised classification of polarimetric GB-SAR image data.

• 한국지진공학회논문집
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• 제22권6호
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• pp.311-322
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• 2018

As a method of seismic-design for pile-supported wharves, equivalent static analysis, response spectrum analysis, and time history analysis method are applied. Among them, the response spectrum analysis is widely used to obtain the maximum response of a structure. Because the ground is not modeled in the response spectrum analysis of pile-supported wharves, the amplified input ground acceleration should be calculated by ground classification or seismic response analysis. However, it is difficult to calculate the input ground acceleration through ground classification because the pile-supported wharf is build on inclined ground, the methods to calculate the input ground acceleration proposed in the standards are different. Therefore, in this study, the dynamic centrifuge model tests and the response spectrum analysis were carried out to calculate the appropriate input ground acceleration. The pile moment in response spectrum analysis and the dynamic centrifuge model tests were compared. As a result of comparison, it was shown that the response spectrum analysis results using the amplified acceleration in the ground surface were appropriate.

• 한국수산과학회지
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• 제35권3호
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• pp.273-281
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• 2002

해양의 표층퇴적물을 분류하는 일반적인 방법은 ground frothing에 의한 것으로 시료채취 정점에 국한된 자료라는 한계성을 가지고 있다. 최근에는 원격분류방법의 개발로 인하여 이러한 한계성을 극복한 연속적인 자료를 얻을 수 있도록 가능하게 되었다 본 연구에서는 해저면의 원격분류결과를 실시간 수치화된 자료로 얻을 수 있는 음향장비인 QTC View라는 기기를 이용해 부산 수영만의 표층 퇴적물을 원격분류 하였다. QTC View는 50kHz의 음향측심기와 연결하였고 측정장비의 설정환경은 조사동안 일정하게 유지하였다. Ground trothing에 의한 시료 분석결과 수영만은 slightly gravelly sand, slightly gravelly sandy mud. gravelly muddy sand, clayey sand, sandy mud, slightly gravelly muddy sand 그리고 rocky bottom의 총 7개의 퇴적물형으로 분류되었다. QTC View를 이용한 1차 원격분류결과 이들 7개 중 slightly gravelly sand, gravelly muddy sand, sandy mud 및 rocky bottom 등 4개의 퇴적물형이 구분되었으며 이는 2차 원격분류결과에서도 유사하게 분포하는 것으로 확인되었다. Ground frothing에 의한 분류자료와 원격분류 자료를 비교한 결과 퇴적물형을 구분할 때 소량성분에 의해 서로 다르게 구분된 경우는 다소 차이가 있으나 연구지역 전반에 걸친 퇴적물의 분포양상은 잘 일치하는 것으로 나타났다. 따라서 QTC View는 해저퇴적물을 원격분류하는데 유효하게 이용될 수 있을 것으로 본다.

• Journal of Radiation Protection and Research
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• 제41권3호
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• pp.260-267
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• 2016

Background: This study analyzes the differences in the annual averaged atmospheric dispersion factor and ground deposition factor produced using two classification methods of atmospheric stability, which are based on a vertical temperature difference and the standard deviation of horizontal wind direction fluctuation. Materials and Methods: Daedeok and Wolsong nuclear sites were chosen for an assessment, and the meteorological data at 10 m were applied to the evaluation of atmospheric stability. The XOQDOQ software program was used to calculate atmospheric dispersion factors and ground deposition factors. The calculated distances were chosen at 400 m, 800 m, 1,200 m, 1,600 m, 2,400 m, and 3,200 m away from the radioactive material release points. Results and Discussion: All of the atmospheric dispersion factors generated using the atmospheric stability based on the vertical temperature difference were shown to be higher than those from the standard deviation of horizontal wind direction fluctuation. On the other hand, the ground deposition factors were shown to be same regardless of the classification method, as they were based on the graph obtained from empirical data presented in the Nuclear Regulatory Commission's Regulatory Guide 1.111, which is unrelated to the atmospheric stability for the ground level release. Conclusion: These results are based on the meteorological data collected over the course of one year at the specified sites; however, the classification method of atmospheric stability using the vertical temperature difference is expected to be more conservative.

• 제어로봇시스템학회논문지
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• 제17권5호
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• pp.436-443
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• 2011

Recognition of structures in urban environments is a fundamental ability for unmanned ground vehicles. In this paper we propose the geometrical featured voxel which has not only 3-D coordinates but also the type of geometrical properties of point cloud. Instead of dealing with a huge amount of point cloud collected by range sensors in urban, the proposed voxel can efficiently represent and save 3-D urban structures without loss of geometrical properties. We also provide an urban structure classification algorithm by using the proposed voxel and machine learning techniques. The proposed method enables to recognize urban environments around unmanned ground vehicles quickly. In order to evaluate an ability of the proposed map representation and the urban structure classification algorithm, our vehicle equipped with the sensor system collected range data and pose data in campus and experimental results have been shown in this paper.