• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.232-232
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• 2020

하천기본계획 수립이나 생태하천 조성사업 등 다양한 하천사업에서 하천측량은 대상 하천의 지형 현황과 과거 사업이후의 변화량을 확인할 수 있는 중요한 요소이다. 국내 측량 기준인 공공측량작업규정(국토지리정보원)에서 하천 측량은 육지부에서는 횡단측량을 수부에서는 수심측량을 실시하고 수심측량은 음향측심기 사용을 원칙으로 한다. 국내 대부분의 수심측량은 단빔 음향측심기를 사용하고 있는 실정이며 일부 수심 확보 구간 또는 연구목적으로 멀티빔 음향측심기를 적용한 사례가 일부 보고된 바가 있다. 최근 수심측정이 가능한 항공수심측량(Airbone LiDAR Bathymetry) 장비 중 핵심계측기기인 Green LiDAR 센서 국산화 및 경량화에 관한 연구가 진행중이다. 이에 본 연구는 국내 하천 여건에서 개발 센서가 어느 정도의 활용성을 확보할 수 있는지를 검토하였다. 우선 환경부가 운영중인 수질자동측정망 71개 지점의 정기측정성과 중 탁도에 관한 일자료를 확보가 가능한 45개 지점을 대상으로 G-LiDAR 센서의 SD(Secchi Depth)를 기준으로 가용계측일을 산정해 보았다. 분석기간은 '12. 7.부터 '19.12.까지이며 분석기간중 SD 1.5m(1.94 NTU 추정) 기준을 만족하는 기간은 한강 2.07년, 낙동강 0.64년, 금강 2.21년, 영산강 2.71년으로 나타났다. 또한 지점별 가용기간 분석결과 분석기간인 7.33년 동안 탁도 기준이하인 운영 가능 기간은 연중 평균 80여일(2.74개월)로 나타나 제한적이나마 활용이 가능할 것으로 확인되었다. 향후 현장조사를 통해 공공측량 성과와 대상수계의 탁도 실측자료와의 연계분석을 통해 정확한 활용성 검토를 수행할 예정이다. 향후 적용 센서의 개발 성능목표를 달성한다면 하천내의 다양한 분야에서 활용이 가능할 것으로 기대된다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.5
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• pp.163-181
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• 2023

Improving the detection performance of facilities corresponding to the sensors of autonomous vehicles helps driving safety. In the road and transportation field, research is being conducted to improve the detection performance of sensors by road infrastructure or facilities. As part of this on the development of autonomous driving support infrastructure, the shape of traffic cones and drums to ensure sufficient LiDAR detection performance even rainy conditions and maintain the line-of-sight guidance function in construction zones improvement effect. The principle was to increase reflection performance and ensure no significant difference in shape from existing facilities. Traffic cones were manufactured in square pyramid shapes instead of cones, and drums were manufactured in hexagonal and octagonal pillar shapes instead of cylinders. LiDAR detection data for the facility was confirmed on a clear day and with 20 mm/h and 40 mm/h rainfall. The detection performance of the square pyramid-shaped traffic cone and octagonal column-shaped drum was to the existing facility. On the other hand, deviations occurred due to repeated measurements, and significance could not be confirmed through statistical analysis. By reflecting these results, future studies will seek a form in which data can be obtained uniformly despite the diversity of measurement environments.

• Tunnel and Underground Space
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• v.30 no.1
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• pp.76-86
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• 2020

In this study, a small autonomous driving robot was developed for underground mines using the Light Detection and Ranging (LiDAR) sensor. The developed robot measures the distances to the left and right wall surfaces using the LiDAR sensor, and automatically controls its steering to drive along the centerline of mine tunnel. A field experiment was conducted in an underground amethyst mine to test the driving performance of developed robot. During five repeated driving tests, the robot showed stable driving performance overall. There were no collision accidents with the wall of mine tunnel.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.5
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• pp.253-273
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• 2022

Automated vehicles rely on information collected through sensors to drive. Therefore, the uncertainty of the information collected from a sensor is an important to address. To this end, research is conducted in the field of road and traffic to solve the uncertainty of these sensors through infrastructure or facilities. Therefore, this study developed a traffic cone that can maintaing the gaze guidance function in the construction site by securing sufficient LiDAR detection performance even in rainy conditions and verified its improvement effect through demonstration. Two types of cones were manufactured, a cross-type and a flat-type, to increase the reflective performance compared to an existing cone. The demonstration confirms that the flat-type traffic cone has better detection performance than an existing cone, even in 50 mm/h rainfall, which affects a driver's field of vision. In addition, it was confirmed that the detection level on a clear day was maintained at the 20 mm/h rain for both cones. In the future, improvement measures should be developed so that the traffic cones, that can improve the safety of automated driving, can be applied.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.10
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• pp.1453-1461
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• 2022

The accuracy of peripheral object recognition algorithms using 3D data sensors such as LiDAR in autonomous vehicles has been increasing through many studies, but this requires high performance hardware and complex structures. This object recognition algorithm acts as a large load on the main processor of an autonomous vehicle that requires performing and managing many processors while driving. To reduce this load and simultaneously exploit the advantages of 3D sensor data, we propose 2D data-based recognition using the ROI generated by extracting physical properties from 3D sensor data. In the environment where the brightness value was reduced by 50% in the basic image, it showed 5.3% higher accuracy and 28.57% lower performance time than the existing 2D-based model. Instead of having a 2.46 percent lower accuracy than the 3D-based model in the base image, it has a 6.25 percent reduction in performance time.

• Journal of the Korean Geotechnical Society
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• v.38 no.7
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• pp.5-24
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• 2022

The current performance evaluation of slope anchors qualitatively determines the physical bonding between the anchor head and ground as well as cracks or breakage of the anchor head. However, such performance evaluation does not measure these primary factors quantitatively. Therefore, the time-dependent management of the anchors is almost impossible. This study is an evaluation of the 3D numerical model by SfM which combines UAS images with terrestrial LiDAR to collect numerical data on the damage factors. It also utilizes the data for the quantitative maintenance of the anchor system once it is installed on slopes. The UAS 3D model, which often shows relatively low precision in the z-coordinate for vertical objects such as slopes, is combined with terrestrial LiDAR scan data to improve the accuracy of the z-coordinate measurement. After validating the system, a field test is conducted with ten anchors installed on a slope with arbitrarily damaged heads. The damages (such as cracks, breakages, and rotational displacements) are detected and numerically evaluated through the orthogonal projection of the measurement system. The results show that the introduced system at the resolution of 8K can detect cracks less than 0.3 mm in any aperture with an error range of 0.05 mm. Also, the system can successfully detect the volume of the damaged part, showing that the maximum damage area of the anchor head was within 3% of the original design guideline. Originally, the ground adhesion to the anchor head, where the z-coordinate is highly relevant, was almost impossible to measure with the UAS 3D numerical model alone because of its blind spots. However, by applying the combined system, elevation differences between the anchor bottom and the irregular ground surface was identified so that the average value at 20 various locations was calculated for the ground adhesion. Additionally, rotation angle and displacement of the anchor head less than 1" were detected. From the observations, the validity of the 3D numerical model can obtain quantitative data on anchor damage. Such data collection can potentially create a database that could be used as a fundamental resource for quantitative anchor damage evaluation in the future.

• Journal of Korean Society for Geospatial Information Science
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• v.17 no.4
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• pp.61-66
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• 2009

This Paper describes a new program called DEM Generator need to process DEM from LiDAR data or digital map data. It is difficult to generate raster DEM from LiDAR mass point data sets and digital maps too large to fit into memory. The DEM Generator was designed to process DEM and shaded relief image of GeoTiff format in order of streaming meshes; I/O minimize tag, delaunay triangle, natural neighborhood or TIN, temporary files and grid. It is expected that we can be improved the precision of DEM and solved the time consuming problem of DEM generating of a wider area.

• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.1009-1024
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• 2018

With the increased use of LiDAR (Light Detection and Ranging) that can obtain over millions of point dataset, methodologies for efficient search and dimensionality reduction for the point cloud became a crucial technique. The existing octree-based "parametric algorithm" has proved its efficiency and contributed as a part of PCL (Point Cloud Library). However, the implementation of the algorithm on GPU (Graphics Processing Unit) is considered very difficult because of structural constraints of the octree implemented in PCL. In this paper, we present a method for the parametric algorithm on GPU environment and implement a projection of the queried points on four directions with an improved noise reduction.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.39 no.9
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• pp.929-936
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• 2015

Nacelle anemometers are mounted on wind-turbine nacelles behind blade roots to measure the free-stream wind speed projected onto the wind turbine for control purposes. However, nacelle anemometers measure the transformed wind speed that is due to the wake effect caused by the blades' rotation and the nacelle geometry, etc. In this paper, we derive the Nacelle Transfer Function (NTF) to calibrate the nacelle wind speed to the free-stream wind speed, as required to carry out the performance test of wind turbines according to the IEC 61400-12-2 Wind-Turbine Standard. For the reference free-stream wind data, we use the Light Detection And Ranging (LiDAR) measurement at the Shinan wind power plant located on the Bigeumdo Island shoreline. To improve the simple linear regression NTF, we derive the multiple nonlinear regression NTF. The standard error of the wind speed was found to have decreased by a factor of 9.4, whereas the mean of the power-output residual distribution decreased by 6.5 when the 2-parameter NTF was used instead of the 1-parameter NTF.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1709-1718
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• 2021

The waveform data of the Airborne Bathymetric LiDAR (ABL; LiDAR: Light Detection And Ranging) system provides data with improved accuracy, resolution, and reliability compared to the discrete-return data, and increases the user's control over data processing. Furthermore, we are able to extract additional information about the return signal. Waveform decomposition is a technique that separates each echo from the received waveform with a mixture of water surface and seabed reflections, waterbody backscattering, and various noises. In this study, a new waveform decomposition technique based on a Gaussian model was developed to improve the point extraction performance from the ABL waveform data. In the existing waveform decomposition techniques, the number of decomposed echoes and decomposition performance depend on the peak detection results because they use waveform peaks as initial values. However, in the study, we improved the approximation accuracy of the decomposition model by adding the estimated potential peak candidates to the initial peaks. As a result of an experiment using waveform data obtained from the East Coast from the Seahawk system, the precision of the decomposition model was improved by about 37% based on evaluating RMSE compared to the Gaussian decomposition method.