• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.642-645
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• 2007

Mounted in aircraft, LiDAR (Light Detection And Ranging) technology uses pulses of light to collect data about the terrain below. The main objective of this study was to extract reliable the individual tree and analysis techniques to facilitate the used LiDAR data for estimating tree crown diameter by measuring individual trees identifiable on the three dimensional LiDAR surface. In addition, this study can be quantitative analysis of individual tree through the canopy parameter.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.422-424
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• 2021

In this paper, it leads the approach of fusing multiple RGB cameras for visual objects recognition based on deep learning with convolution neural network and 3D Light Detection and Ranging (LiDAR) to observe the environment and match into a 3D world in estimating the distance and position in a form of point cloud map. The goal of perception in multiple cameras are to extract the crucial static and dynamic objects around the autonomous vehicle, especially the blind spot which assists the AV to navigate according to the goal. Numerous cameras with object detection might tend slow-going the computer process in real-time. The computer vision convolution neural network algorithm to use for eradicating this problem use must suitable also to the capacity of the hardware. The localization of classified detected objects comes from the bases of a 3D point cloud environment. But first, the LiDAR point cloud data undergo parsing, and the used algorithm is based on the 3D Euclidean clustering method which gives an accurate on localizing the objects. We evaluated the method using our dataset that comes from VLP-16 and multiple cameras and the results show the completion of the method and multi-sensor fusion strategy.

• Korean Journal of Optics and Photonics
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• v.24 no.4
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• pp.176-183
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• 2013

In this paper, we report the design, fabrication and characterization of the 3-Dimensional optical receiver for a Laser Detection And Ranging (LADAR) system. The optical receiver is composed of three parts; $16{\pm}16$ Geiger Mode InGaAs Avalanche Photodiode (APD) array device operated at 1560 nm wavelength, Read Out Integrated Circuit (ROIC) measuring the Time-Of-Flight (TOF) of the return signal reflected from target objects, a package and cooler maintaining the proper operational condition of the detector and control electronics. We can confirm that the LADAR system can detect the signal from a target up to 1.2 km away, and it showed low Dark Count Rate (DCR) of less than 140 kHz, and higher than 28%-Photon Detection Efficiency (PDE). This is considered to be the best performance of the $16{\pm}16$ FPA APD optical receiver for a LADAR system.

• IEMEK Journal of Embedded Systems and Applications
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• v.12 no.1
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• pp.43-62
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• 2017

Mobile pulse scanning Light Detection And Ranging (LIDAR) are essential components of intelligent vehicles capable of autonomous travel. Obstacle detection functions of autonomous vehicles require very low failure rates. With the increasing number of autonomous vehicles equipped with scanning LIDARs to detect and avoid obstacles and navigate safely through the environment, the probability of mutual interference becomes an important issue. The reception of foreign laser pulses can lead to problems such as ghost targets or a reduced signal-to-noise ratio. This paper will show the probability that any two scanning LIDARs will interfere mutually by considering spatial and temporal overlaps. We have conducted four experiments to investigate the occurrence of the mutual interference between scanning LIDARs. These four experimental results introduced the effects of mutual interference and indicated that the interference has spatial and temporal locality. It is hard to ignore consecutive mutual interference on the same line or the same angle because it is possible the real object not noise or error. It may make serious faults because the obstacle detection functions of autonomous vehicle rely on heavily the scanning LIDAR.

• Current Optics and Photonics
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• v.6 no.2
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• pp.171-182
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• 2022

A single-photon laser and mid-wave infrared (MWIR) common aperture optical system was designed and developed to detect and range a long-distance civil aviation aircraft. The secondary mirror of the Ritchey-Chretien (R-C) optical system was chosen as a dichroic lens to realize the design of a common aperture system for the laser and MWIR. Point spread function (PSF) ellipticity was introduced to evaluate the coupling efficiency of the laser receiving system. A small aperture stop and narrow filter were set in the secondary image plane and an afocal light path of the laser system, respectively, and the stray light suppression ability of the small aperture stop was verified by modeling and simulation. With high-precision manufacturing technology by single point diamond turning (SPDT) and a high-efficiency dichroic coating, the laser/MWIR common aperture optical system with a 𝜑300 mm aluminum alloy mirror obtained images of buildings at a distance of 5 km with great quality. A civil aviation aircraft detection experiment was conducted. The results show that the common aperture system could detect and track long-distance civil aviation aircraft effectively, and the coverage was more than 450 km (signal-to-noise ratio = 6.3). It satisfied the application requirements for earlier warning and ranging of long-range targets in the area of aviation, aerospace and ground detection systems.

• Applied Chemistry for Engineering
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• v.33 no.1
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• pp.17-27
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• 2022

Fluorescence imaging is widely used to image cells or small animals due to its high temporal and spatial resolution. Because conventional fluorescence imaging uses visible light, the penetration depth of light within the tissue is low, phototoxicity may occur due to visible light, and the detection sensitivity is lowered due to interference by background autofluorescence. In order to overcome this limitation, long-wavelength light should be used, and fluorescence imaging using near-infrared-I (NIR-I) in the region of 700~900 nm has been developed. To further improve imaging quality, researchers are interested in using a longer wavelength light, near-infrared-II (NIR-II) ranging from 1000 to 1700 nm. In the NIR-II region, light scattering is further minimized, and the penetration depth of light in the tissue is improved up to about 10 mm, and autofluorescence of the tissue is reduced, enabling high sensitivity and resolution fluorescence imaging. In this review, among various NIR-II fluorescence imaging probes, inorganic nanoparticle-based probes with excellent photostability and easily tunable emission wavelength were described, focusing on single-walled carbon nanotubes, quantum dots, and lanthanide nanoparticles.

• Journal of Korean Society of Forest Science
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• v.100 no.3
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• pp.315-326
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• 2011

In recent years, domestic and international interests focus on climate change, and importance of forest as carbon sink have been also increased. Particularly REDD+ mechanism expanded from REDD (Reduced Emissions from Deforestation and Degradation) is expected to perform a new mechanism for reducing greenhouse gas in post 2012. To conduct this mechanism, countries which try to get a carbon credit have to certify effectiveness of their activities by MRV (Measuring, Reporting and Verification) system. This study analyzed the approaches for detecting land cover change and estimating carbon stock by remote sensing technology which is considered as the effective method to develop MRV system. The most appropriate remote sensing for detection of land cover change is optical medium resolution sensors and satellite SAR (Synthetic Aperture Radar) according to cost efficiency and uncertainty assessment. In case of estimating carbon stock, integration of low uncertainty techniques, airborne LiDAR (Light Detection and Ranging), SAR, and cost efficient techniques, optical medium resolution sensors and satellite SAR, could be more appropriate. However, due to absence of certificate authority, guideline, and standard of uncertainty, we should pay continuously our attention on international information flow and establish appropriate methods. Moreover, to apply monitoring system to developing countries, close collaboration and monitoring method reflected characteristics of each countries should be considered.

• 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.

• Korean Journal of Remote Sensing
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• v.39 no.5_4
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• pp.1155-1164
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• 2023

Currently, the National Forest Inventory (NFI) collects tree information by human, so the range and time of the survey are limited. Research is actively being conducted to extract tree information from a large area using aerial Light Detection And Ranging (LiDAR) and aerial photographs, but it does not reflect the characteristics of forest areas in Korea because it is conducted in areas with wide tree spacing or evenly spaced trees. Therefore, this study proposed a methodology for generating Digital Surface Model (DSM), Digital Elevation Model (DEM), and Canopy Height Model (CHM) images using aerial LiDAR, extracting the tree height through the local Maxima, and calculating the Diameter at Breath Height (DBH) through the DBH-tree height formula. The detection accuracy of trees extracted through the proposed methodology was 88.46%, 86.14%, and 84.31%, respectively, and the Root Mean Squared Error (RMSE) of DBH calculated based on the tree height formula was around 5cm, confirming the possibility of using the proposed methodology. It is believed that if standardized research on various types of forests is conducted in the future, the scope of automation application of the manual national forest resource survey can be expanded.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1095-1109
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• 2021

The technology used to recognize the location and surroundings of autonomous vehicles is called SLAM. SLAM standsfor Simultaneously Localization and Mapping and hasrecently been actively utilized in research on autonomous vehicles,starting with robotic research. Expensive GPS, INS, LiDAR, RADAR, and Wheel Odometry allow precise magnetic positioning and mapping in centimeters. However, if it can secure similar accuracy as using cheaper Cameras and GPS data, it will contribute to advancing the era of autonomous driving. In this paper, we present a method for converging monocular camera with RTK-enabled GPS data to perform RMSE 33.7 cm localization and mapping on the urban road.