• Journal of Auto-vehicle Safety Association
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• v.12 no.4
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• pp.23-30
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• 2020

Recently, self-driving research has been actively studied in various institutions. Accurate recognition is important because information about surrounding objects is needed for safe autonomous driving. This study mainly deals with the signal processing of LiDAR among sensors for object recognition. LiDAR is a sensor that is widely used for high recognition accuracy. First, we clustered and tracked objects by predicting relative position and speed of objects. The characteristic points of all objects were extracted using point cloud data of each objects through proposed algorithm. The Classification between vehicle and pedestrians is estimated using number of characteristic points and distances among characteristic points. The algorithm for classifying cars and pedestrians was implemented and verified using test vehicle equipped with LiDAR sensors. The accuracy of proposed object classification algorithm was about 97%. The classification accuracy was improved by about 13.5% compared with deep learning based algorithm.

• Korean Journal of Remote Sensing
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• v.22 no.1
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• pp.49-61
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• 2006

The lifting scheme has been found to be a flexible method for constructing scalar wavelets with desirable properties. In this paper, it is extended to the UDAR data compression. A newly developed data compression approach to approximate the UDAR surface with a series of non-overlapping triangles has been presented. Generally a Triangulated Irregular Networks (TIN) are the most common form of digital surface model that consists of elevation values with x, y coordinates that make up triangles. But over the years the TIN data representation has become an important research topic for many researchers due its large data size. Compression of TIN is needed for efficient management of large data and good surface visualization. This approach covers following steps: First, by using a Delaunay triangulation, an efficient algorithm is developed to generate TIN, which forms the terrain from an arbitrary set of data. A new interpolation wavelet filter for TIN has been applied in two steps, namely splitting and elevation. In the splitting step, a triangle has been divided into several sub-triangles and the elevation step has been used to 'modify' the point values (point coordinates for geometry) after the splitting. Then, this data set is compressed at the desired locations by using second generation wavelets. The quality of geographical surface representation after using proposed technique is compared with the original UDAR data. The results show that this method can be used for significant reduction of data set.

• Current Optics and Photonics
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• v.4 no.3
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• pp.174-185
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• 2020

Multiscattering occurs when a laser transmits into dense atmosphere targets (e.g. fogs, smoke or clouds), which can cause depolarization effects even though the scattering particles are spherical. In addition, multiscattering effects have additional information about microphysical properties of scatterers. Thus, multiscattering can be utilized to study the microphysical properties of the liquid water cloud. In this paper, a Monte Carlo method was used to simulate multi-scattering transmission properties of Lidar signals in the cloud. The results showed the slope of the degree of linear polarization (SLDLP) can be used to invert the extinction coefficient, and then the cloud effective size (CES) and the liquid water content (LWC) may be easily obtained by using the extinction coefficient and saturation of the degree of linear polarization (SADLP). Based on calculation results, a microphysical properties inversion method for a liquid cloud was presented. An innovative multiscattering polarization Lidar (MSPL) system was constructed to measure the LWC and CES of the liquid cloud, and a new method based on the polarization splitting ratio of the Polarization Beam Splitter (PBS) was developed to calibrate the polarization channels of MSPL. By analyzing the typical observation data of MSPL observation in the northern suburbs of Nanjing, China, the LWC and CES of the liquid water cloud were obtained. Comparisons between the results from the MSPL, MODIS and the Microwave radar data showed that, the microphysical properties of liquid cloud could be retrieved by combining our MSPL and the inversion method.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.1099-1110
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• 2023

The recognition system of autonomous driving and robot navigation performs vision work such as object recognition, tracking, and lane detection after multi-sensor fusion to improve performance. Currently, research on a deep learning model based on the fusion of a camera and a lidar sensor is being actively conducted. However, deep learning models are vulnerable to adversarial attacks through modulation of input data. Attacks on the existing multi-sensor-based autonomous driving recognition system are focused on inducing obstacle detection by lowering the confidence score of the object recognition model.However, there is a limitation that an attack is possible only in the target model. In the case of attacks on the sensor fusion stage, errors in vision work after fusion can be cascaded, and this risk needs to be considered. In addition, an attack on LIDAR's point cloud data, which is difficult to judge visually, makes it difficult to determine whether it is an attack. In this study, image scaling-based camera-lidar We propose an attack method that reduces the accuracy of LCCNet, a fusion model (camera-LiDAR calibration model). The proposed method is to perform a scaling attack on the point of the input lidar. As a result of conducting an attack performance experiment by size with a scaling algorithm, an average of more than 77% of fusion errors were caused.

• Journal of Korean Society for Geospatial Information Science
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• v.19 no.4
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• pp.139-144
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• 2011

This study proposed a classification method of LIDAR data by using simultaneously the color information (R, G, B) and reflection intensity information (I) obtained from terrestrial LIDAR and by analyzing the association between these data through the use of statistical classification methods. To this end, first, the factors that maximize variance were calculated using the variables, R, G, B, and I, whereby the factor matrix between the principal factor and each variable was calculated. However, although the factor matrix shows basic data by reducing them, it is difficult to know clearly which variables become highly associated by which factors; therefore, Varimax method from orthogonal rotation was used to obtain the factor matrix and then the factor scores were calculated. And, by using a non-hierarchical clustering method, K-mean method, a cluster analysis was performed on the factor scores obtained via K-mean method as factor analysis, and afterwards the classification accuracy of the terrestrial LiDAR data was evaluated.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2010.04a
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• pp.341-345
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• 2010

It is known that the accuracy of DEM is related with terrain morphology, sampling density, and interpolation method. However, the theoretical reasons for these correlations have rarely been accounted for so far. This study aimed to verify a theoretical basis that DEM accuracy can be assessed based on approximation theory when we generate a DEM using lots of precise and accurate source data such as digital maps and LIDAR data.

• Current Optics and Photonics
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• v.1 no.3
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• pp.175-185
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• 2017

The aerosol optical properties such as depolarization ratio (${\delta}$) and aerosol extinction-to-backscatter ratios (S, LIDAR ratio) and ${\AA}ngstr{\ddot{o}m$ exponent (${\AA}$) derived from measurement with AERONET sun/sky radiometer at Gangneung-Wonju National University (GWNU), Gangneung, Korea ($37.77^{\circ}N$, $128.87^{\circ}E$) during a winter season (December 2014 - February 2015) are presented. The PM concentration measurements are conducted simultaneously and used to identify the high-PM events. The observation period was divided into three cases according to the PM concentrations. We analysed the ${\delta}$, S, and ${\AA}$ during these high PM-events. These aerosol optical properties are calculated by the sun/sky radiometer data and used to classify a type of aerosols (e.g., dust, anthropogenic pollution). The higher values of ${\delta}$ with lower values of S and ${\AA}$ were measured for the dust particles. The mean values of ${\delta}$, S, and ${\AA}$ at 440-870 nm wavelength pair (${\AA}_{440-870}$) for the Asia dust were 0.19-0.24, 36-56 sr, and 0.48, respectively. The anthropogenic aerosol plumes are distinguished with the lower values of ${\delta}$ and higher values of ${\AA}$. The mean values of spectral ${\delta}$ and ${\AA}_{440-870}$ for this case varied 0.06-0.16 and 1.33-1.39, respectively. We found that aerosol columnar optical properties obtained from the sun/sky radiometer measurement are useful to identify the aerosol type. Moreover, the columnar aerosol optical properties calculated based on sun/sky radiometer measurements such as ${\delta}$, S, and ${\AA}$ will be further used for the validation of aerosol parameters obtained from LIDAR observation as well as for quantification of the air quality.

• Proceedings of the Korean Environmental Sciences Society Conference
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• 1996.04a
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• pp.22-22
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• 1996

;The Kwinana Shoreline Fumigation Experiment(KSFE) took place in Fremantle, WA, Australia between 23 January and 8 February, 1995. All measurement systems performed to expectation. The CSIRO DAR(Division of Atmospheric Research) LIDAR measured plume sections from near the Kwinana Power Station(KPS) stacks to up to about 5 km downstream. It also measured boundary layer aerosols and the structure of the boundary layer on some occasions. Both stages A and C of KPS were used as tracers at different times. Radiosonde and double theodolite sounding systems measured temperature, humidity, air pressure and wind structure at the coast(Woodman Point) and at the inland(ALCOA residue dump) site at intervals of roughly two hours. These were supplemented by mid afternoon soundings(radiosonde and single theodolite) by Department of Environmental Protection(DEP) at Swanbourne. The Flinders aircraft measured wind, turbulence and temperature structure of the atmospheric boundary layer, concentrations of $C0_2,\;0_3,\;S0_2\;and\;NO_x$ in the smoke plumes and surface radiation over both land and sea. CSIRO DCET(Division of Coal and Energy Technology) vehicle successfully interceptde many smoke plumes and using a range of tracers will be able to identify the various sources much of the time. Routine data from the DEP and Kwinana Industrial Council(KIC) air quality monitoring networks were also automatically logged. Murdoch University measured surface heat flux at Hope Valldy monitoring station and also at Wattleup monitoring station for the last five days. The heart of the LIDAR system is a Neodymium-doped Yttrium-aluminumgarnet(Nd:Y AG) laser operating at a fundamental wavelength of 1064 nm, with harmonics fo 532 nm and 355 nm. A small fraction of the laser beam is scattered back to the LIDAR, collected by a telescope and detedted by a photomultiplier tube. The intensity of the signal as a function of time is a measure of the particle concentration as a function of distance along the line of the laser shot. The results of nine days special field observations are summarized in detail.etail.

• Current Optics and Photonics
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• v.2 no.2
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• pp.195-202
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• 2018

A Scanning Rayleigh Doppler lidar for wind profiling based on a non-polarized beam splitter cube optically contacted FPI is developed for wind measurement from high troposphere to low stratosphere in 5-35 km. Non-polarized beam splitter cube optically contacted to the FPI are used for a stable optical receiver. Zero Doppler shift correction is used to correct for laser or FPI frequency jitter and drift and the timing sequence is designed. Stability of the receiver for Doppler shift discrimination is validated by measuring the transmissions of FPI in different days and analyzed the response functions. The maximal relative wind deviation due to the stability of the optical receiver is about 4.1% and the standard deviation of wind velocity is 1.6% due to the stability. Wind measurement comparison experiments were carried out in Jiuquan ($39.741^{\circ}N$, $98.495^{\circ}E$), Gansu province of China in 2015, showing good agreement with radiosonde result data. Continuous wind field observation was performed from October 16th to November 12th and semi-continuous wind field of 19 nights are presented.

• Korean Journal of Remote Sensing
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• v.32 no.2
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• pp.119-131
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• 2016

Descriptions are provided of the automated aerosol-type classification and mass concentration calculation algorithm for real-time data processing and aerosol products in Korea Aerosol Lidar Observation Network (KALION, http://www.kalion.kr). The KALION algorithm provides aerosol-cloud classification and three aerosol types (clean continental, dust, and polluted continental/urban pollution aerosols). It also generates vertically resolved distributions of aerosol extinction coefficient and mass concentration. An extinction-to-backscatter ratio (lidar ratio) of 63.31 sr and aerosol mass extinction efficiency of $3.36m^2g^{-1}$ ($1.39m^2g^{-1}$ for dust), determined from co-located sky radiometer and $PM_{10}$ mass concentration measurements in Seoul from June 2006 to December 2015, are deployed in the algorithm. To assess the robustness of the algorithm, we investigate the pollution and dust events in Seoul on 28-30 March, 2015. The aerosol-type identification, especially for dust particles, is agreed with the official Asian dust report by Korean Meteorological Administration. The lidar-derived mass concentrations also well match with $PM_{10}$ mass concentrations. Mean bias difference between $PM_{10}$ and lidar-derived mass concentrations estimated from June 2006 to December 2015 in Seoul is about $3{\mu}g\;m^{-3}$. Lidar ratio and aerosol mass extinction efficiency for each aerosol types will be developed and implemented into the KALION algorithm. More products, such as ice and water-droplet cloud discrimination, cloud base height, and boundary layer height will be produced by the KALION algorithm.