• 한국지형공간정보학회:학술대회논문집
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• 2005.08a
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• pp.51-59
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• 2005

With industrial development, industrial products became more refined and upscale, resulting in increased necessity for manufacturers to come up with a method to check whether products have been produced to meet the needs of the customer. For thisreason, attention was drawn to industrial photogrammetry to obtain data rapidly without contacting the target object. In this study, the experiment has been conducted with the O-2A aircraft to determine the applicability of industrial photogrammetry.

• Journal of Korean Society for Atmospheric Environment
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• v.12 no.4
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• pp.407-419
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• 1996

Characteristics of visual air quality in Seoul have been investigated between June 13 and 21, 1994. Optical properties (extinction coefficient and particle scattering coefficient), meteorological parameters (relative humidity, temperature, wind speed, wind direction, and cloud cover), particle characteristics (mass size distribution, components) were measured and analyzed. During measurement periods, northwest wind with less than 2m/sec of wind speed deteriorates visibility. Effects of relative humidity are though to be not a direct factor which influence to visibility through the size change due to hygroscopic species in aerosol. During the smoggy period both the aerosol mass concentration and fine particle fraction of the size distribution are increased compared to the clear period. Sulfate, organic carbon, and elemental carbon in aerosol are the major species in determining the occurrence and severity of a smog in Seoul.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.5
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• pp.2414-2433
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• 2019

The aim of this paper is to design a 3D laser radar prototype based on laser triangulation. The mathematical model of distance sensitivity is deduced; a pixel-distance conversion formula is discussed and used to complete 3D scanning. The center position extraction algorithm of the spot is proposed, and the error of the linear laser, camera distortion and installation are corrected by using the proposed weighted average algorithm. Finally, the three-dimensional analytic computational algorithm is given to transform the measured distance into point cloud data. The experimental results show that this 3D laser radar can accomplish the 3D object scanning and the environment 3D reconstruction task. In addition, the experiment result proves that the product of the camera focal length and the baseline length is the key factor to influence measurement accuracy.

• Journal of Sensor Science and Technology
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• v.33 no.4
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• pp.187-195
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• 2024

Light detection and ranging (LiDAR), a widely used sensor in mobile robots and autonomous vehicles, has its most important function as measuring the range of objects in three-dimensional space and generating point clouds. These point clouds consist of the coordinates of each reflection point and can be used for various tasks, such as obstacle detection and environment recognition. However, several processing steps are required, such as three-dimensional modeling, mesh generation, and rendering. Efficient data processing is crucial because LiDAR provides a large number of real-time measurements with high sampling frequencies. Despite the rapid development of controller computational power, simplifying the computational algorithm is still necessary. This paper presents a method for estimating the presence of curbs, humps, and ground tilt using range measurements from a single horizontal or vertical scan instead of point clouds. These features can be obtained by data segmentation based on linearization. The effectiveness of the proposed algorithm was verified by experiments in various environments.

• Atmosphere
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• v.21 no.4
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• pp.429-438
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• 2011

The climatology of surface UV radiation for Seoul, presented in Cho et al. (1998; 2001), has been updated using measurement of surface erythemal ultraviolet (EUV) and total ultraviolet (TUV) irradiance (wavelength 286.5~363.0 nm) by a Brewer Spectrophotometer (MK-IV) for the period 2004~2010. The analysis was also carried out together with the broadband total (global) solar irradiance (TR ; 305~2800 nm) and cloud amount to compare with the UV variations, measured by Seoul meteorological station of Korean Meteorological Agency located near the present study site. Under all-sky conditions, the day-to-day variability of EUV exhibits annual mean of 98% in increase and 31% in decrease. It has been also shown that the EUV variability is 17 times as high as the total ozone in positive change, whereas this is 6 times higher in negative change. Thus, the day to day variability is dominantly caused rather by the daily synoptic situations than by the ozone variability. Annual mean value of daily EUV and TUV shows $1.62kJm^{-2}$ and $0.63MJm^{-2}$ respectively, whereas mean value of TR is $12.4MJm^{-2}$ ($143.1Wm^{-2}$). The yearly maximum in noon-time UV Index (UVI) varies between 9 and 11 depending on time of year. The highest UVI shows 11 on 20 July, 2008 during the period 2004~2010, but for the period 1994~2000, the index of 12 was recorded on 13 July, 1994 (Cho et al., 2001). A 40% of daily maximum UVI belongs to "low (UVI < 2)", whereas the UVI less than 5% of the maximum show "very high (8 < UVI < 10)". On average, the maximum UVI exceeded 8 on 9 days per year. The values of Tropospheric Emission Monitoring Internet Service (TEMIS) EUV and UVI under cloud-free conditions are 1.8 times and 1.5 times, respectively, higher than the all-sky measurements by the Brewer. The trend analysis in fractional deviation of monthly UV from the reference value shows a decrease of -0.83% and -0.90% $decade^{-1}$ in the EUV and TUV, respectively, whereas the TR trend is near zero (+0.11% $decade^{-1}$). The trend is statistically significant except for TR trend (p = 0.279). It is possible that the recent UV decrease is mainly associated with increase in total ozone, but the trend in TR can be attributed to the other parameters such as clouds except the ozone. Certainly, the cloud effects suggest that the reason for the differences between UV and TR trends can be explained. In order to estimate cloud effects, the EUV, TUV and TR irradiances have been also evaluated for clear skies (cloud cover < 25%) and cloudy skies (cloud cover ${\geq}$ 75%). Annual mean values show that EUV, TUV and TR are $2.15kJm^{-2}$, $0.83MJm^{-2}$, and $17.9MJm^{-2}$ for clear skies, and $1.24kJm^{-2}$, $0.46MJm^{-2}$, and $7.2MJm^{-2}$ for cloudy skies, respectively. As results, the transmission of radiation through clouds under cloudy-sky conditions is observed to be 58%, 55% and 40% for EUV, TUV and TR, respectively. Consequently, it is clear that the cloud effects on EUV and TUV are 18% and 15%, respectively lower than the effects on TR under cloudy-sky conditions. Clouds under all-sky conditions (average of cloud cover is 5 tenths) reduced the EUV and TUV to about 25% of the clear-sky (cloud cover < 25%) values, whereas for TR, this was 31%. As a result, it is noted that the UV radiation is attenuated less than TR by clouds under all weather conditions.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.294-297
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• 2007

Steam and advection fogs are frequently observed in the Yellow Sea located between Korea and China during the periods of March-April and June-July respectively. This study uses the remote sensing (RS) data for monitoring sea fog. Meteorological data obtained from the Ieodo Ocean Research Station provided an informative synopsis for the occurrence of steam and advection fogs through a ground truth. The RS data used in this study was GOES-9, MTSAT-1R images and QuikSCAT wind data. A dual channel difference (DCD) approach using IR and near-IR channel of GOES-9 and MTSAT-1R satellites was applied to estimate the extension of the sea fog. For the days examined, it was found that not only the DCD but also the texture-related measurement and the weak wind condition are required to separate the sea fog from the low cloud. The QuikSCAT wind is used to provide a weak wind area less than threshold under stable condition of the surface wind around a fog event. The Laplacian computation for a measurement of the homogeneity was designed. A new combined method of DCD, QuikSCAT wind speed and Laplacian was applied in the twelve cases with GOES-9 and MTSAT-1R. The threshold values for DCD, QuikSCAT wind speed and Laplacian are -2.0 K, 8 m $s^{-1}$ and 0.1, respectively. The validation methods such as Heidke skill score, probability of detection, probability of false detection, true skill score and odds ratio show that the new combined method improves the detection of sea fog rather than DCD method.

• Atmosphere
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• v.26 no.2
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• pp.243-256
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• 2016

Total number concentration of aerosols larger than 10 nm ($N_{CN10}$), 3 nm ($N_{CN3}$), and cloud condensation nuclei ($N_{CCN}$) were measured during four different ship cruises over the Yellow Sea. Average values of $N_{CN10}$ and $N_{CCN}$ at 0.6% supersaturation were 6914 and $3353cm^{-3}$, respectively, and the minimum value of $N_{CN10}$ was $2000cm^{-3}$, suggesting significant anthropogenic influence even at relatively clean marine environment. Although $N_{CN10}$ and $N_{CN3}$ increased near the coast due to anthropogenic influence, $N_{CCN}$ was relatively constant and therefore $N_{CCN}/N_{CN10}$ ratio tended to decrease, suggesting that coastal aerosols were relatively less hygroscopic. In general $N_{CN10}$, $N_{CN3}$, and $N_{CCN}$ during the cruises seemed to be significantly influenced by wet scavenging effects (e.g. fog) and boundary layer height variation. Only one new particle formation (NPF) event was observed during the measurement period. Interestingly, the NPF event occurred during a dust storm event and spatial scale of the NPF event was estimated to be larger than 100 km. These results demonstrate that aerosol and CCN concentration over the Yellow Sea can vary due to various different factors.

• Korean Journal of Remote Sensing
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• v.24 no.1
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• pp.1-16
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• 2008

Steam and advection fogs are frequently observed in the Yellow Sea from March to July except for May. This study uses remote sensing (RS) data for the monitoring of sea fog. Meteorological data obtained from the Ieodo Ocean Research Station provided a valuable information for the occurrence of steam and advection fogs as a ground truth. The RS data used in this study were GOES-9, MTSAT-1R images and QuikSCAT wind data. A dual channel difference (DCD) approach using IR and shortwave IR channel of GOES-9 and MTSAT-1R satellites was applied to detect sea fog. The results showed that DCD, texture-related measurement and the weak wind condition are required to separate the sea fog from the low cloud. The QuikSCAT wind data was used to provide the wind speed criteria for a fog event. The laplacian computation was designed for a measurement of the homogeneity. A new combined method, which includes DCD, QuikSCAT wind speed and laplacian computation, was applied to the twelve cases with GOES-9 and MTSAT-1R. The threshold values for DCD, QuikSCAT wind speed and laplacian are -2.0 K, $8m\;s^{-1}$ and 0.1, respectively. The validation results showed that the new combined method slightly improves the detection of sea fog compared to DCD method: improvements of the new combined method are $5{\sim}6%$ increases in the Heidke skill score, 10% decreases in the probability of false detection, and $30{\sim}40%$ increases in the odd ratio.

• Journal of Auto-vehicle Safety Association
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• v.13 no.4
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• pp.14-19
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• 2021

This paper presents LiDAR static obstacle map based vehicle dynamic state estimation algorithm for urban autonomous driving. In an autonomous driving, state estimation of host vehicle is important for accurate prediction of ego motion and perceived object. Therefore, in a situation in which noise exists in the control input of the vehicle, state estimation using sensor such as LiDAR and vision is required. However, it is difficult to obtain a measurement for the vehicle state because the recognition sensor of autonomous vehicle perceives including a dynamic object. The proposed algorithm consists of two parts. First, a Bayesian rule-based static obstacle map is constructed using continuous LiDAR point cloud input. Second, vehicle odometry during the time interval is calculated by matching the static obstacle map using Normal Distribution Transformation (NDT) method. And the velocity and yaw rate of vehicle are estimated based on the Extended Kalman Filter (EKF) using vehicle odometry as measurement. The proposed algorithm is implemented in the Linux Robot Operating System (ROS) environment, and is verified with data obtained from actual driving on urban roads. The test results show a more robust and accurate dynamic state estimation result when there is a bias in the chassis IMU sensor.

• The Journal of the Korea Contents Association
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• v.22 no.3
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• pp.54-61
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• 2022

Ship is a huge system including a variety of pipe arrangements. Pipes are installed according to the design layout, however the end poistion of pipes are not well matched owing to its measurement and construction errors. In this situation, the customized pipe fitting is frequently designed to connect with both pipes, the position of which are manually measured. This paper focused that these two coordinates are measured by point cloud from RGBD sensor and the relative transformation induced by positional and orientational differences is calculated by inverse kinematics in robotics theory. Therefore, the result applies for the methodology of the pipe connection design. The pipe coordinate that is estimated by the matching and the probabilistic RANSAC method will be verified by experiments. The kinematic design parameters are computationally calculated by using the minimum degree of freedom that connects both pipe coordinates.