54 research outputs found

    3-dimensional Modeling and Mining Analysis for Open-pit Limestone Mine Stope Using a Rotary-wing Unmanned Aerial Vehicle

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    The purpose of this study is to show the possibility of 3-dimensional modeling of open-pit limestone mine by using a rotary-wing unmanned aerial vehicle, a drone, and to estimate the amount of mining before and after mining of limestone by explosive blasting. Analysis of the image duplication of the mine has shown that it is possible to achieve high image quality. Analysis of each axis error at the shooting position after analyzing the distortions through camera calibration was shown the allowable range. As a result of estimating the amount of mining before and after explosive blasting, it was possible to estimate the amount of mining of a wide range quickly and accurately in a relatively short time. In conclusion, it is considered that the drone of a rotary-wing unmanned aerial vehicle can be usefully used for the monitoring of open-pit limestone mines and the estimation of the amount of mining. Furthermore, it is expected that this method will be utilized for periodic monitoring of construction sites and road slopes as well as open-pit mines in the future

    Unmanned aerial vehicle remotely sensed datasets, a reference dataset for coastal topography change and shoreline analysis

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    To analyze tendency of temporal and spatial change of coast using long-term topography and shoreline change data is important. In this study, high-resolution digital elevation model and orthorectified image data were generated using rotary-wing UAV(unmanned aerial vehicle) system for coastal topography and shoreline change analysis. The UAV system has advantage of low cost and high efficiency compared to satellite remote sensing platform so UAV system easily acquire time series image data. The spatial resolution of generated digital elevation model and orthorectified images are very high, in centimeter. Therefore, the above image data can be used in various fields of remote sensing and geography such as detailed coastal topography

    UAV based infrared aerial thermal imaging and analysis for the estimation of crop water stress

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๋†์—…์ƒ๋ช…๊ณผํ•™๋Œ€ํ•™ ๋ฐ”์ด์˜ค์‹œ์Šคํ…œ๊ณตํ•™๊ณผ, 2023. 2. ๊น€๊ธฐ์„.๊ณผ์ˆ˜ ์ƒ์‚ฐ์— ์žˆ์–ด ํ•ต์‹ฌ์ ์ธ ํšจ์œจ์ ์ธ ๊ด€์ˆ˜ ๊ด€๋ฆฌ๋Š” ์™ธ๊ธฐ ์˜ํ–ฅ์— ๋”ฐ๋ฅธ ๊ณผ์ˆ˜ ์ž‘๋ฌผ์˜ ์ˆ˜๋ถ„ ์ƒํƒœ๋ฅผ ์ •ํ™•ํ•˜๊ฒŒ ํŒŒ์•…ํ•˜๋Š” ๊ฒƒ์œผ๋กœ๋ถ€ํ„ฐ ์‹œ์ž‘ํ•œ๋‹ค. ๋”ฐ๋ผ์„œ ์ž‘๋ฌผ์˜ ์ˆ˜๋ถ„์ŠคํŠธ๋ ˆ์Šค ์ •๋Ÿ‰ํ™”์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋Š” ์ง€์†์ ์œผ๋กœ ์ด๋ฃจ์–ด์ ธ ์™”์œผ๋ฉฐ ์ตœ๊ทผ์—๋Š” ์›๊ฒฉํƒ์‚ฌ ๊ธฐ์ˆ ์„ ์ด์šฉํ•ด ๋„“์€ ๋ฉด์ ์˜ ๋ถ„ํฌํ•œ ์ž‘๋ฌผ์˜ ์ˆ˜๋ถ„์ŠคํŠธ๋ ˆ์Šค๋ฅผ ์งง์€ ์‹œ๊ฐ„ ์•ˆ์— ๋” ์ •๋ฐ€ํ•˜๊ฒŒ ํ‰๊ฐ€ํ•˜๊ธฐ ์œ„ํ•œ ์—ฐ๊ตฌ๋“ค์ด ์ฃผ๋ฅผ ์ด๋ฃจ๊ณ  ์žˆ๋‹ค. ํŠนํžˆ ์—ฝ์˜จ์„ ์ด์šฉํ•œ ๊ฒฝํ—˜์  ์ˆ˜๋ถ„์ŠคํŠธ๋ ˆ์Šค ์ง€์ˆ˜(crop water stress index , CWSI)๋Š” ์ž‘๋ฌผ์˜ ์ˆ˜๋ถ„ ์ƒํƒœ๋ฅผ ํ‰๊ฐ€ํ•˜๋Š” ์ง€ํ‘œ๋กœ์„œ ๋„๋ฆฌ ํ™œ์šฉ๋˜์–ด ์™”๋Š”๋ฐ, ๋ฌด์ธํ•ญ๊ณต๊ธฐ์— ํƒ‘์žฌ๋œ ์ ์™ธ์„  ์—ด์˜์ƒ ์„ผ์„œ๋ฅผ ์ด์šฉํ•ด ๋„“์€ ๋ฉด์ ์˜ ํ•ญ๊ณต์—ด์˜์ƒ์„ ํš๋“ํ•˜๊ณ  ์—ด์˜์ƒ์—์„œ ์ถ”์ถœ๋œ ์—ฝ์˜จ ๋ฐ์ดํ„ฐ์™€ ๋Œ€๊ธฐํ™˜๊ฒฝ ๋ฐ์ดํ„ฐ๋ฅผ ์ด์šฉํ•ด ๊ฒฝํ—˜์  CWSI๋ฅผ ๋„์ถœํ•˜๋Š” ์—ฐ๊ตฌ๊ฐ€ ๋งŽ์ด ์ด๋ฃจ์–ด์กŒ๋‹ค. ํ•˜์ง€๋งŒ ๋ฌด์ธํ•ญ๊ณต๊ธฐ๋Š” ์žฅ๊ธฐ๊ฐ„ ์—ฐ์†์ ์ธ ๊ด€์ธก์ด ์–ด๋ ต๊ธฐ ๋•Œ๋ฌธ์— ๋‹ค์–‘ํ•œ ํ™˜๊ฒฝ์—์„œ์˜ ๊ฒฝํ—˜์  ๋ฐ์ดํ„ฐ์— ๊ธฐ๋ฐ˜ํ•ด ์ˆ˜๋ถ„์ŠคํŠธ๋ ˆ์Šค์˜ ์ƒํ•œ๊ณผ ํ•˜ํ•œ์— ๋Œ€ํ•œ ๊ธฐ์ค€์„ ์ด ํ•„์š”ํ•œ ๊ฒฝํ—˜์  CWSI ์—ฐ๊ตฌ์— ์ตœ์ ์˜ ๋ฐ์ดํ„ฐ ์ˆ˜์ง‘ ๋ฐฉ๋ฒ•์ด๋ผ๊ณ  ๋ณด๊ธฐ๋Š” ์–ด๋ ต๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ํ•ญ๊ณต์—ด์˜์ƒ ์˜จ๋„ ๋ณด์ • ๋ชจ๋ธ์„ ์ œ์‹œ, ์—ฝ์˜จ ์ถ”์ถœ์„ ์œ„ํ•œ ์˜์ƒ์ฒ˜๋ฆฌ ๊ธฐ๋ฒ•์„ ๊ฐœ์„ , ์ธ๊ณต์‹ ๊ฒฝ๋ง ๊ธฐ๋ฐ˜์˜ ์ƒˆ๋กœ์šด ์ˆ˜๋ถ„์ŠคํŠธ๋ ˆ์Šค ์ง„๋‹จ ๋ชจ๋ธ์„ ๊ฐœ๋ฐœ์„ ํ†ตํ•ด ์ˆ˜๋ถ„์ŠคํŠธ๋ ˆ์Šค ํ‰๊ฐ€์˜ ์ •ํ™•๋„๋ฅผ ํ–ฅ์ƒํ•˜๊ณ  ๋ฌด์ธํ•ญ๊ณต๊ธฐ๋ฅผ ์ด์šฉํ•œ ์ˆ˜๋ถ„์ŠคํŠธ๋ ˆ์Šค ํ‰๊ฐ€์— ์ ํ•ฉํ•œ ๋ชจ๋ธ์„ ์ œ์‹œํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ๋จผ์ € ํ•ญ๊ณต์—ด์˜์ƒ ์˜จ๋„ ๋ณด์ • ๋ชจ๋ธ์€ ํœด๋Œ€์šฉ ๋Œ€๋ฉด์ (300mmร—300mm) ํ‘์ฒด์˜ ์˜จ๋„๋ฅผ ์ง€์ƒ๊ณผ ํ•ญ๊ณต์—์„œ ๊ฐ๊ฐ ์ธก์ •ํ•˜์—ฌ ๊ทธ ์ฐจ์ด๋ฅผ ์˜ˆ์ธกํ•˜๋Š” ์ธ๊ณต์‹ ๊ฒฝ๋ง ๋ชจ๋ธ๋กœ ๊ตฌ์„ฑํ•˜์˜€๋‹ค. ํˆฌ์ž… ๋ณ€์ˆ˜๋กœ๋Š” ์ดฌ์˜๊ณ ๋„, ๋Œ€๊ธฐ์˜จ๋„, ์ƒ๋Œ€์Šต๋„, ์ผ์‚ฌ๋Ÿ‰, ํ’์† ๋“ฑ์ด ์‚ฌ์šฉ๋˜์—ˆ์œผ๋ฉฐ, ์˜จ๋„ ๋ณด์ • ๊ฒฐ๊ณผ ์‹ค์ œ ํ‘์ฒด ์˜จ๋„์™€ ๋ณด์ •๋œ ์˜จ๋„ ๊ฐ„์˜ RMSE๊ฐ€ 0.68 โ„ƒ๋กœ ์ž‘์€ ์˜ค์ฐจ๋ฅผ ๋ณด์˜€๋‹ค. ํ•ญ๊ณต ์ธก์ • ์—ฝ์˜จ์— ๋ณด์ • ๋ชจ๋ธ์„ ์ ์šฉํ•˜์—ฌ ์ง€์ƒ ์ ์™ธ์„  ์—ด์˜์ƒ ์„ผ์„œ์™€ ๊ธฐ๊ณต์ „๋„๋„ ์„ผ์„œ์—์„œ ์ธก์ •๋œ ์—ฝ์˜จ๊ณผ ๋น„๊ตํ•œ ๊ฒฐ๊ณผ RMSE๋Š” ๊ฐ๊ฐ 0.73 โ„ƒ, 1.13 โ„ƒ๋กœ ํฌ๊ฒŒ ๊ฐœ์„ ๋˜์—ˆ๋‹ค. ํ•œํŽธ, ํ•ญ๊ณต์˜์ƒ์—์„œ์˜ ์ˆ˜๊ด€ ์˜์—ญ ๋ถ„ํ• ์„ ํ†ตํ•œ ์ •๋ฐ€ ์—ฝ์˜จ ์ถ”์ถœ์„ ์œ„ํ•ด์„œ CHM(Canopy Height Model, ์ˆ˜๊ด€๋†’์ด๋ชจ๋ธ)๊ณผ ๋”ฅ๋Ÿฌ๋‹ Mask R-CNN(Mask Regional-Convolutional Neural Network) ๋ฐฉ๋ฒ•์„ ๋น„๊ตํ•˜์˜€์œผ๋ฉฐ, Mask R-CNN ๊ธฐ๋ฐ˜์˜ ์ˆ˜๊ด€ ์˜์—ญ ๋ถ„ํ•  ๊ฒฐ๊ณผ, ํ‰๊ท  ์ •ํ™•๋„ ๊ธฐ์ค€ 0.95์˜ ์„ฑ๋Šฅ์„ ๋ณด์—ฌ CHM์— ๋น„ํ•ด ๋” ๋‚˜์€ ์ˆ˜๊ด€ ์˜์—ญ ๋ถ„ํ•  ๊ฒฐ๊ณผ๋ฅผ ๋ณด์˜€๋‹ค. ์ˆ˜๊ด€ ์˜์—ญ ๋ถ„ํ•  ๊ฒฐ๊ณผ ์ถ”์ถœ๋œ ์—ฝ์˜จ ๋ฐ์ดํ„ฐ๋ฅผ ์ด์šฉํ•ด ๊ณผ์ˆ˜ ์ˆ˜๋ถ„์ŠคํŠธ๋ ˆ์Šค ์ง„๋‹จ ๋ชจ๋ธ์„ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. ์ˆ˜๋ถ„์ŠคํŠธ๋ ˆ์Šค ์ง„๋‹จ ๋ชจ๋ธ์€ ์—ฝ์˜จ, ๋Œ€๊ธฐ์˜จ๋„, ์ƒ๋Œ€์Šต๋„๋ฅผ ์ด์šฉํ•ด ๊ธฐ๊ณต์ „๋„๋„๋ฅผ ์˜ˆ์ธกํ•˜๋Š” ์ธ๊ณต์‹ ๊ฒฝ๋ง์œผ๋กœ ๊ตฌ์„ฑ๋˜์—ˆ๋‹ค. ๋ชจ๋ธ ์˜ˆ์ธก ๊ฒฐ๊ณผ์™€ ๊ธฐ๊ณต์ „๋„๋„ ๊ฐ’ ๊ฐ„์˜ ์ƒ๊ด€๊ณ„์ˆ˜๋Š” 0.86๋กœ ๊ฒฝํ—˜์  CWSI์™€ ๊ธฐ๊ณต์ „๋„๋„ ๊ฐ’ ๊ฐ„์˜ ์ƒ๊ด€๊ณ„์ˆ˜์ธ 0.56๋ณด๋‹ค ํฌ๊ฒŒ ๊ฐœ์„ ๋˜๋Š” ๊ฒƒ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ด๋Ÿฌํ•œ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋Š” ๋ณธ ์—ฐ๊ตฌ์—์„œ ์ œ์‹œ๋œ ๋ชจ๋ธ์ด ๊ธฐ์กด์˜ ๊ฒฝํ—˜์  CWSI์— ๋น„ํ•ด ๋ฌด์ธํ•ญ๊ณต๊ธฐ ๊ธฐ๋ฐ˜์˜ ์ˆ˜๋ถ„์ŠคํŠธ๋ ˆ์Šค ํ‰๊ฐ€์— ๋” ์ ํ•ฉํ•˜๋‹ค๋Š” ๊ฒƒ์„ ๋ณด์—ฌ์ฃผ์—ˆ๋‹ค๋Š” ์ ์—์„œ ์˜์˜๊ฐ€ ์žˆ์—ˆ๋‹ค.Efficient irrigation management, an important key to improve productivity, begins with accurate monitoring of the water status of crops. Therefore, researches on the crop water stress has been conducted, and in the present days, studies focus on using remote sensing technology in monitoring crop water stress of wider area in shorter time. Canopy temperature has been used as a major indicator for crop water stress evaluation for especially tree crops. Therefore, measuring crop water stress based on the aerial thermal image of canopy using unmanned aerial vehicle (UAV) has been widely attempted. In particular, the empirical crop water stress index (CWSI) which is calculated based on the canopy temperature has been used as an indicator of crop water status. However, although computation of empirical CWSI requires continuously collected data, UAVs are bound to have intermittent observations due to realistic constraints. Therefore, empirical CWSI may not be the best way to evaluate water stress when using UAV. The major objective of the present study is to improve the accuracy of crop water stress measurement based on aerial infrared thermal imaging and also to suggest a novel crop water stress evaluation model which is more suitable for UAV based observations. A temperature calibration modeling, image processing techniques for canopy area segmentation, and the novel artificial neural network (ANN)-based crop water stress evaluation model are suggested to achieve the main goals. First, we developed an ANN-based temperature calibration model using a large-area(300mmร—300mm) blackbody which is a material that has emissivity of 1. The goal of calibration model was to predict temperature difference between the ground (ground truth) and airborne measurement of blackbody temperature. The flight altitude, air temperature, relative humidity, solar radiation, and wind speed were used as input variables. As a result of the calibration process, the RMSE between the calibrated airborne measurement and ground measurement was 0.68 โ„ƒ which can be considered as a minor error. Next, canopy area segmentation performance of the canopy height model (CHM) and Mask R-CNN instance segmentation model were compared to determine the better model for accurate canopy temperature extraction. Mask R-CNN instance segmentation model showed better performance in canopy area segmentation. An average precision (AP)50 of 0.95 was achieved by the model meaning that of all the tree canopy area predictions by the model, 95% of them were true positives. Finally, a novel crop water stress evaluation model was developed. The main concept of the model was to develop an ANN model that predicts stomatal conductance (g_sw) of the canopy using the canopy temperature, air temperature, and relative humidity data. The R^2 between the prediction and the actual g_sw was 0.86, which showed a significant improvement compared to the result that the R^2 between conventional empirical CWSI and the g_sw was 0.56. However, the R^2 decreased to 0.79 when VPD was additionally used as an input variable and R^2 also decreased to 0.84 when air temperature variable was replaced by VPD. The results of this study are significant in that it showed the novel ANN-based model is more suitable and precise in crop water stress evaluation using UAV compared to the conventional empirical CWSI.์ œ 1 ์žฅ ์„œ ๋ก  1 1.1 ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ 1 1.2 ์—ฐ๊ตฌ์˜ ๋ชฉ์  4 1.3 ์—ฐ๊ตฌ์‚ฌ 6 1.3.1 ํ•ญ๊ณต์—ด์˜์ƒ ๋ณด์ •์— ๊ด€ํ•œ ์—ฐ๊ตฌ 6 1.3.2 ํ•ญ๊ณต์˜์ƒ์ฒ˜๋ฆฌ์— ๊ด€ํ•œ ์—ฐ๊ตฌ 7 1.3.3 ์›๊ฒฉํƒ์‚ฌ ๊ธฐ๋ฐ˜ ์ž‘๋ฌผ ์ˆ˜๋ถ„์ŠคํŠธ๋ ˆ์Šค์— ๊ด€ํ•œ ์—ฐ๊ตฌ 9 ์ œ 2 ์žฅ ์žฌ๋ฃŒ ๋ฐ ๋ฐฉ๋ฒ• 11 2.1 ์‹œํ—˜ํฌ์žฅ ๋ฐ ๋ฌด์ธํ•ญ๊ณต๊ธฐ(UAV) ์‹œ์Šคํ…œ 11 2.1.1 ์‹œํ—˜ํฌ์žฅ 11 2.1.2 ๋ฌด์ธํ•ญ๊ณต๊ธฐ(UAV) ์‹œ์Šคํ…œ ๋ฐ ๋น„ํ–‰ 14 2.1.3 ํ•ญ๊ณต์ดฌ์˜ ๋ฐ ์ง€์ƒ์‹คํ—˜ 17 2.2 ํ•ญ๊ณต์—ด์˜์ƒ ์ธก์ • ๋ฐ ๋ณด์ • 19 2.2.1 ์ ์™ธ์„  ์—ด์˜์ƒ ์ธก์ • ์‹œ์Šคํ…œ 19 2.2.2 ํ‘์ฒด ์‹œ์Šคํ…œ 21 2.2.3 ํ•ญ๊ณต์—ด์˜์ƒ ๋ณด์ • ๋ชจ๋ธ ๊ฐœ๋ฐœ 24 2.3 ํ•ญ๊ณต์˜์ƒ ๋ฐ ์—ด์˜์ƒ ์˜์ƒ์ฒ˜๋ฆฌ 34 2.3.1 ์˜์ƒ์ •ํ•ฉ(Orthomosaic) ๋ฐ ํฌ์ธํŠธํด๋ผ์šฐ๋“œ ์ƒ์„ฑ 34 2.3.2 CHM ๊ธฐ๋ฐ˜ ์ˆ˜๊ด€ ์˜์—ญ ๋ถ„ํ•  38 2.3.3 ๋”ฅ๋Ÿฌ๋‹ ๊ฐœ์ฒด ๋ถ„ํ•  ๊ธฐ๋ฐ˜ ์ˆ˜๊ด€ ์˜์—ญ ๋ถ„ํ•  41 2.3.4 ์—ฝ์˜จ ์ถ”์ถœ 45 2.4 ์—ฝ์˜จ ๋ฐ์ดํ„ฐ๋ฅผ ์ด์šฉํ•œ ์ˆ˜๋ถ„์ŠคํŠธ๋ ˆ์Šค ์ถ”์ธก์ • ๋ชจ๋ธ ๊ฐœ๋ฐœ 46 2.4.1 CWSI ๋ถ„์„ 46 2.4.2 ์ง€์ƒ์—์„œ์˜ ์—ฝ์˜จ ์ธก์ •๊ณผ CWSI ๋ถ„์„ 47 2.4.3 ์ธ๊ณต์‹ ๊ฒฝ๋ง์„ ์ด์šฉํ•œ ์ˆ˜๋ถ„์ŠคํŠธ๋ ˆ์Šค ์ง„๋‹จ ๋ชจ๋ธ ๊ฐœ๋ฐœ ์—ฐ๊ตฌ 51 ์ œ 3 ์žฅ ๊ฒฐ๊ณผ ๋ฐ ๊ณ ์ฐฐ 54 3.1 ํ‘์ฒด ์‹œ์Šคํ…œ ๊ธฐ๋ฐ˜์˜ ์—ด์˜์ƒ ๋ณด์ • 54 3.1.1 ํ‘์ฒด ์‹œ์Šคํ…œ ์„ฑ๋Šฅ ํ‰๊ฐ€ 54 3.1.2 ํ•ญ๊ณต์—ด์˜์ƒ ๋ณด์ • ๋ชจ๋ธ ๊ฐœ๋ฐœ 59 3.2 ํ•ญ๊ณต์˜์ƒ ๋ฐ ์—ด์˜์ƒ ์˜์ƒ์ฒ˜๋ฆฌ ๊ฒฐ๊ณผ 65 3.2.1 CHM(CHM) ๊ธฐ๋ฐ˜ ์ˆ˜๊ด€ ์˜์—ญ ๋ถ„ํ•  ๊ฒฐ๊ณผ 65 3.2.2 ๋”ฅ๋Ÿฌ๋‹ ๊ฐœ์ฒด๋ถ„ํ•  ๊ธฐ๋ฐ˜ ์ˆ˜๊ด€ ์˜์—ญ ๋ถ„ํ•  ๊ฒฐ๊ณผ 70 3.2.3 ์ˆ˜๊ด€ ์˜์—ญ ๋ถ„ํ• ๊ณผ ์—ฝ์˜จ ์ถ”์ถœ 74 3.3 ์ธ๊ณต์‹ ๊ฒฝ๋ง ๊ธฐ๋ฐ˜ ์ˆ˜๋ถ„์ŠคํŠธ๋ ˆ์Šค ์ถ”์ธก์ • ๋ชจ๋ธ ๊ฐœ๋ฐœ ์—ฐ๊ตฌ 76 3.3.1 ์ง€์ƒ ์ ์™ธ์„  ์—ด์˜์ƒ ์„ผ์„œ๋ฅผ ์ด์šฉํ•œ ์ˆ˜๋ถ„์ŠคํŠธ๋ ˆ์Šค ๋ถ„์„ 76 3.3.2 ๊ธฐ๊ณต์ „๋„๋„ ์„ผ์„œ๋ฅผ ์ด์šฉํ•œ ์ˆ˜๋ถ„์ŠคํŠธ๋ ˆ์Šค ๋ถ„์„ 81 3.3.3 ์ ์™ธ์„  ํ•ญ๊ณต์—ด์˜์ƒ ๊ธฐ๋ฐ˜ ์ˆ˜๋ถ„์ŠคํŠธ๋ ˆ์Šค ๋ถ„์„ 85 3.3.4 ์ธ๊ณต์‹ ๊ฒฝ๋ง ๊ธฐ๋ฐ˜ ์ˆ˜๋ถ„์ŠคํŠธ๋ ˆ์Šค ์ง„๋‹จ ๋ชจ๋ธ ๊ฐœ๋ฐœ 90 ์ œ 4 ์žฅ ์š”์•ฝ 94 ์ฐธ๊ณ ๋ฌธํ—Œ 96 Abstract 100์„

    A Study on Laws and Regimes for the Revitalization of the Autonomous Marine Vehicles

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    ๋ฌด์ธ์ด๋™์ฒด(Autonomous Vehicles, AV)๋Š” โ€œ๊ธฐ๊ธฐ๊ฐ€ ์™ธ๋ถ€ ํ™˜๊ฒฝ์„ ์ธ์‹ํ•˜๊ณ  ์Šค์Šค๋กœ ํŒ๋‹จํ•˜์—ฌ ์ด๋™ํ•˜๊ฑฐ๋‚˜ ์™ธ๋ถ€์—์„œ ์›๊ฒฉ์œผ๋กœ ์กฐ์ข…ํ•  ์ˆ˜ ์žˆ๋Š” ์ด๋™์ฒดโ€๋ฅผ ๋งํ•œ๋‹ค. ๋ฌด์ธ์ด๋™์ฒด(AV)๋Š” ์‚ฌ์šฉ์ด ๊ฐ€๋Šฅํ•œ ์ง€์—ญ์— ๋”ฐ๋ผ ํ•ญ๊ณตํ˜•(Aerial Vehicle), ์œก์ƒํ˜•(Ground Vehicle), ํ•ด์–‘ํ˜•(Marine Vehicle)์œผ๋กœ ๊ตฌ๋ถ„ํ•  ์ˆ˜ ์žˆ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ํ•ด์–‘๋ฌด์ธ์ด๋™์ฒด๋Š” ์ˆ˜์ƒ์˜ ๋ฌด์ธ์„ ๋ฐ•๊ณผ ์ˆ˜์ค‘์˜ ๋ฌด์ธ์ด๋™์ฒด๋กœ ๊ตฌ๋ถ„์ด ๊ฐ€๋Šฅํ•˜๋‹ค. ์ตœ๊ทผ ์„ธ๊ณ„ ๊ฐ๊ตญ์˜ ์ •๋ถ€์—์„œ๋Š” ๋ฌด์ธ์ด๋™์ฒด ๊ด€๋ จ ๋ถ€ํ’ˆโ€ค์†Œ์žฌ, ์šด์˜๊ธฐ์ˆ  ๋“ฑ์„ ๊ฐœ๋ฐœํ•˜๊ธฐ ์œ„ํ•ด ๋Œ€ํ•™, ์—ฐ๊ตฌ๊ธฐ๊ด€ ๋“ฑ๊ณผ ํ•จ๊ป˜ ๋…ธ๋ ฅํ•ด ์™”๋‹ค. ๋ฌด์ธํ•ญ๊ณต๊ธฐ(๋“œ๋ก )์™€ ์ž์œจ์ฃผํ–‰์ž๋™์ฐจ์„ ์ด์šฉํ•œ ํƒ๋ฐฐ ์‹คํ—˜์ด ์„ฑ๊ณตํ•˜๋ฉด์„œ ๋ฌด์ธ์ด๋™์ฒด์— ๋Œ€ํ•œ ๊ด€์‹ฌ์ด ์„ธ๊ณ„์ ์œผ๋กœ ์ฆ๊ฐ€ํ•˜๊ณ  ์žˆ๋‹ค. ๋ฌด์ธํ•ญ๊ณต๊ธฐ์˜ ๊ฒฝ์šฐ ๊ณผ๊ฑฐ์—๋Š” ์ฃผ๋กœ ๊ตญ๋ฐฉโ€ค๊ตฐ์ˆ˜๋ถ„์•ผ์—์„œ ํ™œ์šฉ๋˜์—ˆ์œผ๋‚˜ ์ตœ๊ทผ์—๋Š” ํ™”๋ฌผ์ˆ˜์†ก, ์‚ฐ๋ฆผ๋ณดํ˜ธ, ํ•ด์•ˆ๊ฐ์‹œ, ๊ตญํ† ์กฐ์‚ฌ, ๋†์—…์ง€์›, ์ดฌ์˜ยท๋ ˆ์ € ๋“ฑ ๋ฏผ๊ฐ„โ€ค์ƒ์—…๋ถ„์•ผ์—์„œ ๋ฌด์ธํ•ญ๊ณต๊ธฐ์˜ ์ˆ˜์š”๊ฐ€ ์ฆ๊ฐ€ํ•˜์˜€๋‹ค. ๋ฏธ๊ตญ, ์œ ๋Ÿฝ, ์ค‘๊ตญ, ์ผ๋ณธ ์ •๋ถ€์—์„œ๋Š” ๋ฌด์ธํ•ญ๊ณต๊ธฐ(Unmanned Aerial Vehicle, UAV)์˜ ์—ฐ๊ตฌ์ง€์›๊ณผ ํ•จ๊ป˜ ๊ด€๋ จ ๋ฒ• ์ œ์ •, ๊ฐœ์ • ๋“ฑ์„ ํ†ตํ•œ ๋ฒ•์ œ ๋งˆ๋ จ์— ๋…ธ๋ ฅํ•˜๊ณ  ์žˆ๋‹ค. ์ง€๊ธˆ๊นŒ์ง€๋Š” ๋ฌด์ธํ•ญ๊ณต๊ธฐ ์ˆ˜์š”๊ฐ€ ๊ฐ€์žฅ ๋งŽ์•˜์œผ๋‚˜ 2020๋…„๊ฒฝ์—๋Š” ์ž์œจ์ฃผํ–‰์ฐจ๊ฐ€ ๋ฌด์ธํ•ญ๊ณต๊ธฐ ๋ณด๋‹ค ์‹œ์žฅ ์ ์œ ์œจ์ด ๋” ๋†’์•„์ง€๊ฒŒ ๋˜๊ณ  ๋ฌด์ธ๋†๊ธฐ๊ณ„ ์‹œ์žฅ๋„ ํฌ๊ฒŒ ์„ฑ์žฅํ•  ๊ฒƒ์œผ๋กœ ์˜ˆ์ƒ๋œ๋‹ค. 2016๋…„ ์ดํ›„ ๋ฏธ๊ตญ ๊ตํ†ต๋ถ€์™€ ๋„๋กœ๊ตํ†ต์•ˆ์ „๊ตญ์€ ์ฃผ ์ •๋ถ€ ๋ฐ ๋ฏธ๊ตญ์ž๋™์ฐจ๊ด€๋ฆฌํ˜‘ํšŒ(AAMVA)์™€ ํ•จ๊ป˜ ์ž์œจ์ฃผํ–‰์ž๋™์ฐจ์˜ ์šดํ–‰, ์•ˆ์ „์„ฑ ํ‰๊ฐ€์— ๋Œ€ํ•œ ์ง€์นจ์„ ๋งˆ๋ จํ•˜๊ธฐ ์œ„ํ•ด ๋…ธ๋ ฅํ•˜์˜€๋‹ค. ์šฐ๋ฆฌ๋‚˜๋ผ์˜ ๊ฒฝ์šฐ ํ•ญ๊ณต์•ˆ์ „๋ฒ•์—์„œ ์ดˆ๊ฒฝ๋Ÿ‰๋น„ํ–‰์žฅ์น˜ ์•ˆ์ „์„ฑ ์ธ์ฆ๊ฒ€์‚ฌ ๋Œ€์ƒ์„ ์ตœ๋Œ€์ด๋ฅ™์ค‘๋Ÿ‰ ๋“ฑ์„ ๊ธฐ์ค€์œผ๋กœ ์žˆ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์ดˆ๊ฒฝ๋Ÿ‰๋น„ํ–‰์žฅ์น˜๋ฅผ ์กฐ์ข…ํ•˜๊ธฐ ์œ„ํ•œ ํ•ญ๊ณต์ข…์‚ฌ์ž ์ž๊ฒฉ์ฆ๋ช… ์‹œํ—˜์š”๋ น์„ ๊ณ ์‹œํ•˜์—ฌ ๊ตํ†ต์•ˆ์ „๊ณต๋‹จ์—์„œ ์กฐ์ข…์‚ฌ ์ž๊ฒฉ์ฆ๋ช…์‹œํ—˜์„ ์‹œํ–‰ํ•˜๊ณ  ์žˆ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์šฐ๋ฆฌ๋‚˜๋ผ ์ž๋™์ฐจ๊ด€๋ฆฌ๋ฒ•์ด ๊ฐœ์ •๋˜์–ด ์ž์œจ์ฃผํ–‰์ž๋™์ฐจ๋Š” ์ž„์‹œ์šดํ–‰ ํ—ˆ๊ฐ€๋ฅผ ๋ฐ›์•„์„œ ์‹œํ—˜ยท์—ฐ๊ตฌ ๋ชฉ์ ์œผ๋กœ ์šดํ–‰์„ ํ•  ์ˆ˜ ์žˆ๋„๋ก ๋˜์—ˆ์œผ๋ฉฐ ํ–ฅํ›„ ๋„๋กœ๊ตํ†ต๋ฒ•์—์„œ ์šด์ „์ž์˜ ์ž๋™์ฐจ ์กฐ์ž‘์„ ์˜๋ฌด์ ์œผ๋กœ ๊ทœ์ •ํ•˜๋Š” ์กฐํ•ญ์ด ๊ฐœ์ • ๋˜๋ฉด ์•ž์œผ๋กœ ์‹ค์ œ ๋„๋กœ์—์„œ ์ž์œจ์ฃผํ–‰์ฐจ๊ฐ€ ์ฃผํ–‰ํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋‹ค. ์•ž์„œ ์‚ดํŽด๋ณธ ๋ฐ”์™€ ๊ฐ™์ด ํ•ญ๊ณต๋“œ๋ก , ์ž์œจ์ฃผํ–‰์ฐจ๊ณผ ๊ฐ™์€ ํ•ญ๊ณต, ์œก์ƒ์˜ ๋ฌด์ธ์ด๋™์ฒด๋Š” ์‹ค์ œ ์ƒํ™œ์— ํ™œ์šฉ์ด ๊ฐ€๋Šฅํ•œ ๋‹จ๊ณ„๋กœ ๊ธฐ์ˆ ์ , ๋ฒ•์ œ์  ์ˆ˜์ค€์— ๋„๋‹ฌ ํ–ˆ์œผ๋‚˜ ํ•ด์–‘๋ฌด์ธ๋™์ฒด์˜ ๊ฒฝ์šฐ ์•„์ง ์šฐ๋ฆฌ๋‚˜๋ผ์˜ ํ•ต์‹ฌ๊ธฐ์ˆ ์€ ์„ ์ง„๊ตญ์— ๋น„ํ•˜์—ฌ ๋’ค๋–จ์–ด์ ธ ์žˆ๋Š” ์‹ค์ •์ด๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ํ•ด์–‘๋ฌด์ธ์ด๋™์ฒด ์ค‘์—์„œ ์ˆ˜์ค‘๋กœ๋ด‡, ์ˆ˜์ค‘๊ธ€๋ผ์ด๋”์™€ ๊ฐ™์€ ์ˆ˜์ค‘์˜ ๋ฌด์ธ์ด๋™์ฒด๋ฅผ ํ™œ์„ฑํ™”๋ฅผ ์œ„ํ•ด ํ•ด์‚ฌ๋ฒ•, ํ•ด์–‘์ˆ˜์‚ฐ๊ณผํ•™๊ธฐ์ˆ ์œก์„ฑ๋ฒ•, ํ•ด์–‘๊ณต๊ฐ„๊ณ„ํš ๋ฐ ๊ด€๋ฆฌ์— ๊ด€ํ•œ ์ž…๋ฒ•๋ก ์  ๊ฐœ์„ ์•ˆ์„ ์ค‘์‹ฌ์œผ๋กœ ์—ฐ๊ตฌ๋ฅผ ์ˆ˜ํ–‰ํ•˜๊ณ  ๊ฐœ์„ ๋ฐฉ์•ˆ์„ ๋„์ถœํ•˜์˜€๋‹ค.๋ชฉ ์ฐจ ๊ตญ๋ฌธ ์š”์•ฝ โ…ฒ Abstract โ…ด ์ œ1์žฅ ์„œ ๋ก  1 ์ œ1์ ˆ ์—ฐ๊ตฌ์˜ ๋ชฉ์  1 ์ œ2์ ˆ ์—ฐ๊ตฌ์˜ ๋‚ด์šฉ ๋ฐ ๋ฐฉ๋ฒ• 5 ์ œ2์žฅ ๋ฌด์ธ์ด๋™์ฒด์— ๊ด€ํ•œ ์ผ๋ฐ˜์  ๊ฒ€ํ†  8 ์ œ1์ ˆ ํ•ญ๊ณต๋ฌด์ธ์ด๋™์ฒด 8 โ… . ๊ตญ๋‚ด ํ•ญ๊ณต๋ฌด์ธ์ด๋™์ฒด 8 1. ๊ฐœ์š” 8 2. ์•ˆ์ „์„ฑ ์ธ์ฆ๊ฒ€์‚ฌ 9 3. ์ดˆ๊ฒฝ๋Ÿ‰๋น„ํ–‰์žฅ์น˜ ๋น„ํ–‰๊ณต์—ญ 12 4. ์ดˆ๊ฒฝ๋Ÿ‰๋น„ํ–‰์žฅ์น˜ ๋น„ํ–‰์ž๊ฒฉ ์ฆ๋ช…์ œ๋„ 16 โ…ก. ์ฃผ์š”๊ตญ์˜ ํ•ญ๊ณต๋ฌด์ธ์ด๋™์ฒด 17 1. ๋ฏธ๊ตญ 17 2. ์˜๊ตญ ๋ฐ ์œ ๋Ÿฝ(EU) 23 ์ œ2์ ˆ ์ž์œจ์ฃผํ–‰์ž๋™์ฐจ 27 โ… . ๊ตญ๋‚ด ์ž์œจ์ฃผํ–‰์ž๋™์ฐจ 27 1. ๊ฐœ์š” 28 2. ๊ตญ๋‚ด๋ฒ•์ œ 28 โ…ก. ์ฃผ์š”๊ตญ์˜ ์ž์œจ์ฃผํ–‰์ž๋™์ฐจ 31 1. ๋ฏธ๊ตญ ๋ฒ•์ œ 31 2. ์˜๊ตญ ๋ฒ•์ œ 38 ์ œ3์ ˆ ๋ฌด์ธ์ด๋™์ฒด์˜ ํ‘œ์ค€, ์ธ์ • ๋ฐ ์ธ์ฆ์ œ๋„ 42 โ… . ํ‘œ์ค€์ œ๋„ 42 1. ๊ตญ๋‚ดํ‘œ์ค€ 42 2. ๊ตญ์ œ ๋ฐ ์ง€์—ญํ‘œ์ค€ 47 โ…ก. ์ธ์ • ๋ฐ ์ธ์ฆ์ œ๋„ 51 1. ์ธ์ •์ œ๋„ 52 2. ์ธ์ฆ์ œ๋„ 53 ์ œ3์žฅ ํ•ด์–‘๋ฌด์ธ์ด๋™์ฒด์˜ ๋ฒ•์ œ 64 ์ œ1์ ˆ ํ•ด์–‘๋ฌด์ธ์ด๋™์ฒด์˜ ๋ฒ•์ œ ํ˜„ํ™ฉ 64 โ… . ๊ตญ๋‚ด ํ•ด์–‘๋ฌด์ธ์ด๋™์ฒด์˜ ๋ฒ•์ œ 64 1. ํ•ด์–‘๋ฌด์ธ์ด๋™์ฒด์˜ ์ •์˜ 64 2. ํ•ด์‚ฌ๋ฒ• 64 3. ํ•ด์–‘์ˆ˜์‚ฐ๊ณผํ•™๊ธฐ์ˆ ์œก์„ฑ๋ฒ• 72 4. ํ•ด์–‘๊ณต๊ฐ„๊ณ„ํš ๋ฐ ๊ด€๋ฆฌ์— ๊ด€ํ•œ ๋ฒ•๋ฅ  75 โ…ก. ์ฃผ์š”๊ตญ์˜ ํ•ด์–‘๋ฌด์ธ์ด๋™์ฒด์˜ ๋ฒ•์ œ 76 1. ๋ฏธ๊ตญ ๋ฒ•์ œ 76 2. ๊ตญ์ œ๋ฒ• ๋ฐ ์ œ๋„ 77 ์ œ2์ ˆ ํ•ด์–‘๋ฌด์ธ์ด๋™์ฒด์˜ ํ™œ์„ฑํ™”์— ๋Œ€ํ•œ ๋ฌธ์ œ์  79 โ… . ํ•ด์‚ฌ๋ฒ•์ƒ ๋ฌธ์ œ์  79 1. ์„ ๋ฐ•์„ฑ์˜ ๋ฌธ์ œ 79 2. ์„ ๋ฐ•์•ˆ์ „๊ฒ€์‚ฌ ๋ฌธ์ œ 80 3. ์กฐ์ข…์‚ฌ ์ž๊ฒฉ์ฆ๋ช… ๋ฌธ์ œ 81 โ…ก. ํ•ด์–‘์ˆ˜์‚ฐ๊ณผํ•™๊ธฐ์ˆ ์œก์„ฑ๋ฒ•์ƒ ๋ฌธ์ œ์  81 1. ํ‘œ์ค€ ๋ฌธ์ œ 81 2. ์ธ์ • ๋ฐ ์ธ์ฆ ๋ฌธ์ œ 82 โ…ข. ํ•ด์–‘๊ณต๊ฐ„๊ณ„ํš ๋ฐ ๊ด€๋ฆฌ์— ๊ด€ํ•œ ๋ฒ•๋ฅ ์ƒ ๋ฌธ์ œ์  83 1. ํ•ด์–‘์šฉ๋„๊ตฌ์—ญ ๋ฌธ์ œ 83 2. ์ „์šฉ์‹œํ—˜์žฅ ๋ฌธ์ œ 84 ์ œ4์žฅ ํ•ด์–‘๋ฌด์ธ์ด๋™์ฒด์˜ ๋ฒ•์ œ์— ๊ด€ํ•œ ๊ฐœ์„ ๋ฐฉ์•ˆ 85 ์ œ1์ ˆ ํ•ด์‚ฌ๋ฒ• ๊ฐœ์„ ๋ฐฉ์•ˆ 85 โ… . ์„ ๋ฐ•์˜ ์ •์˜ ๊ฐœ์ • 85 โ…ก. ์„ ๋ฐ•์•ˆ์ „๋ฒ• ๊ฐœ์ • 87 โ…ข. ์„ ๋ฐ•์ง์›๋ฒ• ๊ฐœ์ • 89 ์ œ2์ ˆ ํ•ด์–‘์ˆ˜์‚ฐ๊ณผํ•™๊ธฐ์ˆ ์œก์„ฑ๋ฒ• ๊ฐœ์„ ๋ฐฉ์•ˆ 90 โ… . ํ‘œ์ค€ ์ œ์ • 90 โ…ก. ์ธ์ • ๋ฐ ์ธ์ฆ์ œ๋„ ๋„์ž… 91 ์ œ3์ ˆ ํ•ด์–‘๊ณต๊ฐ„๊ณ„ํš ๋ฐ ๊ด€๋ฆฌ์— ๊ด€ํ•œ ๊ฐœ์„ ๋ฐฉ์•ˆ 93 โ… . ํ•ด์–‘์šฉ๋„๊ตฌ์—ญ ์ง€์ • 93 โ…ก. ์ „์šฉ์‹œํ—˜์žฅ ๋ฒ•์ œ ๋„์ž… 95 ์ œ5์žฅ ๊ฒฐ ๋ก  97 ์ฐธ๊ณ ๋ฌธํ—Œ 101 ๋ถ€๋ก 1. ๋ถ€์ฒ˜๋ณ„ ์ธ์ฆ์ œ๋„ ํ˜„ํ™ฉ 105 ๋ถ€๋ก 2. PART 107 ์†Œํ˜• ๋ฌด์ธํ•ญ๊ณต๊ธฐ ์‹œ์Šคํ…œ ๋ชฉ์ฐจ 114 ๋ถ€๋ก 3. Part 107 ์ฃผ์š” ๊ทœ์ • ์š”์•ฝ 116 ํ‘œ ๋ชฉ ์ฐจ ์ดˆ๊ฒฝ๋Ÿ‰๋น„ํ–‰์žฅ์น˜ ์ž๊ฒฉ์ฆ๋ช… ๋ฐ ์•ˆ์ „์„ฑ ๊ฒ€์‚ฌ 11 ๊ณต์—ญ์˜ ์‚ฌ์šฉ๋ชฉ์ ์— ๋”ฐ๋ฅธ ๊ตฌ๋ถ„ 13 ์ดˆ๊ฒฝ๋Ÿ‰๋น„ํ–‰์žฅ์น˜ ๋น„ํ–‰๊ณต์—ญ ํ˜„ํ™ฉ 14 ์˜๊ตญ์˜ ์œ ํ˜•๋ณ„ ๋“œ๋ก ์˜ ํ™œ์šฉ ํ˜„ํ™ฉ๊ณผ ํŠน์ง• 24 ์˜๊ตญ์˜ ๋“œ๋ก ์˜ ๋น„ํ–‰ ์š”๊ฑด 26 ๋ฏธ๊ตญ์ž๋™์ฐจ๊ณตํ•™ํšŒ(SAE International) ๋ถ„๋ฅ˜ ์ž๋™ํ™” 6๋‹จ๊ณ„ 31 ํ•œ๊ตญ์‚ฐ์—…ํ‘œ์ค€(KS) ๋ถ„๋ฅ˜์ฒด๊ณ„ 46 ๋ถ€์ฒ˜๋ณ„ ๋“ฑ๋ก์ธ์ฆ ํ˜„ํ™ฉ 54 ํ•ด์–‘๊ธฐ์ž์žฌ ๊ฒ€์‚ฌยท์Šน์ธ ์ข…๋ฅ˜ 59 ๊ตญ์ œํ•ด์‚ฌ๊ธฐ๊ตฌ(IMO)์—์„œ ์ธ์ •๋ฐ›์€ ๊ตญ๋‚ด ์ธ์ฆ์‹œํ—˜๊ธฐ๊ด€ 62 ์„ ๋ฐ•๋ฒ• ๊ฐœ์ •์•ˆ 86 ํ‘œ์ค€์˜ ์„ธ๋ถ€๊ธฐ์ค€ ์˜ˆ์‹œ 89 ์„ ๋ฐ•์ง์›๋ฒ• ๊ฐœ์ •์•ˆ 90 ํ•œ๊ตญ์‚ฐ์—…ํ‘œ์ค€(KS) ๋ถ„๋ฅ˜์ฒด๊ณ„ ๊ฐœ์ •์•ˆ 91 ํ•ด์–‘์ˆ˜์‚ฐ๊ณผํ•™๊ธฐ์ˆ ์œก์„ฑ๋ฒ• ์‹œํ–‰๊ทœ์น™ ๊ฐœ์ •์•ˆ 92 ํ•ด์–‘์‹ ๊ธฐ์ˆ ์ธ์ฆ์„ ์œ„ํ•œ ์„ธ๋ถ€๊ธฐ์ค€(์˜ˆ์‹œ) 92 ๊ณต๊ฐ„์ •๋ณด์˜ ๊ตฌ์ถ• ๋ฐ ๊ด€๋ฆฌ ๋“ฑ์— ๊ด€ํ•œ ๋ฒ•๋ฅ  ๊ฐœ์ •์•ˆ 94 ํ•ด์–‘๊ณต๊ฐ„๊ณ„ํš ๋ฐ ๊ด€๋ฆฌ์— ๊ด€ํ•œ ๋ฒ•๋ฅ  ๊ฐœ์ •์•ˆ 96Maste

    ๋ ˆ์ด๋” ์ŠคํŽ™ํŠธ๋กœ๊ทธ๋žจ์„ ์‚ฌ์šฉํ•œ ์ปจ๋ณผ๋ฃจ์…˜ ์‹ ๊ฒฝ๋ง ๊ธฐ๋ฐ˜ ๋ฌด์ธํ•ญ๊ณต๊ธฐ ๋ถ„๋ฅ˜

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์œตํ•ฉ๊ณผํ•™๊ธฐ์ˆ ๋Œ€ํ•™์› ์œตํ•ฉ๊ณผํ•™๋ถ€(์ง€๋Šฅํ˜•์œตํ•ฉ์‹œ์Šคํ…œ์ „๊ณต), 2021. 2. ๊ณฝ๋…ธ์ค€.With the upsurge in using Unmanned Aerial Vehicles (UAVs) in various fields, identifying them in real-time is becoming an important issue. However, the identification of UAVs is difficult due to their characteristics such as Low altitude, Slow speed and Small radar cross-section (LSS). To identify UAVs with existing deterministic systems, the algorithm becomes more complex and requires large computations, making it unsuitable for real-time systems. Hence, we need a new approach to these threats. Deep learning models extract features from a large amount of data by themselves and have shown outstanding performance in various tasks. Using these advantages, deep learning-based UAV classification models using various sensors are being studied recently. In this paper, we propose a deep learning-based classification model that learns the micro-Doppler signatures (MDS) of targets represented on radar spectrogram images. To enable this, first, we recorded five LSS targets (three types of UAVs and two different types of human activities) with a frequency modulated continuous wave (FMCW) radar in various scenarios. Then, we converted signals into spectrograms in the form of images by Short-time Fourier transform (STFT). After the data refinement and augmentation, we made our own radar spectrogram dataset. Secondly, we analyzed characteristics of the radar spectrogram dataset using the ResNet-18 model and designed the lightweight ResNet-SP model for the real-time system. The results show that the proposed ResNet-SP has a training time of 242 seconds and an accuracy of 83.39%, which is superior to the ResNet-18 that takes 640 seconds for training with an accuracy of 79.88%.๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š”, ๋ ˆ์ด๋” ์ŠคํŽ™ํŠธ๋กœ๊ทธ๋žจ ์ƒ์— ํ˜•์„ฑ๋œ ์„œ๋กœ ๋‹ค๋ฅธ ์ด๋™ํ‘œ์ ์˜ ๊ณ ์œ ํ•œ ๋งˆ์ดํฌ๋กœ ๋„ํ”Œ๋Ÿฌ์‹ ํ˜ธ๋ฅผ ํ•™์Šตํ•˜๋Š” ๋”ฅ๋Ÿฌ๋‹ ๊ธฐ๋ฐ˜ ๋ถ„๋ฅ˜๋ชจ๋ธ์„ ์ œ์•ˆํ•œ๋‹ค. ์ด๋ฅผ์œ„ํ•ด ์šฐ๋ฆฌ๋Š” ๋‹ค์„ฏ๊ฐ€์ง€ ์†Œํ˜• ์ด๋™ํ‘œ์ (๋ฌด์ธํ•ญ๊ณต๊ธฐ 3์ข…๊ณผ ์‚ฌ๋žŒํ–‰๋™ 2์ข…)์„ ์„ ์ •ํ•˜์—ฌ ์ฃผํŒŒ์ˆ˜๋ณ€์กฐ ์—ฐ์†ํŒŒ๋ ˆ์ด๋”๋กœ ํ‘œ์ ๋“ค์˜ ๋‹ค์–‘ํ•œ ์›€์ง์ž„์„ ์ธก์ •ํ•˜๊ณ  ์ธก์ •ํ•œ ์‹ ํ˜ธ์— ๋‹จ์‹œ๊ฐ„ ํ‘ธ๋ฆฌ์— ๋ณ€ํ™˜์˜ ์‹ ํ˜ธ์ฒ˜๋ฆฌ๊ณผ์ •๊ณผ ๋ฐ์ดํ„ฐ ์ •์ œ ๋ฐ ์ฆ๊ฐ•์˜ ์ „์ฒ˜๋ฆฌ๊ณผ์ •์„ ์ ์šฉํ•˜์—ฌ ์ž์ฒด ๋ ˆ์ด๋” ์ŠคํŽ™ํŠธ๋กœ๊ทธ๋žจ ๋ฐ์ดํ„ฐ์…‹์„ ์ƒ์„ฑํ•œ๋‹ค. ์ดํ›„ ๊ด‘ํ•™์ด๋ฏธ์ง€ ๋ถ„๋ฅ˜๋ชจ๋ธ์ธ ResNet-18์„ ์‚ฌ์šฉํ•˜์—ฌ ๋ ˆ์ด๋” ์ŠคํŽ™ํŠธ๋กœ๊ทธ๋žจ ๋ฐ์ดํ„ฐ์…‹์˜ ํŠน์„ฑ์„ ๋ถ„์„ํ•œ๋‹ค. ๋ ˆ์ด๋”์‹ ํ˜ธ๋ฅผ ๊ด‘ํ•™์ด๋ฏธ์ง€๋กœ ๋ณ€ํ˜•ํ•˜๋Š” ๊ณผ์ •์—์„œ์˜ ์ •๋ณด์™œ๊ณก ๋ฐ ์†์‹ค์„ ๊ฐ€์ •ํ•˜์—ฌ ์„ธ๊ฐ€์ง€ ๋ ˆ์ด๋” ์‹ ํ˜ธํ˜•ํƒœ์— ๋”ฐ๋ฅธ ์„ฑ๋Šฅ์„ ๋น„๊ตํ•˜๊ณ  ์ตœ์ ์˜ ๋ฐ์ดํ„ฐํ˜•ํƒœ๋ฅผ ํ™•์ธํ•œ๋‹ค. ๋…ธ์ด์ฆˆ ์‹œํ—˜ ๋ฐ ๊ตฌ์กฐ์— ๋”ฐ๋ฅธ ์„ฑ๋Šฅ๋ณ€ํ™”๋ฅผ ํ†ตํ•ด ๋ชจ๋ธ์ด ํ•™์Šตํ•˜๋Š” ์ฃผ์š”ํ•œ ๋ฐ์ดํ„ฐ ํŠน์ง•๊ณผ ์ด์ƒ์ ์ธ ๋ชจ๋ธ๊ตฌ์กฐ๋ฅผ ํ™•์ธํ•œ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ๋ ˆ์ด๋” ์ŠคํŽ™ํŠธ๋กœ๊ทธ๋žจ ๋ฐ์ดํ„ฐ์…‹ ํŠน์„ฑ๋ถ„์„์„ ๊ธฐ๋ฐ˜์œผ๋กœ ์ถ”๊ฐ€์ ์ธ ๊ฒฝ๋Ÿ‰ํ™” ๋ฐ ์•ˆ์ •ํ™” ๊ธฐ๋ฒ•์„ ์ ์šฉํ•˜์—ฌ ์‹ค์‹œ๊ฐ„ ์‹œ์Šคํ…œ์„ ์œ„ํ•œ ResNet-SP ๋ชจ๋ธ์„ ์„ค๊ณ„ํ•˜๊ณ  ResNet-18๋ชจ๋ธ๊ณผ์˜ ์„ฑ๋Šฅ๋น„๊ต๋ฅผ ํ†ตํ•˜์—ฌ ์—ฐ์‚ฐ์†๋„ ์ฆ๊ฐ€์™€ ์•ˆ์ •์„ฑ ๋ฐ ์ •ํ™•์„ฑ ํ–ฅ์ƒ ๋“ฑ์˜ ์„ฑ๋Šฅ๊ฐœ์„ ์„ ํ™•์ธํ•œ๋‹ค.Abstract . . . . . . . . . . . . . . i Contents . . . . . . . . . . . . . . ii List of Tables . . . . . . . . . . . . iv List of Figures . . . . . . . . . . . . v 1 Introduction . . . . . . . . . . . . . . . . . . . . 1 2 Related Works . . . . . . . . . . . . . . . . . . . 5 2.1 Micro Doppler Signature (MDS) . . . . . . . . 5 2.2 Classification of UAVs using MDS . . . . . . . 6 3 Dataset Generation . . . . . . . . . . . . . . . . . 9 3.1 Measurement . . . . . . . . . . . . . . . . . . 10 3.2 Pre-processing . . . . . . . . . . . . . . . . . . 12 4 Models . . . . . . . . . . . . . . . . . . . . . . . 21 4.1 ResNet-18 . . . . . . . . . . . . . . . . . . . 22 4.2 ResNet-SP . . . . . . . . . . . . . . . . . . . . 27 5 Experiment . . . . . . . . . . . . . . . . . . . . . 32 5.1 Experiment Result . . . . . . . . . . . . . . . 32 5.2 Training Details . . . . . . . . . . . . . . . . . 33 6 Conclusion . . . . . . . . . . . . . . . . . . . . . 34 Abstract (In Korean) . . . . . . . . . . . . . . . . . 38Maste

    ์ถฉ๋ŒํšŒํ”ผ๋ฅผ ๊ณ ๋ คํ•œ ๋ฌด์ธํ•ญ๊ณต๊ธฐ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€, 2015. 2. ๊น€์œ ๋‹จ.Unmanned aerial vehicle (UAV) has developed to perform military missions including patrol, surveillance, and reconnaissance. Recently, the applications of UAV are expanding to the private markets of UAV agriculture, aerial photography and etc. For these various applications, collision avoidance of UAV is an essential problem, because collision to obstacles may cause not only mission failure but also destruction of UAV and fatal accident including loss of human life. In this study, modified A* path planning algorithm is proposed for UAV systems. In various robot applications, standard A* algorithm is widely used, which does not apply dynamics of UAV. Therefore, the results from the path planning algorithm should be post-processed to reflect the dynamic constraint of an UAV such as limited turning angle. To deal with this problem, search direction achievable by the UAV is considered in the proposed A* algorithm. The proposed algorithm can be implemented on the on-board system of the UAV in real-time, and does not need post processing to follow the result of path planning. Nonlinear guidance algorithm and PID controller to follow the path are also designed. The performance of the proposed algorithm is demonstrated using numerical simulations. Six degree-of-freedom simulation model was obtained by system identification flight test. Finally, the integrated algorithm is verified by the flight test.1. Introduction 2. Guidance and Control of UAV 3 2.1 Guidance of UAV for Path Following 3 2.1.1 Longitudinal Guidance 3 2.1.2 Lateral Guidance[10] 4 2.2 Autopilot system of UAV 6 2.2.1 Longitudinal Control 6 2.2.2 Lateral Control 7 3. Path Planning of UAV 9 3.1 A* Path Planning Algorithm 9 3.2 Modified A* Path Planning Algorithm for UAV 13 3.2.1 Problems of Standard A* Search 13 3.2.2 Search Candidate Point Selection 14 3.2.3 Cost and Heuristic Function Design 18 3.2.4 Modified A* Path Planning Simulation 21 4. Numerical Simulation 26 4.1 UAV System Modeling 26 4.1.1 UAV Model 26 4.1.2 UAV System ID 26 4.1.3 Longitudinal UAV System Model 27 4.1.4 Lateral UAV System Model 28 4.2 Linear Model Based A* Simulation 28 4.2.1 Simulink Simulator Block 28 4.2.2 Angle of Sight Target Point Calculation 34 4.2.3 Simulation Result 36 5. Flight Test 44 5.1 UAV System 44 5.1.1 UAV System Introduction 44 5.1.2 Flight Control Computer 47 5.2 Flight Test Preparation 49 5.3 Flight Test Result 49 6. Conclusion 56Maste

    ๋ฌด์ธํ•ญ๊ณต๊ธฐ ์šด์˜์„ ์œ„ํ•œ ๋ฎ๊ฐœ ๋ชจ๋ธ ๊ธฐ๋ฐ˜์˜ ๋Œ€๊ทœ๋ชจ ์ตœ์ ํ™” ๊ธฐ๋ฒ•

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์‚ฐ์—…๊ณตํ•™๊ณผ, 2021. 2. ๋ฌธ์ผ๊ฒฝ.There is increasing interest in the unmanned aerial vehicle (UAV) in various fields of the industry, starting from the surveillance to the logistics. After introducing the smart city, there are attempts to utilize UAVs in the public service sector by connecting individual components of the system with both information and physical goods. In this dissertation, the UAV operation problems in the public service sector is modeled in the set covering approach. There is a vast literature on the facility location and set covering problems. However, when operating UAVs in the system, the plan has to make the most of the flexibility of the UAV, but also has to consider its physical limitation. We noticed a gap between the related, existing approaches and the technologies required in the field. That is, the new characteristics of the UAV hinder the existing solution algorithms, or a brand-new approach is required. In this dissertation, two operation problems to construct an emergency wireless network in a disaster situation by UAV and one location-allocation problem of the UAV emergency medical service (EMS) facility are proposed. The reformulation to the extended formulation and the corresponding branch-and-price algorithm can overcome the limitations and improve the continuous or LP relaxation bounds, which are induced by the UAV operation. A brief explanation of the UAV operation on public service, the related literature, and the brief explanation of the large-scale optimization techniques are introduced in Chapter 1, along with the research motivations and contributions, and the outline of the dissertations. In Chapter 2, the UAV set covering problem is defined. Because the UAV can be located without predefined candidate positions, more efficient operation becomes feasible, but the continuous relaxation bound of the standard formulation is weakened. The large-scale optimization techniques, including the Dantzig-Wolfe decomposition and the branch-and-price algorithm, could improve the continuous relaxation bound and reduce the symmetries of the branching tree and solve the realistic-scaled problems within practical computation time. To avoid numerical instability, two approximation models are proposed, and their approximation ratios are analyzed. In Chapter 3, UAV variable radius set covering problem is proposed with an extra decision on the coverage radius. While implementing the branch-and-price algorithm to the problem, a solvable equivalent formulation of the pricing subproblem is proposed. A heuristic based on the USCP is designed, and the proposed algorithm outperformed the benchmark genetic algorithm proposed in the literature. In Chapter 4, the facility location-allocation problem for UAV EMS is defined. The quadratic variable coverage constraint is reformulated to the linear equivalent formulation, and the nonlinear problem induced by the robust optimization approach is linearized. While implementing the large-scale optimization techniques, the structure of the subproblem is analyzed, and two solution approaches for the pricing subproblem are proposed, along with a heuristic. The results of the research can be utilized when implementing in the real applications sharing the similar characteristics of UAVs, but also can be used in its abstract formulation.ํ˜„์žฌ, ์ง€์—ญ ๊ฐ์‹œ์—์„œ ๋ฌผ๋ฅ˜๊นŒ์ง€, ๋ฌด์ธํ•ญ๊ณต๊ธฐ์˜ ๋‹ค์–‘ํ•œ ์‚ฐ์—…์—์˜ ์‘์šฉ์ด ์ฃผ๋ชฉ๋ฐ›๊ณ  ์žˆ๋‹ค. ํŠนํžˆ, ์Šค๋งˆํŠธ ์‹œํ‹ฐ์˜ ๊ฐœ๋…์ด ๋Œ€๋‘๋œ ์ดํ›„, ๋ฌด์ธํ•ญ๊ณต๊ธฐ๋ฅผ ๊ณต๊ณต ์„œ๋น„์Šค ์˜์—ญ์— ํ™œ์šฉํ•˜์—ฌ ๊ฐœ๋ณ„ ์‚ฌํšŒ ์š”์†Œ๋ฅผ ์—ฐ๊ฒฐ, ์ •๋ณด์™€ ๋ฌผ์ž๋ฅผ ๊ตํ™˜ํ•˜๊ณ ์ž ํ•˜๋Š” ์‹œ๋„๊ฐ€ ์ด์–ด์ง€๊ณ  ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๊ณต๊ณต ์„œ๋น„์Šค ์˜์—ญ์—์„œ์˜ ๋ฌด์ธํ•ญ๊ณต๊ธฐ ์šด์˜ ๋ฌธ์ œ๋ฅผ ์ง‘ํ•ฉ๋ฎ๊ฐœ๋ฌธ์ œ ๊ด€์ ์—์„œ ๋ชจํ˜•ํ™”ํ•˜์˜€๋‹ค. ์„ค๋น„์œ„์น˜๊ฒฐ์ • ๋ฐ ์ง‘ํ•ฉ๋ฎ๊ฐœ๋ฌธ์ œ ์˜์—ญ์— ๋งŽ์€ ์—ฐ๊ตฌ๊ฐ€ ์ง„ํ–‰๋˜์–ด ์žˆ์œผ๋‚˜, ๋ฌด์ธํ•ญ๊ณต๊ธฐ๋ฅผ ์šด์˜ํ•˜๋Š” ์‹œ์Šคํ…œ์˜ ๊ฒฝ์šฐ ๋ฌด์ธํ•ญ๊ณต๊ธฐ๊ฐ€ ๊ฐ–๋Š” ์ž์œ ๋„๋ฅผ ์ถฉ๋ถ„ํžˆ ํ™œ์šฉํ•˜๋ฉด์„œ๋„ ๋ฌด์ธํ•ญ๊ณต๊ธฐ์˜ ๋ฌผ๋ฆฌ์  ํ•œ๊ณ„๋ฅผ ๊ณ ๋ คํ•œ ์šด์˜ ๊ณ„ํš์„ ํ•„์š”๋กœ ํ•œ๋‹ค. ์šฐ๋ฆฌ๋Š” ๋ณธ ๋ฌธ์ œ์™€ ๊ด€๋ จ๋œ ๊ธฐ์กด ์—ฐ๊ตฌ์™€ ํ˜„์žฅ์ด ํ•„์š”๋กœ ํ•˜๋Š” ๊ธฐ์ˆ ์˜ ๊ดด๋ฆฌ๋ฅผ ์ธ์‹ํ•˜์˜€๋‹ค. ์ด๋Š” ๋‹ค์‹œ ๋งํ•ด, ๋ฌด์ธํ•ญ๊ณต๊ธฐ๊ฐ€ ๊ฐ€์ง€๋Š” ์ƒˆ๋กœ์šด ํŠน์„ฑ์„ ๊ณ ๋ คํ•˜๋ฉด ๊ธฐ์กด์˜ ๋ฌธ์ œ ํ•ด๊ฒฐ ๋ฐฉ๋ฒ•์„ ํ†ตํ•ด ํ’€๊ธฐ ์–ด๋ ต๊ฑฐ๋‚˜, ํ˜น์€ ์ƒˆ๋กœ์šด ๊ด€์ ์—์„œ์˜ ๋ฌธ์ œ ์ ‘๊ทผ์ด ํ•„์š”ํ•˜๋‹ค๋Š” ๊ฒƒ์ด๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์žฌ๋‚œ์ด ๋ฐœ์ƒํ•œ ์ง€์—ญ์— ๋ฌด์ธํ•ญ๊ณต๊ธฐ๋ฅผ ์ด์šฉํ•˜์—ฌ ๊ธด๊ธ‰๋ฌด์„ ๋„คํŠธ์›Œํฌ๋ฅผ ๊ตฌ์„ฑํ•˜๋Š” ๋‘๊ฐ€์ง€ ๋ฌธ์ œ์™€, ๋ฌด์ธํ•ญ๊ณต๊ธฐ๋ฅผ ์ด์šฉํ•˜์—ฌ ์‘๊ธ‰์˜๋ฃŒ์„œ๋น„์Šค๋ฅผ ์ œ๊ณตํ•˜๋Š” ์‹œ์„ค์˜ ์œ„์น˜์„ค์ • ๋ฐ ํ• ๋‹น๋ฌธ์ œ๋ฅผ ์ œ์•ˆํ•œ๋‹ค. ํ™•์žฅ๋ฌธ์ œ๋กœ์˜ ์žฌ๊ณต์‹ํ™”์™€ ๋ถ„์ง€ํ‰๊ฐ€๋ฒ•์„ ํ™œ์šฉํ•˜์—ฌ, ๋ฌด์ธํ•ญ๊ณต๊ธฐ์˜ ํ™œ์šฉ์œผ๋กœ ์ธํ•ด ๋ฐœ์ƒํ•˜๋Š” ๋ฌธ์ œ ํ•ด๊ฒฐ ๋ฐฉ๋ฒ•์˜ ํ•œ๊ณ„๋ฅผ ๊ทน๋ณตํ•˜๊ณ  ์™„ํ™”ํ•œ๊ณ„๋ฅผ ๊ฐœ์„ ํ•˜์˜€๋‹ค. ๊ณต๊ณต ์„œ๋น„์Šค ์˜์—ญ์—์„œ์˜ ๋ฌด์ธํ•ญ๊ณต๊ธฐ ์šด์˜, ๊ด€๋ จ๋œ ๊ธฐ์กด ์—ฐ๊ตฌ์™€ ๋ณธ ๋…ผ๋ฌธ์—์„œ ์‚ฌ์šฉํ•˜๋Š” ๋Œ€๊ทœ๋ชจ ์ตœ์ ํ™” ๊ธฐ๋ฒ•์— ๋Œ€ํ•œ ๊ฐœ๊ด„์ ์ธ ์„ค๋ช…, ์—ฐ๊ตฌ ๋™๊ธฐ ๋ฐ ๊ธฐ์—ฌ์™€ ๋…ผ๋ฌธ์˜ ๊ตฌ์„ฑ์„ 1์žฅ์—์„œ ์†Œ๊ฐœํ•œ๋‹ค. 2์žฅ์—์„œ๋Š” ๋ฌด์ธํ•ญ๊ณต๊ธฐ ์ง‘ํ•ฉ๋ฎ๊ฐœ๋ฌธ์ œ๋ฅผ ์ •์˜ํ•œ๋‹ค. ๋ฌด์ธํ•ญ๊ณต๊ธฐ๋Š” ๋ฏธ๋ฆฌ ์ •ํ•ด์ง„ ์œ„์น˜ ์—†์ด ์ž์œ ๋กญ๊ฒŒ ๋น„ํ–‰ํ•  ์ˆ˜ ์žˆ๊ธฐ ๋•Œ๋ฌธ์— ๋” ํšจ์œจ์ ์ธ ์šด์˜์ด ๊ฐ€๋Šฅํ•˜๋‚˜, ์•ฝํ•œ ์™„ํ™”ํ•œ๊ณ„๋ฅผ ๊ฐ–๊ฒŒ ๋œ๋‹ค. Dantzig-Wolfe ๋ถ„ํ•ด์™€ ๋ถ„์ง€ํ‰๊ฐ€๋ฒ•์„ ํฌํ•จํ•œ ๋Œ€๊ทœ๋ชจ ์ตœ์ ํ™” ๊ธฐ๋ฒ•์„ ํ†ตํ•ด ์™„ํ™”ํ•œ๊ณ„๋ฅผ ๊ฐœ์„ ํ•  ์ˆ˜ ์žˆ์œผ๋ฉฐ, ๋ถ„์ง€๋‚˜๋ฌด์˜ ๋Œ€์นญ์„ฑ์„ ์ค„์—ฌ ์‹ค์ œ ๊ทœ๋ชจ์˜ ๋ฌธ์ œ๋ฅผ ์‹ค์šฉ์ ์ธ ์‹œ๊ฐ„ ์•ˆ์— ํ•ด๊ฒฐํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ˆ˜์น˜์  ๋ถˆ์•ˆ์ •์„ฑ์„ ํ”ผํ•˜๊ธฐ ์œ„ํ•˜์—ฌ, ๋‘ ๊ฐ€์ง€ ์„ ํ˜• ๊ทผ์‚ฌ ๋ชจํ˜•์ด ์ œ์•ˆ๋˜์—ˆ์œผ๋ฉฐ, ์ด๋“ค์˜ ๊ทผ์‚ฌ ๋น„์œจ์„ ๋ถ„์„ํ•˜์˜€๋‹ค. 3์žฅ์—์„œ๋Š” ๋ฌด์ธํ•ญ๊ณต๊ธฐ ์ง‘ํ•ฉ๋ฎ๊ฐœ๋ฌธ์ œ๋ฅผ ์ผ๋ฐ˜ํ™”ํ•˜์—ฌ ๋ฌด์ธํ•ญ๊ณต๊ธฐ ๊ฐ€๋ณ€๋ฐ˜๊ฒฝ ์ง‘ํ•ฉ๋ฎ๊ฐœ๋ฌธ์ œ๋ฅผ ์ •์˜ํ•œ๋‹ค. ๋ถ„์ง€ํ‰๊ฐ€๋ฒ•์„ ์ ์šฉํ•˜๋ฉด์„œ ํ•ด๊ฒฐ ๊ฐ€๋Šฅํ•œ ํ‰๊ฐ€ ๋ถ€๋ฌธ์ œ๋ฅผ ์ œ์•ˆํ•˜์˜€์œผ๋ฉฐ, ํœด๋ฆฌ์Šคํ‹ฑ์„ ์„ค๊ณ„ํ•˜์˜€๋‹ค. ์ œ์•ˆํ•œ ํ’€์ด ๋ฐฉ๋ฒ•๋“ค์ด ๊ธฐ์กด ์—ฐ๊ตฌ์—์„œ ์ œ์•ˆํ•œ ๋ฒค์น˜๋งˆํฌ ์œ ์ „ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ๋Šฅ๊ฐ€ํ•˜๋Š” ๊ฒฐ๊ณผ๋ฅผ ๋‚˜ํƒ€๋‚ด์—ˆ๋‹ค. 4์žฅ์—์„œ๋Š” ๋ฌด์ธํ•ญ๊ณต๊ธฐ ์‘๊ธ‰์˜๋ฃŒ์„œ๋น„์Šค๋ฅผ ์šด์˜ํ•˜๋Š” ์‹œ์„ค์˜ ์œ„์น˜์„ค์ • ๋ฐ ํ• ๋‹น๋ฌธ์ œ๋ฅผ ์ •์˜ํ•˜์˜€๋‹ค. 2์ฐจ ๊ฐ€๋ณ€๋ฐ˜๊ฒฝ ๋ฒ”์œ„์ œ์•ฝ์ด ์„ ํ˜•์˜ ๋™์น˜์ธ ์ˆ˜์‹์œผ๋กœ ์žฌ๊ณต์‹ํ™”๋˜์—ˆ์œผ๋ฉฐ, ๊ฐ•๊ฑด์ตœ์ ํ™” ๊ธฐ๋ฒ•์œผ๋กœ ์ธํ•ด ๋ฐœ์ƒํ•˜๋Š” ๋น„์„ ํ˜• ๋ฌธ์ œ๋ฅผ ์„ ํ˜•ํ™”ํ•˜์˜€๋‹ค. ๋Œ€๊ทœ๋ชจ ์ตœ์ ํ™” ๊ธฐ๋ฒ•์„ ์ ์šฉํ•˜๋ฉด์„œ, ํ‰๊ฐ€ ๋ถ€๋ฌธ์ œ์˜ ๊ตฌ์กฐ๋ฅผ ๋ถ„์„ํ•˜์—ฌ ๋‘ ๊ฐ€์ง€ ํ’€์ด ๊ธฐ๋ฒ•๊ณผ ํœด๋ฆฌ์Šคํ‹ฑ์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๊ฒฐ๊ณผ๋Š” ๋ฌด์ธํ•ญ๊ณต๊ธฐ์™€ ๋น„์Šทํ•œ ํŠน์ง•์„ ๊ฐ€์ง€๋Š” ์‹ค์ œ ์‚ฌ๋ก€์— ์ ์šฉ๋  ์ˆ˜ ์žˆ์œผ๋ฉฐ, ์ถ”์ƒ์ ์ธ ๋ฌธ์ œ๋กœ์จ ๋‹ค์–‘ํ•œ ๋ถ„์•ผ์— ๊ทธ๋Œ€๋กœ ํ™œ์šฉ๋  ์ˆ˜๋„ ์žˆ๋‹ค.Abstract i Contents vii List of Tables ix List of Figures xi Chapter 1 Introduction 1 1.1 Unmanned aerial vehicle operation on public services 1 1.2 Facility location problems 3 1.3 Large-scale optimization techniques 4 1.4 Research motivations and contributions 6 1.5 Outline of the dissertation 12 Chapter 2 Unmanned aerial vehicle set covering problem considering fixed-radius coverage constraint 14 2.1 Introduction 14 2.2 Problem definition 20 2.2.1 Problem description 22 2.2.2 Mathematical formulation 23 2.2.3 Discrete approximation model 26 2.3 Branch-and-price approach for the USCP 28 2.3.1 An extended formulation of the USCP 29 2.3.2 Branching strategies 34 2.3.3 Pairwise-conflict constraint approximation model based on Jung's theorem 35 2.3.4 Comparison of the approximation models 40 2.3.5 Framework of the solution algorithm for the PCBP model 42 2.4 Computational experiments 44 2.4.1 Datasets used in the experiments 44 2.4.2 Algorithmic performances 46 2.5 Solutions and related problems of the USCP 61 2.6 Summary 64 Chapter 3 Unmanned aerial vehicle variable radius set covering problem 66 3.1 Introduction 66 3.2 Problem definition 70 3.2.1 Mathematical model 72 3.3 Branch-and-price approach to the UVCP 76 3.4 Minimum covering circle-based approach 79 3.4.1 Formulation of the pricing subproblem II 79 3.4.2 Equivalence of the subproblem 82 3.5 Fixed-radius heuristic 84 3.6 Computational experiments 86 3.6.1 Datasets used in the experiments 88 3.6.2 Solution algorithms 91 3.6.3 Algorithmic performances 94 3.7 Summary 107 Chapter 4 Facility location-allocation problem for unmanned aerial vehicle emergency medical service 109 4.1 Introduction 109 4.2 Related literature 114 4.3 Location-allocation model for UEMS facility 117 4.3.1 Problem definition 118 4.3.2 Mathematical formulation 120 4.3.3 Linearization of the quadratic variable coverage distance function 124 4.3.4 Linear reformulation of standard formulation 125 4.4 Solution algorithms 126 4.4.1 An extended formulation of the ULAP 126 4.4.2 Branching strategy 129 4.4.3 Robust disjunctively constrained integer knapsack problem 131 4.4.4 MILP reformulation approach 132 4.4.5 Decomposed DP approach 133 4.4.6 Restricted master heuristic 136 4.5 Computational experiments 137 4.5.1 Datasets used in the experiments 137 4.5.2 Algorithmic performances 140 4.5.3 Analysis of the branching strategy and the solution approach of the pricing subproblem 150 4.6 Summary 157 Chapter 5 Conclusions and future research 160 5.1 Summary 160 5.2 Future research 163 Appendices 165 A Comparison of the computation times and objective value of the proposed algorithms 166 Bibliography 171 ๊ตญ๋ฌธ์ดˆ๋ก 188 ๊ฐ์‚ฌ์˜ ๊ธ€ 190Docto

    A Legal Research on Human Elements of Maritime Autonomous Surface Ships (MASS) for Securing of Maritime Traffic Safety

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    This paper focuses on the normative issues and improvement plans related to maritime traffic safety of Maritime Autonomous Surface Ships (hereinafter referred to asโ€œMASSโ€) related to human elements, including prior research on the normative study autonomous vessel. This work tried to find out what the regulatory barriers for securing maritime traffic safety in earnest when an autonomous vessel with the automated system. And it proposed the improvement plans to review any restriction conditions and problems of The United Nations Convention on the Law of the Sea(UNCLOS) and the International Maritime Convention. The purpose of this research is to suggest an improvement plan for the safety of maritime traffic through legal issues related to MASS that will change the paradigm of the future maritime industry including Shipping and Port and comparative legal consideration. This paper analyzed the characteristics and definitions of MASS and the autonomy in order to secure the safety of maritime traffic. The Autonomous airplane and Self-driving car field which have advanced in autonomy have been examined comparatively. In MASS, It has been concluded that this research faithfully fulfill both reliability of the technology and normative maintenance. Also, it is desirable to make a system that does not replace humans but that requires human intervention and keeps safety in common. In addition, the International Convention and the matters related to the safety of maritime traffic on autonomous vessel were compared with each other in domestic and foreign laws and regulations. This research have sought to explore whether there are any problems in applying the existing laws and regulations for MASS which completely different from the existing ship operation forms. As a result, This paper have determined that MASS can enjoy their status as vessels regardless of the human factors that played an essential role in conventional vessels. It was proved that very difficult for MASS to operate and secure safety within the framework of the UNCLOS and the International Maritime Convention. Therefore, if the technical reliability of MASS is seriously suspected, or if they are not expressly contrary to international conventions or flag laws, it is the general principle of the international community to treat vessels under the principle of international reciprocity or international comity. This paper examined the MASS in terms of human, ethical, and physical as to whether there are any regulatory barriers that violate the UNCLOS, the International Convention, and flag laws & regulations. The issues of human regulatory barriers for securing maritime traffic safety were examined by focusing on COLREG, STCW, LLMC, SOLAS(chapter VI, V, โ…จ, XI). In relation to the MASS ethical dilemma, this research looked at what to consider and decide on navigation algorithm programming. MASS are based on sophisticated computer system, so ethical problems applied to Artificial Intelligence(AI) are very important. Ethical considerations also need to be studied in the future. Consequently, even if it is the MASS lacking human factors, it is an essential element that can not be abandoned and it is a great advantage in terms of securing the safety of maritime traffic. Therefore, it is appropriate to review the matters and related regulations by absorbing and integrating them in the existing normative system rather than the special law system that recognizes the specificity or exceptionality of MASS. In this regard, this research introduced the legislative proposals for the introduction and operation of MASS regarding the safety of life and maritime traffic and the prevention of marine environmental pollution at sea. Finally, Amendments for the domestic maritime law proposed relating to the definition of MASS, the permission of the provisional operation of MASS, the safety operation requirements, the collection of sailing information and suspension & punishment for MASS, Master's authority and duty for onboard reflecting the specificity of the sea.็›ฎ ๆฌก Abstract โ…ฒ ็ฌฌ1็ซ  ์„œ๋ก  1 ์ œ1์ ˆ ์—ฐ๊ตฌ์˜ ๋ชฉ์  ๋ฐ ๋ฐฐ๊ฒฝ 1 ์ œ2์ ˆ ์—ฐ๊ตฌ์˜ ๋ฐฉ๋ฒ• ๋ฐ ๋ฒ”์œ„ 10 ์ œ3์ ˆ ์„ ํ–‰์—ฐ๊ตฌ 12 ็ฌฌ2็ซ  ์ž์œจ์šดํ•ญ์„ ๋ฐ•์˜ ๊ฐœ๋…๊ณผ ํƒ€ ์ž์œจ์‹œ์Šคํ…œ๊ณผ์˜ ๋น„๊ต ๊ณ ์ฐฐ 17 ์ œ1์ ˆ ์ž์œจ์šดํ•ญ์„ ๋ฐ•์˜ ๊ฐœ๋… 17 โ… . ์ž์œจ์šดํ•ญ์„ ๋ฐ•์˜ ์ •์˜ 17 โ…ก. ์ž์œจ์šดํ•ญ์„ ๋ฐ•์˜ ์ž์œจํ™” ๋‹จ๊ณ„ 23 ์ œ2์ ˆ ์ž์œจ์šดํ•ญ์„ ๋ฐ•๊ณผ ์ž์œจ๋น„ํ–‰ ํ•ญ๊ณต๊ธฐ ์ž์œจํ™” ๋น„๊ต 30 โ… . ์ž์œจ๋น„ํ–‰ ํ•ญ๊ณต๊ธฐ ์ž์œจํ™” ํ˜„ํ™ฉ 30 โ…ก. ์ž์œจ์šดํ•ญํ•ญ๊ณต๊ธฐ์™€ ์ž์œจ์šดํ•ญ์„ ๋ฐ•์˜ ์ž์œจํ™” ๋‹จ๊ณ„ ๋น„๊ต 33 ์ œ3์ ˆ ์ž์œจ์šดํ•ญ์„ ๋ฐ•๊ณผ ์ž์œจ์ฃผํ–‰ ์ž๋™์ฐจ ์ž์œจํ™” ๋น„๊ต 36 โ… . ์ž์œจ์ฃผํ–‰ ์ž๋™์ฐจ ์ž์œจํ™” ํ˜„ํ™ฉ 36 โ…ก. ์ž์œจ์ฃผํ–‰ ์ž๋™์ฐจ์™€ ์ž์œจ์šดํ•ญ์„ ๋ฐ•์˜ ์ž์œจํ™” ๋‹จ๊ณ„ ๋น„๊ต 39 ็ฌฌ3็ซ  ์ž์œจ์šดํ•ญ์„ ๋ฐ•์˜ ์„ ๋ฐ•์ง€์œ„์— ๋”ฐ๋ฅธ ๊ทœ๋ฒ”์  ๊ณ ์ฐฐ 44 ์ œ1์ ˆ ์ž์œจ์šดํ•ญ์„ ๋ฐ•์˜ ์„ ๋ฐ•์„ฑ์— ๊ด€ํ•œ ๊ณ ์ฐฐ 44 ์ œ2์ ˆ ์œ ์—”ํ•ด์–‘๋ฒ•ํ˜‘์•ฝ์ƒ ์ž์œจ์šดํ•ญ์„ ๋ฐ•์˜ ๊ด€ํ• ๊ถŒ 49 โ… . ์œ ์—”ํ•ด์–‘๋ฒ•ํ˜‘์•ฝ์ƒ ๊ธฐ๊ตญ์˜ ๊ด€ํ• ๊ถŒ๊ณผ MASS์˜ ์Ÿ์ ๋ถ„์„ 50 โ…ก. ์œ ์—”ํ•ด์–‘๋ฒ•ํ˜‘์•ฝ์ƒ ์—ฐ์•ˆ๊ตญ์˜ ๊ด€ํ• ๊ถŒ๊ณผ MASS์˜ ์Ÿ์ ๋ถ„์„ 52 โ…ข. ์œ ์—”ํ•ด์–‘๋ฒ•ํ˜‘์•ฝ์ƒ ํ•ญ๋งŒ๊ตญ ๊ด€ํ• ๊ถŒ๊ณผ MASS์˜ ์Ÿ์ ๋ถ„์„ 55 ็ฌฌ4็ซ  ์ž์œจ์šดํ•ญ์„ ๋ฐ•๊ณผ ํ•ด์ƒ๊ตํ†ต์•ˆ์ „์˜ ์ธ์ ์š”์†Œ์— ๊ด€ํ•œ ๊ณ ์ฐฐ 58 ์ œ1์ ˆ ์ž์œจ์šดํ•ญ์„ ๋ฐ• ์›๊ฒฉ์šดํ•ญ์ž์˜ ์„ ์›์„ฑ์— ๊ด€ํ•œ ํŒ๋‹จ 60 โ… . ์„ ๋ฐ•์˜ ํ•„์ˆ˜์š”์†Œ๋กœ์„œ์˜ ์„ ์žฅ๊ณผ ์„ ์› 60 โ…ก. ์ž์œจ์šดํ•ญ์„ ๋ฐ• ์›๊ฒฉ์šดํ•ญ์ž์˜ ์—ญํ•  ๋ฐ ์ธ์  ๊ฐํ•ญ์„ฑ 60 ์ œ2์ ˆ ์ž์œจ์šดํ•ญ์„ ๋ฐ•์˜ ์ธ์ ์š”์†Œ์™€ ์„ ์›์˜ ์ƒ๋ฌด ์ ์šฉ์— ๊ด€ํ•œ ๊ณ ์ฐฐ 68 โ… . ์ž์œจ์šดํ•ญ์„ ๋ฐ•์— ์„ ์›์˜ ์ƒ๋ฌด ์ ์šฉ ๊ทผ๊ฑฐ์™€ ๋‚ด์šฉ 68 โ…ก. ์ž์œจ์šดํ•ญ์„ ๋ฐ• ์„ ์›์˜ ์ƒ๋ฌด ์ ์šฉ์— ๋Œ€ํ•œ ์Ÿ์ ์‚ฌํ•ญ 71 ็ฌฌ5็ซ  ์ž์œจ์šดํ•ญ์„ ๋ฐ• ํ•ด์ƒ๊ตํ†ต์•ˆ์ „ ๊ด€๋ จ ๊ทœ์ œ ์žฅ๋ฒฝ ๋ฐ ๊ฐœ์„ ๋ฐฉ์•ˆ 76 ์ œ1์ ˆ ์ž์œจ์šดํ•ญ์„ ๋ฐ• ํ•ด์ƒ๊ตํ†ต์•ˆ์ „ ํ™•๋ณด๋ฅผ ์œ„ํ•œ ์Ÿ์  ๋ฐ ๊ฐœ์„ ๋ฐฉ์•ˆ 76 โ… . ์ž์œจ์šดํ•ญ์„ ๋ฐ• ํ•ด์ƒ๊ตํ†ต์•ˆ์ „ ํ™•๋ณด๋ฅผ ์œ„ํ•œ ์ธ์ ๊ทœ์ œ ์Ÿ์ ๋ถ„์„ ๋ฐ ๊ฐœ์„ ๋ฐฉ์•ˆ 77 โ…ก. ์ž์œจ์šดํ•ญ์„ ๋ฐ• ํ•ด์ƒ๊ตํ†ต์•ˆ์ „ ํ™•๋ณด๋ฅผ ์œ„ํ•œ ์œค๋ฆฌ์  ์Ÿ์ ๋ถ„์„ ๋ฐ ๊ฐœ์„ ๋ฐฉ์•ˆ 87 ์ œ2์ ˆ ์ž์œจ์šดํ•ญ์„ ๋ฐ• ํ•ด์ƒ๊ตํ†ต์•ˆ์ „์„ ์œ„ํ•œ ๊ตญ์ œํ˜‘์•ฝ๊ณผ ๊ตญ๋‚ด๋ฒ• ๊ฐœ์ •๋ฐฉํ–ฅ ๋ฐ ๊ฐœ์ •์›์น™ 95 โ… . ์ž์œจ์šดํ•ญ์„ ๋ฐ• ํ•ด์ƒ๊ตํ†ต์•ˆ์ „์„ ์œ„ํ•œ ๊ทœ๋ฒ”์˜ ๊ฐœ์ •๋ฐฉํ–ฅ ๋ฐ ์›์น™ 96 โ…ก. ์ž์œจ์šดํ•ญ์„ ๋ฐ• ๋„์ž… ๋ฐ ํ•ด์ƒ๊ตํ†ต์•ˆ์ „ ํ™•๋ณด๋ฅผ ์œ„ํ•œ ์ž…๋ฒ•ํ™” ์›์น™ 99 โ…ข. ์„ ์žฅ์˜ ๊ถŒํ•œ๊ณผ ์ฑ…์ž„์— ๊ด€ํ•œ ์›์น™ 102 ็ฌฌ6็ซ  ๊ฒฐ๋ก  104 ์ œ1์ ˆ ์š”์•ฝ ๋ฐ ์ •๋ฆฌ 104 ์ œ2์ ˆ ๊ฒฐ๋ก  106 ๅƒ่€ƒๆ–‡็ป 111Maste

    ์ฐจ๋Ÿ‰ ๊ฐ„ GPS ๊ณตํ†ต ๊ฐ€์‹œ์œ„์„ฑ ๊ฒ€์ƒ‰์„ ํ†ตํ•œ ์ƒ๋Œ€์œ„์น˜ ์ถ”์ • ์ •ํ™•๋„ ํ–ฅ์ƒ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ

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    ๋ณธ ๋…ผ๋ฌธ์€ ์ €๊ฐ€์˜ GPS ์ˆ˜์‹ ๊ธฐ์™€ MEMS๊ธ‰ IMU, B-CDMA ๋ฌด์„  ํ†ต์‹  ๋ชจ๋“ˆ์„ ์ด์šฉํ•œ ๋‹ค์ˆ˜ ์ฐจ๋Ÿ‰์˜ ์ƒ๋Œ€์œ„์น˜ ์ถ” ์ •์— ๊ด€ํ•œ ์—ฐ๊ตฌ์ด๋‹ค. ์ฐจ๋Ÿ‰์˜ ์ƒ๋Œ€์œ„์น˜๋ฅผ ์ถ”์ •ํ•จ์— ์žˆ์–ด์„œ, ๊ฐ ์ฐจ๋Ÿ‰์˜ ๊ฐ€์‹œ ์œ„์„ฑ ์กฐํ•ฉ์ด ๋ถˆ์ผ์น˜ ํ•  ๊ฒฝ์šฐ ์˜ค์ฐจ๊ฐ€ ๊ธ‰์ฆํ•˜๋Š” ํ˜„์ƒ์ด ๋ฐœ์ƒํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š”, ์ด๋ฅผ ๊ฐœ์„ ํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์ธก์ •์น˜ ๊ธฐ๋ฐ˜์œผ๋กœ ์ƒ๋Œ€์œ„์น˜๋ฅผ ๊ณ„์‚ฐํ•˜๋Š” RGPS ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ œ์•ˆํ•œ๋‹ค. ๋™์‹œ์— GPS/INS ํ†ตํ•ฉ ํ•ญ๋ฒ• ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ ์šฉํ•˜์—ฌ ๊ฐ ์ฐจ๋Ÿ‰์˜ ๋ฐฉํ–ฅ๊ฐ๊ณผ ์†๋„๋ฅผ ์ถ”์ •ํ•œ๋‹ค. ์ตœ์ข…์ ์œผ๋กœ RGPS ์•Œ๊ณ ๋ฆฌ์ฆ˜๊ณผ ๊ฐ ์ฐจ๋Ÿ‰์˜ GPS/INS ํ†ตํ•ฉํ•ญ๋ฒ• ์•Œ๊ณ ๋ฆฌ์ฆ˜ ๊ฒฐ๊ณผ๋ฅผ ์‚ฌ์šฉํ•œ Position Integration Filter ์•Œ๊ณ ๋ฆฌ์ฆ˜์œผ๋กœ๋ถ€ํ„ฐ ์ตœ์ข…์ ์ธ ์ƒ๋Œ€์œ„์น˜์™€ ์ƒ๋Œ€์†๋„๋ฅผ ์ถ”์ •ํ•œ๋‹ค. ์ด์™€ ๊ฐ™์€ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋ฅผ ์ฆ๋ช…ํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์‹ค์ œ ์‹ค ํ—˜์„ ํ†ตํ•˜์—ฌ ์ถ”์ • ๊ฒฐ๊ณผ๋ฅผ ํ™•์ธํ•˜์˜€๋‹ค. ์‹ค์‹œ๊ฐ„ ํ”„๋กœ๊ทธ๋žจ๊ณผ ์‹คํ—˜์šฉ ๋ชจํ˜• ์ฐจ๋Ÿ‰์„ ์ œ์ž‘ํ•˜์—ฌ ์ƒ๋Œ€์œ„์น˜, ์ƒ๋Œ€์†๋„ ์ถ” ์ • ์‹คํ—˜์„ ์‹ค์‹œ, ์‹ค์ œ ํ™˜๊ฒฝ์—์„œ์˜ ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ์„ฑ๋Šฅ์„ ๊ฒ€์ฆํ•˜์˜€๋‹ค.In this paper, we present relative positioning algorithm for moving land vehicle using GPS, MEMS IMU and B-CDMA module. This algorithm does not calculate precise absolute position but calculates relative position directly, so additional infrastructure and I2V communication device are not required. Proposed algorithm has several steps. Firstly, unbiased relative position is calculated using pseudorange difference between two vehicles. Simultaneously, the algorithm estimates position of each vehicle using GPS/INS integration. Secondly, proposed algorithm performs filtering and finally estimates relative position and relative velocity. Using proposed algorithm, we can obtain more precise relative position for moving land vehicles with short time interval as IMU sensor has. The simulation is performed to evaluate this algorithm and the several field tests are performed with real time program and miniature vehicles for verifying performance of proposed algorithm.OAIID:oai:osos.snu.ac.kr:snu2012-01/102/0000003405/9SEQ:9PERF_CD:SNU2012-01EVAL_ITEM_CD:102USER_ID:0000003405ADJUST_YN:NEMP_ID:A000360DEPT_CD:446CITE_RATE:0FILENAME:05 ํ•œ์˜๋ฏผ(927~934).pdfDEPT_NM:๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€EMAIL:[email protected]_YN:NCONFIRM:
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