• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.2
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• pp.58-76
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• 2019

As the number of reported injuries has tended to increase over time, large hospitalization expenditure from excessive medical treatments and hospitalization, and insurance frauds associated with moral hazard in minor collisions have caused a global societal problem. Many occupants of rear-ended vehicles involved in rear-end collisions complain of whiplash injury, which is also known as neck injury, without any anatomical and radiological evidence. With only clinical symptoms, stating that a whiplash injury is a type of injury defined by the Abbreviated Injury Scale would be difficult. Therefore, this study focuses on minor rear-end collisions, where the rear-ender vehicle collides with the rear-ended vehicle at rest. The mathematics dynamic model is employed to simulate a total of 100 rear-end collision scenarios based on various weights and collision speeds and identify how the weights and speeds of both vehicles influence the risk of whiplash injury in occupants involved in minor rear-end collisions. The possibility of an injury is very high when the same-weight vehicles are involved in accidents at collision speeds of 15 km/h or higher. The possibilities are 36% and 84% with collision speeds of 15 km/h and 20 km/h, respectively, if weights are disregarded.

• Journal of the Korea Society for Simulation
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• v.27 no.4
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• pp.61-68
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• 2018

This paper deals with ground clutter model for FOD (foreign object debris) surveillance FMCW (frequency modulated continuous waveform) radar. In the FOD surveillance radar, it has received not only the signals reflected by FOD, but also the clutters of the surface of the runway and the grassland simultaneously. However, to detect the FOD, the clutter rejection algorithm is necessary because the RCS (radar cross section) of FOD is nearly same to RCS of the grassland. In addition, it is difficult to apply the MTI (moving target indicator) algorithm as the clutter rejection algorithm because both the FOD and the clutter coexist stationarily. Hence, to remove the stationary clutter, it is crucial to accurately generate clutter map considering the surface of road. In this paper, in order to generate the clutter map, the respective beat signal at every range bin is generated in the case of only the surface without FOD, and then the beat signal accumulated 100 times. And also, Weibull distribution is applied to the RCS value to take the scattering distribution of clutter into consideration. The simulation results show that FOD can be well detected by applying the generated clutter map to the FOD FMCW radar.

• Journal of Advanced Navigation Technology
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• v.22 no.6
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• pp.602-609
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• 2018

Differential GPS (DGPS) is known as a positioning method using pseudo range correction (PRC) which is communicating between a refence receiver and moving receivers. In real world, a moving receiver loses communication with the reference receiver, resulting in loss of PRC real-time communication. In this paper, we assume that the transmission of the pseudo range correction isinterrupted in the middle of real-time positioning situations, in which calibration information is received in the DGPS method. Under the disconnected communication, we propose 'predict DGPS' that real-time virtual PRC model which is modeled by a machine learning algorithm with previously acquired PRC data from a reference receiver. To verify predict DGPS method, we compared and analyzed positioning solutions acquired from real PRC and the virtual PRC. In addition, we show that positioning using the DGPS prediction method on a real road can provide an improved positioning solution assuming a scenario in which PRC communication was cut off.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.5
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• pp.27-35
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• 2019

The purpose of this study is to analyze the displacement characteristics in slow-moving landslide area using digital elevation model and airborne LiDAR when unpredictable disaster such as slow-moving landslide occurred. We also aimed to provide basic data for establishing a rapid, reasonable and effective restoration plan. In this study, slow-moving landslide occurrence cracks were selected through the airborne LiDAR data, and the topographic changes and the scale of occurrence were quantitatively analyzed. As a result of the analysis, the study area showed horseshoe shape similar to the general form of slow-moving landslide occurrence in Korea, and the direction of movement was in the north direction. The total area of slow-moving landslide damage was estimated to about 2.5ha, length of landsldie scrap 327.3m, average width 19.3m, and average depth 8.6m. The slow-moving landslides did not occur on a large scale but occurred on the adjacent slope where roads were located, caused damage to retaining walls and roads. The field survey of slow-moving landslides was limited by accessibility and safety issues, but there was an advantage that accurate analysis was possible through the airborne LiDAR. However, because airborne LiDAR has costly disadvantages, it has proposed a technique to mount LiDAR on UAV for rapidity, long-term monitoring. In a slow-moving landslide damage area, information such as direction of movement of cracks and change of scale should be acquired continuously to be used in restoration planning and prevention of damage.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.32 no.6
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• pp.481-495
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• 2020

After the resonator on the basis of the wave-filter theory was designed to control the waves with a specific frequency range surging into the harbor, the several case with the use of resonator have been reported in some part of sea, including the port of Long Beach, USA, and yacht harbor at Rome, Italy in order to control the long-period wave motion from the vessels. Recently, the utility and applicability of the resonator has been sufficiently verified in respect of the control of tsunami approximated as the solitary wave and/or the super long-period waves. However, the case with the application of tsunami in the real sea have not been reported yet. In this research, the respective case with the use of existing resonator at the port of Mukho and Imwon located in the eastern coast of South Korea were studied by using the numerical analysis through the COMCOT model adapting the reduction rate of 1983 Central East Sea tsunami and 1993 Hokkaido Southwest off tsunami. Consequently, the effectiveness of resonator against tsunami in the real sea was confirmed through the reduction rate of maximum 40~50% at the port of Mukho, and maximum 21% at the port of Imwom, respectively. In addition, it was concluded that it is necessary to study about the various case with application of different shape, arrangement, and size of resonator in order to design the optimal resonator considering the site condition.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.32 no.6
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• pp.496-505
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• 2020

In the previous research, the effectiveness of resonator was confirmed through the numerical analysis on two cases with the use of existing resonator at the Mukho and Imwon ports located in the eastern coast of South Korea by discussing the reduction rates of 1983 Central East Sea tsunami, and 1993 Hokkaido Southwest off tsunami, respectively. In this study, the reduction rates of tsunami height with three different resonators, Type I, II-1, and II-2, at the Samcheok port were examined respectively through the numerical analysis using COMCOT model under the same condition as the previous study. It was discussed the spatial distribution of maximum height of tsunami, change of water level, and effectiveness of resonator with the presence of new types of resonator, and change of their sizes. As a result, the effectiveness of resonator was verified through the application of new types of resonator reducing about maximum 40% of tsunami height. In order to design the optimal resonator for the variety of site condition, it is necessary to research about the various cases applying different shape, arrangement, and size of resonator as further study.

• Korean Journal of Remote Sensing
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• v.36 no.6_2
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• pp.1579-1590
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• 2020

In this paper, spatially-adaptive denormalization (SPADE) based U-Net is proposed to detect changes by using high-resolution satellite images. The proposed network is to preserve spatial information using SPADE. Change detection methods using high-resolution satellite images can be used to resolve various urban problems such as city planning and forecasting. For using pixel-based change detection, which is a conventional method such as Iteratively Reweighted-Multivariate Alteration Detection (IR-MAD), unchanged areas will be detected as changing areas because changes in pixels are sensitive to the state of the environment such as seasonal changes between images. Therefore, in this paper, to precisely detect the changes of the objects that consist of the city in time-series satellite images, the semantic spatial objects that consist of the city are defined, extracted through deep learning based image segmentation, and then analyzed the changes between areas to carry out change detection. The semantic objects for analyzing changes were defined as six classes: building, road, farmland, vinyl house, forest area, and waterside area. Each network model learned with KOMPSAT-3A satellite images performs a change detection for the time-series KOMPSAT-3 satellite images. For objective assessments for change detection, we use F1-score, kappa. We found that the proposed method gives a better performance compared to U-Net and UNet++ by achieving an average F1-score of 0.77, kappa of 77.29.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.991-1005
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• 2022

Satellite imaging is an effective supplementary data source for detecting and verifying nuclear activity. It is also highly beneficial in regions with limited access and information, such as nuclear installations. Time series analysis, in particular, can identify the process of preparing for the conduction of a nuclear experiment, such as relocating equipment or changing facilities. Differences in the semantic segmentation findings of time series photos were employed in this work to detect changes in meaningful items connected to nuclear activity. Building, road, and small object datasets made of KOMPSAT 3/3A photos given by AIHub were used to train deep learning models such as U-Net, PSPNet, and Attention U-Net. To pick relevant models for targets, many model parameters were adjusted. The final change detection was carried out by including object information into the first change detection, which was obtained as the difference in semantic segmentation findings. The experiment findings demonstrated that the suggested approach could effectively identify altered pixels. Although the suggested approach is dependent on the accuracy of semantic segmentation findings, it is envisaged that as the dataset for the region of interest grows in the future, so will the relevant scope of the proposed method.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.6
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• pp.393-399
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• 2021

In this study, experiments were conducted to estimate the PM concentrations by learning the nighttime CCTV images of various PM concentrations environments. In the case of daytime images, there have been many related studies, and the various texture and brightness information of images is well expressed, so the information affecting learning is clear. However, nighttime images contain less information than daytime images, and studies using only nighttime images are rare. Therefore, we conducted an experiment combining nighttime images with non-uniform characteristics due to light sources such as vehicles and streetlights and building roofs, building walls, and streetlights with relatively constant light sources as an ROI (Region of Interest). After that, the correlation was analyzed compared to the daytime experiment to see if deep learning-based PM concentrations estimation was possible with nighttime images. As a result of the experiment, the result of roof ROI learning was the highest, and the combined learning model with the entire image showed more improved results. Overall, R² exceeded 0.9, indicating that PM estimation is possible from nighttime CCTV images, and it was calculated that additional combined learning of weather data did not significantly affect the experimental results.

• Journal of the Society of Disaster Information
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• v.17 no.1
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• pp.165-175
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• 2021

Purpose: The purpose of this study is to quantitatively present the carbon dioxide(CO₂) emission change according to the application of autonomous driving technology at the network level for waste vehicles in the metropolitan area. Method: The target year was set to 2030, and the analysis method estimated the carbon dioxide (CO₂) emissions for each road link through user equilibrium assignment when unapplied scenario. The application scenario performed traffic assignment using route data on the premise that the group was running in accordance with the application of autonomous driving technology to waste vehicles. In addition, the other means estimated the carbon dioxide emissions through user balance allocation by reflecting the results of the waste vehicle allocation. Result: As a result of the analysis, carbon dioxide(CO₂) emissions were found to be reduced by about 56.9ton/day from the national network level, and the Seoul metropolitan area was analyzed to be reduced by about 54.7ton/day. Conclusion: This study quantitatively presented environmental impacts among various social effects that autonomous driving technology will bring, and in the future, development of various analytical methodologies and related studies should be continuously conducted.