• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.3
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• pp.255-263
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• 2014

Today, a number of CCTV on the highway is to observe the flow of traffics. There have been a number of studies where traffic data (e.g., the speed of vehicles and the amount of traffic on the road) are transferred back to the centralized server so that an appropriate action can be taken. This paper introduces a system that detects the changes of traffic flows caused by an accident or unexpected stopping (i.e., vehicle remains idle) by monitoring each lane separately. The traffic flows of each lane are level spacing curve that shows Wigner distribution for location vector. Applying calogero-moser system and Hamiltonian system, probability equation for each level-spacing curve is derived. The high level of modification of the signal means that the lane is in accident situation. This is different from previous studies in that it does more than looking for the signal from only one lane, now it is able to detect an accident in entire flow of traffic. In process of monitoring traffic flow of each lane, when camera recognizes a shadow of vehicle as a vehicle, it will affect the accident detecting capability. To prevent this from happening, the study introduces how to get rid of such shadow. The system using Basian network method is being compared for capability evaluation of the system of the study. As a result, the system of the study appeared to be better in performance in detecting the modification of traffic flow caused by idle vehicle.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.5
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• pp.271-279
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• 2014

In this paper, the two design techniques of the embedded system which provides a wireless image observation with temporary ad-hoc network are proposed and developed. The first method is based on the embedded system design technique for a nearly real-time wireless short observation application, having a specific remote monitoring node with a built-in image processing function, and having the maximum rate of 1 fps (frame per second) wireless image transmission capability of a $160{\times}128$size image. The second technique uses the embedded system for a general wireless long observation application, consisting of the main node, the remote monitoring node, and the system controller with built-in image processing function, and the capability of the wireless image transmission rate of 1/3 fps. The proposed system uses the wireless ad-hoc network which is widely accepted as a short range, low power, and bidirectional digital communication, the hardware are consisted of the general developed modules, a small digital camera, and a PC, and the embedded software based upon the Zigbee stack and the user interface software are developed and tested on the implemented module. The wireless environment analysis and the performance results are presented.

• Korean Journal of Remote Sensing
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• v.35 no.6_2
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• pp.1149-1160
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• 2019

In this study, two radiometric correction methods deriving reflectance from UAV multispectral image for monitoring forest vegetation were applied and evaluated. Multispectral images were obtained from a small multispectral camera having 5 spectral bands. Reflectance were derived by applying the two methods: (1) the direct method using downwelling irradiance measurement and (2) the empirical line correction method by linking a set of field reflectance measured simultaneous with the image capture. Field reflectance were obtained using a spectroradiometer during the flight and used for building the linear equation for the empirical method and for the validation of image reflectance derived. Although both methods provided the high correlations between field reflectance and image-derived reflectance, their distributions were somewhat different. While the direct method provided rather stable and consistent distribution of reflectance all over the entire image area, the empirical method showed very unstable and inconsistent reflectance distribution. The direct method would be more appropriate for relatively wide area that requires more time to acquire image and may vary in downwelling irradiance and atmospheric conditions.

• Korean Chemical Engineering Research
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• v.57 no.2
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• pp.274-282
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• 2019

In recent years, big data analysis has been expanded to include automatic control through reinforcement learning as well as prediction through modeling. Research on the utilization of image data is actively carried out in various industrial fields such as chemical, manufacturing, agriculture, and bio-industry. In this paper, we applied NASNet, which is an AutoML reinforced learning algorithm, to DeepU-Net neural network that modified U-Net to improve image semantic segmentation performance. We used BRATS2015 MRI data for performance verification. Simulation results show that DeepU-Net has more performance than the U-Net neural network. In order to improve the image segmentation performance, remove dropouts that are typically applied to neural networks, when the number of kernels and filters obtained through reinforcement learning in DeepU-Net was selected as a hyperparameter of neural network. The results show that the training accuracy is 0.5% and the verification accuracy is 0.3% better than DeepU-Net. The results of this study can be applied to various fields such as MRI brain imaging diagnosis, thermal imaging camera abnormality diagnosis, Nondestructive inspection diagnosis, chemical leakage monitoring, and monitoring forest fire through CCTV.

• The Journal of Engineering Geology
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• v.28 no.4
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• pp.701-714
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• 2018

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.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.577-589
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• 2021

Drone mapping systems can be applied to many fields such as disaster damage investigation, environmental monitoring, and construction process monitoring. To integrate individual sensors attached to a drone, it was essential to undergo complicated procedures including time synchronization. Recently, a variety of composite sensors are released which consist of visual sensors and GPS/INS. Composite sensors integrate multi-sensory data internally, and they provide geotagged image files to users. Therefore, to use composite sensors in drone mapping systems, mapping accuracies from composite sensors should be examined. In this study, we analyzed the mapping accuracies of a composite sensor, focusing on the data acquisition area and pre-calibration effect. In the first experiment, we analyzed how mapping accuracy varies with the number of ground control points. When 2 GCPs were used for mapping, the total RMSE has been reduced by 40 cm from more than 1 m to about 60 cm. In the second experiment, we assessed mapping accuracies based on whether pre-calibration is conducted or not. Using a few ground control points showed the pre-calibration does not affect mapping accuracies. The formation of weak geometry of the image sequences has resulted that pre-calibration can be essential to decrease possible mapping errors. In the absence of ground control points, pre-calibration also can improve mapping errors. Based on this study, we expect future drone mapping systems using composite sensors will contribute to streamlining a survey and calibration process depending on the data acquisition circumstances.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1479-1488
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• 2022

Accidents in which oil spills occur intermittently in the ocean due to ship collisions and sinkings. In order to prepare prompt countermeasures when such an accident occurs, it is necessary to accurately identify the current status of spilled oil. To this end, the Coast Guard patrols the target area with a fixed-wing airplane or helicopter and checks it with the naked eye or video, but it was difficult to determine the area contaminated by the spilled oil and its exact location on the map. Accordingly, this study develops a technology for direct ortho-rectification by automatically geo-referencing aerial images collected by the Coast Guard without individual ground reference points to identify the current status of spilled oil. First, meta information required for georeferencing is extracted from a visualized screen of sensor information such as video by optical character recognition (OCR). Based on the extracted information, the external orientation parameters of the image are determined. Images are individually orthorectified using the determined the external orientation parameters. The accuracy of individual orthoimages generated through this method was evaluated to be about tens of meters up to 100 m. The accuracy level was reasonably acceptable considering the inherent errors of the position and attitude sensors, the inaccuracies in the internal orientation parameters such as camera focal length, without using no ground control points. It is judged to be an appropriate level for identifying the current status of spilled oil contaminated areas in the sea. In the future, if real-time transmission of images captured during flight becomes possible, individual orthoimages can be generated in real time through the proposed individual orthorectification technology. Based on this, it can be effectively used to quickly identify the current status of spilled oil contamination and establish countermeasures.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1219-1230
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• 2022

Multi-spectral drones in agricultural observation require quantitative and reliable data based on physical quantities such as radiance or reflectance in crop yield analysis. In the case of remote sensing data for crop monitoring, images taken in the same area over time-series are required. In particular, biophysical data such as leaf area index or chlorophyll are analyzed through time-series data under the same reference, it can be directly analyzed. So, comparable reflectance data are required. Orthoimagery using drone images, the entire image pixel values are distorted or there is a difference in pixel values at the junction boundary, which limits accurate physical quantity estimation. In this study, reflectance and vegetation index based on drone images were calculated according to the correction method of drone images for time-series crop monitoring. comparing the drone reflectance and ground measured data for spectral characteristics analysis.

• Economic and Environmental Geology
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• v.55 no.5
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• pp.497-510
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• 2022

Monthly monitoring, 3D scan survey, and electrical resistivity survey were conducted from January 2018 to August 2022 to identify the cause of rockfall occurring in Daljeon-ri Columnar Joint (Natural Monument No. 415), Pohang. A total of 3,231 rocks fell from the columnar joint over the past 5 years, and 1,521 (47%) of the falling rocks were below 20 cm in length, 978 (30.3%) of 20-30 cm, and 732 (22.7%) of rocks over 30 cm. While the number of rockfalls by year has decreased since 2018, the frequency of rockfalls bigger than 30 cm tends to increase. Large-scale rockfalls occurred mainly during the thawing season (March-April) and the rainy season (June-July), and the analysis of the relationship between cumulative rainfall and rockfall occurrence showed that cumulative rainfall for 3 to 4 days is also closely related to the occurrence of rockfall. Smectite and illite, which are expansible clay minerals, were observed in XRD analysis of the slope material (filling minerals) in the columnar joint, and the presence of a fault fracture zone was confirmed in the electrical resistivity survey. In addition, the confirmed fault fracture zone and the maximum erosion point analyzed through 3D precision measurement coincided with the main rockfall occurrence point observed by the BTC-6PXD camera. Therefore, the main cause of rockfall at Daljeon-ri columnar joint in Pohang is a combination of internal factors (development of fault fracture zones and joints, weathering of rocks, presence of expansive clay minerals) and external factors (precipitation, rapid thawing phenomenon), resulting in large-scale rockfall. Meanwhile, it was also confirmed that the Pohang-Gyeongju earthquake, which was continuously raised, was not the main cause.

• The Bulletin of The Korean Astronomical Society
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• v.40 no.1
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• pp.52.3-53
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• 2015

The GMT-Consortium Large Earth Finder (G-CLEF) is a fiber-fed, optical band high dispersion echelle spectrograph that selected as the first light instrument for the Giant Magellan Telescope (GMT). This G-CLEF has been designed to be a general- purpose echelle spectrograph with the precisional radial velocity (PRV) capability of 10 cm/sec as a goal. The preliminary design review (PDR) was held on April 8 to 10, 2015 and the scientific observations will be started in 2022 with four mirrors installed on GMT. We have been participating in this preliminary design study in flexure control camera (slit monitoring system), calibration lamp sources, dichroic assembly and the fabrication of the proto-Mangin Mirror. We present the design concept on the parts KASI undertaken, introducing the specifications and capabilities of G-CLEF.