• Bulletin of the Korean Chemical Society
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• v.30 no.12
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• pp.3049-3052
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• 2009

Methyl tert-butyl ether (MTBE) is added to gasoline to enhance the octane number of gasoline, tert-butyl alcohol (TBA) is major degradation intermediate of MTBE in environment, and benzene, toluene, ethyl benzene and xylene (BTEX) are also major constituents of gasoline. In this study, a simplified headspace analysis method was adapted for simultaneous determination of MTBE, TBA and BTEX in ground water samples. The sample 5.0 mL and 2 g NaCl were placed in a 10 mL vial and the solution was spiked with fluorobenzene as an internal standard and sealed with a cap. The vial was placed in a heating block at 85 $^{\circ}C$ for 30 min. The detection limits of the assay were 0.01 ${\mu}$g/L for MTBE and BTEX, and 0.02 ${\mu}$g/L for TBA. The method was used to analyze 110 ground water samples from various regions in Korea, and to survey the their background concentration in ground water in Korea. The samples revealed MTBE concentrations in the range of 0.01 - 0.45 ${\mu}$g/L (detection frequency of 57.3%), TBA concentrations in the range of 0.02 - 0.08 ${\mu}$g/L (detection frequency of 5.5%), and total BTEX concentrations in the range of 0.01 - 2.09 ${\mu}$g/L (detection frequency of 87.3%). The developed method may be used when simultaneously determining the amount of MTBE, TBA and BTEX in water.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.1
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• pp.54-60
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• 2009

In this paper, a precision landing approach is implemented based on real-time image processing. A full-scale landmark for automatic landing is used. canny edge detection method is applied to identify the outside quadrilateral while circular hough transform is used for the recognition of inside circle. Position information on the ground landmark is uplinked to the unmanned helicopter via ground control computer in real time so that the unmanned helicopter control the air vehicle for accurate landing approach. Ground test and a couple of flight tests for autonomous landing approach show that the image processing and automatic landing operation system have good performance for the landing approach phase at the altitude of $20m{\sim}1m$ above ground level.

• Journal of electromagnetic engineering and science
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• v.13 no.4
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• pp.214-223
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• 2013

This paper proposes a theoretical analysis of hidden device detection and a design of multiband circular polarization patch array antenna for non-linear junction detector system application. A good axial ratio of circular polarization patch antenna is realized by a new approach that employs inclined slots, two rectangular grooves and a truncated ground for the conventional antenna. A good axial ratio of the 1.5 dB lower is measured by having an asymmetric gap distance between the ground planes of the coplanar waveguide feeding structure. The common ground plane of the linear array has an optimum trapezoidal slot array to reduce the mutual coupling without increasing the distance between the radiators. The higher gain of about 1 dBi is realized by using the novel common ground structure. The measured return loss, gain, and axial ratio of the proposed single radiator, as well as the proposed array antennas, showed a good agreement with the simulated results.

• Geomechanics and Engineering
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• v.38 no.1
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• pp.29-44
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• 2024

This research investigates the effectiveness of data augmentation techniques in the automated analysis of B-scan images from ground-penetrating radar (GPR) using deep learning. In spite of the growing interest in automating GPR data analysis and advancements in deep learning for image classification and object detection, many deep learning-based GPR data analysis studies have been limited by the availability of large, diverse GPR datasets. Data augmentation techniques are widely used in deep learning to improve model performance. In this study, we applied four data augmentation techniques (geometric transformation, color-space transformation, noise injection, and applying kernel filter) to the GPR datasets obtained from a testbed. A deep learning model for GPR data analysis was developed using three models (Faster R-CNN ResNet, SSD ResNet, and EfficientDet) based on transfer learning. It was found that data augmentation significantly enhances model performance across all cases, with the mAP and AR for the Faster R-CNN ResNet model increasing by approximately 4%, achieving a maximum mAP (Intersection over Union = 0.5:1.0) of 87.5% and maximum AR of 90.5%. These results highlight the importance of data augmentation in improving the robustness and accuracy of deep learning models for GPR B-scan analysis. The enhanced detection capabilities achieved through these techniques contribute to more reliable subsurface investigations in geotechnical engineering.

• Proceedings of the IEEK Conference
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• 2000.11c
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• pp.81-84
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• 2000

In this paper, we propose an efficient method to extract a goal area which may be closely related to the scoring highlight. In our method, the boundary between the ground and the non-ground area is used. An efficient methods for a rapid detection of both the boundary and then the goal area are proposed. Our simulation results show that our method is very reliable and takes less processing time compared with previous methods. This performance improvements may be caused by the use of a general simple feature.

• Proceedings of the Korean Geotechical Society Conference
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• 2005.03a
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• pp.376-383
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• 2005

In this study, we conducted geophysical investigations for the organization of integrated geophysical methods to detect underground cavities of ground subsidence area at the field test site, located at Yongweol-ri, Muan-gun. We examined the applicability of geophysical methods such as electrical resistivity, electromagnetic, and microgravity to cavity detection with the aid of borehole survey results. Underground cavities are widely present within the limestone bedrock overlain by the alluvial deposits in the area of the test site where the ground subsidences have occurred in the past. The limestone cavities are mostly filled with groundwater and clays in the test site. Thus, cavities have low electrical resistivity and density compared to the surrounding host bedrock. The results of the study have shown that the zones of low resistivity and density correspond to the zones of the cavities identified in the boreholes at the site, and that the geophysical methods used are very effective to detect underground cavities. Furthermore, we could map the distribution of cavities more precisely with the test results incorporated from the various geophysical methods. It is also important to notice that the microgravity method is a very promising tool since it has rarely used for the cavity detection in korea. Beyond the investigation of underground cavities, the geophysical methods are required to provide useful information for the reinforcement design for the ground subsidence areas. It is, therefore, necessary to develop integrated geophysical technique incorporating different geophysical methods to precisely map underground cavities and image the subsurface of the ground subsidence areas.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.696-701
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• 2002

This paper describes some results of change detection in Tokyo metropolitan area, Japan , using the Landsat TM data, and methods to quantify the ground cover classes. The changes are analyzed using the measures of not only conventional spectral classes but also a set of landscape indices to describe spatial properties of ground cove types using fractal dimension of objects, entropy in the specific windows defining the neighbors of focusing locations. In order eliminate the seasonal radiometric effects on TM data, an automated class labeling method is also attempted. Urban areas are also delineated automatically by defining the boundaries of the urban area. These procedures for urban change detection were implemented by the unified image computing methods proposed by the author, they can be automated in coherent and systematic ways, and it is anticipated to automate the whole procedures. The results of this analysis suggest that Tokyo metropolitan area was extended to the suburban areas along the new transportation networks and the high density area of Tokyo were also very much extended during the period between 1985 and 1995.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.9B
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• pp.1704-1712
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• 1999

This research features efficient detection of obstacles, especially vehicles, in the forward direction of navigation for the development of unmanned automous vehicle. We separate image regions into ground and non-ground planes using the Helmholtz shearing technique in order to reliably exclude regions that do not contain obstacles. We propose a computationally simple and efficient method for the detection of vehicles in the forward direction by analysis of horizontally and vertically projected histograms of residual disparity map obtained from Helmholtz shearing. We have experimented the proposed method on real outdoor stereo data. Experimental results show that our method gives accurate detection of forward vehicles and is computationally very efficient.

• The Journal of Korea Robotics Society
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• v.10 no.2
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• pp.104-111
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• 2015

In this paper, we proposed visual information to provide a highly maneuverable system for a tele-operator. The visual information image is bird's eye view from UFR(Unmanned Flying Robot) shows around UGR(Unmanned Ground Robot). We need UGV detection and tracking method for UFR following UGR always. The proposed system uses TLD(Tracking Learning Detection) method to rapidly and robustly estimate the motion of the new detected UGR between consecutive frames. The TLD system trains an on-line UGR detector for the tracked UGR. The proposed system uses the extended Kalman filter in order to enhance the performance of the tracker. As a result, we provided the tele-operator with the visual information for convenient control.

• Journal of Astronomy and Space Sciences
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• v.33 no.4
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• pp.313-318
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• 2016

Communication, Ocean And Meteorological Satellite (COMS) Meteorological Imager (MI) images are processed for radiometric and geometric correction from raw image data. When intermediate image data are matched and compared with reference landmark images in the geometrical correction process, various techniques for edge detection can be applied. It is essential to have a precise and correct edged image in this process, since its matching with the reference is directly related to the accuracy of the ground station output images. An edge detection method based on neural networks is applied for the ground processing of MI images for obtaining sharp edges in the correct positions. The simulation results are analyzed and characterized by comparing them with the results of conventional methods, such as Sobel and Canny filters.