• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.5
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• pp.676-682
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• 2014

In this paper, precipitation echo(PRE) and non-precipitaion echo(N-PRE)(including ground echo and clear echo) through weather radar data are identified with the aid of neuro-fuzzy algorithm. The accuracy of the radar information is lowered because meteorological radar data is mixed with the PRE and N-PRE. So this problem is resolved by using RBFNN and judgement module. Structure expression of weather radar data are analyzed in order to classify PRE and N-PRE. Input variables such as Standard deviation of reflectivity(SDZ), Vertical gradient of reflectivity(VGZ), Spin change(SPN), Frequency(FR), cumulation reflectivity during 1 hour(1hDZ), and cumulation reflectivity during 2 hour(2hDZ) are made by using weather radar data and then each characteristic of input variable is analyzed. Input data is built up from the selected input variables among these input variables, which have a critical effect on the classification between PRE and N-PRE. Echo judgment module is developed to do echo classification between PRE and N-PRE by using testing dataset. Polynomial-based radial basis function neural networks(RBFNNs) are used as neuro-fuzzy algorithm, and the proposed neuro-fuzzy echo pattern classifier is designed by combining RBFNN with echo judgement module. Finally, the results of the proposed classifier are compared with both CZ and DZ, as well as QC data, and analyzed from the view point of output performance.

• Journal of the Korea Institute of Military Science and Technology
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• v.9 no.2 s.25
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• pp.87-95
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• 2006

The performance of a microwave missile seeker and radar operating in an air-to-air look-down mode is strongly influenced by the presence of ground clutter In order to correctly account for the effects of ground clutter, it is required to develop a model capable of representing clutter characteristics as a function of range and/or frequency. In this paper, a program to estimate the clutter reflectivity for various ground conditions is developed, using the actually measured data and the data available from open literatures. In addition, clutter characteristics measured for various ground conditions such as sea, agricultural area, urban city and industrial area through the captive flight tests are presented.

• The Journal of Engineering Geology
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• v.33 no.4
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• pp.689-699
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• 2023

Assessing ground stability is critical to the construction of underground transportation infrastructure. Surface displacement is a key indicator of ground stability, and can be measured using interferometric synthetic aperture radar (InSAR). This study measured time-series surface displacement using permanent scatterer InSAR applied to Sentinel-1 SAR images acquired from January 2017 to June 2023 for the area around a deep underground expressway under construction to connect Mandeok-dong and Centum City in Busan, South Korea. Regions of seasonal subsidence and uplift were identified, as were regions with severe subsidence after summer 2022. To evaluate stability of the ground in the construction area, the mean displacement velocity, final surface displacement, cumulative surface displacement, and difference between minimum and maximum surface displacement were analyzed. Considering the time-series surface displacement characteristics of the study area, the difference between minimum and maximum surface displacement since June 2022 was found to be the most suitable parameter for evaluating ground stability. The results identified highly unstable ground in the construction area as being to the north of the mid-lower reaches of the Oncheon-cheon River and to the west of the Suyeong River at the point where both rivers meet, with the difference between minimum and maximum surface displacement of 40~60 mm.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.26 no.10
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• pp.924-931
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• 2015

When a target locates at low altitude over a curved earth surface and far away from a radar, we examine the accuracy of the conventional formulations to compute the radar echo signal. The 4-ray model is used to calculate the scattering by the target to consider the ground plane effect. In this paper, the diffracted wave is not included. Based on the required parameters able to be calculated by known equations for the estimation of the wave propagation phenomena, the radar echo signal is computed and verified by comparing with measured data sets.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.1
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• pp.1-8
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• 2023

In this paper, we developed a mid-range automotive radar systems based on the performance requirements and test procedures of the intelligent transport systems, that is lane change decision aid systems (LCDAS). The mid-range automotive radar has the maximum detection range up to 80m and an update time within 50ms. The computational loads of a signal processing were reduced by using ROI preprocessing technique. Considering actual driving environments, radar performance evaluations were conducted in two driving scenarios at an automotive proving ground.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.23 no.11
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• pp.1307-1314
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• 2012

To reduce the effects of clutters with subsurface inhomogenities in ground-penetrating radar(GPR) images, an eigenimage based signal-processing technique is presented. If the conventional eigenimage filtering technique is applied to B-scan images of a GPR survey, relatively homogeneous clutters such as antenna ringing, direct coupling between transmitting and receiving antennas, and soil-surface reflection, can be removed sufficiently. However, since random clutters of subsurface inhomogenities still remain in the images, target signals are distorted and obscured by the clutters. According to a comparison of the eigenimage filtering results, there is different coherency between subsurface clutters and target signals. To reinforce the pixels with high coherency and reduce the pixels with low coherency, the pixel-by-pixel geometric-mean process after the eigenimage filtering is proposed here. For the validity of the proposed approach, GPR survey for detection of a metal target in a randomly inhomogeneous soil is numerically simulated by using a random media generation technique and the finite-difference time-domain(FDTD) method. And the proposed signal processing is applied to the B-scan data of the GPR survey. We show that the proposed approach provides sufficient enhancement of target signals as well as remarkable reduction of subsurface inhomogeneous clutters in comparison with the conventional eigenimage filtering.

• Journal of Wetlands Research
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• v.15 no.3
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• pp.413-422
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• 2013

In recent, the rainfall is showing different properties in space and time but the ground rain gauge only can observe rainfall at a point. This means the ground rain gauge has the limitations in spatial and temporal resolutions to measure rainfall and so there is a need to utilize radar rainfall which can consider spatial distribution of rainfall This study tried to apply radar rainfall for runoff simulation on an urban drainage system. The study area is Guro-gu, Seoul and we divided study area into subbasins based on rain gauge network of AWS(Automatic Weather station). Then the radar rainfalls were adjusted using rainfall data of rain gauge stations the areal rainfalls were obtained. The runoffs were simulated by using XP-SWMM model in subbasins of an urban drainage system. As the results, the adjusted radar rainfalls were underestimated in the range of 60 to 95% of rain gauge rainfalls and so the simulated runoffs from the adjusted radar and gauge rainfalls also showed the differences. The runoff peak time from radar rainfall was occurred more fast than that from gauge rainfall.

• Journal of Korea Water Resources Association
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• v.38 no.12 s.161
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• pp.1039-1049
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• 2005

The objective of this study is to produce optimal radar-derived rainfall for hydrologic utilization. The ground clutter and beam blockage effects from Mt. Kwanak station (E.L 608m) are removed from radar reflectivities by POD analysis. The reflectivities are used to produce radar rainfall data in the form of rain rates (mm/h) by the application of the Marshall-Palmer reflectivity versus rainfall relationship. However, these radar-derived rainfall are underestimated in temporal and spatial scale compared with observed one, so it is necessary to hire a correction scheme based on the gauge-to-radar (G/R) statistical adjustment technique. The selected watershed for studying the real-time correction of radar-rainfall estimation is the Soyang dam site, which is located approximately 100km east of Kwanak radar station. The results indicate that adjusted radar rainfall with the gauge measurement have reasonal G/R ratio ranged on 0.95-1.32 and less uncertainty with that mean standard deviation of G/R ratio are decreased by $9-28\%$. Mean areal precipitation from adjusted radar rainfall are well agreed to the observed one on the Soyang River watershed. It is concluded that the real-time bias adjustment scheme is useful to estimate accurate basin-based radar rainfall for hydrologic application.

• Journal of Wetlands Research
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• v.14 no.2
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• pp.243-254
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• 2012

Recently, the use of radar rainfall data that can help tracking of the development and movement of rainfall's spatial distribution is drawing much attention in hydrology. The reliability of existing radar rainfall compared to gauge rainfall data on the ground has not yet been confirmed and so we have difficulties to apply the radar rainfall in hydrology. The radar rainfall for the applications in hydrology are adjusted merging method derived from gage. This study uses the Mean-Field Bias (MFB) and Statistical Objective Analysis (SOA) as correction methods to create adjusted grid-based radar rainfall data which can represent the temporal and spatial distribution of rainfall. This study used a storm event occurred in August 2010 for the adjustment of radar rainfall. In addition, the grid-based distributed rainfall-runoff model (Vflo), which enables more detailed examinations of spatial flux changes in the basin rather than the lumped hydrological models, has been applied to Gamcheon river basin which is a tributary of Nakdong River located in south-eastern part of the Korean peninsular and the basin area is $1005km^2$. The simulated runoff was compared with the observed runoff in an attempt to evaluate the usability of radar rainfall data and the reliability of the correction methods. The error range of peak discharge using each correction method was within 20 percent and the efficiency of the model was between 60 and 80 percent. In particular, the SOA method showed better results than MFB method. Therefore, the SOA method could be used for the adjustment of grid-based radar rainfall and the adjusted radar rainfall can be used as an input data of rainfall-runoff models.

• Geomechanics and Engineering
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• v.33 no.2
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• pp.203-209
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• 2023

One major advantage of ground penetrating radar (GPR) over other field test methods is its ability to obtain subsurface images of roads in an efficient and non-intrusive manner. Not only can the strata of pavement structure be retrieved from the GPR scan images, but also various irregularities, such as cracks and internal cavities. This article introduces a deep learning-based approach, focusing on detecting subsurface cracks by recognizing their distinctive hyperbolic signatures in the GPR scan images. Given the limited road sections that contain target features, two data augmentation methods, i.e., feature insertion and generation, are implemented, resulting in 9,174 GPR scan images. One of the most popular real-time object detection models, You Only Learn One Representation (YOLOR), is trained for detecting the target features for two types of subsurface cracks: bottom cracks and full cracks from the GPR scan images. The former represents partial cracks initiated from the bottom of the asphalt layer or base layers, while the latter includes extended cracks that penetrate these layers. Our experiments show the test average precisions of 0.769, 0.803 and 0.735 for all cracks, bottom cracks, and full cracks, respectively. This demonstrates the practicality of deep learning-based methods in detecting subsurface cracks from GPR scan images.