• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.4
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• pp.360-365
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• 2014

There are many types of advanced devices for weather prediction process such as weather radar, satellite, radiosonde, and other weather observation devices. Among them, the weather radar is an essential device for weather forecasting because the radar has many advantages like wide observation area, high spatial and time resolution, and so on. In order to analyze the weather radar observation result, we should know the inside structure and data. Some non-precipitation echoes exist inside of the observed radar data. And these echoes affect decreased accuracy of weather forecasting. Therefore, this paper suggests a method that could remove line-shaped non-precipitation echo from raw radar data. The line-shaped echoes are distinguished from the raw radar data and extracted their own features. These extracted data pairs are used as learning data for naive bayesian classifier. After the learning process, the constructed naive bayesian classifier is applied to real case that includes not only line-shaped echo but also other precipitation echoes. From the experiments, we confirm that the conclusion that suggested naive bayesian classifier could distinguish line-shaped echo effectively.

• Journal of Korea Water Resources Association
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• v.41 no.11
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• pp.1153-1162
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• 2008

The use of radar rainfall for hydrological appraisal has been a challenge due to the limitations in raw data generation followed by the complex analysis needed to come up with precise data interpretation. In this study, RAIDOM (RAdar Image DigitalizatiOn Method) has been developed to convert synthetic radar CAPPI(Constant Altitude Plan Position Indicator) image data from Korea Meteorological Administration into digital format in order to come up with a more practical and useful radar image data. RAIDOM was used to examine a severe local rainstorm that occurred in July 2006 as well as two other separate events that caused heavy floods on both upper and mid parts of the HanRiver basin. A distributed model was developed based on the available radar rainfall data. The Flood Hydrograph simulation has been found consistent with actual values. The results show the potentials of RAIDOM and the distributed model as tools for flood prediction. Furthermore, these findings are expected to extend the usefulness of radar rainfall data in hydrological appraisal.

• 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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.2
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• pp.12-19
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• 2022

Artificial intelligence is driving the Fourth Industrial Revolution and is in the spotlight as a general-purpose technology. As the data collection from the battlefield increases rapidly, the need to us artificial intelligence is increasing in the military, but it is still in its early stages. In order to identify maritime targets, Republic of Korea navy acquires images by ISAR(Inverse Synthetic Aperture Radar) of maritime patrol aircraft, and humans make out them. The radar image is displayed by synthesizing signals reflected from the target after radiating radar waves. In addition, day/night and all-weather observations are possible. In this study, an artificial intelligence is used to identify maritime targets based on radar images. Data of radar images of 24 maritime targets in Republic of Korea and North Korea acquired by ISAR were pre-processed, and an artificial intelligence algorithm(ResNet-50) was applied. The accuracy of maritime targets identification showed about 99%. Out of the 81 warship types, 75 types took less than 5 seconds, and 6 types took 15 to 163 seconds.

• Journal of the Korea Institute of Military Science and Technology
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• v.14 no.6
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• pp.1138-1143
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• 2011

One of the critical items in radar testing is the ability to evaluate the performance of radar systems under real operational environments. But it takes lots of time and cost to operate real targets and analyze the test results due to a large amount of data based on these complicated environments. In this paper, the Radar Simulative T&E Lab. is introduced, and the test and analysis results of the developing radar for predicting the radar system performance are described in the Radar Simulative T&E Lab. This laboratory could be used to test the far-field characteristics of antenna radiation pattern and to perform an effective radar system test and evaluation using a simulative target generator under a low cost repeating test situation.

• Atmosphere
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• v.21 no.3
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• pp.285-300
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• 2011

To discriminate and eliminate chaff echoes in radar measurements, a new removal algorithm in two-dimensional reflectivity composite at the height of 1.5 km has been developed by using the brightness temperature($T_B$) obtained from MTSAT-1R. This algorithm utilizes the fact that chaffs are not appeared in infrared satellite data of MTSAT-1R, but detected in radar measurements due to their significant backscattering in the given radar wavelength. The algorithm is evaluated for three different situations: chaff only, chaff mixed with convective storms, and chaff covered with clouds. The algorithm shows excellent performance for the cases of chaff only and chaff mixed with convective storms. However, the performance of the algorithm significantly depends on the presence of clouds. Thus, the statistical analysis of $T_B$ is performed in order to optimize the monthly threshold.

• Atmosphere
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• v.29 no.2
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• pp.197-211
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• 2019

The observational sensitivity of dual-polarization weather radar was quantitatively analyzed by using two different pulse widths. For this purpose, test radar scan strategy which consisted of consecutive radar scan using long (LP: $2{\mu}s$) and short (SP: $1{\mu}s$) pulses at the same elevation angle was employed. The test scan strategy was conducted at three operational S-band dual-polarization radars (KSN, JNI, and GSN) of Korea Meteorological Administration (KMA). First, the minimum detectable reflectivity (MDR) was analyzed as a function of range using large data set of reflectivity ($Z_H$) obtained from JNI and GSN radars. The MDR of LP was as much as 7~22 dB smaller than that of SP. The LP could measure $Z_H$ greater than 0 dBZ within the maximum observational range of 240 km. Secondly, polarimetric observations and the spatial extent of radar echo between two pulses were compared. The cross-polar correlation coefficient (${\rho}_{hv}$) from LP was greater than that from SP at weak reflectivity (0~20 dBZ). The ratio of $Z_H$ (> 0 dBZ) and ${\rho}_{hv}$(> 0.95) bin to total bin calculated from LP were greater than those from SP (maximum 7.1% and 13.2%). Thirdly, the frequency of $Z_H$ (FOR) during three precipitation events was analyzed. The FOR of LP was greater than that of SP, and the difference in FOR between them increased with increasing range. We conclude that the use of LP can enhance the sensitivity of polarimetric observations and is more suitable for detecting weak echoes.

• Journal of Korea Water Resources Association
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• v.49 no.7
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• pp.625-634
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• 2016

Three leading agencies under different ministries - Korea Meteorological Administration (KMA) in the ministry of Environment, Han river control office in the Ministry of Land, Infrastructure and Transport (MOLIT) and Weather Group of ROK Air Force in the Ministry of National Defense (MND) - have been operated radars in the purpose of observing weather, hydrology and military operational weather in Korea. Eight S-band dual-pol. radars have been newly installed or replaced by these ministries over different places by 2015. However each ministry has different aims of operating radars, observation strategies, data processing algorithms, etc. Due to the differences, there is a wide level of accuracy on observed radar data as well as the composite images made of the cross governmental radar measurement. Gaining fairly high level of accuracy on radar data obtained by different agencies has been shared as a great concern by the ministries. Thus, "an agreement of harmonizing weather and hydrological radar products" was made by the three ministries in 2010. Particularly, this is very important to produce better rainfall estimation using the cross governmental radar measurement. Weather Radar Center(WRC) in KMA has been developed an empirical method using measurements observed by Yongin testbed radar. This study is aiming to examine the efficiency of the empirical method to improve the accuracies of radar rainfalls estimated from cross governmental dual-pol. radar measurements. As a result, the radar rainfalls of three radars (Baengnyeongdo, Biseulsan, and, Sobaeksan Radar) were shown improvement in accuracy (1-NE) up to 70% using data from May to October in 2015. Also, the range of the accuracies in radar rainfall estimation, which were from 30% to 60% before adjusting polarimetric variables, were decreased from 65% to 70% after adjusting polarimetric variables.

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.6
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• pp.253-258
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• 2021

In this paper, we developed a hardware and software platform of the real-time data logging system to verify radar FEM (Front-end Module) and signal-processing algorithms. We developed a hardware platform based on FPGA (Field Programmable Gate Array) and DSP (Digital Signal Processor) and implemented firmware software to verify the various FEMs. Moreover, we designed PC based software platform to control radar logging parameters and save radar data. The developed platform was verified using 24 GHz multiple channel FMCW (Frequency Modulated Continuous Wave) in an environment of stationary and moving targets of chamber room.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.1055-1060
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• 2020

This study is a study on the analysis and measurement of the radar cross-sectional area of a miniature aircraft. Radar cross-sectional area for miniature aircraft in advance were analyzed using an electromagnetic analysis tool, and an actual miniature aircraft was manufactured and measured in an anechoic chamber. When measuring, the old model was used as reference data for RCS(radar cross section) characteristics and applied to the test result data of the actual reduced model. The measurement method improved the accuracy of the measurement by applying time gating to remove the influence on the components scattered inside the anechoic chamber. The RCS test results of the reduced model showed relatively high RCS characteristics in the microwave band, as the previous analysis results. In the future, we plan to utilize the method of RCS analysis and measurement for the target of the radar in the VHF(Very High Frequency)/UHF(Ultra High Frequency) band with a relatively large wavelength.