• Spatial Information Research
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• v.14 no.1 s.36
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• pp.57-65
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• 2006

In general, the investigation for reservoir dredging are conducting a observation on the horizontal position and the depth of water by assembling GPS/Echo Sounder and Total Station/Echo Sounder, and it is computed at a section computation of riverbed, reservoir volume and dredging plan etc. at that times, the detail plane is determinated about soil volume, height for dredging. Planning has a fault that the method of sound detection using the Echo Sounder doesn't check up distribution of reservoir deposit. In this study, the author emphasizes that implementation of dredging with combined Global Positioning System(GPS) and Ground Penetration Radar(GPR) is well-done more than existed GPS/Echo Sounder. the combined equipment can be adapted to computation and dredging reffering to distribution of deposition. First of all, it is executed water tank modelling test through sampling for apply to test area and is estimated the possibility after passed far accuracy verification of equipment.

• Journal of Environmental Science International
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• v.25 no.2
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• pp.267-280
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• 2016

Ground echo is radar return from stationary targets such as buildings and trees. Wind vectors from the wind profile radar in Gangneung are affected by ground echoes due to the complex mountainous terrain located to the west and the south. These ground echoes make a spurious peak close to the direct current (DC) line signal in Doppler spectra. Wind vectors polluted by ground clutters were determined from spectra of oblique beams. After eliminated the terrain echoes, the accuracy of wind vector compared with radiosonde was improved about 68.4% and its relative coefficient was increased from 0.58 to 0.97.

• Tunnel and Underground Space
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• v.7 no.3
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• pp.230-237
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• 1997

As concrete structure is getting old and decrepit, its inspection and diagnosis is getting important. Therefore, it is necessary to estimate the soundness of structure using non-destructive tests for effective repairs and maintenances. But, applications of non-destructive tests in tunnel have been used restrictively, due to accessibility only from one side in tunnel lining and presence of tunnel installations. Recently, the various non-destructive techniques have been studied. Especially, ground penetrating radar(GPR) and impact echo (IE) methods have been researched for tunnel inspection. In this study, the applicability of impact echo test in tunnel lining inspection has been investigated. This paper described the tunnel inspection for lining thickness and internal flaw using impact echo tests. Model tests were carried out using impact echo test systems on two concrete models, Model I is measuring for lining thickness, Model II is detecting for internal flaw. Also, the test were applied for lining inspections in a tunnel constructed by NATM. From the results of impact echo tests, we have concluded that impact echo test is a very useful and effective technique for inspecting the concrete tunnel linings.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.6
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• pp.922-934
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• 2015

In this paper, The classification between precipitation echo(PRE) and non-precipitation echo(N-PRE) (including ground echo and clear echo) is carried out from weather radar data using neuro-fuzzy algorithm. In order to classify between PRE and N-PRE, Input variables are built up through characteristic analysis of radar data. First, the event classifier as the first classification step is designed to classify precipitation event and non-precipitation event using input variables of RBFNNs such as DZ, DZ of Frequency(DZ_FR), SDZ, SDZ of Frequency(SDZ_FR), VGZ, VGZ of Frequency(VGZ_FR). After the event classification, in the precipitation event including non-precipitation echo, the non-precipitation echo is completely removed by the echo classifier of the second classifier step that is built as Type-2 FCM based RBFNNs. Also, parameters of classification system are acquired for effective performance using PSO(Particle Swarm Optimization). The performance results of the proposed echo classifier are compared with CZ. In the sequel, the proposed model architectures which use event classifier as well as the echo classifier of Interval Type-2 FCM based RBFNN show the superiority of output performance when compared with the conventional echo classifier based on RBFNN.

• Korean Journal of Remote Sensing
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• v.25 no.6
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• pp.465-474
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• 2009

Convective/stratiform radar echo classification schemes by Steiner et al. (1995) and Biggerstaff and Listemaa (2000) are examined on a monsoonal front during the summer monsoon-Changma period, which is organized as a cloud cluster with mesoscale convective complex. Target radar is S-band with wavelength of 10cm, spatial resolution of 1km, elevation angle interval of 0.5-1.0 degree, and minimum elevation angle of 0.19 degree at Jindo over the Korean Peninsula. For verification of rainfall amount retrieved from the echo classification, ground-based rain gauge observations (Automatic Weather Stations) are examined, converting the radar echo grid data to the station values using the inverse distance weighted method. Improvement from the echo classification is evaluated based on the correlation coefficient and the scattered diagram. Additionally, an optimal use method was designed to produce combined rainfalls from the radar echo and Tropical Rainfall Measuring Mission Precipitation Radar (TRMM/PR) data. Optimal values for the radar rain and TRMM/PR rain are inversely weighted according to the error variance statistics for each single station. It is noted how the rainfall distribution during the summer monsoon frontal system is improved from the classification of convective/stratiform echo and the use of the optimal use technique.

• 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.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.35 no.3
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• pp.273-281
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• 2002

Determination of sediment type is generally based on ground truthing. This method, however, provides information only for the limited sites. Recent developments of remote classification of seafloor sediments made it possible to obtain continuous profiles of sediment types. QTC View system, which is an acoustic instrument providing digital real-time seabed classification, was used to classify seafloor sediment types in the Suyoung Bay, Pusan. QTC View was connected to 50 kHz echo sounder, All parameters of QTC View and echo sounder are uniformly kept during survey. By ground truthing, the sediments are classified into seven types, such as slightly gravelly sand, slightly gravelly sandy mud, gravelly muddy sand, clayey sand, sandy mud, slightly gravelly muddy sand, and rocky bottom. By the first remote classification using QTC View, four sediment types are clearly identified, such as slightly gravelly sand, gravelly mud, slightly gravelly muddy sand, and rocky bottom. These are similar to the result of the second survey. Also the result of remote classification matches well with that of ground truthing, but for sediment type determined by minor component. Therefore, QTC View can effectively be used for remote classification of seafloor sediments.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.5
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• pp.536-541
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• 2014

Data through meteorological radar includes ground echo, sea-clutter echo, anomalous propagation echo, clear echo and so on. Each echo is a kind of non-precipitation echoes and the characteristic of individual echoes is analyzed in order to identify with non-precipitation. Meteorological radar data is analyzed through pre-processing procedure because the data is given as big data. In this study, echo pattern classifier is designed to distinguish non-precipitation echoes from precipitation echo in meteorological radar data using RBFNNs and echo judgement module. Output performance is compared and analyzed by using both HCM clustering-based RBFNNs and FCM clustering-based RBFNNs.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.89-90
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• 2016

Anomalous propagation echo is a kind of abnormal radar signal occurred by irregularly refracted radar beam caused by temperature or humidity. The echo frequently appears in ground-based weather radar. In order to improve accuracy of weather forecasting, it is important to analyze radar data precisely. Therefore, there are several ongoing researches about identifying the anomalous propagation echo all over the world. This paper conducts researches about a classification method which can distinguish anomalous propagation echo in the radar data using naive Bayes classifier and unique attributes of the echo such as reflectivity, altitude, and so on. It is confirmed that the fine classification results are derived by verifying the suggested naive Bayes classifier using actual appearance cases of the echo.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.6
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• pp.1063-1068
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• 2016

Anomalous propagation echo is a kind of abnormal radar signal occurred by irregularly refracted radar beam caused by temperature or humidity. The echo frequently appears in ground-based weather radar due to its observation principle and disturb weather forecasting process. In order to improve accuracy of weather forecasting, it is important to analyze radar data precisely. Therefore, there are several ongoing researches about identifying the anomalous propagation echo with data mining techniques. This paper conducts researches about implementation of classification method which can separate the anomalous propagation echo in the raw radar data using naive Bayes classifier with various kinds of observation results. Considering that collected data has a class imbalanced problem, this paper includes SMOTE method. It is confirmed that the fine classification results are derived by the suggested classifier with balanced dataset using actual appearance cases of the echo.