• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.64 no.9
• /
• pp.1337-1346
• /
• 2015

In this paper, pattern classifier is designed to classify precipitation and non-precipitation events from weather radar data. The proposed classifier is based on Fuzzy Neural Network(FNN) and consists of three FNNs which operate in parallel. In the proposed network, the connection weights of the consequent part of fuzzy rules are expressed as two polynomial types such as constant or linear polynomial function, and their coefficients are learned by using Least Square Estimation(LSE). In addition, parametric as well as structural factors of the proposed classifier are optimized through Differential Evolution(DE) algorithm. After event classification between precipitation and non-precipitation echo, non-precipitation event is to get rid of all echo, while precipitation event including non-precipitation echo is to get rid of non-precipitation echo by classifier that is also based on Fuzzy Neural Network. Weather radar data obtained from meteorological office is to analysis and discuss performance of the proposed event and echo patter classifier, result of echo pattern classifier compare to QC(Quality Control) data obtained from meteorological office.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.64 no.7
• /
• pp.1114-1124
• /
• 2015

In this paper, precipitation / non-precipitation pattern classification of meteorological radar data is conducted by using neuro-fuzzy algorithm. Structure expression of meteorological radar data information is analyzed in order to effectively classify precipitation and non-precipitation. Also diverse input variables for designing pattern classifier could be considered by exploiting the quantitative as well as qualitative characteristic of meteorological radar data information and then each characteristic of input variables is analyzed. Preferred pattern classifier can be designed by essential input variables that give a decisive effect on output performance as well as model architecture. As the proposed model architecture, neuro-fuzzy algorithm is designed by using FCM-based radial basis function neural network(RBFNN). Two parts of classifiers such as instance classifier part and echo classifier part are designed and carried out serially in the entire system architecture. In the instance classifier part, the pattern classifier identifies between precipitation and non-precipitation data. In the echo classifier part, because precipitation data information identified by the instance classifier could partially involve non-precipitation data information, echo classifier is considered to classify between them. The performance of the proposed classifier is evaluated and analyzed when compared with existing QC method.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.63 no.5
• /
• pp.676-682
• /
• 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 Korean Institute of Intelligent Systems
• /
• v.24 no.5
• /
• pp.536-541
• /
• 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.

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

• Journal of Biomedical Engineering Research
• /
• v.27 no.1
• /
• pp.22-29
• /
• 2006

This paper proposes an efficient array beamforming method using spatial matched filtering for ultrasound imaging. In the proposed method, ultrasound waves are transmitted from an array subaperture with fixed transmit focus as in conventional array imaging. At receive, radio frequency (RF) echo signals from each receive channel are passed through a spatial matched filter that is constructed based on the system transmit-receive spatial impulse response. The filtered echo signals are then summed. The filter remaps and spatially registers the acoustic energy from each element so that the pulse-echo impulse response of the summed output is focused with acceptably low side lobes. Analytical beam pattern analysis and simulation results using a linear array show that the proposed spatial filtering method can provide more improved spatial resolution and contrast-to-noise ratio (CNR) compared with conventional dynamic receive focusing (DRF) method by implementing two-way dynamically focused beam pattern throughout the field.

• Journal of the Korean Society of Fisheries and Ocean Technology
• /
• v.36 no.3
• /
• pp.195-201
• /
• 2000

This paper discusses methods for the stable bottom detection and the optimum bottom offset which enable to separate the fish echoes from the bottom echoes with echo integration of demersal fish. In preprocessing of the echo signal, the bottom detection has to be done stably against the fluctuation of echo level and the bottom offset has to be set to a minimum height such that near bottom fish echoes are included Two methods of bottom detection, namely echo level threshold method and maximum echo slope method were compared and analyzed. The echo level method works well if the ideal threshold level was given but it sometimes misses the bottom because of the fluctuation of the echo. Another method to detect the bottom which uses maximum echo slope indicates the simple and stable bottom detection. In addition, the bottom offset has to be set near to the bottom but not to include the bottom echo. Optimum bottom offset should be set a few samples before the detected bottom echo which relates the beginning of pulse shape and acoustic beam pattern to the bottom feature.

• Geophysics and Geophysical Exploration
• /
• v.16 no.3
• /
• pp.145-153
• /
• 2013

High-resolution chirp profiles were analyzed to investigate the echo types of near-surface sediments in the Yellow Sea off the Baegryeong Island. On the basis of seafloor morphology and subbottom echo characters, 7 echo types were identified. Flat seafloor with no internal reflectors or moderately to well-developed subbottom reflectors (echo type 1-1 and 1-2) is mainly distributed in the southern part of the study area. Flat seafloor with superposed wavy bedforms (echo type 1-3) is also distributed in the middle part. Mounded seafloor with either smooth surface or superposed bedforms (echo type 2-1, 2-2, and 2-3) occurs in the middle part of the study area. Irregular and eroded seafloor with no subbottom reflectors (echo type 3-1) is present in the northern part of the study area off the Baegryeong Island. According to the distribution pattern and sedimentary facies of echo types, depositional environments can be divided into three distinctive areas: (1) active erosional zone due to strong tidal currents in the northern part; (2) formation of tidal sand ridges in response to tidal currents associated with sea-level rise distributed in the middle part; and (3) transgressive sand sheets in the southern part. Such a depositional pattern, including 7 echo types, in this area reflects depositional process related to the sea-level rise and strong tidal currents during the Holocene transgression.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.23 no.6
• /
• pp.545-550
• /
• 2013

In pattern recognition field, data classification is an essential process for extracting meaningful information from data. Adaptive boosting algorithm, known as AdaBoost algorithm, is a kind of improved boosting algorithm for applying to real data analysis. It consists of weak classifiers, such as random guessing or random forest, which performance is slightly more than 50% and weights for combining the classifiers. And a strong classifier is created with the weak classifiers and the weights. In this paper, a research is performed using AdaBoost algorithm for detecting chaff echo which has similar characteristics to precipitation echo and interrupts weather forecasting. The entire process for implementing chaff echo classifier starts spatial and temporal clustering based on similarity with weather radar data. With them, learning data set is prepared that separated chaff echo and non-chaff echo, and the AdaBoost classifier is generated as a result. For verifying the classifier, actual chaff echo appearance case is applied, and it is confirmed that the classifier can distinguish chaff echo efficiently.

• Journal of the Korean Institute of Telematics and Electronics
• /
• v.21 no.2
• /
• pp.24-28
• /
• 1984

A new type of digital echo canceler for two-wire digital transmission is presented. The new principle estimates an echo signal by use of the arithmetic means estimate for each transmitted data pattern, which leads to relatively simple hardware. The principle is compared with adaptive digital filter methods through theoretical analysis and computer simulation. The results show that the proposed method has fast convergence property with respect to its hardware simplicity and that the convergence time is independent of echo level. Quantization effects are also analyzed.