• Journal of the Korean Society for Nondestructive Testing
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• v.33 no.1
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• pp.69-72
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• 2013

In long-range ultrasonic testing, a phased array probe composed of multiple identical transducers with an uniform interval of one quarter wavelength is usually used for the transmission or reception directivity control. This paper shows that the propagation directions of individual echoes can be identified in real time by displaying the inputs of a process for summing the constitution reception signals after compensating the phase difference due to the transducer interval, together with the output of the process. A constructive interference of the constitution echoes indicates a forward direction echo propagating along an intended direction while a destructive interference implies a reverse direction echo propagating along the direction opposite to the intended one.

• 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.

• Proceedings of the Korean Society of Fisheries Technology Conference
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• 2003.10a
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• pp.9-18
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• 2003

현재까지 어종식별에 이용되어진 방법으로는 크게 4가지로 나눌수 있다. 1) 주파수특성법: 광대역 혹은 복수주파수에 의한 어군의 음향산란의 주파수특성의 차이를 이용하는 방법(Madureira et at., 1993; Simmonds et at., 1996),2) 분포특징법 :어군형태와 분포특성에 근거한 방법 (LeFeuvre et at., 2000), 3) 신호특징법: 어군에코의 포락선등의 신호의 특징에 근거한 방법Rose and Leggett, 1988; Scalabrin et at., 1996), 4) 음향결과법: SV, TS, 에코트레스해석에 의한 유영속도 등 과학어탐에 의해서 얻어진 정보를 이용한 방법이 있다 (Richards et al., 1991). (중략)

• 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.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.6
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• pp.545-550
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• 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 Korea Academia-Industrial cooperation Society
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• v.14 no.12
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• pp.6114-6118
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• 2013

This study examined the ultrasonic pulse reflection method(UPRM) for testing each ultrasonic pulse waveform model(UPWM) based on weld defects. The sharp crack of a clear signal was generated. The echo height of the defective probes changed according to the location. In a long crack in a circle around the defective probes, the Swivel scanning echo height when using the particle was reduced drastically. The peaks in the echo were thin because the needle was pointed. The porosity defects arising from a single echo was sharp and crisp, but a number of pores of the collective reflection overlapped and ajagged echo was observed. Slag, slag inclusions, cracks, and defects at the Swivel scan of each particle using the echo shape showed difference in the degree. Cracks were revealed as sudden changes in the echo height of the slag inclusions: increase ${\rightarrow}$ decrease ${\rightarrow}$ increase ${\rightarrow}$ decrease. In addition, the location of a number of defects in the dense pore geometry, such as a typical echo sundry, revealed the shape in the slag. Poor penetration of the defect echo, revealed the cracks to have a sharp-edged, crack-like shape with an echo.

• 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.

• The Journal of the Acoustical Society of Korea
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• v.33 no.3
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• pp.210-215
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• 2014

In spite of distortion caused by noise and echo, the audio fingerprinting technique must identify successfully an audio source. This audio fingerprinting technique is applying for TV commercial advertisement identification. In this paper, we propose a robust audio fingerprinting method for TV commercial advertisement identification. In the proposed method, a prominent audio peak pair fingerprint based on constant Q transform improves the accuracy of the audio fingerprinting system in real noisy environments. Experimental results confirm that the proposed method is quite robust than previous audio fingerprinting method in different noise conditions and achieves promising accurate results.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.39 no.2
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• pp.92-98
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• 2003

The quantification of fish school abundance was carried out by using luminance of pixel on scanning sonar image, and compared with the indices of fish school abundance e.g. school number, school area and weighted school area. The survey was carried out in Funka Bay off southern Hokkaido, Japan using research vessel Ushio-Maru during December 1999. A 180-degree scanning sonar with a frequency of 164kHz was used. The school number was counted both left and right 40-degree radial lines from the center of own vessel mark on a scanning image. The school area was measured approximately as an ellipse from the school length and width. The weighted school area was calculated by multiplying school area and average value of inner pixel luminance. A quantification of pixel luminance was also measured to integrate squared pixel luminance value on these lines. Fish school and school bottom were discriminated by the produced sonar echogram using pixel luminance value on these lines. The relationships between the quantified luminance value and other abundance indices such as school area and weighted school area revealed a good correlation. Therefore, the quantified luminance is a useful method in estimating fish school abundance in the acoustic survey using sonar.

• Proceedings of the Korean Information Science Society Conference
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• 2006.10b
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• pp.475-479
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• 2006

환자의 체동은 MRI에 의해 제공된 영상의 화질을 저하시키는 주된 원인이 되고 있다. 이에 본 논문에서는 MRI내 3차원 강체운동에 기인한 아티팩트를 제거하는 방법을 제안한다. 이러한 제거 목표를 달성하기 위해 MRI 영상 데이터를 얻기위한 2차원 다-슬라이스 방법(a multiple two dimensional slice technique)이 사용되어 왔다. 대상물체의 운동에 해당하는 수집된 MRI 데이터는 불균일한 표본화와 위상오차에 의해 영향을 받게된다. 3차원 강체운동에 대해 주어진 운동 파라메타와 장면간의 영향이라는 가정하에 양선형 보간법과 중첩법으로 다-슬라이스 데이터를 사용하는 방법에 기초한 재구성 알고리즘을 MRI 아티팩트를 제거하는데 사용한다. 미지의 체동 파라메타를 추정하기 위해 3차원 강체운동은 다-슬라이스 취득방법의 각 영상과 결합된 관심영역 바깥쪽에서 측정된 에너지를 증가시킨다는 사실을 이용하는 최소에너지법을 사용한다. 본 방법의 유효성을 확인하기 위해 3차원 강체운동에 의해 화질이 저하된 스핀-에코우 영상에 적용한 결과 화질이 식별될 수 있을 정도로 개선됨을 확인하였다.