• 비파괴검사학회지
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• 제29권5호
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• pp.466-472
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• 2009

고온, 고압의 원자력 배관 누설 판별을 위해 음향방출기법(AE)을 이용한 누설감지 시스템인 ALMS 기법이 적용되고 있다. 이 시스템은 단지 AE 센서로 전해진 신호의 RMS값을 이용하여 누설의 유무만을 판단할 뿐, 누설 발생시 누설부의 크기나 형태를 평가하는 것에는 어려움이 있었다. 따라서 본 논문에서는 이러한 문제를 해결하기 위하여 AE센서와 가속도센서를 동시에 이용한 이중 센서 시스템을 제안하였다. 빠른 학습 속도와 정확성을 위해 Levenberg-Marquardt 학습 알고리즘을 이용한 인공신경회로망을 적용시키고, 이를 통해 신뢰성 있는 분석 결과를 얻을 수 있다. 배관내 압력과 누설부의 크기와 모양에 따른 실험신호들을 학습시키고 그 판별 정확성을 확인하였다. 추가적으로 배관 두께에 따라 발생하는 파(wave)의 종류와 특성이 달라지는 것을 이론과 실험을 통하여 알아보았다.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2005년도 춘계학술대회 논문집
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• pp.108-113
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• 2005

In this study, rotary type diamond dressing system for ultra-precision centerless grinding for ferrule was developed at the first time and experiments were conducted with AE sensor and hall sensor system to verify the optimum dressing condition for ultra-precision centerless grinding for ferrule. The correlations with the condition of dressing are evaluated by AE signal analysis with root mean square (RMS) and frequency analysis. And current signals from hall sensor are also studied as a factor of dressing optimum condition selection. Dressing process was conducted to investigate the effects of depth of cut, rotating speed, and the number of overlap to select the optimum condition of rotary dressing system of ultra-precision centerless grinding machine for ferrule fabrication. In order to verify the optimum condition of dressing, AE and current signals were compared with the surface quality of dressing wheel and grinding wheel for ultra-precision ferrule grinding. All of these experiments were completed by Taguchi Methodology to reduce experimental time. Hence, the optimum condition of rotary dressing system for ultra-precision centerless grinding for ferrule fabrication can be selected following to the experiment result from signals of AE and hall sensor.

• 한국생산제조학회지
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• 제8권1호
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• pp.52-59
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• 1999

An experimental study on the grinding temperature, surface roughness and Acoustic Emission(AE) signals was conducted with different shapes of wheel. The grinding characteristics for slotted shapes of wheel changed by width and helical angle, were compared with those by general one. Lower grinding temperature was obtained for 30$^{\circ}$helical angle with 10mm width and Root Mean square(RMS) values of AE signals were lower for slotted shapes rather than general one. Surface roughness characteristics of slotted shapes found to be rough but the value of roughness for 45$^{\circ}$helical angel with 6mm width, represented to similar tendency general one.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1997년도 춘계학술대회 논문집
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• pp.832-836
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• 1997

An experimental study on the grinding temperature, surface roughness and Acoustic Emission(AE) signal was conducted with different shapes of wheel. The grinding characteristics by slotted shapes of wheel changed by width and helical angle,were compared with those by general one. Lower grinding temperature was obtained for 30 .deg. helical angel with 10mm width and Root Mean Square(RMS) values of AE signals were lower for slotted shapes rather than general one. Surface roughness characteristic of slotted shapes found to be rough,but the value of roughness for 45 .deg. helical angel with 6mm width, represented to similar tendency general one.

• 대한기계학회논문집
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• 제17권7호
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• pp.1783-1793
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• 1993

Pattern recognition technique using fuzzy c-means algorithm and multilayer perceptron was applied to classify tool wear states in turning. The tool wear states were categorized into the three regions 'Initial', 'Normal', 'Severe' wear. The root mean square(RMS) value of acoustic emission(AE) and current signal was used for the classification of tool wear states. The simulation results showed that a fuzzy c-means algorithm was better than the conventional pattern recognition techniques for classifying ambiguous informations. And normalized RMS signal can provide good results for classifying tool wear. In addition, a fuzzy c-means algorithm(success rate for tool wear classification : 87%) is more efficient than the multilayer perceptron(success rate for tool wear classification : 70%).

• Journal of Advanced Marine Engineering and Technology
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• 제33권5호
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• pp.672-678
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• 2009

Acoustic Emission (AE) technique is a non-destructive testing method and widely used for the early detection of faults in rotating machines in these days, because the sensitivity of AE transducers is higher than normal accelerometers. So it can detect low energy vibration signals. The faults in the rotating machines are generally occurred at bearings and gearboxes which are the principal parts of the machines. It was studied to detect the bearing faults by envelop analysis in several decade years. And the researches showed that AE had a possibility of the application in condition monitoring system(CMS) using the envelope analysis for the rolling bearing. And peak ratio (PR) was developed for expression of the bearing condition in condition monitoring system using AE. Noise level is needed to reduce to take exact PR value because the PR is calculated from total root mean square (RMS) and the harmonics peak levels of the defect frequencies of the bearing. Therefore, in this paper, the discrete wavelet transform (DWT) was added in the envelope analysis to reduce the noise level in the AE signals. And then, the PR was calculated and compared with general envelope analysis result and the result of envelope analysis added the DWT. In the experiment result about inner fault of bearing, defect frequency was difficult to find about only envelop analysis. But it's easy to find defect frequency after wavelet transform. Therefore, Envelop analysis added wavelet transform was useful method for early detection of default in signal process.

• 방사선산업학회지
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• 제11권3호
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• pp.139-144
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• 2017

To evaluation of clinical usefulness for B1+RMS limits, we compared image quality of Routine, Specific absorption rate (SAR) and Root mean square (RMS) protocol. 5 volunteers underwent Magnetic Resonance Imaging (MRI) scan of the brain using three different protocols. We draw Region of interest ROI in cortex, white matter, gray matter, putamen and thalamus of axial plan. Signal to noise ratio (SNR) were evaluated in each area and Contrast to noise ration (CNR) were evaluated between white matter and gray matter. Qualitative evaluation was used to score each ROI. B1+RMS is confirmed its usefulness compared to conventional SAR standard on the aspect of improvement of image quality, reduction of scan time and easy adjusting parameter.

• 한국감성과학회:학술대회논문집
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• 한국감성과학회 2009년도 춘계학술대회
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• pp.249-252
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• 2009

본 연구는 화면전환 및 필기 등의 프레젠테이션을 편리하게 제어 할 수 있는 시스템을 구현하고자 한다. 카메라를 이용한 컴퓨터 비전 분야에서 주변의 조명 환경의 영향을 적게 받는 적외선을 이용한 연구가 활발하게 진행되고 있다. 제안하는 방법에서는 USB(Universal Serial Bus) 타입의 카메라를 적외선 환경에 맞게 개조하고, 이를 이용하여 적외선 LED(Light Emitting Diode)를 이용한 전자 분필 영상을 취득하여, 실시간으로 전자 분필의 위치를 검출한다. 검출된 영역은 사전에 사용자가 설정한 '필기 가능 영역'과 변형 함수에 기반하여, 실제 모니터 좌표로 사상되어 화면에 표시되게 된다. 제안하는 시스템은 저렴한 가격의 웹 캠과 적외선 조명 이외에는 별도의 장비가 필요하지 않으면서도, 대형 스크린을 터치 방식으로 활용할 수 있다는 장점을 가진다. 실험 결과, 계산된 모니터 좌표와 사용자가 의도한 위치간의 평균 RMS(Root Mean Square) 오차는 약 15픽셀로 나타나서, 글씨를 필기하고 화면의 특정 부분을 강조하기 위한 활용이 가능함을 확인할 수 있었다.

• 한국지구과학회지
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• 제41권4호
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• pp.356-366
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• 2020

The accuracy of satellite-observed sea surface salinity (SSS) was evaluated in comparison with in-situ salinity measurements from ARGO floats and buoys in the seas around the Korean Peninsula, the northwest Pacific, and the global ocean. Differences in satellite SSS and in-situ measurements (SSS errors) indicated characteristic dependences on geolocation, sea surface temperature (SST), and other oceanic and atmospheric conditions. Overall, the root-mean-square (rms) errors of non-averaged SMOS SSSs ranged from approximately 0.8-1.08 psu for each in-situ salinity dataset consisting of ARGO measurements and non-ARGO data from CTD and buoy measurements in both local seas and the ocean. All SMOS SSSs exhibited characteristic negative bias errors at a range of -0.50- -0.10 psu in the global ocean and the northwest Pacific, respectively. Both rms and bias errors increased to 1.07 psu and -0.17 psu, respectively, in the East Sea. An analysis of the SSS errors indicated dependence on the latitude, SST, and wind speed. The differences of SMOS-derived SSSs from in-situ salinity data tended to be amplified at high latitudes (40-60°N) and high sea water salinity. Wind speeds contributed to the underestimation of SMOS salinity with negative bias compared with in-situ salinity measurements. Continuous and extensive validation of satellite-observed salinity in the local seas around Korea should be further investigated for proper use.

• 대한원격탐사학회지
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• 제27권6호
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• pp.729-741
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• 2011

Wind speeds have long been estimated from C-band VV-polarized SAR data by using the CMOD algorithms such as CMOD4, CMOD5, and CMOD_IFR2. Some SAR data with HH-polarization without any observations in VV-polarization mode should be converted to VV-polarized value in order to use the previous algorithms based on VV-polarized observation. To satisfy the necessity of polarization ratio (PR) for the conversion, we retrieved the conversion parameter from full-polarized SIR-C SAR image off the east coast of Korea. The polarization ratio for SIR-C SAR data was estimated to 0.47. To assess the accuracy of the polarization ratio coefficient, pseudo VV-polarized normalized radar cross section (NRCS) values were calculated and compared with the original VV-polarized ones. As a result, the estimated psudo values showed a good agreement with the original VV-polarized data with an root mean square error by 0.99 dB. We applied the psudo NRCS to the estimation of wind speeds based on the CMOD wind models. Comparison of the retrieved wind field with the ECMWF and NCEP/NCAR reanalysis wind data showed relatively small rms errors of 1.88 and 1.91 m/s, respectively. SIR-C HH-polarized SAR wind retrievals met the requirement of the scatterometer winds in overall. However, the polarization ratio coefficient revealed dependence on NRCS value, wind speed, and incident angle.