• Proceedings of the Korean Radioactive Waste Society Conference
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• 2008.11a
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• pp.393-394
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• 2008

• 대한방사선방어학회:학술대회논문집
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• 2009.04a
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• pp.226-227
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• 2009

• Proceedings of the Korean Magnestics Society Conference
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• 2013.12a
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• pp.17-19
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• 2013

• Journal of Radiation Protection and Research
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• v.16 no.1
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• pp.25-32
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• 1991

MCNP code was used to calculate conversion factor H(d)ma at the depths of 0.07 and 10mm within a water phantom recommended by IAEA and within a PMMA phantom required by the US dosimeter proficiency testing programmes. The calculations were performed for an expanded parrallel beam of monoenergetic photons of perpendicular incidence on one faces of the phantom. The results can be used as conversion factor in calibrating individual dosemeters in terms of the dose equivalent quantities defined directly in the phantom.

• The Monthly Diabetes
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• s.194
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• pp.28-31
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• 2006

뇌졸증 예방을 위해서는 대부분의 당뇨병환자에서 혈당조절 이외에 동반된 여러 위험인자들을 같이 교정해주는 것이 중요하다. 또한 환자에게 당뇨병이 뇌졸증의 위험인자임을 인식하도록하고 이에 상응하는 증상이 발생할 경우에는 적절한 시간 내에 병원을 방문하여 검사를 받도록 하여야 한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.05a
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• pp.410-413
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• 2003

We have developed the measurement system for the high power laser The absorber is made of gold-plated copper cavity. The calibration heater is using a manganin(CuMn12Ni ; Isabellenhutte) coil, and output power is measured by using of resistance bridge with composed manganin and copper coil. Developed system can measure for 5∼1000w laser output power range. Calibration factors are 489.13 J/mV at 100W range and 489.13 J/mV at 500W range. Correction factors are 0.99 at 100W range and 1.006 at 500W range.

• Proceedings of the Korea Information Processing Society Conference
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• 2006.05a
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• pp.673-676
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• 2006

본 논문은 산업현장의 불균등한 조명 조건에서 정확한 카메라 교정을 수행할 수 있는 방법을 제안한다. 비접촉 계측을 위한 카메라 교정법은 패턴에서 교정점들을 정확하게 추출할 수 있어야 하며, 평면 패턴을 사용하는 교정 방법은 최소 7개의 교정점을 알아야 한다. 그러나 비접촉 치수 계측기가 설치된 산업현장에서 카메라 교정에 알맞은 조명을 기대하기 힘들다. 본 논문에서는 최적조명제어가 어려운 산업현장에서 치수계측을 위한 카메라 교정을 효과적으로 수행할 수 있는 반자동 카메라 교정방법을 제안한다. 교정패턴상의 최소 4점을 사용자가 지정함에 의해, 조명제어의 어려움으로 인해 교정점 추출이 실패한 교정패턴의 불완전 교정점을 사용하여 이상적인 조명상태에서의 교정점 정보를 예측하고, 이 정보로부터 다시 정확한 교정인자들을 반복적으로 추출하는 방법을 적용한다. 제시된 방법은 렌즈의 투사왜곡에 의한 교정패턴에서도 성공적으로 적용될 수 있음을 실험을 통해 확인하였다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.3 no.4
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• pp.867-873
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• 1999

Camera calibration is a procedure which calculates internal and external parameters of a camera with the Down world coordinates of the control points. Accurate camera calibration is required for achieving accurate visual measurements. In this paper, we propose a simple and flexible camera calibration using neural networks which doesn't require a special knowledge of 3D geometry and camera optics. There are some applications which are not in need of the values of the internal and external parameters. The proposed method is very useful to these applications. Also, the proposed camera calibration has advantage that resolves the ill-condition as object plane is near parallel image plane. The ill-condition is frequently met in product inspection. For little more accurate calibration, acquired image is divided into two regions according to radial distortion of lens and neural network is applied to each region. Experimental results and comparison with Tsai's algorithm prove the validity of the proposed camera calibration.

• Progress in Medical Physics
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• v.13 no.1
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• pp.27-31
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• 2002

Output factors for 6MeV electron were directly measured under the condition of an individual beam cutout and these factors were compared with the output factors by 1D method which is an easy means to predict the output factors of electron beam. Output factors by 1D method are defined as output factors of rectangular fields where one side is always equal to the side of the square reference field and the output of an arbitrary rectangular field X, Y is given by the product of the 1D output factors. The output of very large square fields is overestimated using 1D method for the 6MeV electron, but it results in agreement with measured data under the condition of an individual cutout within 1% error adopting a correction factor $CF=C\times$[(X-10)(Y-10)/$\mid$(X-10)(Y-10)$\mid^{1/2}]$.

• Journal of Chest Surgery
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• v.39 no.8 s.265
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• pp.579-587
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• 2006

Background: Preoperative risk analysis for Fontan candidates is still less than optimal in that patients with apparently low risks may have poor surgical outcome; prolonged pleural drainage, protein losing enteropathy, pulmonary thromboembolism and death. We hypothesized that low pulmonary vascular compliance (PVC) is a risk factor for prolonged pleural effusion drainage after the Fontan operation. Material and Method: A retrospective review of 96 consecutive patients who underwent the Extracardiac Fontan procedures (median age: 3.9 years) was performed. Fontan risk score (FRS) was calculated from 12 categorized preoperative anatomic and physiologic variables. PVC $(mm^2/m^2{\cdot}mmHg)$ was defined as pulmonary artery index $(mm^2/m^2)$ divided by total pulmonary resistance $(W.U{\cdot}/m^2)$ and pulmonary blood flow $(L/min/m^2)$ based on the electrical circuit analogue of the pulmonary circulation. Chest tube indwelling time was log-transformed (log indwelling time, LIT) to fit normal distribution, and the relationship between preoperative predictors and LIT was analyzed by multiple linear regression. Result: Preoperative PVC, chest tube indwelling time and LIT ranged from 6 to 94.8 $mm^2/mmHg/m^2$ (median: 24.8), 3 to 268 days (median: 20 days), and 1.1 to 5.6 (mean: 2.9, standard deviation: 0.8), respectively. FRS, PVC, cardiopulmonary bypass time (CPB) and central venous pressure at postoperative 12 hours were correlated with LIT by univariable analyses. By multiple linear regression, PVC (p=0.0018) and CPB (p=0.0024) independently predicted LIT, explaining 21.7% of the variation. The regression equation was LIT=2.74-0.0158 PVC+0.00658 CPB. Conclusion: Low pulmonary vascular compliance is an important risk factor for prolonged pleural effusion drainage after the extracardiac Fontan procedure.