• Radiation Oncology Journal
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• v.18 no.2
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• pp.138-149
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• 2000

Purpose : It was studied that the relationship between radiation dose, dose rate and the frequency of chromosomal aberrations in peripheral lymphocytes. Methods and Materials : Peripheral lymphocytes were irradiated in vitro with 6 MeV X-ray at dose ranges from 50 cGy to 800 cGy. The variations of the frequency of chromosomal aberrations were observed according to different radiation dose rate from 20 cGy/min to 400 cGy/min at constant total dose of 400 cGy which it was considered as factor to correct biological radiation dose measurement. Results : The yields of lymphocytes with chromosomal aberrations (dicentric chromosome, ring chromosome, acentric fragment pairs) are 0% at 50 cGy, 9% at 100 cGy, 20% at 200 cGy, 27% at 300 cGy, 55% at 400 cGy, 88% at 600 cGy, and 100% at 800 cGy. The value of Ydr is 0.000 at 50 cGy, 0.093 at 100 cGy, 0.200 at 200 cGy, 0.354 at 300 cGy, 0.612 at 400 cGy, 2.040 at 600 cGy, and 2.846 at 800 cGy. The relationship between radiation (D) and the frequency of dicentrlc chromosomes and ring Chromosomes (Ydr) can be expressed as Ydr=0.188${\times}$10$^{-2}$ D/Gy+0.422${\times}$10$^{-4}$/Gy$^{2}$${\times}$D$^{2}$ The Value of Qdr is 0.000 at 50 cGy, 1.000 at 100 cGy, 1.000 at 200 cGy, 1.333 at 300 cGy, 1.118 at 400 cGy, 2.318 at 600 cGy, and 2.846 at 800 cGy. When 400 cGy is irradiated with different dose rate each of 20, 40, 60, 80, 100, 160, 240, 320, and 400 cGy/min, Ydr is each of 0.982, 0.837, 0.860, 0.732, 0.763, 0.966, 0.909, 1.006, and 0.806, and Qdr is each of 1.839, 1.555, 1.654, 1.333, 1.381, 1.750, 1.6000, 1.710, and 1.318. Conclusion : There are not the significant variations of Ydr and Qdr values according to different dose rate. And so radiation damage is influenced by total exposed radiation doses and is influenced least of all by different dose rate when it is acute single exposure.

• Journal of Soil and Groundwater Environment
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• v.14 no.3
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• pp.1-13
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• 2009

Alkalinity and total carbon contents were measured by acid neutralizing titration (ANT), back titration (BT), gravitational weighing (GW), non-dispersive infrared-total carbon (NDIR-TC) methods for assessing precision and accuracy of alkalinity and total carbon concentration in $CO_2$-rich water. Artificial $CO_2$-rich water(ACW: pH 6.3, alkalinity 68.8 meq/L, $HCO_3^-$ 2,235 mg/L) was used for comparing the measurements. When alkalinity measured in 0 hr, percent errors of all measurement were 0~12% and coefficient of variation were less than 4%. As the result of post-hoc analysis after repeated measure analysis of variance (RM-AMOVA), the differences between the pair of methods were not significant (within confidence level of 95%), which indicates that the alkalinity measured by any method could be accurate and precise when it measured just in time of sampling. In addition, alkalinity measured by ANT and NDIR-TC were not change after 24 and 48 hours open to atmosphere, which can be explained by conservative nature of alkalinity although $CO_2$ degas from ACW. On the other hand, alkalinity measured by BT and GW increased after 24 and 48 hours open to atmosphere, which was caused by relatively high concentration of measured total carbon and increasing pH. The comparison between geochemical modeling of $CO_2$ degassing and observed data showed that pH of observed ACW was higher than calculated pH. This can be happen when degassed $CO_2$ does not come out from the solution and/or exist in solution as $CO_{2(g)}$ bubble. In that case, $CO_{2(g)}$ bubble doesn't affect the pH and alkalinity. Thus alkalinity measured by ANT and NDIR-TC could not detect the $CO_2$ bubble although measured alkalinity was similar to the calculated alkalinity. Moreover, total carbon measured by ANT and NDIR-TC could be underestimated. Consequently, it is necessary to compare the alkalinity and total carbon data from various kind of methods and interpret very carefully. This study provide technical information of measurement of dissolve $CO_2$ from $CO_2$-rich water which could be natural analogue of geologic sequestration of $CO_2$.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.947-958
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• 2021

This study was conducted to provide basic data for crop monitoring by comparing and analyzing changes in reflectance and vegetation index by sensor of multi-spectral sensors mounted on unmanned aerial vehicles. For four types of unmanned aerial vehicle-mounted multispectral sensors, such as RedEdge-MX, S110 NIR, Sequioa, and P4M, on September 14 and September 15, 2020, aerial images were taken, once in the morning and in the afternoon, a total of 4 times, and reflectance and vegetation index were calculated and compared. In the case of reflectance, the time-series coefficient of variation of all sensors showed an average value of about 10% or more, indicating that there is a limit to its use. The coefficient of variation of the vegetation index by sensor for the crop test group showed an average value of 1.2 to 3.6% in the crop experimental sites with high vitality due to thick vegetation, showing variability within 5%. However, this was a higher value than the coefficient of variation on a clear day, and it is estimated that the weather conditions such as clouds were different in the morning and afternoon during the experiment period. It is thought that it is necessary to establish and implement a UAV flight plan. As a result of comparing the NDVI between the multi-spectral sensors of the unmanned aerial vehicle, in this experiment, it is thought that the RedEdeg-MX sensor can be used together without special correction of the NDVI value even if several sensors of the same type are used in a stable light environment. RedEdge-MX, P4M, and Sequioa sensors showed a linear relationship with each other, but supplementary experiments are needed to evaluate joint utilization through off-set correction between vegetation indices.

• Journal of the Korean Applied Science and Technology
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• v.35 no.3
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• pp.667-675
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• 2018

Increasing emissions regulations and demand of high-efficiency cars that travels a lot of distance with less fuel, there is growing interest in Energy Consumption Efficiency. Korean energy consumption efficiency compute combined Fuel Economy by driven city & highway driving mode and present final Energy Consumption Efficiency as using 5-cycle correction formula. Energy consumption efficiency is computed Carbon-balance-method, when used burning fuel play a key role in vehicle performance & Energy Consumption Efficiency. In Korea, vehicle fuel is circulate by Petroleum and Petroleum Alternative Business Act, there is property difference in quality standard because petroleum sector's refine method or type of crude oil. It does not appear a big difference according to fuel, because it sets steady quality standard, it may affect the performance of automobile. Thus, in research We purchase a few diesel fuel which circulated in the market in summer season though directly-managed-gas station by petroleum sector, resolve property each of fuel, we compute Fuel Economy each of them. We analyze into change depend on applying for property as nowadays utilizing Energy Consumption Efficiency calculating formula of gasoline and diesel fuel. As result, Density each of sample fuel has a maximum difference roughly 0.9%, net heat value each of sample fuel has difference 1.6%, result of current Energy Consumption Efficiency each of sample fuel has a difference roughly 1% at city drive mode, 1.4% at highway drive mode. Result of use gasoline calculator formula shows less 6% result than nowadays utilizing Energy Consumption Efficiency calculating formula, each of sample's Energy Consumption Efficiency shows maximum roughly 1.4% result in city & highway drive mode.

• Korean Journal of Food Science and Technology
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• v.29 no.6
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• pp.1119-1124
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• 1997

This study was carried out for improvement and correction of the traditional pressure variation method (PVM) in the respiration rate measurements of fresh fruits and vegetables using a microcomputer system and a differential pressure sensor. Water vapor pressure in the container was calculated by equations for psychrometric calculations. At the beginning of experimental period water vapor pressure in the container was increased and maintained constantly in the most experimental period, but was decreased dramatically after $CO_2$ scrubbing. The percentages of water vapor pressure on total differential pressure were $33{\sim}46%$ at $1^{\circ}C$, $23{\sim}45%$ at $11^{circ}C$ and $35{\sim}53%$ at $21^{\circ}C$. The differences between the respiration rates determined by gas chromatography and corrected pressure variation method (CPVM) were $0.2{\sim}0.3\;mgCO_2kg^{-1}h^{-1}$ at $1^{\circ}C$, $0.2{\sim}2.9\;mgCO_2kg^{-1}h^{-1}$ at $11^{\circ}C$ and 1.0{\sim}9.0\;mgCO_2kg^{-1}h^{-1}$ at $21^{circ}C$, while those between gas chromatography and normal pressure variation method (PVM) were $0.8{\sim}1.2\;mgCO_2kg^{-1}h^{-1}$ at $1^{\circ}C$, $3.9{\sim}11.0\;mgCO_2kg^{-1}h^{-1}$ at $11^{\circ}C$ and $8.0{\sim}32.0\;mgCO_2kg^{-1}h^{-1}$ at $21^{circ}C$, respectively. The differences of the respiration rates with CPVM were smaller than those with PVM. CPVM, therefore, were more exact and convenient method than PVM in the measurement of respiration rate of fresh produce.

• The Korean Journal of Nuclear Medicine
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• v.39 no.6
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• pp.421-429
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• 2005

Purpose: It has been postulated that dopamine release in the striatum underlies the reinforcing properties of nicotine. Substantial evidence in the animal studies demonstrates that nicotine interacts with dopaminergic neuron and regulates the activation of the dopaminergic system. The aim of this study was to visualize the dopamine release by smoking in human brain using PET scan with $[^{11}C]raclopride$. Materials and Methods: Five male non-smokers or ex-smokers with an abstinence period longer than 1 year (mean age of $24.4{\pm}1.7$ years) were enrolled in this study $[^{11}C]raclopride$, a dopamine D2 receptor radioligand, was administrated with bolus-plus-constant infusion. Dynamic PET was performed during 120 minutes ($3{\times}20s,\;2{\times}60s,\;2{\times}120s,\;1{\times}180s\;and\;22{\times}300s$). following the 50 minute-scanning, subjects smoked a cigarette containing 1 mg of nicotine while in the scanner. Blood samples for the measurement of plasma nicotine level were collected at 0, 5, 10, 15, 20, 25, 30, 45, 60, and 90 minute after smoking. Regions for striatal structures were drawn on the coronal summed PET images guided with co-registered MRI. Binding potential, calculated as (striatal-cerebellar)/cerebellar activity, was measured under equilibrium condition at baseline and smoking session. Results: The mean decrease in binding potential of $[^{11}C]raclopride$ between the baseline and smoking in caudate head, anterior putamen and ventral striatum was 4.7%, 4.0% and 7.8%, respectively. This indicated the striatal dopamine release by smoking. Of these, the reduction in binding potential in the ventral striatum was significantly correlated with the cumulated plasma level of the nicotine (Spearman's rho=0.9, p=0.04). Conclusion: These data demonstrate that in vivo imaging with $[^{11}C]raclopride$ PET could measure nicotine-induced dopamine release in the human brain, which has a significant positive correlation with the amount or nicotine administered bt smoking.

• Journal of radiological science and technology
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• v.31 no.1
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• pp.17-23
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• 2008

Purpose : Because there is a difference depending on the environment as for an inspection equipment the important part of bone density scan and the precision/accuracy of a tester, the management of quality must be made systematically. The equipment failure caused by overload effect due to the aged equipment and the increase of a patient was made frequently. Thus, the replacement of equipment and additional purchases of new bonedensity equipment caused a compatibility problem in tracking patients. This study wants to know whether the clinical changes of patient's bonedensity can be accurately and precisely reflected when used it compatiblly like the existing equipment after equipment replacement and expansion. Materials and methods : Two equipments of GE Lunar Prodigy Advance(P1 and P2) and the Phantom HOLOGIC Spine Road(HSP) were used to measure equipment precision. Each device scans 20 times so that precision data was acquired from the phantom(Group 1). The precision of a tester was measured by shooting twice the same patient, every 15 members from each of the target equipment in 120 women(average age 48.78, 20-60 years old)(Group 2). In addition, the measurement of the precision of a tester and the cross-calibration data were made by scanning 20 times in each of the equipment using HSP, based on the data obtained from the management of quality using phantom(ASP) every morning (Group 3). The same patient was shot only once in one equipment alternately to make the measurement of the precision of a tester and the cross-calibration data in 120 women(average age 48.78, 20-60 years old)(Group 4). Results : It is steady equipment according to daily Q.C Data with $0.996\;g/cm^2$, change value(%CV) 0.08. The mean${\pm}$SD and a %CV price are ALP in Group 1(P1 : $1.064{\pm}0.002\;g/cm^2$, $%CV=0.190\;g/cm^2$, P2 : $1.061{\pm}0.003\;g/cm^2$, %CV=0.192). The mean${\pm}$SD and a %CV price are P1 : $1.187{\pm}0.002\;g/cm^2$, $%CV=0.164\;g/cm^2$, P2 : $1.198{\pm}0.002\;g/cm^2$, %CV=0.163 in Group 2. The average error${\pm}$2SD and %CV are P1 - (spine: $0.001{\pm}0.03\;g/cm^2$, %CV=0.94, Femur: $0.001{\pm}0.019\;g/cm^2$, %CV=0.96), P2 - (spine: $0.002{\pm}0.018\;g/cm^2$, %CV=0.55, Femur: $0.001{\pm}0.013\;g/cm^2$, %CV=0.48) in Group 3. The average error${\pm}2SD$, %CV, and r value was spine : $0.006{\pm}0.024\;g/cm^2$, %CV=0.86, r=0.995, Femur: $0{\pm}0.014\;g/cm^2$, %CV=0.54, r=0.998 in Group 4. Conclusion: Both LUNAR ASP CV% and HOLOGIC Spine Phantom are included in the normal range of error of ${\pm}2%$ defined in ISCD. BMD measurement keeps a relatively constant value, so showing excellent repeatability. The Phantom has homogeneous characteristics, but it has limitations to reflect the clinical part including variations in patient's body weight or body fat. As a result, it is believed that quality control using Phantom will be useful to check mis-calibration of the equipment used. A value measured a patient two times with one equipment, and that of double-crossed two equipment are all included within 2SD Value in the Bland - Altman Graph compared results of Group 3 with Group 4. The r value of 0.99 or higher in Linear regression analysis(Regression Analysis) indicated high precision and correlation. Therefore, it revealed that two compatible equipment did not affect in tracking the patients. Regular testing equipment and capabilities of a tester, then appropriate calibration will have to be achieved in order to calculate confidential BMD.

• Progress in Medical Physics
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• v.17 no.2
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• pp.105-113
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• 2006

This study peformed to confirm the corrected dose In different electron density materials using the superposition/FFT convolution method in radiotherapy Planning system. The experiments of the $K_2HPO_4$ diluted solution for bone substitute, Cork for lung and n-Glucose for soft tissue are very close to effective atomic number of tissue materials. The image data acquisited from the 110 KVp and 130 KVp CT scanner (Siemes, Singo emotions). The electron density was derived from the CT number (H) and adapted to planning system (Xio, CMS) for heterogeneity correction. The heterogeneity tissue phantom used for measurement dose comparison to that of delivered computer planning system. In the results, this investigations showed the CT number is highly affected in photoelectric effect in high Z materials. The electron density in a given energy spectrum showed the relation of first order as a function of H in soft tissue and bone materials, respectively. In our experiments, the ratio of electron density as a function of H was obtained the 0.001026H+1.00 in soft tissue and 0.000304H+1.07 for bone at 130 KVp spectrum and showed 0.000274H+1.10 for bone tissue in low 110 KVp. This experiments of electron density calibrations from CT number used to decide depth and length of photon transportation. The Computed superposition and FFT convolution dose showed very close to measurements within 1.0% discrepancy in homogeneous phantom for 6 and 15 MV X rays, but it showed -5.0% large discrepancy in FFT convolution for bone tissue correction of 6 MV X rays. In this experiments, the evaluated doses showed acceptable discrepancy within -1.2% of average for lung and -2.9% for bone equivalent materials with superposition method in 6 MV X rays. However the FFT convolution method showed more a large discrepancy than superposition in the low electron density medium in 6 and 15 MV X rays. As the CT number depends on energy spectrum of X rays, it should be confirm gradient of function of CT number-electron density regularly.

• Journal of Korean Tunnelling and Underground Space Association
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• v.19 no.6
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• pp.915-936
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• 2017

In this study, current status of Korean hazard mitigation guideline for tunnel operation is summarized. It shows that requirement for CCTV installation has been gradually stricted and needs for tunnel incident detection system in conjunction with the CCTV in tunnels have been highly increased. Despite of this, it is noticed that mathematical algorithm based incident detection system, which are commonly applied in current tunnel operation, show very low detectable rates by less than 50%. The putative major reasons seem to be (1) very weak intensity of illumination (2) dust in tunnel (3) low installation height of CCTV to about 3.5 m, etc. Therefore, an attempt in this study is made to develop an deep-learning based tunnel incident detection system, which is relatively insensitive to very poor visibility conditions. Its theoretical background is given and validating investigation are undertaken focused on the moving vehicles and person out of vehicle in tunnel, which are the official major objects to be detected. Two scenarios are set up: (1) training and prediction in the same tunnel (2) training in a tunnel and prediction in the other tunnel. From the both cases, targeted object detection in prediction mode are achieved to detectable rate to higher than 80% in case of similar time period between training and prediction but it shows a bit low detectable rate to 40% when the prediction times are far from the training time without further training taking place. However, it is believed that the AI based system would be enhanced in its predictability automatically as further training are followed with accumulated CCTV BigData without any revision or calibration of the incident detection system.