• Journal of the Korean Society of Radiology
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• v.12 no.6
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• pp.769-776
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• 2018

According IEC 60601-1 ed3.1 and IEC 60601-2-45 regulation, diagnostic X-ray equipment should be display to measured and calculated air kerma area product. On the clinical X ray equipment, air kerma area product dosimeter would like to have an evidence for dosimeter accuracy and energy dependency. This study was performed to indirect calibration and energy dependency test for attached type air kerma area product (KAP) dosimeter by RQR standards beam quality. On the RQR5 beam quality, attached KAP dosimeter error showed -7.5%, respectably. On the RQR9 beam quality, attached KAP dosimeter error showed -10.4%, respectably. All RQR beam quality, average absolute error was $8.30%{\pm}2.85%$, respectably. On this study, attached KAP dosimeter was satisfied to IEC 60580 and AAPM TG 190. This calibration method of KAP dosimeter will help to performance maintain for clinical KAP dosimeter.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1009-1029
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• 2023

Urban trees play a vital role in urban ecosystems,significantly reducing impervious surfaces and impacting carbon cycling within the city. Although previous research has demonstrated the efficacy of employing artificial intelligence in conjunction with airborne light detection and ranging (LiDAR) data to generate urban tree information, the availability and cost constraints associated with LiDAR data pose limitations. Consequently, this study employed freely accessible, high-resolution multispectral satellite imagery (i.e., Sentinel-2 data) to estimate fractional tree canopy cover (FTC) within the urban confines of Suwon, South Korea, employing machine learning techniques. This study leveraged a median composite image derived from a time series of Sentinel-2 images. In order to account for the diverse land cover found in urban areas, the model incorporated three types of input variables: average (mean) and standard deviation (std) values within a 30-meter grid from 10 m resolution of optical indices from Sentinel-2, and fractional coverage for distinct land cover classes within 30 m grids from the existing level 3 land cover map. Four schemes with different combinations of input variables were compared. Notably, when all three factors (i.e., mean, std, and fractional cover) were used to consider the variation of landcover in urban areas(Scheme 4, S4), the machine learning model exhibited improved performance compared to using only the mean of optical indices (Scheme 1). Of the various models proposed, the random forest (RF) model with S4 demonstrated the most remarkable performance, achieving R² of 0.8196, and mean absolute error (MAE) of 0.0749, and a root mean squared error (RMSE) of 0.1022. The std variable exhibited the highest impact on model outputs within the heterogeneous land covers based on the variable importance analysis. This trained RF model with S4 was then applied to the entire Suwon region, consistently delivering robust results with an R² of 0.8702, MAE of 0.0873, and RMSE of 0.1335. The FTC estimation method developed in this study is expected to offer advantages for application in various regions, providing fundamental data for a better understanding of carbon dynamics in urban ecosystems in the future.

• The Journal of Korean Society for Radiation Therapy
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• v.28 no.2
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• pp.179-186
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• 2016

Purpose : To figure out if the treatment plan for rectum, bladder and prostate that have a lot of interfraction errors satisfies dosimetric limits without adaptive plan by analyzing MR image. Materials and Methods : This study was based on 5 prostate cancer patients who had IMRT(total dose: 70Gy) Using ViewRay MRIdian System(ViewRay, ViewRay Inc., Cleveland, OH, USA) The treatment plans were made on the same CT images to compare with the plan quality according to adaptive plan, and the Eclipse(Ver 10.0.42, Varian, USA) was used. After registrate the 5 treatment MR images to the CT images for treatment plan to analyze the interfraction changes of organ, we measured the dose volume histogram and the changes of the absolute volume for each organ by appling the first treatment plan to each image. Over 5 fractions, the total dose for PTV was $V_{36.25}$ Gy $${\geq_-}$$ 95%. To confirm that the prescription dose satisfies the SBRT dose limit for prostate, we measured $V_{100%}$, $V_{95%}$, $V_{90%}$ for CTV and $V_{100%}$, $V_{90%}$, $V_{80%}$ $V_{50%}$ of rectum and bladder. Results : All dose average value of CTV, rectum and bladder satisfied dose limit, but there was a case that exceeded dose limit more than one after analyzing the each image of treatment. After measuring the changes of absolute volume comparing the MR image of the first treatment plan with the one of the interfraction treatment, the difference values were maximum 1.72 times at rectum and maximum 2.0 times at bladder. In case of rectum, the expected values were planned under the dose limit, on average, $V_{100%}=0.32%$, $V_{90%}=3.33%$, $V_{80%}=7.71%$, $V_{50%}=23.55%$ in the first treatment plan. In case of rectum, the average of absolute volume in first plan was 117.9 cc. However, the average of really treated volume was 79.2 cc. In case of CTV, the 100% prescription dose area didn't satisfy even though the margin for PTV was 5 mm because of the variation of rectal and bladder volume. Conclusion : There was no case that the value from average of five fractions is over the dosimetric limits. However, dosimetric errors of rectum and bladder in each fraction was significant. Therefore, the precise delivery is needed in case of prostate SBRT. The real-time tracking and adaptive plan is necessary to meet the precision delivery.

• Journal of radiological science and technology
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• v.39 no.4
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• pp.607-614
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• 2016

The study investigates the necessity of 3 dimensional dose distribution evaluation instead of point dose and 2 dimensional dose distribution evaluation. Treatment plans were generated on the RANDO phantom to measure the precise dose distribution of the treatment site 0.5, 1, 1.5, 2, 2.5, 3 cm with the prescribed dose; 1,200 cGy, 5 fractions. Gamma analysis (3%/3 mm, 2%/2 mm) of dose distribution was evaluated with gafchromic EBT2 film and ArcCHECK phantom. The average error of absolute dose was measured at $0.76{\pm}0.59%$ and $1.37{\pm}0.76%$ in cheese phantom and ArcCHECK phantom respectively. The average passing ratio for 3%/3 mm were $97.72{\pm}0.02%$ and $99.26{\pm}0.01%$ in gafchromic EBT2 film and ArcCHECK phantom respectively. The average passing ratio for 2%/2 mm were $94.21{\pm}0.02%$ and $93.02{\pm}0.01%$ in gafchromic EBT2 film and ArcCHECK phantom respectively. There was a more accurate dose distribution of 3D volume phantom than cheese phantom in patients DQA using tomotherapy. Therefor it should be evaluated simultaneously 3 dimensional dose evaluation on target and peripheral area in rotational radiotherapy such as tomotherapy.

• The Korean Journal of Applied Statistics
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• v.26 no.5
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• pp.835-845
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• 2013

Travel-time is considered the most typical and preferred traffic information for intelligent transportation systems(ITS). This paper proposes a real-time travel-time prediction method for a national highway. In this paper, the K-nearest neighbor(KNN) method is used for travel time prediction. The KNN method (a nonparametric method) is appropriate for a real-time traffic management system because the method needs no additional assumptions or parameter calibration. The performances of various models are compared based on mean absolute percentage error(MAPE) and coefficient of variation(CV). In real application, the analysis of real traffic data collected from Korean national highways indicates that the proposed model outperforms other prediction models such as the historical average model and the Kalman filter model. It is expected to improve travel-time reliability by flexibly using travel-time from the proposed model with travel-time from the interval detectors.

• Journal of Korean Society on Water Environment
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• v.35 no.6
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• pp.580-588
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• 2019

The flow of the middle and downstream of the Yeongsan River is stagnant by two weirs of Seungchon and Juksan and the estuary dam and maintained in freshwater. In this study, the Environmental Fluid Dynamics Code-National Institute of Environment Research(EFDC-NIER) model was applied to the Yeongsan River to simulate water flow, temperature, and salinity stratification. The EFDC-NIER model is an improved model which can simulate multi-functional weirs operation, multiple algal species, and the vertical movement mechanism of algal based on the EFDC model. The simulation results for the water level, water temperature, velocity, and salinity reproduced the observed values well. The mean absolute error(MAE) of the model calibration in the annual variations of the water level was 0.1-0.3 m, water temperature was 0.8-1.7 ℃, velocity was 4.5-7.1 cm/sec, and salinity was 1.5 psu, respectively. In the case of scenario simulation for the full opening of the estuary dam, the water level of the estuary dam was directly impacted by the tide so it was predicted to rise - 1.35 m to 0.2 m on average sea level. The velocity was also predicted to increase from 2.7 cm/sec to 50.8 cm/sec, and the flow rate to increase from 53 ㎥/sec to 5,322 ㎥/sec.

• Journal of Broadcast Engineering
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• v.22 no.5
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• pp.608-617
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• 2017

Methods of contrast enhancement have problem such as side effect of over-enhancement with non-gaussian histogram distribution, tradeoff enhancement efficiency against brightness preserving. In order to enhance contrast at various histogram distribution, segmentation to region with gaussian distribution and then enhance contrast each region. First, we segment an image into several regions using GMM(Gaussian Mixture Model)fitting by that k-mean clustering and EM(Expectation-Maximization) in $L^*a^*b^*$ color space. As a result region segmentation, we get the region map and probability map. Then we apply local contrast enhancement algorithm that mean shift to minimum overlapping of each region and preserve brightness histogram equalization. Experiment result show that proposed region based contrast enhancement method compare to the conventional method as AMBE(AbsoluteMean Brightness Error) and AE(Average Entropy), brightness is maintained and represented detail information.

• Journal of Korea Water Resources Association
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• v.52 no.12
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• pp.963-973
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• 2019

This study reviews a urban flooding risk criteria estimation model to predict risk criteria in areas where flood risk criteria are not precalculated by using watershed characteristic data and limit rainfall based on damage history. The risk criteria estimation model was designed using Support Vector Machine, one of the machine learning algorithms. The learning data consisted of regional limit rainfall and watershed characteristic. The learning data were applied to the SVM algorithm after normalization. We calculated the mean absolute error and standard deviation using Leave-One-Out and K-fold cross-validation algorithms and evaluated the performance of the model. In Leave-One-Out, models with small standard deviation were selected as the optimal model, and models with less folds were selected in the K-fold. The average accuracy of the selected models by rainfall duration is over 80%, suggesting that SVM can be used to estimate flooding risk criteria.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.8
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• pp.18-26
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• 2015

Measuring the heart rate during exercise is important to properly control the amount of exercise. With the recent advent of smart device usage, there is a dramatic increase in interest in devices for the real-time measurement of the heart rate during exercise. During intensive exercise, accurate heart rate estimation from wrist-type photoplethysmography (PPG) signals is a very difficult problem due to motion artifact (MA). In this study, we propose an efficient algorithm for an accurate estimation of the heart rate from wrist-type PPG signals. For the twelve data sets, the proposed algorithm achieves the average absolute error of 1.38 beat per minute (BPM) and the Pearson correlation between the estimates and the ground-truth of heart rate was 0.9922. The proposed algorithm presents the strengths in an accurate estimation together with a fast computation speed, which is attractive in application to wearable devices.

• Journal of Korea Water Resources Association
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• v.44 no.3
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• pp.221-230
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• 2011

The climate change has been observed in Korea as well as in the entire world recently. The rainstorm has been gradually increased and then the damage has been grown. It is getting important to predict short-term rainfall. The Korea Meteorological Administration (KMA) generates numerical model outputs which are computed by Global Data Assimilation and Prediction System (GDAPS) and Regional Data Assimilation and Prediction System (RDAPS). The KMA predicts rainfall using RDAPS results. RDAPS model generates 48 hours data which is organized 3 hours data accumulated at 00UTC and 12UTC. RDAPS results which are organized 3 hours time scale are converted into daily rainfall to compare observed daily rainfall. In this study, 9 cases are applied to convert RDAPS results to daily rainfall data. The MAP (mean areal precipitation) in Geum river basin are computed by using KMA which are 2005 are used. Finally, the best case which gives the close value to the observed rainfall data is obtained using the average absolute relative error (AARE) especially for the Geum River basin.