• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.2
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• pp.123-129
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• 2017

This paper proposes a new nonnegative matrix factorization (NMF) based direction-of-arrival (DOA) estimation method for multiple sound sources using a dual microphone array. First of all, sound signals coming from the dual microphone array are segmented into consecutive analysis frames, and a steered-response power phase transform (SRP-PHAT) beamformer is applied to each frame so that stereo signals of each frame are represented in a time-direction domain. The time-direction outputs of SRP-PHAT are stored for a pre-defined number of frames, which is referred to as a time-direction block. Next, In order to estimate DOAs robust to noise, each time-direction block is normalized along the time by using a block subtraction technique. After that, an unsupervised NMF method is applied to the normalized time-direction block in order to cluster the directions of each sound source in a multiple sound source environments. In particular, the activation and basis matrices are used to estimate the number of sound sources and their DOAs, respectively. The DOA estimation performance of the proposed method is evaluated by measuring a mean absolute error (MAE) and the standard deviation of errors between the oracle and estimated DOAs under a three source condition, where the sources are located in [$-35{\circ}$, 5m], [$12{\circ}$, 4m], and [$38{\circ}$, 4.m] from the dual microphone array. It is shown from the experiment that the proposed method could relatively reduce MAE by 56.83%, compared to a conventional SRP-PHAT based DOA estimation method.

• Journal of Korean Society for Geospatial Information Science
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• v.24 no.1
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• pp.81-87
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• 2016

It is important to analyze the exact damage information for fast recovery when natural disasters cause damage on river-side facilities such as dams, bridges, embankments etc. In this study, we shows the method to effectively damage analysis plan using UAV(Unmanned aerial vehicle) images and accuracy assessment of it. The UAV images are captured on area near the river-side facilities and the core methodology for damage analysis are image matching and change detection algorithm. The result(point cloud) from image matching is to construct 3-dimensional data using by 2-dimensional images, it extracts damage areas by comparing the height values on same area with reference data. The results are tested absolute locational precision compared by post-processed aerial LiDAR data named reference data. The assessment analysis test shows our matching results 10-20 centimeter level precision if external orientation parameters are very accurate. This study shows suggested method is very useful for damage analysis in a large size structure like river-side facilities. But the complexity building can't apply this method, it need to the other method for damage analysis.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1013-1027
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• 2021

Images taken with KOMPSAT-3 have additional NIR and PAN bands, as well as RGB regions of the visible ray band, compared to imagestaken with a standard camera. Furthermore, electrical and optical properties must be considered because a wide radius area of approximately 17 km or more is photographed at an altitude of 685 km above the ground. In other words, the camera sensor of KOMPSAT-3 is distorted by each CCD pixel, characteristics of each band,sensitivity and time-dependent change, CCD geometry. In order to solve the distortion, correction of the sensors is essential. In this paper, we propose a method for detecting uniform regions in side-slider-based KOMPSAT-3 images using segment-based noise analysis. After detecting a uniform area with the corresponding algorithm, a correction table was created for each sensor to apply the non-uniformity correction algorithm, and satellite image correction was performed using the created correction table. As a result, the proposed method reduced the distortion of the satellite image,such as vertical noise, compared to the conventional method. The relative radiation accuracy index, which is an index based on mean square error (RA) and an index based on absolute error (RE), wasfound to have a comparative advantage of 0.3 percent and 0.15 percent, respectively, over the conventional method.

• 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.

• Progress in Medical Physics
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• v.20 no.1
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• pp.43-50
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• 2009

The 4 bank mico-MLC (mMLC; Acculeaf, Direx, Isral) has been commissioned for clinical use of linac based stereotactic radiosurgery. The geometrical parameters to control the leaves were determined and comparisons between measured and calculated by the calculation model were performed in terms of absolute dose (cGy/100 MU). As a result of evaluating calculated dose for various field sizes and depths of 5 and 10 cm in water in the geometric condition of fixed SSD (source to surface distance) and fixed SCD (source to chamber distance), most of differences were within 1% for 6 MV and 15 MV x-rays. The penumbral widths at the isocenter were approximately evaluated to 0.29~0.43 cm depending on the field size for 6 MV and 0.36~0.51 cm for 15 MV x-rays. The average transmission and leakage for 6 MV and 15 MV x-rays were 6.6% and 7.4% respectively in single level of leaves fully closed. In case of dual level of leaves fully closed the measured transmission is approximately 0.5% for both 6 MV and 15 MV x-rays. Through the commissiong procedure we could verify the dose characteristics of mMLC and approximately evaluate the error ranges for treatment planning system.

• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.85-98
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• 2016

This study makes an empirical comparison of various realized volatilities (RVs) in terms of overnight returns. In financial asset markets, during overnight or holidays, no or few trading data are available causing a difficulty in computing RVs for a whole span of a day. A review will be made on several RVs reflecting overnight return variations. The comparison is made for forecast accuracies of several RVs for some financial assets: the US S&P500 index, the US NASDAQ index, the KOSPI (Korean Stock Price Index), and the foreign exchange rate of the Korea won relative to the US dollar. The RV of a day is compared with the square of the next day log-return, which is a proxy for the integrated volatility of the day. The comparison is made by investigating the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE). Statistical inference of MAE and RMSE is made by applying the model confidence set (MCS) approach and the Diebold-Mariano test. For the three index data, a specific RV emerges as the best one, which addresses overnight return variations by inflating daytime RV.

• Korean Journal of Construction Engineering and Management
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• v.15 no.4
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• pp.150-158
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• 2014

A bunch of asphalt roads have been damaged frequently in relation to the rapid climate change. To solve and prevent this type of problems, many nationalities in the world have performed various researches. In this regard, the objective of this study is to develop prediction model as to the number of potholes occurred in seoul. At the same time, we have utilized empirical and statistical approaches in order for us to identify factors which is affecting the actual occurrence. The predictive model was determinded by using BHS (Basic Harmony Search) algorithm. Prediction was based on the weather and traffic data as well as data occurrence data of porthole. To assess the influences which are PAR(Pitch Adjusting Rate) and HMCR(Harmony Memory Considering Rate), we determined suitability by changing the values. In the process of the determining a predictive model, the predictive model composed Training data (2011, 2012 and 2013yrs data). To determine the suitability of the model, we have utilized Testing Set (2009 and 2010 yrs data). The suitability of the basic prediction model has been from RMSE(Root Mean Squared Error), MAE(Mean Absolute Error) and Coefficient of determination.

• Journal of Korea Water Resources Association
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• v.56 no.8
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• pp.471-484
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• 2023

Deep learning models based on generative adversarial neural networks are specialized in generating new information based on learned information. The deep generative models (DGMR) model developed by Google DeepMind is an generative adversarial neural network model that generates predictive radar images by learning complex patterns and relationships in large-scale radar image data. In this study, the DGMR model was trained using radar rainfall observation data from the Ministry of Environment, and rainfall prediction was performed using an generative adversarial neural network for a heavy rainfall case in August 2021, and the accuracy was compared with existing prediction techniques. The DGMR generally resembled the observed rainfall in terms of rainfall distribution in the first 60 minutes, but tended to predict a continuous development of rainfall in cases where strong rainfall occurred over the entire area. Statistical evaluation also showed that the DGMR method is an effective rainfall prediction method compared to other methods, with a critical success index of 0.57 to 0.79 and a mean absolute error of 0.57 to 1.36 mm in 1 hour advance prediction. However, the lack of diversity in the generated results sometimes reduces the prediction accuracy, so it is necessary to improve the diversity and to supplement it with rainfall data predicted by a physics-based numerical forecast model to improve the accuracy of the forecast for more than 2 hours in advance.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.6
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• pp.536-542
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• 2023

Satellites represent cutting-edge technology, of ering significant advantages in spatial and temporal observations. National agencies worldwide harness satellite data to respond to marine accidents and analyze ocean fluctuations effectively. However, challenges arise with high-resolution satellite-based sea surface temperature data (Operational Sea Surface Temperature and Sea Ice Analysis, OSTIA), where gaps or empty areas may occur due to satellite instrumentation, geographical errors, and cloud cover. These issues can take several hours to rectify. This study addressed the issue of missing OSTIA data by employing LaMa, the latest deep learning-based algorithm. We evaluated its performance by comparing it to three existing image processing techniques. The results of this evaluation, using the coefficient of determination (R²) and mean absolute error (MAE) values, demonstrated the superior performance of the LaMa algorithm. It consistently achieved R² values of 0.9 or higher and kept MAE values under 0.5 ℃ or less. This outperformed the traditional methods, including bilinear interpolation, bicubic interpolation, and DeepFill v1 techniques. We plan to evaluate the feasibility of integrating the LaMa technique into an operational satellite data provision system.

• Radiation Oncology Journal
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• v.25 no.4
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• pp.249-260
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• 2007

Purpose: In order to enhance the quality of IMRT as employed in Korea, we developed a remote monitoring system. The feasibility of the system was evaluated by conducting a pilot study. Materials and Methods: The remote monitoring system consisted of a head and neck phantom and a user manual. The phantom contains a target and three OARs (organs at risk) that can be detected on CT images. TLD capsules were inserted at the center of the target and at the OARs. Two film slits for GafchromicEBT film were located on the axial and saggital planes. The user manual contained an IMRT planning guide and instructions for IMRT planning and the delivery process. After the manual and phantom were sent to four institutions, IMRT was planed and delivered. Predicted doses were compared with measured doses. Dose distribution along the two straight lines that intersected at the center of the axial film was measured and compared with the profiles predicted by the plan. Results: The measurements at the target agreed with the predicted dose within a 3% deviation. Doses at the OARs that represented the thyroid glands showed larger deviations (minimum 3.3% and maximum 19.8%). The deviation at OARs that represented the spiral cord was $0.7{\sim}1.4%$. The percentage of dose distributions that showed more than a 5% of deviation on the lines was $7{\sim}27%$ and $7{\sim}14%$ along the horizontal and vertical lines, respectively. Conculsion: Remote monitoring of IMRT using the developed system was feasible. With remote monitoring, the deviation at the target is expected to be small while the deviation at the OARs can be very large. Therefore, a method that is able to investigate the cause of a large deviation needs to be developed. In addition, a more clinically relevant measure for the two-dimensional dose comparison and pass/fail criteria need to be further developed.