• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2013.06a
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• pp.274-277
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• 2013

CT(Computed Tomography)영상에서 선량과 화질은 중요한 요소이다. 선량은 환자에게 직접적으로 악영향을 끼치는 요소이며, 화질은 환자의 병변을 판단하는데 매우 중요하게 작용한다. 반복적 재구성 알고리즘을 이용하면 저선량 영상에서도 고화질의 영상을 얻을 수 있는지 FBP와 정량적, 정성적으로 비교하였다. 촬영 프로토콜은 관전압 80, 100, 120kVp에서 관전류를 동일하게 200mA로 촬영하여 획득하였으며, 정량적 평가를 위해 SD(Standard Deviation), SNR(Signal to Noise Ratio), MTF(Modulation Transfer Function)를 측정하여 분석하였다. 선량은 80kVp일 때 가장 낮았으며, 120kVp일 때 가장 높았다. 80kVp의 영상을 Toshiba 사(社)의 AIDR 3D(Adaptive Iterative Reduction integrated into $^{SURE}Exposure$)로 재구성하고, 120kVp의 영상에 FBP로 재구성한 다음 정량적 비교를 한 결과 AIDR 3D를 적용한 영상의 SD가 낮게 나왔으며, SNR이 높게 나타났고, MTF 곡선은 유사하게 나타났다. 그리고 FWHM(Full Width at Half Maximum) 값의 오차가 거의 없었다. 결론적으로 AIDR 3D는 저선량에서도 높은 화질을 나타냄을 확인하였다.

• Journal of Satellite, Information and Communications
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• v.10 no.3
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• pp.114-120
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• 2015

In this paper, we show the design requirements of the cryptographic module and its security algorithm designed to prevent the exposure of the command signal applied to Flight Termination System. The cryptographic module consists of two separate devices that are Command Insertion Device and Command Generation Device. The cryptographic module designed to meet the 3 principles(Confidentiality, Integrity and Availability) for the information security. AES-256 block encryption algorithm and SHA-256 Hash function were applied to the encrypted symmetric key encryption method. The proposed cryptographic module is expected to contribute to the security and reliability of the Flight Termination System for Space Launch Vehicle.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.31 no.7
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• pp.515-520
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• 2018

A large number of lithium-ion batteries are arranged in series and parallel in battery packs, such as those in electric vehicles or energy storage systems. As battery packs age, their output power and energy density drop because of voltage deviation, constant and non-uniform exposure to abnormal environments, and increased contact resistance between batteries; this reduces application system efficiency. Despite the balancing circuit and logic of the battery management system, the output of the battery pack is concentrated in the most severely aged unit cell and the output is frequently limited by power derating. In this study, we implemented a cell imbalance detection algorithm and selected parameters to detect a sudden decrease in battery pack output. In addition, we propose a method to increase efficiency by applying the measured testing values considering the operating conditions and abnormal conditions of the battery pack.

• Nuclear Engineering and Technology
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• v.51 no.3
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• pp.723-730
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• 2019

Acquiring instrumentation signals generated from nuclear power plants (NPPs) is essential to maintain nuclear reactor integrity or to mitigate an abnormal state under normal operating conditions or severe accident circumstances. However, various safety-critical instrumentation signals from NPPs cannot be accurately measured on account of instrument degradation or failure under severe accident circumstances. Reactor vessel (RV) water level, which is an accident monitoring variable directly related to reactor cooling and prevention of core exposure, was predicted by applying a few signals to deep neural networks (DNNs) during severe accidents in NPPs. Signal data were obtained by simulating the postulated loss-of-coolant accidents at hot- and cold-legs, and steam generator tube rupture using modular accident analysis program code as actual NPP accidents rarely happen. To optimize the DNN model for RV water level prediction, a genetic algorithm was used to select the numbers of hidden layers and nodes. The proposed DNN model had a small root mean square error for RV water level prediction, and performed better than the cascaded fuzzy neural network model of the previous study. Consequently, the DNN model is considered to perform well enough to provide supporting information on the RV water level to operators.

• Journal of Nutrition and Health
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• v.55 no.1
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• pp.1-9
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• 2022

In the past few decades, great progress has been made on understanding the interaction between nutrition and health status. But despite this wealth of knowledge, health problems related to nutrition continue to increase. This leads us to postulate that the continuing trend may result from a lack of consideration for intra-individual biological variation on dietary responses. Precision nutrition utilizes personal information such as age, gender, lifestyle, diet intake, environmental exposure, genetic variants, microbiome, and epigenetics to provide better dietary advices and interventions. Recent technological advances in the artificial intelligence, big data analytics, cloud computing, and machine learning, have made it possible to process data on a scale and in ways that were previously impossible. A big data platform is built by collecting numerous parameters such as meal features, medical metadata, lifestyle variation, genome diversity and microbiome composition. Sophisticated techniques based on machine learning algorithm can be used to integrate and interpret multiple factors and provide dietary guidance at a personalized or stratified level. The development of a suitable machine learning algorithm would make it possible to suggest a personalized diet or functional food based on analysis of intra-individual metabolic variation. This novel precision nutrition might become one of the most exciting and promising approaches of improving health conditions, especially in the context of non-communicable disease prevention.

• International conference on construction engineering and project management
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• 2007.03a
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• pp.509-519
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• 2007

Private financing is playing an increasing role in public infrastructure construction projects worldwide. However, private investors/operators are exposed to the financial risk of low profitability due to the inaccurate estimation of facility demand, operation income, maintenance costs, etc. From the operator's perspective, a sound and thorough financial feasibility study is required to establish the appropriate capital structure of a project. Operators tend to reduce the equity amount to minimize the level of risk exposure, while creditors persist to raise it, in an attempt to secure a sufficient level of financial involvement from the operators. Therefore, it is important for creditors and operators to reach an agreement for a balanced capital structure that synthetically considers both profitability and repayment capacity. This paper presents an optimal capital structure model for successful private infrastructure investment. This model finds the optimized point where the profitability is balanced with the repayment capacity, with the use of the concept of utility function and multi-objective GA (Generic Algorithm)-based optimization. A case study is presented to show the validity of the model and its verification. The research conclusions provide a proper capital structure for privately-financed infrastructure projects through a proposed multi-objective model.

• Wind and Structures
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• v.36 no.6
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• pp.355-366
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• 2023

Although machine learning (ML) techniques have been widely used in various fields of engineering practice, their applications in the field of wind engineering are still at the initial stage. In order to evaluate the feasibility of machine learning algorithms for prediction of wind loads on high-rise buildings, this study took the exposure category type, wind direction and the height of local wind force as the input features and adopted four different machine learning algorithms including k-nearest neighbor (KNN), support vector machine (SVM), gradient boosting regression tree (GBRT) and extreme gradient (XG) boosting to predict wind force coefficients of CAARC standard tall building model. All the hyper-parameters of four ML algorithms are optimized by tree-structured Parzen estimator (TPE). The result shows that mean drag force coefficients and RMS lift force coefficients can be well predicted by the GBRT algorithm model while the RMS drag force coefficients can be forecasted preferably by the XG boosting algorithm model. The proposed machine learning based algorithms for wind loads prediction can be an alternative of traditional wind tunnel tests and computational fluid dynamic simulations.

• Korean Journal of Artificial Intelligence
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• v.12 no.1
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• pp.1-6
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• 2024

Recently, various techniques are being applied through the development of medical AI, and research has been conducted on the application of super-resolution AI models. In this study, evaluate the results of the application of the super-resolution AI model to brain CT as the basic data for future research. Acquiring CT images of the brain, algorithm for brain and bone windowing setting, and the resolution was downscaled to 5 types resolution image based on the original resolution image, and then upscaled to resolution to create an LR image and used for network input with the original imaging. The SRCNN model was applied to each of these images and analyzed using PSNR, SSIM, Loss. As a result of quantitative index analysis, the results were the best at 256×256, the brain and bone window setting PSNR were the same at 33.72, 35.2, and SSIM at 0.98 respectively, and the loss was 0.0004 and 0.0003, respectively, showing relatively excellent performance in the bone window setting CT image. The possibility of future studies aimed image quality and exposure dose is confirmed, and additional studies that need to be verified are also presented, which can be used as basic data for the above studies.

• Korean Journal of Digital Imaging in Medicine
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• v.17 no.1
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• pp.13-18
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• 2015

Purpose : Skip the repetitive HRCT axial scan in order to reduce the exposure of patients during chest HRCT scan, Helical Scan Data into a reconstructed image, and exposure of the patient change and visually evaluate the usefulness of the HRCT images. Materials and method : Patients were enrolled in the survey are 50 people who underwent chest CT scans of patients who presented to the hospital from January 2015 to March 2015. 50 people surveyed 22 people men and 28 people women people showed an average distribution of 30 to 80 years age was 48 years. 50 patients to Somatom Sensation 64 ch (Siemens) model with 120 kVp tube voltage to a reference mAs tube current to mAs (Care dose, Siemens) as a whole, including the lungs and the chest CT scan was performed. Scan upon each patient CARE dose 4D (Automatic exposure control, Siemens Medical Solution Erlangen, Germany) was to maintain the proper radiation dose scan every cross-section through a device that automatically adjusts the tube current of. CT scan is the rotation time of the Tube slice collimation, slice width 0.6 mm, pitch factor was made under the terms of 1.4. CT scan obtained after the raw data (raw data) to the upper surface of the axial images and coronal images for each slice thickness 1 mm, 5 mm intervals in the high spatial frequency calculation method (hight spatial resolution algorithm, B60 sharp) was the use of the lung window center -500 HU, windows were reconstructed into images in the interval -1000 HU to see. Result : 1. Measure the total value of DLP 50 patients who proceed to chest CT group A (Helical Scan after scan performed with HRCT) and group B (Helical Scan after the HR image reconstruction to the original data) compared with the group divided, analysis As a result of the age, but show little difference for each age group it had a decreased average dose of about 9%. 2. A Radiation read the results of the two Radiologist and a doctor upper lobe and middle lobe of the lung takes effect the visual evaluation is not a big difference between the two images both, depending on the age of the patient, especially if the blood vessels of the lower lobe (A: 3.4, B: 4.6) and bronchi(A: 3.8, B4.7) image shake caused by breathing in anxiety (blurring lead) to the original data (raw data) showed that the reconstructed image is been more useful in diagnostic terms. Conclusion : Scan was confirmed a continuous, rapid motion video to get Helical scan is much lower lobe lung reduction in visual blurring, Helical scan data to not repeat the examination by obtaining HRCT images reorganization reduced the exposure of the patient.

• Journal of Radiation Protection and Research
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• v.42 no.3
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• pp.158-165
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• 2017

Background: Nuclear facilities in South Korea have generally adopted pressurized ion chambers to measure ambient gamma ray exposure rates for monitoring the impact of radiation on the surrounding environment. The rates assessed with pressurized ion chambers do not distinguish between natural and man-made radiation, so a further step is needed to identify the cause of abnormal variation. In contrast, using NaI(Tl) scintillation detectors to detect gamma energy rates can allow an immediate assessment of the cause of variation through an analysis of the energy spectra. Against this backdrop, this study was conducted to propose a more effective way to monitor ambient gamma exposure rates. Materials and Methods: The following methods were used to analyze gamma energy spectra measured from January to November 2016 with NaI detectors installed at the Korea Atomic Energy Research Institute (KAERI) dormitory and Hanbat University. 1) Correlations of the variation of rates measured at the two locations were determined. 2) The dates, intervals, duration, and weather conditions were identified when rates increased by $5nSv{\cdot}h^{-1}$ or more. 3) Differences in the NaI spectra on normal days and days where rates spiked by $5nSv{\cdot}h^{-1}$ or more were studied. 4) An algorithm was derived for automatically calculating the net variation of the rates. Results and Discussion: The rates measured at KAERI and Hanbat University, located 12 kilometers apart, did not show a strong correlation (coefficient of determination = 0.577). Time gaps between spikes in the rates and rainfall were factors that affected the correlation. The weather conditions on days where rates went up by $5nSv{\cdot}h^{-1}$ or more featured rainfall, snowfall, or overcast, as well as an increase in peaks of the gamma rays emitted from the radon decay products of $^{214}Pb$ and $^{214}Bi$ in the spectrum. This study assumed that $^{214}Pb$ and $^{214}Bi$ exist at a radioactive equilibrium, since both have relatively short half-lives of under 30 minutes. Provided that this assumption is true and that the gamma peaks of the 352 keV and 1,764 keV gamma rays emitted from the radionuclides have proportional count rates, no man-made radiation should be present between the two energy levels. This study proved that this assumption was true by demonstrating a linear correlation between the count rates of these two gamma peaks. In conclusion, if the count rates of these two peaks detected in the gamma energy spectrum at a certain time maintain the ratio measured at a normal time, such variation can be confirmed to be caused by natural radiation. Conclusion: This study confirmed that both $^{214}Pb$ and $^{214}Bi$ have relatively short half-lives of under 30 minutes, thereby existing in a radioactive equilibrium in the atmosphere. If the gamma peaks of the 352 keV and 1,764 keV gamma rays emitted from these radionuclides have proportional count rates, no man-made radiation should exist between the two energy levels.