• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.9
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• pp.126-132
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• 2016

This study numerically investigates the characteristics of chemical reactions and thermal deformation in a steam reformer. These phenomena are significantly affected by the high-temperature burner gas and the process gas conditions. Because the high temperature of the burner gas ranges from 800 to 1000 K, the reformer tubes undergo substantial thermal deformation, eventually resulting in structural failure. Thus, it is necessary to understand the characteristics of the reaction and thermal deformation under the operating conditions to evaluate the reformer tubes for sustainable, stable operation. Extensive numerical simulations were carried out using commercial CFD code (ANSYS FLUENT/MECHANICA Ver. 13.0) while considering three-dimensional turbulent flows and combined heat transfer including conduction, convection, and radiation. Structural analysis considering conjugated heat transfer between solid tubes and fluid flows was conducted using the Fluid-Solid Interaction (FSI) method. The results show that when the injection temperature of the process gas and burner gas decreased, the hydrogen production rate decreased significantly, and thermal deformation decreased by at least 15 to 20%.

• Nuclear Engineering and Technology
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• v.45 no.4
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• pp.513-522
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• 2013

The ODSCC detected in the TSP position of Ulchin 3&4 SGs are typical ODSCC of Alloy 600MA tubes. The causative chemical environment is formed by concentration of impurities inside the occluded region formed by the tube surface, egg crate strips, and sludge deposit there. Most cracks are detected at or near the line contacts between the tube surface and the egg crate strips. The region of dense crack population, as defined as between $4^{th}$ and $9^{th}$ TSPs, and near the center of hot leg hemisphere plane, coincided well with the region of preferential sludge deposition as defined by thermal hydraulics calculation using SGAP computer code. The cracks developed homogeneously in a wide range of SGs, so that the number of cracks detected each outage increased very rapidly since the first detection in the $8^{th}$ refueling outage. The root cause assessment focused on investigation of the difference in microstructure and manufacturing residual stress in order to reveal the cause of different susceptibilities to ODSCC among identical six units. The manufacturing residual stress as measured by XRD on OD surface and by split tube method indicated that the high residual stress of Alloy 600MA tube played a critical role in developing ODSCC. The level of residual stress showed substantial variations among the six units depending on details of straightening and OD grinding processes. Youngwang 3&4 tubes are less susceptible to ODSCC than U3 and U4 tubes because semi-continuous coarse chromium carbides are formed along the grain boundary of Y3&4 tubes, while there are finer less continuous chromium carbides in U3 and U4. The different carbide morphology is caused by the difference in cooling rate after mill anneal. There is a possibility that high chromium content in the Y3&4 tubes, still within the allowable range of Alloy 600, has made some contribution to the improved resistance to ODSCC. It is anticipated that ODSCC in Y5&6 SGs will be retarded more considerably than U3 SGs since the manufacturing residual stress in Y5&6 tubes is substantially lower than in U3 tubes, while the microstructure is similar with each other.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.126-136
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• 2018

The purpose of this study was to develop a severity-adjustment model for predicting mortality in acute stroke patients using machine learning. Using the Korean National Hospital Discharge In-depth Injury Survey from 2006 to 2015, the study population with disease code I60-I63 (KCD 7) were extracted for further analysis. Three tools were used for the severity-adjustment of comorbidity: the Charlson Comorbidity Index (CCI), the Elixhauser comorbidity index (ECI), and the Clinical Classification Software (CCS). The severity-adjustment models for mortality prediction in patients with acute stroke were developed using logistic regression, decision tree, neural network, and support vector machine methods. The most common comorbid disease in stroke patients were hypertension, uncomplicated (43.8%) in the ECI, and essential hypertension (43.9%) in the CCS. Among the CCI, ECI, and CCS, CCS had the highest AUC value. CCS was confirmed as the best severity correction tool. In addition, the AUC values for variables of CCS including main diagnosis, gender, age, hospitalization route, and existence of surgery were 0.808 for the logistic regression analysis, 0.785 for the decision tree, 0.809 for the neural network and 0.830 for the support vector machine. Therefore, the best predictive power was achieved by the support vector machine technique. The results of this study can be used in the establishment of health policy in the future.

• Journal of Korean Neurosurgical Society
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• v.62 no.4
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• pp.442-449
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• 2019

Objective : Bone mineral density (BMD) is an important consideration during fusion surgery. Although dual X-ray absorptiometry is considered as the gold standard for assessing BMD, quantitative computed tomography (QCT) provides more accurate data in spine osteoporosis. However, QCT has the disadvantage of additional radiation hazard and cost. The present study was to demonstrate the utility of artificial intelligence and machine learning algorithm for assessing osteoporosis using Hounsfield units (HU) of preoperative lumbar CT coupling with data of QCT. Methods : We reviewed 70 patients undergoing both QCT and conventional lumbar CT for spine surgery. The T-scores of 198 lumbar vertebra was assessed in QCT and the HU of vertebral body at the same level were measured in conventional CT by the picture archiving and communication system (PACS) system. A multiple regression algorithm was applied to predict the T-score using three independent variables (age, sex, and HU of vertebral body on conventional CT) coupling with T-score of QCT. Next, a logistic regression algorithm was applied to predict osteoporotic or non-osteoporotic vertebra. The Tensor flow and Python were used as the machine learning tools. The Tensor flow user interface developed in our institute was used for easy code generation. Results : The predictive model with multiple regression algorithm estimated similar T-scores with data of QCT. HU demonstrates the similar results as QCT without the discordance in only one non-osteoporotic vertebra that indicated osteoporosis. From the training set, the predictive model classified the lumbar vertebra into two groups (osteoporotic vs. non-osteoporotic spine) with 88.0% accuracy. In a test set of 40 vertebrae, classification accuracy was 92.5% when the learning rate was 0.0001 (precision, 0.939; recall, 0.969; F1 score, 0.954; area under the curve, 0.900). Conclusion : This study is a simple machine learning model applicable in the spine research field. The machine learning model can predict the T-score and osteoporotic vertebrae solely by measuring the HU of conventional CT, and this would help spine surgeons not to under-estimate the osteoporotic spine preoperatively. If applied to a bigger data set, we believe the predictive accuracy of our model will further increase. We propose that machine learning is an important modality of the medical research field.

• Journal of radiological science and technology
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• v.44 no.4
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• pp.359-365
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• 2021

A heavy particle accelerator is a device that accelerates particles using high energy and is used in various fields such as medical and industrial fields as well as research. However, secondary neutrons and particle fragments are generated by the high-energy particle beam, and among them, the neutrons do not have an electric charge and directly interact with the nucleus to cause radiation of the material. Quantitative evaluation of the radioactive material produced in this way is necessary, but there are many difficulties in actual measurement during or after operation. Therefore, this study compared and evaluated the generated radioactive material in the concrete shield for protons and carbon ions of specific energy by using the simulation code FLUKA. For the evaluation of each energy of proton beam and carbon ion, the reliability of the source term was secured within 2% of the relative error with the data of the NASA Space Radiation Laboratory(NSRL), which is an internationally standardized data. In the evaluation, carbon ions exhibited higher neutron flux than protons. Afterwards, in the evaluation of radioactive materials under actual operating conditions for disposal, a large amount of short-lived beta-decay nuclides occurred immediately after the operation was terminated, and in the case of protons with a high beam speed, more radioactive products were generated than carbon ions. At this time, radionuclides of ⁴⁴Sc, ³H and ²²Na were observed at a high rate. In addition, as the cooling time elapsed, the ratio of long-lived nuclides increased. For nonparticulate radionuclides, ³H, ²²Na, and for particulate radionuclides, ⁴⁴Ti, ⁵⁵Fe, ⁶⁰Co, ¹⁵²Eu, and ¹⁵⁴Eu nuclides showed a high ratio. In this study, it is judged that it is possible to use the particle accelerator as basic data for facility maintenance, repair and dismantling through the prediction of radioactive materials in concrete according to the cooling time after operation and termination of operation.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.6
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• pp.583-592
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• 2020

This study assessed source depletion in the vadose zones of contaminated sites. The possible range of infiltration rate in Korea was statistically analyzed. The results showed a trend of decreasing leachate concentration of 13 pollutants used for risk assessment. Among them, benzene, ethylbenzene, toluene, and xylene showed a lower leachate concentration in groundwater over time due to their low distribution coefficient and also possible biodegradation effects. The average values of the relative concentration could be taken as a default index due to a very small range of uncertainties. In the case of heavy metals, it was shown that the leachate concentration in a pollutant does not decrease over time. Considering the annually different infiltration, a site-specific source-depletion scenario was applied to Cheongju in North Chungcheong Province. The result was expressed as a time series of the relative concentration of the leachate concentration, and this was compared to the trend by averaged Korean infiltration. Finally, an open-source code that used Python was used to help calculate the leachate concentration by this site-specific infiltration scenario.

• Journal of Cadastre & Land InformatiX
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• v.52 no.1
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• pp.105-118
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• 2022

The demand for information related to 3D spatial objects model in metaverse, smart cities, digital twins, autonomous vehicles, urban air mobility will be increased. 3D model construction for spatial objects is possible with various equipments such as satellite-, aerial-, ground platforms and technologies such as modeling, artificial intelligence, image matching. However, it is not easy to quickly detect and convert spatial objects that need updating. In this study, based on spatial information (features) and attributes, using matching elements such as address code, number of floors, building name, and area, the converged building DB and the detected building DB are constructed. Both to support above and to verify the suitability of object selection that needs to be updated, one system prototype was developed. When constructing the converged building DB, the convergence of spatial information and attributes was impossible or failed in some buildings, and the matching rate was low at about 80%. It is believed that this is due to omitting of attributes about many building objects, especially in the pilot test area. This system prototype will support the establishment of an efficient drone shooting plan for the rapid update of 3D spatial objects, thereby preventing duplication and unnecessary construction of spatial objects, thereby greatly contributing to object improvement and cost reduction.

• Journal of Internet Computing and Services
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• v.24 no.4
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• pp.25-36
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• 2023

Today, as AI (Artificial Intelligence) technology is introduced in various fields, including security, the development of technology is accelerating. However, with the development of AI technology, attack techniques that cleverly bypass malicious behavior detection are also developing. In the classification process of AI models, an Adversarial attack has emerged that induces misclassification and a decrease in reliability through fine adjustment of input values. The attacks that will appear in the future are not new attacks created by an attacker but rather a method of avoiding the detection system by slightly modifying existing attacks, such as Adversarial attacks. Developing a robust model that can respond to these malware variants is necessary. In this paper, we propose two methods of generating Adversarial attacks as efficient Adversarial attack generation techniques for improving Robustness in AI models. The proposed technique is the XAI-based attack technique using the XAI technique and the Reference based attack through the model's decision boundary search. After that, a classification model was constructed through a malicious code dataset to compare performance with the PGD attack, one of the existing Adversarial attacks. In terms of generation speed, XAI-based attack, and reference-based attack take 0.35 seconds and 0.47 seconds, respectively, compared to the existing PGD attack, which takes 20 minutes, showing a very high speed, especially in the case of reference-based attack, 97.7%, which is higher than the existing PGD attack's generation rate of 75.5%. Therefore, the proposed technique enables more efficient Adversarial attacks and is expected to contribute to research to build a robust AI model in the future.

• Structural Engineering and Mechanics
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• v.81 no.2
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• pp.243-258
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• 2022

In this paper, tensile behavior of joint filling has been investigated under experimental test and numerical simulation (particle flow code). Two concrete slabs containing semi cylinder hole were prepared. These slabs were attached to each other by glue and one cubic specimen with dimension of 19 cm×15 cm×6 cm was prepared. This sample placed in the universal testing machine where the direct tensile stress can be applied to this specimen by implementing a special type of load transferring device which converts the applied compressive load to that of the tensile during the test. In the present work, two different joint filling thickness i.e., 3 mm and 6 mm were prepared and tested in the laboratory to measure their direct tensile strengths. Concurrent with experimental test, numerical simulation was performed to investigate the effect of hole diameter, length of edge notch, filling thickness and filling length on the tensile behavior of joint filling. Model dimension was 19 cm×15 cm. hole diameter was change in four different values of 2.5 cm, 5 cm, 7.5 cm and 10 cm. glue lengths were different based on the hole diameter, i.e., 12.5 cm for hole diameter of 2.5 cm, 10 cm for hole diameter of 5 cm, 7.5 cm for hole diameter of 7.5 cm and 5 cm for hole diameter of 10 cm. length of edge notch were changed in three different value i.e., 10%, 30% and 50% of glue length. Filling thickness were changed in three different value of 3 mm, 6 mm and 9 mm. Tensile strengths of glue and concrete were 2.37 MPa and 6.4 MPa, respectively. The load was applied at a constant rate of 1 kg/s. Results shows that hole diameter, length of edge notch, filling thickness and filling length have important effect on the tensile behavior of joint filling. In fixed glue thinks and fixed joint length, the tensile strength was decreased by increasing the hole diameter. Comparing the results showed that the strength, failure mechanism and fracture patterns obtained numerically and experimentally were similar for both cases.

• The Journal of Korean Medicine
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• v.44 no.2
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• pp.20-33
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• 2023

Objectives: This study is designed to statistically analyze the features of patients who visited to Department of Acupuncture and Moxibustion at OO Korean Medicine Hospital for 3 years. Methods: This study retrospectively analyzed the medical records of patients at Department of Acupuncture and Moxibustion at OO Korean Medicine Hospital using IBM SPSS ver. 27.0 for Windows. Results: 1. In the analysis of the total number of patients, those in their 30s accounted for the highest percentage, male patients had a higher rate. 2. In the analysis of KCD-8 code, spinal stenosis and fractures in areas other than the spine were the most common in the 70s and older, and cervical sprains were the most common in other. 3. In the analysis of the number of hospitalizations, patients classified as illness or higher age had more re-hospitalization than patients classified as injury or lower age in first-time care. 4. In the analysis of hospitalization duration, patients classified as illness, female or higher age had longer hospitalization duration than patients classified as accident, male or lower age in first-time care. Conclusion: We expect that the results of this study would be used as reference materials for analyzing medical consumption condition of Department of Acupuncture and Moxibustion.