• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2017.05a
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• pp.1135-1139
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• 2017

Spatial distribution of chemical species in flame is a important indicator understanding the flame structure and combustion characteristics, and optical emission spectroscopy has been widely used for the measurement because of its simple and non-intrusive methodology. In this study, we suggest the feasibility of the measurement of chemical species (OH radical) distribution in rocket plume using optical emission spectrometer which was developed for the spatially resolved measurement along the line-of-sight. In order to predict the ground state concentration of species from the measured emission intensity by optical emission spectrometer, we consider thermal and chemical excitation mechanisms in flame, and assume thermodynamic equilibrium for the thermally excited species. We also present the spatial resolution and the correction of collection characteristics of the optical emission spectrometer depending on object distance.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.17-17
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• 2019

IPCC (Intergovernmental Panel on Climate Change) 기후변화 전망보고서에 따르면 RCP 4.5 시나리오 기준, 21세기 전 지구 평균기온은 $2.5^{\circ}C$ 상승(한반도 $+3.0^{\circ}C$)하며, 전 지구 평균강수량은 4.1% 증가(한반도 +16.0%)할 것이라 전망하고 있다(기상청, 2012). 최근 기후변화와 기상이변에 따른 도심지 폭우특성이 변화하고 있음을 많은 연구결과에서 말해주고 있으며, 그 발생 빈도와 강도가 점차 증가하고 있는 추세이다. 특히, 서울시의 경우 인구와 재산이 밀집해 있어 폭우 발생에 의한 시민의 인명과 재산 피해 우려가 크다. 따라서 본 연구에서는 서울시를 대상으로 근미래(~2050년) 기후변화 하에서의 재현기간에 따른 확률강우량 변화 특성을 분석하여 비교 평가한 후 설계 강우량 산정에 활용하고자 하였다. 관측자료 기반 강수량의 변동 특성 분석과 Non-stationary GEV방법을 이용한 비정상성 빈도해석을 수행하였으며, 근미래 폭우특성 변화분석을 위하여 CMIP5 (Coupled Model Intercomparison Project 5)에 참여한 GCMs(General Circulation Models)을 활용한 강우빈도해석을 수행하였다. Mann-Kendall Test와 Quantile Regression을 통한 서울지점 여름철 강수량(June to September)과 기준강수량 초과 강수(30, 50, 80, 100mm/hr), 연간 10th 최대 강수량(Annual Top 10th Precipitation) 등을 분석한 결과 최근 증가 경향이 뚜렷하게 나타났으며, 비정상성 빈도해석에 의한 확률강우량 분석의 가능성과 신뢰성을 확인하였다. 또한 19-GCMs을 통하여 모의된 일(Daily) 단위 강수량자료를 비모수통계적 상세화(Nonparametric Temporal Downscaling) 기법을 적용하여 시간(Hourly) 강우로 다운스케일링하였으며, 서울시 미래 확률강우량에 대한 IDF 곡선(Intensity-Duration-Frequency Curve)을 작성하여 비교?분석한 결과 지속시간 1시간 강우에 대하여 재현기간 30년, 100년 조건에서 확률강우량이 약 4%~11% 수준에서 증가하고 있음을 확인하였다. 본 연구의 결과는 도심지 수공구조물의 설계빈도 영향을 진단하고, 근미래 발생가능한 확률강우량 변화에 따른 시간당 목표 강우량설정의 방법론을 제시하였다는데 의의가 있으며, 서울시의 방재성능목표 설정과 침수취약지역 해소를 위한 기후변화에 따른 수공구조물 설계 시 활용이 가능할 것으로 기대된다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.374-376
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• 2021

For the diagnosis of cancer-related diseases in clinical practice, pathological examination using biopsy is essential after basic diagnosis using imaging equipment. In order to proceed with such a biopsy, the assistance of an oncologist, clinical pathologist, etc. with specialized knowledge and the minimum required time are essential for confirmation. In recent years, research related to the establishment of a system capable of automatic classification of cancer cells using artificial intelligence is being actively conducted. However, previous studies show limitations in the type and accuracy of cells based on a limited algorithm. In this study, we propose a method to identify a total of 4 cancer cells through a convolutional neural network, a kind of deep learning. The optical images obtained through cell culture were learned through EfficientNet after performing pre-processing such as identification of the location of cells and image segmentation using OpenCV. The model used various hyper parameters based on EfficientNet, and trained InceptionV3 to compare and analyze the performance. As a result, cells were classified with a high accuracy of 96.8%, and this analysis method is expected to be helpful in confirming cancer.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.179-181
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• 2023

본 논문에서는 GAN 기반의 영상 생성 방법론을 이용해 delayed PET 영상을 생성하는 연구를 수행하였다. PET은 양전자를 방출하는 방사성 동위원소를 표지한 방사성의약품의 체내 분포를 시각화함으로서 암 세포 진단에 이용되는 의료영상 기법이다. 하지만 PET의 스캔 과정에서 방사성의약품이 체내에 분포하는 데에 걸리는 시간이 오래 걸린다는 문제점이 존재한다. 따라서 본 연구에서는 방사성의약품이 충분히 분포되지 않은 상태에서 얻은 PET 영상을 통해 목표로 하는 충분히 시간이 지난 후에 얻은 PET 영상을 생성하는 모델을 GAN (generative adversarial network)에 기반한 image-to-image translation(I2I)를 통해 수행했다. 특히, 생성 전후의 영상 간의 영상 쌍을 고려한 paired I2I인 Pix2pix와 이를 고려하지 않은 unpaired I2I인 CycleGAN 두 가지의 방법론을 비교하였다. 연구 결과, Pix2pix에 기반해 생성한 delayed PET 영상이 CycleGAN을 통해 생성한 영상에 비해 영상 품질이 좋음을 확인했으며, 또한 실제 획득한 ground-truth delayed PET 영상과의 유사도 또한 더 높음을 확인할 수 있었다. 결과적으로, 딥러닝에 기반해 early PET을 통해 delayed PET을 생성할 수 있었으며, paired I2I를 적용할 경우 보다 높은 성능을 기대할 수 있었다. 이를 통해 PET 영상 획득 과정에서 방사성의약품의 체내 분포에 소요되는 시간을 딥러닝 모델을 통해 줄여 PET 이미징 과정의 시간적 비용을 절감하는 데에 크게 기여할 수 있을 것으로 기대된다.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.5
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• pp.999-1008
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• 2023

Early prediction of chronic diseases such as diabetes is an important issue, and improving the accuracy of diabetes prediction is especially important. Various machine learning and deep learning-based methodologies are being introduced for diabetes prediction, but these technologies require large amounts of data for better performance than other methodologies, and the learning cost is high due to complex data models. In this study, we aim to verify the claim that DNN using the pima dataset and k-fold cross-validation reduces the efficiency of diabetes diagnosis models. Machine learning classification methods such as decision trees, SVM, random forests, logistic regression, KNN, and various ensemble techniques were used to determine which algorithm produces the best prediction results. After training and testing all classification models, the proposed system provided the best results on XGBoost classifier with ADASYN method, with accuracy of 81%, F1 coefficient of 0.81, and AUC of 0.84. Additionally, a domain adaptation method was implemented to demonstrate the versatility of the proposed system. An explainable AI approach using the LIME and SHAP frameworks was implemented to understand how the model predicts the final outcome.

• Nuclear Medicine and Molecular Imaging
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• v.41 no.1
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• pp.30-41
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• 2007

Purpose: Bone metastasis in breast cancer patients are usually assessed by conventional Tc-99m methylene diphosphonate whole-body bone scan, which has a high sensitivity but a poor specificity. However, positron emission tomography with $^{18}F-2-deoxyglucose$ (FDG-PET) can offer superior spatial resolution and improved specificity. FDG-PET/CT can offer more information to assess bone metastasis than PET alone, by giving a anatomical information of non-enhanced CT image. We attempted to evaluate the usefulness of FDG-PET/CT for detecting bone metastasis in breast cancer and to compare FDG-PET/CT results with bone scan findings. Materials and Methods: The study group comprised 157 women patients (range: $28{\sim}78$ years old, $mean{\pm}SD=49.5{\pm}8.5$) with biopsy-proven breast cancer who underwent bone scan and FDG-PET/CT within 1 week interval. The final diagnosis of bone metastasis was established by histopathological findings, radiological correlation, or clinical follow-up. Bone scan was acquired over 4 hours after administration of 740 MBq Tc-99m MDP. Bone scan image was interpreted as normal, low, intermediate or high probability for osseous metastasis. FDG PET/CT was performed after 6 hours fasting. 370 MBq F-18 FDG was administered intravenously 1 hour before imaging. PET data was obtained by 3D mode and CT data, used as transmission correction database, was acquired during shallow respiration. PET images were evaluated by visual interpretation, and quantification of FDG accumulation in bone lesion was performed by maximal SUV(SUVmax) and relative SUV(SUVrel). Results: Six patients(4.4%) showed metastatic bone lesions. Four(66.6%) of 6 patients with osseous metastasis was detected by bone scan and all 6 patients(100%) were detected by PET/CT. A total of 135 bone lesions found on either FDG-PET or bone scan were consist of 108 osseous metastatic lesion and 27 benign bone lesions. Osseous metastatic lesion had higher SUVmax and SUVrel compared to benign bone lesion($4.79{\pm}3.32$ vs $1.45{\pm}0.44$, p=0.000, $3.08{\pm}2.85$ vs $0.30{\pm}0.43$, p=0.000). Among 108 osseous metastatic lesions, 76 lesions showed as abnormal uptake on bone scan, and 76 lesions also showed as increased FDG uptake on PET/CT scan. There was good agreement between FDG uptake and abnormal bone scan finding (Kendall tau-b : 0.689, p=0.000). Lesion showed increased bone tracer uptake had higher SUVmax and SUVrel compared to lesion showed no abnormal bone scan finding ($6.03{\pm}3.12$ vs $1.09{\pm}1.49$, p=0.000, $4.76{\pm}3.31$ vs $1.29{\pm}0.92$, p=0.000). The order of frequency of osseous metastatic site was vertebra, pelvis, rib, skull, sternum, scapula, femur, clavicle, and humerus. Metastatic lesion on skull had highest SUVmax and metastatic lesion on rib had highest SUVrel. Osteosclerotic metastatic lesion had lowest SUVmax and SUVrel. Conclusion: These results suggest that FDG-PET/CT is more sensitive to detect breast cancer patients with osseous metastasis. CT scan must be reviewed cautiously skeleton with bone window, because osteosclerotic metastatic lesion did not showed abnormal FDG accumulation frequently.

• Journal of the Korea institute for structural maintenance and inspection
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• v.22 no.5
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• pp.110-118
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• 2018

Due to recent earthquakes, there is a growing awareness that Korea is not a safe zone for earthquakes any more. Therefore, the review of various aspects of the seismic safety of the infrastructures are being carried out. Because of the characteristics of the underground structure buried in the ground, the electric power utility tunnels must be considered not only for the inertia and load capacity of the structure itself but also the characteristics of the surrounding soils. An extensive and accurate numerical analysis is inevitably required in order to consider the interaction with the ground, but it is difficult to apply the soil-structure interaction analyses, which generally requires high cost and extensive time, to all electric power utility tunnel structures. In this study, the major design variables including soil characteristics are considered as independent variables, and the seismic safety factor, which is the result of the numerical analysis, is considered as a dependent variable. Thus, a method is proposed to select vulnerable electric power utility tunnels with low seismic safety factor while excluding costly and time-consuming numerical analyses through the direct correlation analysis between independent and dependent variables. Equations of boundary limits were derived based on the distribution of the seismic safety factor and the cover depth and rebar amounts with high correlation relationship. Consequently, a very efficient and simple approach is proposed to select vulnerable electric power utility tunnels without intensive numerical analyses. Among the 108 electric power utility tunnels that were investigated in this paper, 30% were screened as fragile structures, and it is confirmed that the screening method is valid by checking the safety factors of the fragile structure. The approach is relatively very simple to use and easy to expand, and can be conveniently applied to additional data to be obtained in the future.

• Journal of the Korea institute for structural maintenance and inspection
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• v.21 no.6
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• pp.113-125
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• 2017

In spite of bulk literature about the tuning of TMD, the effectiveness of TMD in reducing the seismic response of engineering structures is still in a row. This paper deals with the optimum tuning parameters of a passive TMD and simulated on MATLAB with a ten-story numerical shear building. A weighted multi-objective optimization method based on computer experiment consisting of coupled with central composite design(CCD) central composite design and response surface methodology(RSM) was applied to find out the optimum tuning parameters of TMD. After the optimization, the so-conceived TMD turns out to be optimal with respect to the specific seismic event, hence allowing for an optimum reduction in seismic response. The method was employed on above structure by assuming first the El Centro seismic input as a sort of benchmark excitation, and then additional recent strong-motion earthquakes. It is found that the RSM based weighted multi-objective optimized damper improves frequency responses and root mean square displacements of the structure without TMD by 31.6% and 82.3% under El Centro earthquake, respectively, and has an equal or higher performance than the conventionally designed dampers with respect to frequency responses and root mean square displacements and when applied to earthquakes.

• Journal of the Korea institute for structural maintenance and inspection
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• v.24 no.3
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• pp.39-46
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• 2020

Carbonation is a deterioration which degrades structural and material performance by permitting CO₂ and corrosion of embedded steel. Service life evaluation through deterministic method is conventional, however the researches with probabilistic approach on service life considering loading and cold joint effect on carbonation have been performed very limitedly. In this study, probabilistic service life evaluation was carried out through MCS (Monte Carlo Simulation) which adopted random variables such as cover depth, CO₂ diffusion coefficient, exterior CO₂ concentration, and internal carbonatable materials. Probabilistic service life was derived by changing mean value and COV (Coefficient of variation) from 100 % to 300 % and 0.1 ~ 0.2, respectively. From the analysis, maximum reduction ratio (47.7%) and minimum reduction ratio (11.4%) of service life were obtained in cover depth and diffusion coefficient, respectively. In the loading conditions of 30~60% for compressive and tensile stress, GGBFS concrete was effective to reduce cold joint effect on carbonation. In the tensile condition, service life decreased linearly regardless of material types. Additionally service life rapidly decreased due to micro crack propagation in the all cases when 60% loading was considered in compressive condition.

• Journal of Internet Computing and Services
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• v.23 no.3
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• pp.35-45
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• 2022

Even in recent years, treatment of first-aid patients is still often delayed due to a shortage of medical resources in marginalized areas. Research on automating the analysis of medical data to solve the problems of inaccessibility for medical services and shortage of medical personnel is ongoing. Computer vision-based medical inspection automation requires a lot of cost in data collection and labeling for training purposes. These problems stand out in the works of classifying lesion that are rare, or pathological features and pathogenesis that are difficult to clearly define visually. Anomaly detection is attracting as a method that can significantly reduce the cost of data collection by adopting an unsupervised learning strategy. In this paper, we propose methods for detecting abnormal images on chest X-RAY images as follows based on existing anomaly detection techniques. (1) Normalize the brightness range of medical images resampled as optimal resolution. (2) Some feature vectors with high representative power are selected in set of patch features extracted as intermediate-level from lesion-free images. (3) Measure the difference from the feature vectors of lesion-free data selected based on the nearest neighbor search algorithm. The proposed system can simultaneously perform anomaly classification and localization for each image. In this paper, the anomaly detection performance of the proposed system for chest X-RAY images of PA projection is measured and presented by detailed conditions. We demonstrate effect of anomaly detection for medical images by showing 0.705 classification AUROC for random subset extracted from the PadChest dataset. The proposed system can be usefully used to improve the clinical diagnosis workflow of medical institutions, and can effectively support early diagnosis in medically poor area.