• 천문학회보
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• 제45권1호
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• pp.50.4-51
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• 2020

We report our discovery of Raman-scattered He II λ6545 feature in young planetary nebulae NGC 6886 and NGC 6881 which indicates the existence of atomic hydrogen components. Considering sharply increasing cross-section of hydrogen atom near the resonance, Raman-scattered He II features are a useful diagnostic tool to investigate the distribution and kinematics of H I region in planetary nebulae. The high-resolution spectroscopic observation was carried out using BOES installed on the 1.8 m telescope of BOAO. We estimate the column density of H I region and its expansion velocity using our grid-based Monte-Carlo radiative transfer code. We assume that the H I region is uniformly distributed in spherical shell geometry with an opening angle and expands with constant speed. Our best-fit model is shown with the column density NHI = 3 × 1020 cm-2 and expansion speed vexp = 25 km s-1 with the opening angle ~ 25° for NGC 6886, and NHI = 4 × 1020 cm-2 and vexp = 30 km s-1 with the opening angle ~ 35° for NGC 6881. We present brief discussions on the late-stage of evolution of stars with mass > 3 M⊙.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• 제49권3호
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• pp.135-141
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• 2023

Objectives: This study aimed to develop and validate machine learning (ML) models using H₂O-AutoML, an automated ML program, for predicting medication-related osteonecrosis of the jaw (MRONJ) in patients with osteoporosis undergoing tooth extraction or implantation. Patients and Methods: We conducted a retrospective chart review of 340 patients who visited Dankook University Dental Hospital between January 2019 and June 2022 who met the following inclusion criteria: female, age ≥55 years, osteoporosis treated with antiresorptive therapy, and recent dental extraction or implantation. We considered medication administration and duration, demographics, and systemic factors (age and medical history). Local factors, such as surgical method, number of operated teeth, and operation area, were also included. Six algorithms were used to generate the MRONJ prediction model. Results: Gradient boosting demonstrated the best diagnostic accuracy, with an area under the receiver operating characteristic curve (AUC) of 0.8283. Validation with the test dataset yielded a stable AUC of 0.7526. Variable importance analysis identified duration of medication as the most important variable, followed by age, number of teeth operated, and operation site. Conclusion: ML models can help predict MRONJ occurrence in patients with osteoporosis undergoing tooth extraction or implantation based on questionnaire data acquired at the first visit.

• Korean Journal of Radiology
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• 제25권1호
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• pp.86-102
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• 2024

Early diagnosis, accurate assessment, and localization of peritoneal metastasis (PM) are essential for the selection of appropriate treatments and surgical guidance. However, available imaging modalities (computed tomography [CT], conventional magnetic resonance imaging [MRI], and 18fluorodeoxyglucose positron emission tomography [PET]/CT) have limitations. The advent of new imaging techniques and novel molecular imaging agents have revealed molecular processes in the tumor microenvironment as an application for the early diagnosis and assessment of PM as well as real-time guided surgical resection, which has changed clinical management. In contrast to clinical imaging, which is purely qualitative and subjective for interpreting macroscopic structures, radiomics and artificial intelligence (AI) capitalize on high-dimensional numerical data from images that may reflect tumor pathophysiology. A predictive model can be used to predict the occurrence, recurrence, and prognosis of PM, thereby avoiding unnecessary exploratory surgeries. This review summarizes the role and status of different imaging techniques, especially new imaging strategies such as spectral photon-counting CT, fibroblast activation protein inhibitor (FAPI) PET/CT, near-infrared fluorescence imaging, and PET/MRI, for early diagnosis, assessment of surgical indications, and recurrence monitoring in patients with PM. The clinical applications, limitations, and solutions for fluorescence imaging, radiomics, and AI are also discussed.

• Journal of Korea Artificial Intelligence Association
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• 제2권1호
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• pp.7-14
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• 2024

In this paper, we explore the application of RNA-sequencing data and ensemble machine learning to predict lung cancer and treatment strategies for lung cancer, a leading cause of cancer mortality worldwide. The research utilizes Random Forest, XGBoost, and LightGBM models to analyze gene expression profiles from extensive datasets, aiming to enhance predictive accuracy for lung cancer prognosis. The methodology focuses on preprocessing RNA-seq data to standardize expression levels across samples and applying ensemble algorithms to maximize prediction stability and reduce model overfitting. Key findings indicate that ensemble models, especially XGBoost, substantially outperform traditional predictive models. Significant genetic markers such as ADGRF5 is identified as crucial for predicting lung cancer outcomes. In conclusion, ensemble learning using RNA-seq data proves highly effective in predicting lung cancer, suggesting a potential shift towards more precise and personalized treatment approaches. The results advocate for further integration of molecular and clinical data to refine diagnostic models and improve clinical outcomes, underscoring the critical role of advanced molecular diagnostics in enhancing patient survival rates and quality of life. This study lays the groundwork for future research in the application of RNA-sequencing data and ensemble machine learning techniques in clinical settings.

• Nuclear Engineering and Technology
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• 제56권6호
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• pp.1989-2001
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• 2024

Artificial intelligence (AI) techniques are now being considered in the nuclear field, but application faces with the lack of actual plant data. For this reason, most previous studies on AI applications in nuclear power plants (NPPs) have relied on simulators or thermal-hydraulic codes to mimic the plants. However, it remains uncertain whether an AI model trained using a simulator can properly work in an actual NPP. To address this issue, this study suggests the use of metadata, which can give information about parameter trends. Referred to here as robust AI, this concept started with the idea that although the absolute value of a plant parameter differs between a simulator and actual NPP, the parameter trend is identical under the same scenario. Based on the proposed robust AI, this study designs an event diagnosis algorithm to classify abnormal and emergency scenarios in NPPs using prototypical learning. The algorithm was trained using a simulator referencing a Westinghouse 990 MWe reactor and then tested in different environments in Advanced Power Reactor 1400 MWe simulators. The algorithm demonstrated robustness with 100 % diagnostic accuracy (117 out of 117 scenarios). This indicates the potential of the robust AI-based algorithm to be used in actual plants.

• 한국문헌정보학회지
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• 제47권2호
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• pp.181-200
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• 2013

정보활용교육을 효과적으로 전개하기 위해서는 피교육자의 능력을 진단, 측정하고 교육목표의 도달 여부를 판단할 수 있는 검사도구가 필수적이다. 그럼에도 불구하고 초 중등 학생들의 정보활용능력을 종합적으로 측정할 수 있는 검사도구가 미흡한 실정이다. 이에 본 연구에서는 중학생의 정보활용능력 측정을 위한 웹기반의 표준화된 검사도구를 개발하고, 검사도구의 신뢰도와 타당성을 검증하였다. 검사도구의 개발을 위해서 우선 정보활용능력의 구성요인을 추출하고, 길포드의 지능구조모형과 메이커의 학습능력 측정도구를 바탕으로 정보활용능력 검사도구의 프레임을 개발하였다. 본 연구를 통해 개발한 검사도구는 웹 기반의 선다형 30문항으로 구성되어 있으며, 794명의 중학생들을 대상으로 검사를 시행하여 문항의 난이도, 신뢰도, 변별력 지수, 타당도 등을 분석하였다. 또한, 검사도구를 이용하여 학생들의 정보활용능력을 진단하고, 평가하기 위한 표준 점수를 제시하였다.

• 응용통계연구
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• 제24권2호
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• pp.269-278
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• 2011

스코어 변수의 민감도와 특이도와의 관계로 표현한 ROC 곡선을 더욱 정확한 진단을 위하여 분포함수와 공변량을 고려한 연구가 많이 진행되었다. 공변량을 고려하는 회귀분석 방법을 사용하였으며 이때 분포함수를 정규분포로 가정하거나 잔차의 분포함수를 추정하여 ROC 분석을 하였다. 본 연구는 분포함수가 주어지지 않으며 진단에 영향을 주는 공변량을 모르는 일반적인 상황에서 논의하였다. 확률변수인 스코어와 두 개의 보모집단으로 구성된 신용평가 자료에 적합한 분포함수를 추정하기 위하여 여러 개의 정규분포가 혼합된 정규혼합분포를 사용하여 ROC 분석을 한다. 고전적인 비모수적이고 경험적인 ROC 곡선에 적합한지를 파악하기 위하여 AUC 통계량을 사용하여 비교하며, 본 연구에서 제안한 정규혼합분포를 이용한 ROC 곡선이 다른 방법으로 구한 ROC 곡선보다 적합함을 보였다.

• 대기
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• 제26권4호
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• pp.495-508
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• 2016

In order to determine the prediction possibility of heavy rainfall, a variety of analyses was conducted by using three-dimensional data obtained from Korea Local Analysis and Prediction System (KLAPS) re-analysis data. Strong moisture convergence occurring around the time of the heavy rainfall is consistent with the results of previous studies on such continuous production. Heavy rainfall occurred in the cloud system with a thick convective clouds. The moisture convergence, temperature and potential temperature advection showed increase into the heavy rainfall occurrence area. The distribution of integrated liquid water content tended to decrease as rainfall increased and was characterized by accelerated convective instability along with increased buoyant energy. In addition, changes were noted in the various characteristics of instability indices such as K-index (KI), Showalter Stability Index (SSI), and lifted index (LI). The meteorological variables used in the analysis showed clear increases or decreases according to the changes in rainfall amount. These rapid changes as well as the meteorological variables changes are attributed to the surrounding and meteorological conditions. Thus, we verified that heavy rainfall can be predicted according to such increase, decrease, or changes. This study focused on quantitative values and change characteristics of diagnostic variables calculated by using numerical models rather than by focusing on synoptic analysis at the time of the heavy rainfall occurrence, thereby utilizing them as prognostic variables in the study of the predictability of heavy rainfall. These results can contribute to the identification of production and development mechanisms of heavy rainfall and can be used in applied research for prediction of such precipitation. In the analysis of various case studies of heavy rainfall in the future, our study result can be utilized to show the development of the prediction of severe weather.

• 동의신경정신과학회지
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• 제24권3호
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• pp.211-228
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• 2013

Objectives : This report describes the diagnostic and therapeutic procedures of Collaborative Practice between Korean Traditional Medicine and Western Medicine for two dementia patients. Furthermore, through these cases, we suggest a model of collaborative practice between Korean traditional medicine and western medicine for the treatment of dementia. Methods : Two patients suffering from several symptoms related to dementia received collaborative practice between Korean traditional medicine and western medicine. Physicians of deparment which paient first visit interviewed patient and patient's guardians, discussed the symptoms and the status of the patient. Since then, the medical team made a differential diagnosis based on the results of brain imaging, hematology, urine test. and apprehended the status of dementia by the neuropsychological test. Korean traditional physicians examined the physical symptoms and identified the pattern of dementia in Korean traditional medicine. Following this, they decided on the method of acupuncture, moxibustion, cupping and herbal treatment. Western physicians decided on the type of medication after consideration of the patient's other medical conditions. Every intervention was decided by both Korean traditional physicians and western physicians after discussion. The medical team provided education on dementia and counseled the guardians. They also wrote the paper for using the long-term care insurance for the aged. Results : Two patients showed no improvement on the neuropsychologic and activity of daily living tests. However, the patients' subjective physical symptoms were improved. The collaborative practice between Korean traditional medicine and western medicine improved the patients' and guardians' satisfaction. Conclusions : Through these cases, we propose a model of collaborative practice between Korean traditional medicine and western medicine for dementia categorized diagnosis-test, treatment, prevention, management. More specifically, we supplement qigong and psychotherapy which was inadequate in these cases.

• 조명전기설비학회논문지
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• 제21권7호
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• pp.75-83
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• 2007

일반적으로 변압기의 고장진단을 위해 IEC 코드법이 사용되지만, 이 방법은 가스비율이 규정된 범위 내에 존재하지 않거나 경계조건에 있는 경우 숙련된 진단 전문가에게 의뢰하지 않고는 정확한 고장의 원인을 판정하는데 어려움이 있다. 이러한 문제점을 해결하기 위하여 본 논문에서는 SOM을 이용한 전력용 변압기의 고장진단 알고리즘을 제안한다. 제안된 방법은 훈련 데이터의 경쟁학습을 통하여 자기 구성 맵을 구축한 후, 실증 데이터를 구축된 맵에 적용하여 고장의 진단이 이루어진다. 또한 클러스터링 기법에 의해 구축된 정상/고장모델과 정상 데이터를 비교함으로써 고장의 추이 및 열화정도를 분석한다. 제안된 방법의 유용성을 보이기 위한 실험결과에서 기존의 방법들에 비해 향상된 진단결과를 보임을 확인할 수 있었다.