• 천문학회보
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• 제46권2호
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• pp.75.2-76
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• 2021

MESSIER is a science satellite project to observe the Low Surface Brightness (LSB) sky at UV and optical wavelengths. The wide-field, optical system of MESSIER is optimized minimizing optical aberrations through the use of a Linear Astigmatism Free - Three Mirror System (LAF-TMS) combined with freeform mirrors. One of the key factors in observations of the LSB is the shape and spatial variability of the Point Spread Function (PSF) produced by scatterings and diffraction effects within the optical system and beyond (baffle). To assess the various factors affecting the PSF in this design, we use PhoSim, the Photon simulator, which is a fast photon Monte Carlo code designed to include all these effects, and also atmospheric effects (for ground-based telescopes) and phenomena occurring inside of the sensor. PhoSim provides very realistic simulations results and is suitable for simulations of very weak signals. Before the application to the MESSIER optics system, PhoSim had not been validated for confocal off-axis reflective optics (LAF-TMS). As a verification study for the LAF-TMS design, we apply Phosim sequentially. First, we use a single parabolic mirror system and compare the PSF results of the central field with the results from Zemax, CODE V, and the theoretical Airy pattern. We then test a confocal off-axis Cassegrain system and check PhoSim through cross-validation with CODE V. At the same time, we describe the shapes of the freeform mirrors with XY and Zernike polynomials. Finally, we will analyze the LAF-TMS design for the MESSIER optical system.

• 산업경영시스템학회지
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• 제46권1호
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• pp.23-31
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• 2023

Research and interest in sustainable printing are increasing in the packaging printing industry. Currently, predicting the amount of ink required for each work is based on the experience and intuition of field workers. Suppose the amount of ink produced is more than necessary. In this case, the rest of the ink cannot be reused and is discarded, adversely affecting the company's productivity and environment. Nowadays, machine learning models can be used to figure out this problem. This study compares the ink usage prediction machine learning models. A simple linear regression model, Multiple Regression Analysis, cannot reflect the nonlinear relationship between the variables required for packaging printing, so there is a limit to accurately predicting the amount of ink needed. This study has established various prediction models which are based on CART (Classification and Regression Tree), such as Decision Tree, Random Forest, Gradient Boosting Machine, and XGBoost. The accuracy of the models is determined by the K-fold cross-validation. Error metrics such as root mean squared error, mean absolute error, and R-squared are employed to evaluate estimation models' correctness. Among these models, XGBoost model has the highest prediction accuracy and can reduce 2134 (g) of wasted ink for each work. Thus, this study motivates machine learning's potential to help advance productivity and protect the environment.

• 한국환경보건학회지
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• 제49권3호
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• pp.149-158
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• 2023

Background: Injury is one of the major health problems in South Korea. Few studies have evaluated both intentional and unintentional injury when investigating the association between exposure to air pollutants and injury. Objectives: We aimed to explore the association between short-term exposure to ambient air pollution and years of life lost (YLLs) due to injury. Methods: Data on daily YLLs for 2002~2019 were obtained from the the Death Statistics Database of the Korean National Statistical Office. This study estimated short-term exposure to particulate matter with an aerodynamic diameter of <10 ㎛ (PM₁₀), particulate matter with an aerodynamic diameter of <2.5 ㎛ (PM_2.5), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), carbon monoxide (CO), and ozone (O₃). This time series study was conducted using a generalized additive model (GAM) assuming a Gaussian distribution. We also evaluated a delayed effect of ambient air pollution by constructing a lag structure up to seven days. The best-fitting lag was selected based on smallest generalized cross validation (GCV) value. To explore effect modification by intentionality of injury (i.e., intentional injury [self-harm, assault] and unintentional injury), we conducted stratified subgroup analyses. Additionally, we stratified unintentional injury by mechanism (traffic accident, fall, etc.). Results: During the study period, the average daily YLLs due to injury was 307.5 years. In the intentional injury, YLLs due to self-harm and assault showed positive association with air pollutants. In the unintentional injury, YLLs due to fall, electric current, fire and poisoning showed positive association with air pollutants, whereas YLLs due to traffic accident, mechanical force and drowning/submersion showed negative associations with air pollutants. Conclusions: Injury is recognized as preventable, and effective strategies to create a safe society are important. Therefore, we need to establish strategies to prevent injury and consider air pollutants in this regard.

• Journal of Dairy Science and Biotechnology
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• 제41권3호
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• pp.113-125
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• 2023

In this study, we explored the synergistic effects of whey protein concentrate (WPC) and soybean protein components after fermentation with lactic acid bacteria isolated from kimchi, and identified several peptides with desirable physiological functions, proteolysis, and immune effects. Antioxidant activity was determined using 2,2'-azino-bis-3-ethylbenzothiazoline-6-sulphonic acid, 1,1-diphenyl-2-picrylhydrazyl, ferric-reducing antioxidant power, and hydroxyl radical scavenging assays, followed by cross-validation of the four antioxidant activities. These assays revealed that samples with a 8:2 and 9:1 whey to soy ratio possessed higher antioxidant activity than the control samples. Antibacterial potency testing revealed high antibacterial activity in the 9:1 and 8:2 samples. Cytotoxicity testing of samples using 3-(4, 5-dimethyl thiazol-2-yl)-2, 5-diphenyl tetrazolium bromide revealed that only the 10:0, 1:9, and 0:10 samples had <80% viable cells, indicating no significant cytotoxicity. Nitric oxide (NO) assays revealed that NO expression was reduced in 8:2, 5:5, and 0:10 protein ratio fermentations, indicating low inflammatory reaction stimulatory potential. Cytokine expression was confirmed using an enzyme-linked immunosorbent assay kit. The 8:2 sample had the lowest inflammatory cytokine (interleukin [IL]-1α, IL-6, and tumor necrosis factor-α) levels compared with the lipopolysaccharide-treated group. Amino acid profiling of the 8:2 sample identified 17 amino acids. These results suggest that inoculating and fermenting Lactobacillus plantarum DK203 and Lactobacillus paracasei DK209 with an 8:2 mixture of WPC and soybean protein releases bioactive peptides with excellent anti-inflammatory and antioxidant properties, making them suitable for functional food development.

• 스마트미디어저널
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• 제12권8호
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• pp.18-25
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• 2023

2020년 발생한 코로나19는 전세계적으로 지속적인 피해를 미쳤으며, 특히 하늘길 봉쇄 및 외출 자제로 인해 스마트 관광산업은 경제적 직격탄을 맞았다. 해외여행과 국내여행이 크게 감소된 상황에서 계속되는 적자로 인해 휴업과 폐업을 하는 관광호텔들이 늘어나고 있는 상황이다. 따라서 본 연구에서는 행정안전부의 인허가 데이터를 수집한 후 시각화하여 관광숙박업의 운영 현황을 파악하였다. 머신러닝 분류 알고리즘을 적용하여 관광호텔의 생존 예측 모델을 구현하였고 앙상블 알고리즘을 활용하여 예측 모델의 성능을 최적화하였으며 5-Fold 교차검증으로 모델의 성능을 평가하였다. 관광호텔의 생존율이 다소 감소할 것으로 예측되었으나 실제 생존율을 코로나19 이전과 큰 차이를 보이지 않는 것으로 분석되었다. 본 논문의 호텔업 영업 상태 예측을 통해 관광숙박업 전체의 운영 가능성 및 발전 동향을 파악할 수 있는 근거로 활용할 수 있다.

• Journal of Radiation Protection and Research
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• 제48권4호
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• pp.184-196
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• 2023

Background: Radiation protection is crucial in various fields due to the harmful effects of radiation. Shielding is used to reduce radiation exposure, but gamma radiation poses challenges due to its high energy and penetration capabilities. Materials and Methods: This work investigates the radiation shielding properties of polyvinylidene fluoride (PVDF) samples containing different weight fraction of tungsten carbide (WC), tungsten trioxide (WO₃), and tungsten disulfide (WS₂). Parameters such as the mass attenuation coefficient (MAC), half-value layer (HVL), mean free path (MFP), effective atomic number (Z_eff), and macroscopic effective removal cross-section for fast neutrons (Σ_R) were calculated using the Phy-X/PSD software. EpiXS simulations were conducted for MAC validation. Results and Discussion: Increasing the weight fraction of the additives resulted in higher MAC values, indicating improved radiation shielding. PVDF-xWC showed the highest percentage increase in MAC values. MFP results indicated that PVDF-0.20WC has the lowest values, suggesting superior shielding properties compared to PVDF-0.20WO₃ and PVDF-0.20WS₂. PVDF-0.20WC also exhibited the highest Z_eff values, while PVDF-0.20WS₂ showed a slightly higher increase in Z_eff at energies of 0.662 and 1.333 MeV. PVDF-0.20WC has demonstrated the highest Σ_R value, indicating effective shielding against fast neutrons, while PVDF-0.20WS₂ had the lowest Σ_R value. The Monte Carlo N-Particle Transport version 5 (MCNP5) simulations showed that PVDF-xWC attenuates gamma radiation more than pure PVDF, significantly decreasing the dose equivalent rate. Conclusion: Overall, this research provides insights into the radiation shielding properties of PVDF mixtures, with PVDF-xWC showing the most promising results.

• 한국전자통신학회논문지
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• 제18권5호
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• pp.999-1008
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• 2023

당뇨병과 같은 만성 질환의 조기 예측은 중요한 이슈이며, 그중에서도 당뇨 예측의 정확도 향상은 매우 중요하다. 당뇨 예측을 위한 다양한 기계 학습 및 딥 러닝 기반 방법론을 도입하고 있으나, 이러한 기술들은 다른 방법론보다 더 우수한 성능을 위해 대량의 데이터를 필요로 하며, 복잡한 데이터 모델 때문에 학습 비용이 높다. 본 연구에서는 pima 데이터셋과 k-fold 교차 검증을 사용한 DNN이 당뇨 진단 모델의 효율성을 감소시킨다는 주장을 검증하고자 한다. 의사 결정 트리, SVM, 랜덤 포레스트, 로지스틱 회귀, KNN 및 다양한 앙상블 기법과 같은 기계 학습 분류 방법을 사용하여 어떤 알고리즘이 최상의 예측 결과를 내는지 결정하였다. 모든 분류 모델에 대한 훈련 및 테스트 후 제안된 시스템은 ADASYN 방법과 함께 XGBoost 분류기에서 최상의 결과를 제공하였으며, 정확도는 81%, F1 계수는 0.81, AUC는 0.84였다. 또한 도메인 적응 방법이 제안된 시스템의 다양성을 보여주기 위해 구현되었다. LIME 및 SHAP 프레임워크를 사용한 설명 가능한 AI 접근 방식이 모델이 최종 결과를 어떻게 예측하는지 이해하기 위해 구현되었다.

• Composites Research
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• 제36권6호
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• pp.395-401
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• 2023

본 연구에서는 지도학습 기반 분할기법을 이용하여 단층 촬영된 단방향 복합재료의 유한요소모델링을 실시하였다. 우선, 단방향 복합재료의 형상 정보를 얻기 위해 Micro-CT 스캔을 수행하여 단방향 복합재료의 순수 체적(raw volume)을 획득하였고 여기에 몇 개의 단면을 선택하여 재료의 마이크로 구조인 섬유의 형상을 라벨링하였다. 이후 재료의 단면 이미지와 라벨링한 이미지를 각각 입출력으로 U-net 모델을 훈련시켰다. 이를 사용하여 선택되지 않은 단층촬영 이미지를 섬유형상을 구분하는 분할을 수행하였고 이렇게 생성된 3차원 정보를 이용해서 유한요소모델을 생성하였다. 최종적으로 단방향 복합재료 시편과 유한요소모델의 섬유체적비를 비교하여 제안된 방법의 적절성을 확인하였다.

• Korean Journal of Radiology
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• 제22권9호
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• pp.1481-1489
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• 2021

Objective: To construct a CT-based Fagotti scoring system by analyzing the correlations between laparoscopic findings and CT features in patients with advanced ovarian cancer. Materials and Methods: This retrospective cohort study included patients diagnosed with stage III/IV ovarian cancer who underwent diagnostic laparoscopy and debulking surgery between January 2010 and June 2018. Two radiologists independently reviewed preoperative CT scans and assessed ten CT features known as predictors of suboptimal cytoreduction. Correlation analysis between ten CT features and seven laparoscopic parameters based on the Fagotti scoring system was performed using Spearman's correlation. Variable selection and model construction were performed by logistic regression with the least absolute shrinkage and selection operator method using a predictive index value (PIV) ≥ 8 as an indicator of suboptimal cytoreduction. The final CT-based scoring system was internally validated using 5-fold cross-validation. Results: A total of 157 patients (median age, 56 years; range, 27-79 years) were evaluated. Among 120 (76.4%) patients with a PIV ≥ 8, 105 patients received neoadjuvant chemotherapy followed by interval debulking surgery, and the optimal cytoreduction rate was 90.5% (95 of 105). Among 37 (23.6%) patients with PIV < 8, 29 patients underwent primary debulking surgery, and the optimal cytoreduction rate was 93.1% (27 of 29). CT features showing significant correlations with PIV ≥ 8 were mesenteric involvement, gastro-transverse mesocolon-splenic space involvement, diaphragmatic involvement, and para-aortic lymphadenopathy. The area under the receiver operating curve of the final model for prediction of PIV ≥ 8 was 0.72 (95% confidence interval: 0.62-0.82). Conclusion: Central tumor burden and upper abdominal spread features on preoperative CT were identified as distinct predictive factors for high PIV on diagnostic laparoscopy. The CT-based PIV prediction model might be useful for patient stratification before cytoreduction surgery for advanced ovarian cancer.

• International Journal of Computer Science & Network Security
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• 제24권3호
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• pp.125-134
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• 2024

The novel coronavirus 2019 is called COVID-19 has outspread swiftly worldwide. An early diagnosis is more important to control its quick spread. Medical imaging mechanics, chest calculated tomography or chest X-ray, are playing a vital character in the identification and testing of COVID-19 in this present epidemic. Chest X-ray is cost effective method for Covid-19 detection however the manual process of x-ray analysis is time consuming given that the number of infected individuals keep growing rapidly. For this reason, it is very important to develop an automated COVID-19 detection process to control this pandemic. In this study, we address the task of automatic detection of Covid-19 by using a popular deep learning model namely the VGG19 model. We used 1300 healthy and 1300 confirmed COVID-19 chest X-ray images in this experiment. We performed three experiments by freezing different blocks and layers of VGG19 and finally, we used a machine learning classifier SVM for detecting COVID-19. In every experiment, we used a five-fold cross-validation method to train and validated the model and finally achieved 98.1% overall classification accuracy. Experimental results show that our proposed method using the deep learning-based VGG19 model can be used as a tool to aid radiologists and play a crucial role in the timely diagnosis of Covid-19.