• Korean Journal of Remote Sensing
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• v.38 no.2
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• pp.153-166
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• 2022

Waterstorage is one of the factorsthat most directly represent the amount of available water resources. Since the effects of drought can be more intuitively expressed, it is also used in variousstudies for drought evaluation. In a recent study, hydrological drought was evaluated through information on observing reservoirs with optical images. The short observation cycle and diversity of optical satellites provide a lot of data. However, there are some limitations because it is vulnerable to the influence of weather or the atmospheric environment. Therefore, thisstudy attempted to conduct a study on estimating the drought index using Synthetic Aperture Radar (SAR) image with relatively little influence from the observation environment. We produced the waterbody of Baekgok and Chopyeong reservoirs using SAR images of Sentinel-1 satellites and calculated the Reservoir Area Drought Index (RADI), a hydrological drought index. In order to validate the applicability of RADI to drought monitoring, it was compared with Reservoir Storage Drought Index (RSDI) based on measured storage. The two indices showed a very high correlation with the correlation coefficient, r=0.87, Area Under curve, AUC=0.97. These results show the possibility of regional-scale hydrological drought monitoring of SAR-based RADI. As the number of available SAR images increases in the future, it is expected that the utilization of drought monitoring will also increase.

• Journal of the Korean Geotechnical Society
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• v.37 no.11
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• pp.119-129
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• 2021

The process of constructing the DB in the current geotechnical information DB system needs a lot of human and time resource consumption. In addition, it causes accuracy problems frequently because the current input method is a person viewing the PDF and directly inputting the results. Therefore, this study proposes building an automatic digital DB using AI (artificial intelligence) of boring logs. In order to automatically construct DB for various boring log formats without exception, the boring log forms were classified using the deep learning model ResNet 34 for a total of 6 boring log forms. As a result, the overall accuracy was 99.7, and the ROC_AUC score was 1.0, which separated the boring log forms with very high performance. After that, the text in the PDF is automatically read using the robotic processing automation technique fine-tuned for each form. Furthermore, the general information, strata information, and standard penetration test information were extracted, separated, and saved in the same format provided by the geotechnical information DB system. Finally, the information in the boring log was automatically converted into a DB at a speed of 140 pages per second.

• Composites Research
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• v.35 no.5
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• pp.317-321
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• 2022

In this study, the PIC design method with machine learning that automatically assigning different stacking sequences according to loading types was applied bumper beam. The input value and labels of the training data for applying machine learning were defined as coordinates and loading types of reference elements that are part of the total elements, respectively. In order to compare the 2D and 3D implementation method, which are methods of representing coordinate value, training data were generated, and machine learning models were trained with each method. The 2D implementation method is divided FE model into each face and generating learning data and training machine learning models accordingly. The 3D implementation method is training one machine learning model by generating training data from the entire finite element model. The hyperparameter were tuned to optimal values through the Bayesian algorithm, and the k-NN classification method showed the highest prediction rate and AUC-ROC among the tuned models. The 3D implementation method revealed higher performance than the 2D implementation method. The loading type data predicted through the machine learning model were mapped to the finite element model and comparatively verified through FE analysis. It was found that 3D implementation PIC bumper beam was superior to 2D implementation and uni-stacking sequence composite bumper.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.3
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• pp.397-411
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• 2023

The number of deteriorated bridges with a service period of more than 30 years has been rapidly increasing in Korea. Accordingly, the importance of advanced maintenance technologies through the predictions of age-induced deterioration degree, condition, and performance of bridges is more and more noticed. The prediction method of the safety grade of bridges was proposed in this study using the classification models of the Decision Tree and the Random Forest based on machine learning. As a result of analyzing these models for the 8,850 bridges located in national roads with various evaluation indexes such as confusion matrix, balanced accuracy, recall, ROC curve, and AUC, the Random Forest largely showed better predictive performance than that of the Decision Tree. In particular, random under-sampling in the Random Forest showed higher predictive performance than that of other sampling techniques for the C and D grade bridges, with the recall of 83.4%, which need more attention to maintenance because of the significant deterioration degree. The proposed model can be usefully applied to rapidly identify the safety grade and to establish an efficient and economical maintenance plan of bridges that have not recently been inspected.

• Korean Journal of Radiology
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• v.22 no.1
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• pp.106-117
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• 2021

Objective: To explore the value of magnetic resonance imaging (MRI)-based whole tumor texture analysis in differentiating borderline epithelial ovarian tumors (BEOTs) from FIGO stage I/II malignant epithelial ovarian tumors (MEOTs). Materials and Methods: A total of 88 patients with histopathologically confirmed ovarian epithelial tumors after surgical resection, including 30 BEOT and 58 MEOT patients, were divided into a training group (n = 62) and a test group (n = 26). The clinical and conventional MRI features were retrospectively reviewed. The texture features of tumors, based on T2-weighted imaging, diffusion-weighted imaging, and contrast-enhanced T1-weighted imaging, were extracted using MaZda software and the three top weighted texture features were selected by using the Random Forest algorithm. A non-texture logistic regression model in the training group was built to include those clinical and conventional MRI variables with p value < 0.10. Subsequently, a combined model integrating non-texture information and texture features was built for the training group. The model, evaluated using patients in the training group, was then applied to patients in the test group. Finally, receiver operating characteristic (ROC) curves were used to assess the diagnostic performance of the models. Results: The combined model showed superior performance in categorizing BEOTs and MEOTs (sensitivity, 92.5%; specificity, 86.4%; accuracy, 90.3%; area under the ROC curve [AUC], 0.962) than the non-texture model (sensitivity, 78.3%; specificity, 84.6%; accuracy, 82.3%; AUC, 0.818). The AUCs were statistically different (p value = 0.038). In the test group, the AUCs, sensitivity, specificity, and accuracy were 0.840, 73.3%, 90.1%, and 80.8% when the non-texture model was used and 0.896, 75.0%, 94.0%, and 88.5% when the combined model was used. Conclusion: MRI-based texture features combined with clinical and conventional MRI features may assist in differentitating between BEOT and FIGO stage I/II MEOT patients.

• Journal of Chest Surgery
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• v.57 no.1
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• pp.44-52
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• 2024

Background: Visceral pleural invasion (VPI) is a poor prognostic factor that contributes to the upstaging of early lung cancers. However, the preoperative assessment of VPI presents challenges. This study was conducted to examine intraoperative pleural carcinoembryonic antigen (pCEA) level and maximum standardized uptake value (SUVmax) as predictive markers of VPI in patients with clinical T1N0M0 lung adenocarcinoma. Methods: A retrospective review was conducted of the medical records of 613 patients who underwent intraoperative pCEA sampling and lung resection for non-small cell lung cancer. Of these, 390 individuals with clinical stage I adenocarcinoma and tumors ≤30 mm were included. Based on computed tomography findings, these patients were divided into pleural contact (n=186) and non-pleural contact (n=204) groups. A receiver operating characteristic (ROC) curve was constructed to analyze the association between pCEA and SUVmax in relation to VPI. Additionally, logistic regression analysis was performed to evaluate risk factors for VPI in each group. Results: ROC curve analysis revealed that pCEA level greater than 2.565 ng/mL (area under the curve [AUC]=0.751) and SUVmax above 4.25 (AUC=0.801) were highly predictive of VPI in patients exhibiting pleural contact. Based on multivariable analysis, pCEA (odds ratio [OR], 3.00; 95% confidence interval [CI], 1.14-7.87; p=0.026) and SUVmax (OR, 5.25; 95% CI, 1.90-14.50; p=0.001) were significant risk factors for VPI in the pleural contact group. Conclusion: In patients with clinical stage I lung adenocarcinoma exhibiting pleural contact, pCEA and SUVmax are potential predictive indicators of VPI. These markers may be helpful in planning for lung cancer surgery.

• Information Systems Review
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• v.26 no.2
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• pp.137-153
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• 2024

This study aimed to propose early diagnosis and management of dementia, which is increasing in aging societies, and suggest commercial utilization strategies by leveraging digital healthcare technologies, particularly lifelog data collected from wearable devices. By introducing new approaches to dementia prevention and management, this study sought to contribute to the field of dementia prediction and prevention. The research utilized 12,184 pieces of lifelog information (sleep and activity data) and dementia diagnosis data collected from 174 individuals aged between 60 and 80, based on medical pathological diagnoses. During the research process, a multidimensional dataset including sleep and activity data was standardized, and various machine learning algorithms were analyzed, with the random forest model showing the highest ROC-AUC score, indicating superior performance. Furthermore, an ablation test was conducted to evaluate the impact of excluding variables related to sleep and activity on the model's predictive power, confirming that regular sleep and activity have a significant influence on dementia prevention. Lastly, by exploring the potential for commercial utilization strategies of the developed model, the study proposed new directions for the commercial spread of dementia prevention systems.

• The Journal of Korean Obstetrics and Gynecology
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• v.25 no.3
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• pp.95-102
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• 2012

Purpose: The purpose of this study is to define the cut off values of cold hypersensitivity of hands by using digital infrared thermographic imaging(DITI). Methods: Thermographic images of 130 patients with cold hypersensitivity of hands(CHHG, n=65) and non-cold hypersensitivity of hands(NCHHG, n=65) were retrospectively reviewed. We used the temperature difference the palm(PC8) and the upper arm(LU4) for diagnosing cold hypersensitivity of hands. The temperature differences of between two groups were analysed using independent samples t-tests. The cut off values were calculated by ROC curve analysis. Analyses were undertaken using SPSS version 17.0. P value of < 0.05 was considered significant. Results: The temperature difference the palm(PC8) and the upper arm(LU4) were significantly different between groups(p < 0.001). Using receiver operating characteristic curve analysis, the sensitivity, specificity, and area under the curve were 70.8%, 73.8%, respectively both hands. The AUC was 0.822 on right hand and 0.818 on left hand. The optimum cut-off value was defined as $-0.05^{\circ}C$. Conclusions: These results suggest that DITI is a reliable instrument for estimating the cold hypersensitivity of hands.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.11
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• pp.5483-5488
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• 2012

Background: Although nicotine dependence plays a role as a main barrier for smoking cessation, there is still a lack of solid evidence on the validity of biomarkers to determine nicotine dependence in clinical settings. This study aimed to investigate whether urinary cotinine levels could reflect the severity of nicotine dependence in active smokers. Materials and Methods: Data regarding general characteristics and smoking status was collected using a self-administered smoking questionnaire. The Fagerstr$\ddot{o}$m test for nicotine dependence (FTND) was used to determine nicotine dependence of the participants, and a total of 381 participants were classified into 3 groups of nicotine dependence: low (n=205, 53.8%), moderate (n=127, 33.3%), and high dependence groups (n=49, 12.9%). Stepwise multiple linear regression model and receiver operating characteristic (ROC) curves analyses were used to determine the validity of urinary cotinine for high nicotine dependence. Results: In correlation analysis, urinary cotinine levels increased with FTND score (r=0.567, P<0.001). ROC curves analysis showed that urinary cotinine levels predicted the high-dependence group with reasonable accuracy (optimal cut-off value=1,000 ng/mL; AUC=0.82; P<0.001; sensitivity=71.4%; specificity=74.4%). In stepwise multiple regression analysis, the total smoking period (${\beta}$=0.042, P=0.001) and urinary cotinine levels (${\beta}$=0.234, P<0.001) were positively associated with nicotine dependence, whereas an inverse association was observed between highest education levels (>16 years) and nicotine dependence (${\beta}$=-0.573, P=0.034). Conclusions: The results of this study support the validity of using urinary cotinine levels for assessment of nicotine dependence in active smokers.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.1
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• pp.199-203
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• 2012

Purpose: To evaluate the prognostic value of serum CYFRA21-1, CEA and hemoglobin levels regarding long-term survival of patients with esophageal squamous cell carcinoma (ESCC) treated with concurrent chemoradiotherapy (CRT). Methods: Age, gender, Karnofsky Performance Status (KPS), tumor location, tumor length, T stage, N stage and serum hemoglobin, and CYFRA21-1 and CEA levels before concurrent CRT were retrospectively investigated and related to outcome in 113 patients receiving 5-fluorouracil and cisplatin combined with radiotherapy for ESCC. The Kaplan-Meier method was used to analyze prognosis, the log-rank to compare groups, the Cox proportional hazards model for multivariate analysis, and ROC curve analysis for assessment of predictive performance of biologic markers. Results: The median survival time was 20.1 months and the 1-, 2-, 3-, 5- year overall survival rates were 66.4%, 43.4%, 31.9% and 15.0%, respectively. Univariate analysis showed that factors associated with prognosis were KPS, tumor length, T-stage, N-stage, hemoglobin, CYFRA21-1 and CEA level. Multivariate analysis showed T-stage, N-stage, hemoglobin, CYFRA21-1 and CEA level were independent predictors of prognosis. By ROC curve, CYFRA21-1 and hemoglobin showed better predictive performance for OS than CEA (AUC= 0.791, 0.704, 0.545; P=0.000, 0.000, 0.409). Conclusions: Of all clinicopathological and molecular factors, T stage, N stage, hemoglobin, CYFRA21-1 and CEA level were independent predictors of prognosis for patients with ESCC treated with concurrent CRT. Among biomarkers, CYFRA21-1 and hemoglobin may have a better predictive potential than CEA for long-term outcomes.