• Journal of Korean Society of Coastal and Ocean Engineers
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• v.36 no.2
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• pp.61-69
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• 2024

Wave conditions are crucial for offshore wind farm design, particularly in determining structural loads and layout. However, there is limited wave hindcasting research for the Wangdeungdo Island area, a potential offshore wind site. This study used the MIKE21 model for a year-long wave hindcast around Wangdeungdo in 2021. Validation showed high reproducibility for significant wave heights with RMSE values of 0.177 and 0.225 and Pearson correlations of 0.971 and 0.970 at Sangwangdeungdo and Buan buoys. Subsequent analysis of the wave characteristics near Wangdeungdo indicated significant seasonal variations and differences in maximum significant wave heights across locations, which are expected to significantly impact the design loads for offshore wind structures.

• Korean Journal of Radiology
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• v.22 no.4
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• pp.612-623
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• 2021

Objective: To evaluate the diagnostic performance of a deep learning algorithm for the automated detection of developmental dysplasia of the hip (DDH) on anteroposterior (AP) radiographs. Materials and Methods: Of 2601 hip AP radiographs, 5076 cropped unilateral hip joint images were used to construct a dataset that was further divided into training (80%), validation (10%), or test sets (10%). Three radiologists were asked to label the hip images as normal or DDH. To investigate the diagnostic performance of the deep learning algorithm, we calculated the receiver operating characteristics (ROC), precision-recall curve (PRC) plots, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) and compared them with the performance of radiologists with different levels of experience. Results: The area under the ROC plot generated by the deep learning algorithm and radiologists was 0.988 and 0.988-0.919, respectively. The area under the PRC plot generated by the deep learning algorithm and radiologists was 0.973 and 0.618-0.958, respectively. The sensitivity, specificity, PPV, and NPV of the proposed deep learning algorithm were 98.0, 98.1, 84.5, and 99.8%, respectively. There was no significant difference in the diagnosis of DDH by the algorithm and the radiologist with experience in pediatric radiology (p = 0.180). However, the proposed model showed higher sensitivity, specificity, and PPV, compared to the radiologist without experience in pediatric radiology (p < 0.001). Conclusion: The proposed deep learning algorithm provided an accurate diagnosis of DDH on hip radiographs, which was comparable to the diagnosis by an experienced radiologist.

• Archives of Plastic Surgery
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• v.51 no.1
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• pp.30-35
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• 2024

Background Breast aesthetics evaluation often relies on subjective assessments, leading to the need for objective, automated tools. We developed the Seoul Breast Esthetic Scoring Tool (S-BEST), a photometric analysis software that utilizes a DenseNet-264 deep learning model to automatically evaluate breast landmarks and asymmetry indices. Methods S-BEST was trained on a dataset of frontal breast photographs annotated with 30 specific landmarks, divided into an 80-20 training-validation split. The software requires the distances of sternal notch to nipple or nipple-to-nipple as input and performs image preprocessing steps, including ratio correction and 8-bit normalization. Breast asymmetry indices and centimeter-based measurements are provided as the output. The accuracy of S-BEST was validated using a paired t-test and Bland-Altman plots, comparing its measurements to those obtained from physical examinations of 100 females diagnosed with breast cancer. Results S-BEST demonstrated high accuracy in automatic landmark localization, with most distances showing no statistically significant difference compared with physical measurements. However, the nipple to inframammary fold distance showed a significant bias, with a coefficient of determination ranging from 0.3787 to 0.4234 for the left and right sides, respectively. Conclusion S-BEST provides a fast, reliable, and automated approach for breast aesthetic evaluation based on 2D frontal photographs. While limited by its inability to capture volumetric attributes or multiple viewpoints, it serves as an accessible tool for both clinical and research applications.

• Animal Bioscience
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• v.37 no.7
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• pp.1196-1203
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• 2024

Objective: A study was conducted to quantify the performance differences of the near-infrared spectroscopy (NIRS) calibration models developed with different degrees of hay sample preparations. Methods: A total of 227 imported alfalfa (Medicago sativa L.) and another 360 imported timothy (Phleum pratense L.) hay samples were used to develop calibration models for nutrient value parameters such as moisture, neutral detergent fiber, acid detergent fiber, crude protein, and in vitro dry matter digestibility. Spectral data of hay samples prepared by milling into 1-mm particle size or unground were separately regressed against the wet chemistry results of the abovementioned parameters. Results: The performance of the developed NIRS calibration models was evaluated based on R², standard error, and ratio percentage deviation (RPD). The models developed with ground hay were more robust and accurate than those with unground hay based on calibration model performance indexes such as R² (coefficient of determination), standard error, and RPD. Although the R² of calibration models was mainly greater than 0.90 across the feed value indexes, the R² of cross-validations was much lower. The R² of cross-validation varies depending on feed value indexes, which ranged from 0.61 to 0.81 in alfalfa, and from 0.62 to 0.95 in timothy. Estimation of feed values in imported hay can be achievable by the calibrated NIRS. However, the NIRS calibration models must be improved by including a broader range of imported hay samples in the modeling. Conclusion: Although the analysis accuracy of NIRS was substantially higher when calibration models were developed with ground samples, less sample preparation will be more advantageous for achieving rapid delivery of hay sample analysis results. Therefore, further research warrants investigating the level of sample preparations compromising analysis accuracy by NIRS.

• Journal of the Korean Institute of Gas
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• v.28 no.2
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• pp.38-46
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• 2024

The city gas pipeline network is characterized by being widely distributed and hierarchically connected in a complex manner over a wide area. In order to monitor the status of the widely distributed network pressures with high precision, Multi-block PCA(MBPCA) is recommended. However, while MBPCA has excellent performance in identifying faulty sensors as the number of sensors increases, the fault detection performance deteriorates, and also there is a problem that the model needs to be updated entirely even if minor changes occur. In this study, we developed fault detectability index and fault identificability index to determine the effectiveness of MBPCA application block by block. Based on these indices, we distinguished MBPCA and PCA blocks and developed a fault detection and diagnostic system for the city gas pipeline network of Haean Energy Co., Ltd., and were able to solve the problems that arise when there are many sensors.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.123-139
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• 2019

The Korean NPL market was formed by the government and foreign capital shortly after the 1997 IMF crisis. However, this market is short-lived, as the bad debt has started to increase after the global financial crisis in 2009 due to the real economic recession. NPL has become a major investment in the market in recent years when the domestic capital market's investment capital began to enter the NPL market in earnest. Although the domestic NPL market has received considerable attention due to the overheating of the NPL market in recent years, research on the NPL market has been abrupt since the history of capital market investment in the domestic NPL market is short. In addition, decision-making through more scientific and systematic analysis is required due to the decline in profitability and the price fluctuation due to the fluctuation of the real estate business. In this study, we propose a prediction model that can determine the achievement of the benchmark yield by using the NPL market related data in accordance with the market demand. In order to build the model, we used Korean NPL data from December 2013 to December 2017 for about 4 years. The total number of things data was 2291. As independent variables, only the variables related to the dependent variable were selected for the 11 variables that indicate the characteristics of the real estate. In order to select the variables, one to one t-test and logistic regression stepwise and decision tree were performed. Seven independent variables (purchase year, SPC (Special Purpose Company), municipality, appraisal value, purchase cost, OPB (Outstanding Principle Balance), HP (Holding Period)). The dependent variable is a bivariate variable that indicates whether the benchmark rate is reached. This is because the accuracy of the model predicting the binomial variables is higher than the model predicting the continuous variables, and the accuracy of these models is directly related to the effectiveness of the model. In addition, in the case of a special purpose company, whether or not to purchase the property is the main concern. Therefore, whether or not to achieve a certain level of return is enough to make a decision. For the dependent variable, we constructed and compared the predictive model by calculating the dependent variable by adjusting the numerical value to ascertain whether 12%, which is the standard rate of return used in the industry, is a meaningful reference value. As a result, it was found that the hit ratio average of the predictive model constructed using the dependent variable calculated by the 12% standard rate of return was the best at 64.60%. In order to propose an optimal prediction model based on the determined dependent variables and 7 independent variables, we construct a prediction model by applying the five methodologies of discriminant analysis, logistic regression analysis, decision tree, artificial neural network, and genetic algorithm linear model we tried to compare them. To do this, 10 sets of training data and testing data were extracted using 10 fold validation method. After building the model using this data, the hit ratio of each set was averaged and the performance was compared. As a result, the hit ratio average of prediction models constructed by using discriminant analysis, logistic regression model, decision tree, artificial neural network, and genetic algorithm linear model were 64.40%, 65.12%, 63.54%, 67.40%, and 60.51%, respectively. It was confirmed that the model using the artificial neural network is the best. Through this study, it is proved that it is effective to utilize 7 independent variables and artificial neural network prediction model in the future NPL market. The proposed model predicts that the 12% return of new things will be achieved beforehand, which will help the special purpose companies make investment decisions. Furthermore, we anticipate that the NPL market will be liquidated as the transaction proceeds at an appropriate price.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1109-1123
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• 2020

In South Korea with forest as a major land cover class (over 60% of the country), many wildfires occur every year. Wildfires weaken the shear strength of the soil, forming a layer of soil that is vulnerable to landslides. It is important to identify the severity of a wildfire as well as the burned area to sustainably manage the forest. Although satellite remote sensing has been widely used to map wildfire severity, it is often difficult to determine the severity using only the temporal change of satellite-derived indices such as Normalized Difference Vegetation Index (NDVI) and Normalized Burn Ratio (NBR). In this study, we proposed an approach for determining wildfire severity based on machine learning through the synergistic use of Sentinel-1A Synthetic Aperture Radar-C data and Sentinel-2A Multi Spectral Instrument data. Three wildfire cases-Samcheok in May 2017, Gangreung·Donghae in April 2019, and Gosung·Sokcho in April 2019-were used for developing wildfire severity mapping models with three machine learning algorithms (i.e., Random Forest, Logistic Regression, and Support Vector Machine). The results showed that the random forest model yielded the best performance, resulting in an overall accuracy of 82.3%. The cross-site validation to examine the spatiotemporal transferability of the machine learning models showed that the models were highly sensitive to temporal differences between the training and validation sites, especially in the early growing season. This implies that a more robust model with high spatiotemporal transferability can be developed when more wildfire cases with different seasons and areas are added in the future.

• Korean Journal of Remote Sensing
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• v.36 no.6_2
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• pp.1509-1521
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• 2020

Experiments for validation of surface reflectance produced by Korea Multi-Purpose Satellite (KOMPSAT-3A) were conducted using Chinese Baotou (BTCN) data among four sites of the Radical Calibration Network (RadCalNet), a portal that provides spectrophotometric reflectance measurements. The atmosphere reflectance and surface reflectance products were generated using an extension program of an open-source Orfeo ToolBox (OTB), which was redesigned and implemented to extract those reflectance products in batches. Three image data sets of 2016, 2017, and 2018 were taken into account of the two sensor model variability, ver. 1.4 released in 2017 and ver. 1.5 in 2019, such as gain and offset applied to the absolute atmospheric correction. The results of applying these sensor model variables showed that the reflectance products by ver. 1.4 were relatively well-matched with RadCalNet BTCN data, compared to ones by ver. 1.5. On the other hand, the reflectance products obtained from the Landsat-8 by the USGS LaSRC algorithm and Sentinel-2B images using the SNAP Sen2Cor program were used to quantitatively verify the differences in those of KOMPSAT-3A. Based on the RadCalNet BTCN data, the differences between the surface reflectance of KOMPSAT-3A image were shown to be highly consistent with B band as -0.031 to 0.034, G band as -0.001 to 0.055, R band as -0.072 to 0.037, and NIR band as -0.060 to 0.022. The surface reflectance of KOMPSAT-3A also indicated the accuracy level for further applications, compared to those of Landsat-8 and Sentinel-2B images. The results of this study are meaningful in confirming the applicability of Analysis Ready Data (ARD) to the surface reflectance on high-resolution satellites.

• Korean Journal of School Psychology
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• v.17 no.2
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• pp.165-180
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• 2020

As a tiered system of supports, School-Wide Positive Behavior Support (SWPBS) is an evidence-based practice in the educational system of Korea. An important aspect of SWPBS is the ongoing progress monitoring and evaluation of implementation fidelity. This study aimed to develop and validate the Korean Tier 3 School-Wide Positive Behavior Support Implementation Fidelity Checklist (KT3-FC). The preliminary KT3-FC consisted of a 37-item, 6-factor checklist. In the first phase of the study, 10 experts reported that the range of content validity of the KT3-FC was adequate. In the second phase of the study, 185 teachers (52 men and 133 women) who implemented SWPBS completed the KT3-FC, Individualized Supports Questionnaire, School Climate Questionnaire, School Discipline Practice Scale, and PBS Effectiveness Scale. An exploratory factor analysis resulted in a 5-factor structure, with 20 items, instead of 37 items, consisting of: (a) progress monitoring and evaluation of the individualized supports, (b) provision of supports by aligning and integrating mental health and SWPBS, (c) crisis management planning, (d) problem behavior assessment, and (e) establishment of individualized support team. The internal consistency of the KT3-FC was good (full scale α = .950, sub-factor α = .888 ~ .954). In addition, the KT3-FC showed good convergent validity, having statistically significant correlations with the Individualized Support Questionnaire, School Climate Questionnaire, School Discipline Practice Scale, and the PBS Effectiveness Scale. Finally, the confirmatory factor analysis showed that the 5-factor model of the KT3-FC had some good model fits, indicating that the newly developed fidelity measure could be a reliable and valid tool to assess the implementation of Tier 3 supports in Korean schools. Accordingly, the KT3-FC could contribute to implement SWPBS as an evidence-based behavioral intervention for Korean students.

• The Korean Journal of Coaching Psychology
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• v.7 no.1
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• pp.59-89
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• 2023

In this study, change motivation for growth and development is defined as 'the power to set a specific action direction for change based on the perception of one's current behavior in order to achieve a goal that one considers important, and to be willing to act'. In addition, the purpose of this study was to develop and validate a scale to measure the motivation for change for growth and development of general adults. To develop preliminary questions, interviews were conducted with 7 coaching experts and 9 experienced coaches, and an open-ended questionnaire was conducted with 55 adults. Afterwards, 7 factors and 83 questions were selected through three rounds of item classification and content validity verification, and a preliminary survey was conducted targeting 321 general adults, and 42 items, 4 factors, were derived through exploratory factor analysis. did Finally, the main survey was conducted with 631 adults in order to verify the validity of the construct concept of the change motivation scale and the validity of the criterion. Divided into two groups, 315 people in group 1 conducted exploratory factor analysis and 316 people in group 2 conducted confirmatory factor analysis to verify the concept of change motivation scale. As a result of the factor analysis of Group 1, it was found that the 3 factor structure consisting of 31 items was appropriate, and as a result of the confirmatory factor analysis of Group 2, the goodness of fit of the modified model of the 3 factor structure was confirmed, which motivated change. The construct validity of the scale was demonstrated. As a result of analyzing the correlations with various variables for the analysis of convergent validity and criterion-related validity of the Motivation for Change scale, each of the three factors was found to be significantly related to most variables. Finally, the significance, implications and limitations of this study, and future research were discussed.