• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1413-1425
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• 2023

The increasing frequency of wildfires due to climate change is causing extreme loss of life and property. They cause loss of vegetation and affect ecosystem changes depending on their intensity and occurrence. Ecosystem changes, in turn, affect wildfire occurrence, causing secondary damage. Thus, accurate estimation of the areas affected by wildfires is fundamental. Satellite remote sensing is used for forest fire detection because it can rapidly acquire topographic and meteorological information about the affected area after forest fires. In addition, deep learning algorithms such as convolutional neural networks (CNN) and transformer models show high performance for more accurate monitoring of fire-burnt regions. To date, the application of deep learning models has been limited, and there is a scarcity of reports providing quantitative performance evaluations for practical field utilization. Hence, this study emphasizes a comparative analysis, exploring performance enhancements achieved through both model selection and data design. This study examined deep learning models for detecting wildfire-damaged areas using Landsat 8 satellite images in California. Also, we conducted a comprehensive comparison and analysis of the detection performance of multiple models, such as U-Net and High-Resolution Network-Object Contextual Representation (HRNet-OCR). Wildfire-related spectral indices such as normalized difference vegetation index (NDVI) and normalized burn ratio (NBR) were used as input channels for the deep learning models to reflect the degree of vegetation cover and surface moisture content. As a result, the mean intersection over union (mIoU) was 0.831 for U-Net and 0.848 for HRNet-OCR, showing high segmentation performance. The inclusion of spectral indices alongside the base wavelength bands resulted in increased metric values for all combinations, affirming that the augmentation of input data with spectral indices contributes to the refinement of pixels. This study can be applied to other satellite images to build a recovery strategy for fire-burnt areas.

• Journal of Venture Innovation
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• v.6 no.1
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• pp.35-57
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• 2023

The purpose of this study was to analyze the moderating effect of venture start-up and general start-up based on what kinds of entrepreneurs' personal characteristics, business capabilities, and start-up motivation factors affecting start-up satisfaction. This study conducted an online survey of companies who received credit guarantee for start-ups from KCGF(Korea Credit Guarantee Fund), and finally collected 320 survey data. And it conducted statistical analyses such as frequency analysis, factor analysis, reliability analysis, correlation analysis, regression analysis, etc. using SPSS 24.0 statistics program. The results of the study were as follows. First, it is tested that creativity, one of entrepreneurs' characteristics, had a positive effect(+) on start-up satisfaction. Second, it is found that the failure burden, one of entrepreneurs' characteristics, had a negative effect(-) on start-up satisfaction. Third, experiences, one of entrepreneurs' characteristics, had not a significant effect on start-up satisfaction. Fourth, it was analyzed that business capabilities such as technology research and development, marketing, networking, and financing had a positive effect(+) on start-up satisfaction. Fifth, it is tested that the economic and self-realization motivation had a positive effect(+) on start-up satisfaction. Sixth, start-up satisfaction had a positive effect(+) on business performances. Last, it was analyzed that venture start-ups had a more positive effect than general start-up in the creativity, technology research and development, and the self-realization of start-up motivation affecting start-up satisfaction. And, it was found that venture start-ups have a less negative effect than general start-up in the failure burden affecting start-up satisfaction.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.933-948
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• 2023

Atmospheric aerosols not only have adverse effects on human health but also exert direct and indirect impacts on the climate system. Consequently, it is imperative to comprehend the characteristics and spatiotemporal distribution of aerosols. Numerous research endeavors have been undertaken to monitor aerosols, predominantly through the retrieval of aerosol optical depth (AOD) via satellite-based observations. Nonetheless, this approach primarily relies on a look-up table-based inversion algorithm, characterized by computationally intensive operations and associated uncertainties. In this study, a novel high-resolution AOD direct retrieval algorithm, leveraging machine learning, was developed using top-of-atmosphere reflectance data derived from the Geostationary Ocean Color Imager-II (GOCI-II), in conjunction with their differences from the past 30-day minimum reflectance, and meteorological variables from numerical models. The Light Gradient Boosting Machine (LGBM) technique was harnessed, and the resultant estimates underwent rigorous validation encompassing random, temporal, and spatial N-fold cross-validation (CV) using ground-based observation data from Aerosol Robotic Network (AERONET) AOD. The three CV results consistently demonstrated robust performance, yielding R²=0.70-0.80, RMSE=0.08-0.09, and within the expected error (EE) of 75.2-85.1%. The Shapley Additive exPlanations(SHAP) analysis confirmed the substantial influence of reflectance-related variables on AOD estimation. A comprehensive examination of the spatiotemporal distribution of AOD in Seoul and Ulsan revealed that the developed LGBM model yielded results that are in close concordance with AERONET AOD over time, thereby confirming its suitability for AOD retrieval at high spatiotemporal resolution (i.e., hourly, 250 m). Furthermore, upon comparing data coverage, it was ascertained that the LGBM model enhanced data retrieval frequency by approximately 8.8% in comparison to the GOCI-II L2 AOD products, ameliorating issues associated with excessive masking over very illuminated surfaces that are often encountered in physics-based AOD retrieval processes.

• Food Science and Preservation
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• v.30 no.6
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• pp.1056-1071
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• 2023

This study aimed to investigate the quality and microbial population changes for 90 days under two fermentation conditions, outdoors and indoors (35℃), with starters (single or mixed) in soybean paste. Bacillus velezensis NY12-2 (S1), Debaryomyces hansenii D5-P5 (S2), Enterococcus faecium N78-11 (S3), and their mixtures (M) were used for the makjang fermentation. The content of amino-type nitrogen among the makjang samples was highly shown in the indoors, followed by M, S3, and S2. The glutamic and aspartic acid contents in the M sample fermented in the indoors showed the highest values of 867.42±77.27 and 243.20±15.79 mg/g, respectively. By the electronic tongue analysis, the M sample fermented in the indoors exhibited lower saltiness and higher umami than the others. Consequently, we expect that using mixed strains, such as Bacillus, Debaryomyces, and Enterococcus, under constant conditions showed potential to the quality improvement of soy products.

• The Mathematical Education
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• v.63 no.2
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• pp.255-272
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• 2024

This study aims to design mathematics-integrated classes that cultivate artificial intelligence (AI) thinking and to analyze students' AI thinking within these classes. To do this, four classes were designed through the integration of the AI4K12 Initiative's AI Big Ideas with the 2015 revised elementary mathematics curriculum. Implementation of three classes took place with 5th and 6th grade elementary school students. Leveraging the computational thinking taxonomy and the AI thinking components, a comprehensive framework for analyzing of AI thinking was established. Using this framework, analysis of students' AI thinking during these classes was conducted based on classroom discourse and supplementary worksheets. The results of the analysis were peer-reviewed by two researchers. The research findings affirm the potential of mathematics-integrated classes in nurturing students' AI thinking and underscore the viability of AI education for elementary school students. The classes, based on AI Big Ideas, facilitated elementary students' understanding of AI concepts and principles, enhanced their grasp of mathematical content elements, and reinforced mathematical process aspects. Furthermore, through activities that maintain structural consistency with previous problem-solving methods while applying them to new problems, the potential for the transfer of AI thinking was evidenced.

• Journal of Korea Water Resources Association
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• v.57 no.3
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• pp.181-193
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• 2024

The increasing atmospheric imbalance caused by climate change leads to an elevation in precipitation, resulting in a heightened frequency of flooding. Consequently, there is a growing need for technology to detect and monitor these occurrences, especially as the frequency of flooding events rises. To minimize flood damage, continuous monitoring is essential, and flood areas can be detected by the Synthetic Aperture Radar (SAR) imagery, which is not affected by climate conditions. The observed data undergoes a preprocessing step, utilizing a median filter to reduce noise. Classification techniques were employed to classify water bodies and non-water bodies, with the aim of evaluating the effectiveness of each method in flood detection. In this study, the Otsu method and Support Vector Machine (SVM) technique were utilized for the classification of water bodies and non-water bodies. The overall performance of the models was assessed using a Confusion Matrix. The suitability of flood detection was evaluated by comparing the Otsu method, an optimal threshold-based classifier, with SVM, a machine learning technique that minimizes misclassifications through training. The Otsu method demonstrated suitability in delineating boundaries between water and non-water bodies but exhibited a higher rate of misclassifications due to the influence of mixed substances. Conversely, the use of SVM resulted in a lower false positive rate and proved less sensitive to mixed substances. Consequently, SVM exhibited higher accuracy under conditions excluding flooding. While the Otsu method showed slightly higher accuracy in flood conditions compared to SVM, the difference in accuracy was less than 5% (Otsu: 0.93, SVM: 0.90). However, in pre-flooding and post-flooding conditions, the accuracy difference was more than 15%, indicating that SVM is more suitable for water body and flood detection (Otsu: 0.77, SVM: 0.92). Based on the findings of this study, it is anticipated that more accurate detection of water bodies and floods could contribute to minimizing flood-related damages and losses.

• Journal of the Korean Society of Radiology
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• v.84 no.6
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• pp.1309-1323
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• 2023

Purpose To assess the quality of four images obtained using single-breath-hold (SBH), single-shot fast spin-echo (SSFSE) and multiple-breath-hold (MBH) SSFSE with and without deep-learning based reconstruction (DLR) in patients with Crohn's disease. Materials and Methods This study included 61 patients who underwent MR enterography (MRE) for Crohn's disease. The following images were compared: SBH-SSFSE with (SBH-DLR) and without (SBH-conventional reconstruction [CR]) DLR and MBH-SSFSE with (MBH-DLR) and without (MBH-CR) DLR. Two radiologists independently reviewed the overall image quality, artifacts, sharpness, and motion-related signal loss using a 5-point scale. Three inflammatory parameters were evaluated in the ileum, the terminal ileum, and the colon. Moreover, the presence of a spatial misalignment was evaluated. Signal-to-noise ratio (SNR) was calculated at two locations for each sequence. Results DLR significantly improved the image quality, artifacts, and sharpness of the SBH images. No significant differences in scores between MBH-CR and SBH-DLR were detected. SBH-DLR had the highest SNR (p < 0.001). The inter-reader agreement for inflammatory parameters was good to excellent (κ = 0.76-0.95) and the inter-sequence agreement was nearly perfect (κ = 0.92-0.94). Misalignment artifacts were observed more frequently in the MBH images than in the SBH images (p < 0.001). Conclusion SBH-DLR demonstrated equivalent quality and performance compared to MBH-CR. Furthermore, it can be acquired in less than half the time, without multiple BHs and reduce slice misalignments.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.703-711
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• 2024

This study attempted to verify the effect of SBAR-based simulation practice on reporting confidence, communicative competence, nursing competence and debriefing satisfaction of nursing students. This study included 46 students who took the simulation practice course for third-year nursing students at one universities located in one region, The data were collected from October 30 to December 22, 2023 using a self-report questionnaire before and after simulation practice, and is a one group pretest-posttest design study. Data analysis was performed using SPSS/WIN version 26.0 program using frequency analysis, descriptive statistics, Shapiro-Wilk test, and Paired t-test. As a result of the study, the average of the reporting confidence was 5.79±1.47 before the training and 7.13±1.56 after the training, the communicative competence was 3.62±0.44 before the training and the average after the training was 4.34±0.67, the nursing competence was 2.64±0.39 before the training and 3.26±0.51 after the training, and the debriefing satisfaction was 3.57±0.51 before the training and 4.18±0.58 after the training. There was a statistically significant difference in reporting confidence(t=2.84, p=.006), communicative competence(t=-3.28, p=.001), nursing competence(t=-8.16, p<.001), debriefing satisfaction(t=2.72, p<.001) before and after SBAR-based simulation practice. Based on the results of this study, it is thought that communication education using SBAR to nursing students should be systematically carried out from the lower grade curriculum, and it is necessary to strengthen and expand reporting education using SBAR communication in various practice situations as well as simulation practical education to improve nursing competency.

• Journal of Internet of Things and Convergence
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• v.10 no.3
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• pp.35-48
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• 2024

This study analyzed the value empathy of environmentally sustainable fashion products, encompassing environmental, economic, and social values, drawing from existing literature. We sought to verify the relationship between empathic value and the likability and purchase intention towards these products. To validate these relationships, we formulated research hypotheses and conducted an online survey targeting female college students residing in Guangzhou, Guangdong Province, China, who have experience purchasing environmentally sustainable fashion products. The survey was conducted from August 10th to August 20th, 2023, with a total distribution of 352 questionnaires. Among the collected responses, 313 valid responses were utilized for data analysis. The collected survey data underwent frequency analysis, exploratory factor analysis, reliability and validity analysis, correlation analysis, and multiple regression analysis using SPSS 26.0 software. The analysis yielded the following results. First, the empathy value of environmentally sustainable fashion products was classified into environmental protection values, economic values, and social values. Second, the economic and social values of environmentally sustainable fashion products were found to have a positive effect on favorability. Third, it was found that the environmental protection value and social value of environmentally sustainable fashion products had a positive effect on purchase intention. Fourth, it was found that Chinese female college students' favorability toward environmentally sustainable fashion products had a positive effect on their purchase intention. Based on these results, it is judged that companies need to emphasize the characteristics of products such as environmental protective value, economic value, and social value in order to promote consumers' purchase of environmentally sustainable fashion products. The purpose of this study is to help develop marketing strategies for environmentally sustainable fashion products by providing basic data, development ideas, and methods useful for environmentally sustainable fashion-related industries and companies by analyzing the relationship between empathy value, favorability, and purchase intention.

• Journal of Korean Dental Hygiene Science
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• v.7 no.1
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• pp.53-68
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• 2024

Background: Research on the association between renal disease and periodontal conditions has yet to yield definitive results. In this study, we analyzed whether periodontal disease increases the risk of developing renal disease using Korean national cohort data over a period of 11 years. Methods: From 2002 to 2015, a retrospective follow-up investigation was conducted on the 203,538 Korean population using the National Health Insurance Service-National Sample Cohort. Periodontal disease and renal disease were identified through diagnoses using the International Statistical Classification of Diseases and Related Health Problems, 10th revision (ICD-10) codes. The assessment of periodontal status involved considering the number of dental visits related to periodontal disease during the baseline 3-year period. Results: During the 11-year follow-up period, renal disease occurred in 19,868 out of the total 203,538 individuals. After adjusting for age, gender, income, smoking, drinking, physical activity, diabetes, hypertension, obesity, hypercholesterolemia, ischemic heart disease, and advanced periodontal treatment, periodontal disease increased the risk of renal disease occurrence by 1.04 times (adjusted hazard ratio [aHR] = 1.04, 95% CI = 1.01 to 1.08). Additionally, a higher frequency of dental visits attributed to periodontal disease was associated with an increased risk of renal disease,exhibiting a dose-response trend (aHR = 1.02, 95% CI = 1.00 to 1.06 for once; aHR = 1.08, 95% CI = 1.04 to 1.13 for two times; aHR = 1.11, 95% CI = 1.03 to 1.21 for three times). Conclusions: Our data confirmed that periodontal disease is associated witha higher incidence of renal disease.