• Korean Journal of Remote Sensing
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• v.36 no.6_2
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• pp.1579-1590
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• 2020

In this paper, spatially-adaptive denormalization (SPADE) based U-Net is proposed to detect changes by using high-resolution satellite images. The proposed network is to preserve spatial information using SPADE. Change detection methods using high-resolution satellite images can be used to resolve various urban problems such as city planning and forecasting. For using pixel-based change detection, which is a conventional method such as Iteratively Reweighted-Multivariate Alteration Detection (IR-MAD), unchanged areas will be detected as changing areas because changes in pixels are sensitive to the state of the environment such as seasonal changes between images. Therefore, in this paper, to precisely detect the changes of the objects that consist of the city in time-series satellite images, the semantic spatial objects that consist of the city are defined, extracted through deep learning based image segmentation, and then analyzed the changes between areas to carry out change detection. The semantic objects for analyzing changes were defined as six classes: building, road, farmland, vinyl house, forest area, and waterside area. Each network model learned with KOMPSAT-3A satellite images performs a change detection for the time-series KOMPSAT-3 satellite images. For objective assessments for change detection, we use F1-score, kappa. We found that the proposed method gives a better performance compared to U-Net and UNet++ by achieving an average F1-score of 0.77, kappa of 77.29.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.6
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• pp.675-686
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• 2021

This study investigated the ground displacement occurring in a slope below a waste-rock dumping site and estimated the likelihood of a disaster due to a landslide. To start with, photogrammetry was conducted by unmanned aerial vehicles (UAVs) to investigate the size and extent of the ground displacement. From April 2019 to July 2020, the average error rate of the five UAV surveys was 0.011-0.034 m, and an elevation change of 2.97 m occurred due to the movement of the soil layer. Only some areas of the slope showedelevation change, and this was believed to be due to thegroundwater generated during rainfall rather than the effect of the waste-rock load at the top. Sensitivity analysis for LS-RAPID simulation was performed, and the simulation results were compared and analyzed by applying a digital elevation model (DEM) and a digital surface model (DSM)as terrain data with 10 m, 5 m, and 4 m grids. When data with high spatial resolution were used, the extent of the sedimentation of landslide material tended to be excessively expanded in the DEM. In contrast, in the result of applying a DSM, which reflects the topography in detail, the diffusion range was not significantly affected even when the spatial resolution was changed, and the sedimentation behavior according to the river shape could be accurately expressed. As a result, it was concluded that applying a DSM rather than a DEM does not significantly expand the sedimentation range, and results that reflect the site situation well can be obtained.

• Journal of Convergence for Information Technology
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• v.12 no.4
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• pp.111-118
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• 2022

This study is an analysis of auditor's response to audit risk. Specifically, audit risk is measured by the amount of correction of the current financial statements, and auditor's response is measured by the rate of change of audit hour in next auditing. The amount of correction can be viewed as audit risk recognized by auditor because the degree of auditor's correction will increase as the company's financial statement preparation ability is lower or the profit management amount is larger. Auditor's response is measured as the rate of change of audit hour because audit risk would be incorporated in audit plan. Although auditing is performed by a team, auditor's response would differ depending on their roles. It is expected the leaders who establish the audit plan and manage the audit quality would respond more sensitively to audit risk than the other auditors. The results show that when the amount of correction is greater than a certain level, auditors recognize it as audit risk and increase total(and leaders') audit hour in next year audit.

• Journal of Korea Water Resources Association
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• v.55 no.3
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• pp.245-255
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• 2022

Evaluating the qualitative the qualitative process of water resources by using various indicators, as one of the most prevalent methods for optimal managing of water bodies, is necessary for having one regular plan for protection of water quality. In this study, zoning maps were developed on a yearly basis by collecting and reviewing the process, validating, and performing statistical tests on qualitative parameters҆ data of the Iranian aquifers from 1995 to 2020 using Geographic Information System (GIS), and based on Inverse Distance Weighting (IDW), Radial Basic Function (RBF), and Global Polynomial Interpolation (GPI) methods and Kriging and Co-Kriging techniques in three types including simple, ordinary, and universal. Then, minimum uncertainty and zoning error in addition to proximity for ASE and RMSE amount, was selected as the optimum model. Afterwards, the selected model was zoned by using Scholar and Wilcox. General evaluation of groundwater situation of Iran, revealed that 59.70 and 39.86% of the resources are classified into the class of unsuitable for agricultural and drinking purposes, respectively indicating the crisis of groundwater quality in Iran. Finally, for validating the extracted results, spatial changes in water quality were evaluated using the Groundwater Quality Index (GWQI), indicating high sensitivity of aquifers to small quantitative changes in water level in addition to severe shortage of groundwater reserves in Iran.

• The Journal of the Korea Contents Association
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• v.22 no.1
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• pp.368-379
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• 2022

This study aims to understand the characteristics of Korean language learners and propose appropriate teaching plans for them through a focus group interview with Korean language instructors who had experience in teaching Korean at university-attached institutions in Hong Kong. For this purpose, the investigator interviewed ten instructors who taught Korean for at least five years. Korean language learners in Hong Kong who were in their twenties, but there were diverse age groups, including those in their fifties or older. Their motivations for learning Korean included the Korean Wave and the influence of support from the Continuing Education Fund by the Hong Kong government. Korean language learners in Hong Kong were characterized by active learning desire and effort, continuous learning intention, passive performance in speaking, and sensitivity to the disclosure of private information. Based on these findings, the study proposes to devise teaching and learning methods based on various age groups in a class and teaching methods for speaking that reflect the characteristics of Korean language learners in Hong Kong to teach Korean more effectively. The study is significant as a field study that examines the learning motivations, learning attitudes, and difficulties with Korean study of Korean language learners based on an unprecedented survey of the characteristics of common local Korean learners in Hong Kong.

• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
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• v.28 no.1
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• pp.47-53
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• 2022

Recently, intelligent packaging of foods has been increasingly developed in response to the growing interest of consumers in checking food quality. Indicators, an important element in intelligent packaging, change color to detect specific substances or indicate food quality changes. Gas indicators can be built into food packaging to detect volatile substances that are released when food quality changes. Ethylene gas is produced as climacteric fruits ripen. Climacteric fruit ripening results from a rapid increase in ethylene production and respiration. In the case of packaged fruits, the ethylene gas concentration in the headspace is closely related to the ripeness of each fruit variety. If an ethylene gas indicator that can be used in fruit packaging is available, the consumer will be able to eat the fruit at the optimal time. In this paper, the characteristics and pros and cons of the ethylene gas indicators developed so far were analyzed by reviewing various types of indicators such as metal reduction-based indicator, fluorescence-based indicator, pH indicator-based indicator, and liposome-based indicator.

• Journal of the Korean Regional Science Association
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• v.38 no.4
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• pp.59-74
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• 2022

We aim to explore how the COVID-19 affects commercial sales of traditional market in Seoul. We obtain data for commercial sales and several spatial variables that are related to commercial sales from the Seoul Open Data Plaza. In order to estimate the effect of COVID-19 occurrence on commercial sales, we employ fixed-effect panel data analysis models. Unlike our expectation, the empirical results show that the effect of the COVID-19 on commercial sales of traditional market is not significant. However, we found that the effects are significant in some types of businesses when we did the same analyses with subsamples. For example, service sectors are mostly negatively affected by COVID-19, and retail sectors are also second mostly affected by COVID-19. However, there is no significant relationship between COVID-19 and restaurant sectors. In addition, these effects vary by size of traditional market. Our results suggest that government should have a plan to help small businesses in traditional market because they do not have sufficient abilities to adjust to the unexpected economic shock, like COVID-19. Findings also suggest that strategies for helping them should be differentiated by business type and market size.

• Journal of Korean Tunnelling and Underground Space Association
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• v.11 no.4
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• pp.373-385
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• 2009

A new indexing methodology so called KTH-index was developed to quantitatively evaluate a potential level for tunnel collapse hazard, which has been successfully applied to tunnel construction sites to date. In this study, an attempt is made to apply this methodology for validating an outcome of tunnel design by checking the variation of KTH-index along longitudinal tunnel section. In this KTH-index simulation, it is the most important to determine the input factors reasonably. The design factor and construction condition are set up based on the designed outcome. Uncertain ground conditions are arranged based on borehole test and electro-resistivity survey data. Two scenarios for ground conditions, best and worst scenarios, are set up. From this simulation, it is shown that this methodology could be successfully applied for providing quantitative validity of a tunnel design and also potential hazard factors which should be carefully monitored in construction stage. The hazard factors would affect sensitively the hazard level of the tunnel site under consideration.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.61-67
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• 2024

Soldiers serving in military units and institutions are subject to strict security policies and technologies because they handle sensitive and confidential information related to national security, so they are likely to experience security stress. The purpose of this study is to recognize the need to manage the security stress of military personnel and to suggest management measures. To this end, a literature study was conducted on 12 KCI(Korean Journal Citation Index) journals dealing with security stress. Since 2016, research on security stress has been conducted mainly through empirical analysis through surveys. Studies related to security stress were divided into studies dealing with factors that affect stress, the relationship between security stress and security compliance intentions, and factors that reduce security stress. In particular, it was confirmed that factors such as organizational justice, organizational technical support, and security feedback can alleviate security stress. Next, by applying the results of this literature study to the defense security environment, we presented security stress management measures for military personnel in terms of improving security-related organizational justice awareness, technical support, and security feedback. The significance of this study is that we recognized the need to manage military personnel's security stress and reviewed practical measures related to this.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.105-129
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• 2020

This study uses corporate data from 2012 to 2018 when K-IFRS was applied in earnest to predict default risks. The data used in the analysis totaled 10,545 rows, consisting of 160 columns including 38 in the statement of financial position, 26 in the statement of comprehensive income, 11 in the statement of cash flows, and 76 in the index of financial ratios. Unlike most previous prior studies used the default event as the basis for learning about default risk, this study calculated default risk using the market capitalization and stock price volatility of each company based on the Merton model. Through this, it was able to solve the problem of data imbalance due to the scarcity of default events, which had been pointed out as the limitation of the existing methodology, and the problem of reflecting the difference in default risk that exists within ordinary companies. Because learning was conducted only by using corporate information available to unlisted companies, default risks of unlisted companies without stock price information can be appropriately derived. Through this, it can provide stable default risk assessment services to unlisted companies that are difficult to determine proper default risk with traditional credit rating models such as small and medium-sized companies and startups. Although there has been an active study of predicting corporate default risks using machine learning recently, model bias issues exist because most studies are making predictions based on a single model. Stable and reliable valuation methodology is required for the calculation of default risk, given that the entity's default risk information is very widely utilized in the market and the sensitivity to the difference in default risk is high. Also, Strict standards are also required for methods of calculation. The credit rating method stipulated by the Financial Services Commission in the Financial Investment Regulations calls for the preparation of evaluation methods, including verification of the adequacy of evaluation methods, in consideration of past statistical data and experiences on credit ratings and changes in future market conditions. This study allowed the reduction of individual models' bias by utilizing stacking ensemble techniques that synthesize various machine learning models. This allows us to capture complex nonlinear relationships between default risk and various corporate information and maximize the advantages of machine learning-based default risk prediction models that take less time to calculate. To calculate forecasts by sub model to be used as input data for the Stacking Ensemble model, training data were divided into seven pieces, and sub-models were trained in a divided set to produce forecasts. To compare the predictive power of the Stacking Ensemble model, Random Forest, MLP, and CNN models were trained with full training data, then the predictive power of each model was verified on the test set. The analysis showed that the Stacking Ensemble model exceeded the predictive power of the Random Forest model, which had the best performance on a single model. Next, to check for statistically significant differences between the Stacking Ensemble model and the forecasts for each individual model, the Pair between the Stacking Ensemble model and each individual model was constructed. Because the results of the Shapiro-wilk normality test also showed that all Pair did not follow normality, Using the nonparametric method wilcoxon rank sum test, we checked whether the two model forecasts that make up the Pair showed statistically significant differences. The analysis showed that the forecasts of the Staging Ensemble model showed statistically significant differences from those of the MLP model and CNN model. In addition, this study can provide a methodology that allows existing credit rating agencies to apply machine learning-based bankruptcy risk prediction methodologies, given that traditional credit rating models can also be reflected as sub-models to calculate the final default probability. Also, the Stacking Ensemble techniques proposed in this study can help design to meet the requirements of the Financial Investment Business Regulations through the combination of various sub-models. We hope that this research will be used as a resource to increase practical use by overcoming and improving the limitations of existing machine learning-based models.