• Korean Journal of Culture and Social Issue
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• v.18 no.2
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• pp.235-260
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• 2012

This study was aimed at looking into and understanding child-rearing experience of mothers from multicultural families based on the grounded theory, one of the qualitative research methods. For this, twelve mothers from multicultural families were selected and data were collected through in-depth interviews with them and then analyzed through the open coding, axial coding and selective coding. The analysis suggested that main phenomena mothers from multicultural families experienced are the conflict of child-rearing and educational crisis, and the confused identity of parenting. These main phenomena had much to do with contextual factors such as economic hardship, lower level of the Korean language, cultural differences, prejudices and ignorance. The severeness of conflict and crisis of child-rearing and the intensity of identity confusion for parenting depended on arbitral conditions such as family bonds, positive expectation for the future of their children, and the help of supportive systems. Mothers were dealing with difficulties of child-rearing through action and interaction of resignation and denial, introspection and the intention to overcome difficulties, and the capacity building. As a result, mothers from multicultural families acknowledged that there is a limit to their position and roles they can play and tried to change in order to resolve problems related to their children, positioning themselves for more active lives in Korean society. Through discussion based on the findings of the study, this study can help better understand child-rearing experience of mothers from multicultural families and suggest several directions for future researches on multicultural families.

• Informatization Policy
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• v.30 no.4
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• pp.3-39
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• 2023

With the growing importance of information/information system, information security is emphasized, and the significance of information security audit as a tool for maintaining the proper security level is increasing as well. The objectives of the study are to identify the overall research trends and to propose future research areas by analyzing domestic and overseas research in the area. To achieve the objectives, 103 research papers were analyzed based on both general and subject-related criteria. The following are the major research results : In terms of research approach, more empirical studies are needed; For subject "Auditor," studies to develop a framework for related variables (e.g., capability) are needed; For subject "Audit Activities/Procedures," future research should focus on the process/results of detailed audit activities; Future domestic research for "Audit Areas" should look for the new technology/industry/security areas covered by foreign studies; For "Audit Objective/Impact," studies to define the variables (e.g., performance and quality) systematically and comprehensively are needed; For "Audit Standard/Guidelines," research on model/guideline needs to be continued.

• Journal of Advanced Navigation Technology
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• v.26 no.6
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• pp.434-440
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• 2022

The ICAO (International civil aviation organization)recommended the implementation of the GANP (global air navigation plan) for strategic decision-making and air traffic management evaluation. In this study, we proposed a new method for finding the route distance from KPI (key performance indicator) 05 actual route extension presented for air traffic management evaluation. For this purpose, we collected trajectory data for one month and calculated the en-route distances using the methods presented in ICAO and the methods presented by this author. In the ICAO method, the intersection point must be estimated through the equation of a circle for radius 40 NM and the equation of a straight line for an inner and outer point close to a circle in the track data, and four flight distances are calculated to calculate the en-route distance. In the method presented in this study, two flight distances are calculated without estimating the intersection point to calculate the en-route distance. To determine the error between the two methods, we used the performance evaluation index RMSE (root mean square error) and the determination factor R² of the regression model.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1529-1539
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• 2023

The data from Geostationary Ocean Color Imager-II (GOCI-II), which observes the color of the sea to monitor marine environments, undergoes various correction processes in the ground station system, producing data from Raw to Level-2 (L2). Quality issues arising at each processing stage accumulate step by step, leading to an amplification of errors in the satellite data. To address this, improvements were made to the GOCI-II ground station system to measure potential optical quality and geolocation accuracy errors in the Level-1A/B (L1A/B) data. A newly established Radiometric and Geometric Performance Assessment Module (RGPAM) now measures five optical quality factors and four geolocation accuracy factors in near real-time. Testing with GOCI-II data has shown that RGPAM's functions, including data processing, display and download of measurement results, work well. The performance metrics obtained through RGPAM are expected to serve as foundational data for real-time radiometric correction model enhancements, assessment of L1 data quality consistency, and the development of reprocessing strategies to address identified issues related to the GOCI-II detector's sensitivity degradation.

• Korean Journal of Environmental Biology
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• v.41 no.3
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• pp.204-214
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• 2023

This study aimed to conduct a comprehensive assessment of the potential impact of deforestation and forest restoration on carbon storage in North Korea until 2050, employing rigorous analyses of trends of land use change in the past periods and projecting future land use change scenarios. We utilized the CA-Markov model, which can reflect spatial trends in land use changes, and verified the impact of forest restoration strategies on carbon storage by creating land use change scenarios (reforestation and non-reforestation). We employed two distinct periods of land use maps (2000 to 2010 and 2010 to 2020). To verify the overall terrestrial carbon storage in North Korea, our evaluation included estimations of carbon storage for various elements such as above-ground, below-ground, soil, and debris (including litters) for settlement, forest, cultivated, grass, and bare areas. Our results demonstrated that effective forest restoration strategies in North Korea have the potential to increase carbon storage by 4.4% by the year 2050, relative to the carbon storage observed in 2020. In contrast, if deforestation continues without forest restoration efforts, we predict a concerning decrease in carbon storage by 11.5% by the year 2050, compared to the levels in 2020. Our findings underscore the significance of prioritizing and continuing forest restoration efforts to effectively increase carbon storage in North Korea. Furthermore, the implications presented in this study are expected to be used in the formulation and implementation of long-term forest restoration strategies in North Korea, while fostering international cooperation towards this common environmental goal.

• Journal of Preventive Medicine and Public Health
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• v.56 no.5
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• pp.467-474
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• 2023

Objectives: Diabetes mellitus (DM) is a serious public health issue that places a heavy financial, social, and health-related burden on individuals, families, and healthcare systems. Self-reported health-related quality of life (HRQoL) is extensively used for monitoring the general population's health conditions and measuring the effectiveness of interventions. Therefore, this study investigated HRQoL and associated factors among patients with type 2 DM at a primary healthcare center in Indonesia. Methods: A cross-sectional study was conducted in Klaten District, Central Java, Indonesia, from May 2019 to July 2019. In total, 260 patients with DM registered with National Health Insurance were interviewed. HRQoL was measured with the EuroQol Group's validated Bahasa Indonesia version of the EuroQoL 5-Dimension 5-Level (EQ-5D-5L) with the Indonesian value set. Multivariate regression models were used to identify factors influencing HRQoL. Results: Data from 24 patients were excluded due to incomplete information. Most participants were men (60.6%), were aged above 50 years (91.5%), had less than a senior high school education (75.0%), and were unemployed (85.6%). The most frequent health problems were reported for the pain/discomfort dimension (64.0%) followed by anxiety (28.4%), mobility (17.8%), usual activities (10.6%), and self-care (6.8%). The average EuroQoL 5-Dimension (EQ-5D) index score was 0.86 (95% confidence interval [CI], 0.83 to 0.88). In the multivariate ordinal regression model, a higher education level (coefficient, 0.08; 95% CI, 0.02 to 0.14) was a significant predictor of the EQ-5D-5L utility score. Conclusions: Patients with diabetes had poorer EQ-5D-5L utility values than the general population. DM patients experienced pain/discomfort and anxiety. There was a substantial positive relationship between education level and HRQoL.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.12
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• pp.363-370
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• 2023

Object detection technology that accurately recognizes the road and surrounding conditions is a key technology in the field of autonomous driving. In the field of autonomous driving, object detection technology requires real-time performance as well as accuracy of inference services. Task offloading technology should be utilized to apply object detection technology for accuracy and real-time on resource-constrained devices rather than high-performance machines. In this paper, experiments such as performance comparison of task offloading, performance comparison according to input image resolution, and performance comparison according to camera object resolution were conducted and the results were analyzed in relation to the application of task offloading for real-time object detection of autonomous driving in resource-constrained devices. In this experiment, the low-resolution image could derive performance improvement through the application of the task offloading structure, which met the real-time requirements of autonomous driving. The high-resolution image did not meet the real-time requirements for autonomous driving due to the increase in communication time, although there was an improvement in performance. Through these experiments, it was confirmed that object recognition in autonomous driving affects various conditions such as input images and communication environments along with the object recognition model used.

• Journal of Industrial Convergence
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• v.21 no.9
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• pp.41-48
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• 2023

Recently, there has been a sharp increase in the damages caused by ransomware across various sectors of society, including individuals, businesses, and nations. Ransomware is a malicious software that infiltrates user computer systems, encrypts important files, and demands a ransom in exchange for restoring access to the files. Due to its diverse and sophisticated attack techniques, ransomware is more challenging to detect than other types of malware, and its impact is significant. Therefore, there is a critical need for accurate detection and mitigation methods. To achieve precise ransomware detection, an inference engine of a detection system must possess knowledge of ransomware features. In this paper, we propose a model to extract and classify the characteristics of ransomware for accurate detection of ransomware, calculate the similarity of the extracted characteristics, reduce the dimension of the characteristics, group the reduced characteristics, and classify the characteristics of ransomware into attack tools, inflow paths, installation files, command and control, executable files, acquisition rights, circumvention techniques, collected information, leakage techniques, and state changes of the target system. The classified characteristics were applied to the existing ransomware to prove the validity of the classification, and later, if the inference engine learned using this classification technique is installed in the detection system, most of the newly emerging and variant ransomware can be detected.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.395-413
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• 2023

This research studied the effect of customer experience characteristics in offline home appliance stores on perceived value and revisit intention. Among the offline distribution of home appliances with more than 100 stores nationwide, two home appliance retailers (HiMart, E-Land), three hypermarkets (E-Mart, Homeplus, Lotte Hi-Mart), and two home appliance stores (LG Best Shop, Samsung Digital Plaza) were selected, and a survey was conducted on men and women in their 20s or older in Seoul, Gyeonggi, and Incheon who had visited and purchased the home appliance store within the last 6 months. As a result of the survey, a statistical analysis was conducted on a total of 330 samples using the PLS (Partial Least Squares) structural equation model and SPSS statistical package. Through this study, the following research results can be obtained. First, educational experience, deviant experience, and aesthetic experience had a positive (+) effect on the functional value. However, entertainment experience did not affect functional value. Second, educational experience, deviant experience, and aesthetic experience all had a positive (+) effect on emotional value. Third, both functional and sensory values had a positive (+) effect on the revisit intention. Fourth, it was confirmed that brand loyalty had no moderating effect between functional value and sensory value revisit intention. The results of this study show the structural relationship between customer experience characteristics, perceived value (functional value, sensory value), and revisit intention. This result provides guidelines on what activities home appliance offline stores should do at a time when online channels threaten the survival of offline channels.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.325-345
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• 2023

In the rapidly evolving domain of e-commerce, our study presents a cohesive approach to enhance customer satisfaction prediction from online reviews, aligning methodological innovation with practical insights. We integrate the RFE-SHAP feature selection with LDA topic modeling to streamline predictive analytics in e-commerce. This integration facilitates the identification of key features-specifically, narrowing down from an initial set of 28 to an optimal subset of 14 features for the Random Forest algorithm. Our approach strategically mitigates the common issue of overfitting in models with an excess of features, leading to an improved accuracy rate of 84% in our Random Forest model. Central to our analysis is the understanding that certain aspects in review content, such as quality, fit, and durability, play a pivotal role in influencing customer satisfaction, especially in the clothing sector. We delve into explaining how each of these selected features impacts customer satisfaction, providing a comprehensive view of the elements most appreciated by customers. Our research makes significant contributions in two key areas. First, it enhances predictive modeling within the realm of e-commerce analytics by introducing a streamlined, feature-centric approach. This refinement in methodology not only bolsters the accuracy of customer satisfaction predictions but also sets a new standard for handling feature selection in predictive models. Second, the study provides actionable insights for e-commerce platforms, especially those in the clothing sector. By highlighting which aspects of customer reviews-like quality, fit, and durability-most influence satisfaction, we offer a strategic direction for businesses to tailor their products and services.