• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.155-155
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• 2018

Uncertainty in flood forecasting using a coupled meteorological and hydrological model is arisen from various sources, especially the uncertainty comes from the inaccuracy of Quantitative Precipitation Forecasts (QPFs). In order to improve the capability of flood forecast, the uncertainty estimation and mitigation are required to perform. This study is conducted to investigate and reduce such uncertainty. First, ensemble QPFs are generated by using Monte - Carlo simulation, then each ensemble member is forced as input for a hydrological model to obtain ensemble streamflow prediction. Likelihood measures are evaluated to identify feasible member. These members are retained to define upper and lower limits of the uncertainty interval and assess the uncertainty. To mitigate the uncertainty for very short lead time, a blending method, which merges the ensemble QPFs with radar-based rainfall prediction considering both qualitative and quantitative skills, is proposed. Finally, blending bias ratios, which are estimated from previous time step, are used to update the members over total lead time. The proposed method is verified for the two flood events in 2013 and 2016 in the Yeonguol and Soyang watersheds that are located in the Han River basin, South Korea. The uncertainty in flood forecasting using a coupled Local Data Assimilation and Prediction System (LDAPS) and Sejong University Rainfall - Runoff (SURR) model is investigated and then mitigated by blending the generated ensemble LDAPS members with radar-based rainfall prediction that uses McGill algorithm for precipitation nowcasting by Lagrangian extrapolation (MAPLE). The results show that the uncertainty of flood forecasting using the coupled model increases when the lead time is longer. The mitigation method indicates its effectiveness for mitigating the uncertainty with the increases of the percentage of feasible member (POFM) and the ratio of the number of observations that fall into the uncertainty interval (p-factor).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.3199-3218
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• 2019

Location-based services (LBSs) have become popular in recent years due to the ever-increasing usage of smart mobile devices and mobile applications through networks. Although LBS application provides great benefits to mobile users, it also raises a sever privacy concern of users due to the untrusted service providers. In the lack of privacy enhancing mechanisms, most applications of the LBS may discourage the user's acceptance of location services in general, and endanger the user's privacy in particular. Therefore, it is a great interest to discuss on the recent privacy-preserving mechanisms in LBSs. Many existing location-privacy protection-mechanisms (LPPMs) make great efforts to increase the attacker's uncertainty on the user's actual whereabouts by generating a multiple of fake-locations together with user's actual positions. In this survey, we present a study and analysis of existing LPPMs and the state-of-art privacy measures in service quality aware LBS applications. We first study the general architecture of privacy qualification system for LBSs by surveying the existing framework and outlining its main feature components. We then give an overview of the basic privacy requirements to be considered in the design and evaluation of LPPMs. Furthermore, we discuss the classification and countermeasure solutions of existing LPPMs for mitigating the current LBS privacy protection challenges. These classifications include anonymization, obfuscation, and an encryption-based technique, as well as the combination of them is called a hybrid mechanism. Finally, we discuss several open issues and research challenges based on the latest progresses for on-going LBS and location privacy research.

• Journal of the Korea Convergence Society
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• v.10 no.3
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• pp.227-236
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• 2019

The purpose of this study is to examine the effect of TSJ-JSM (Job Stress Management) program, which was developed for improving interpersonal relationship and mitigating job stress among social workers through convergence methods with biomedical and social scientific measurements. In order to achieve the purpose, utilizing quasi-experimental design with non-equivalent control group design was applied. Participants were recruited among social welfare residential facility workers with volunteer intention, and 21 participants divided into three groups randomly: experimental, comparison, and control groups consisted of 7 workers respectively. TSL-JSM program consisted of 12 sessions were implemented to experimental group, on the other hands, other employee assistant program with 12 stages was applied to comparison group. Data analysis for verification of TSL-JSM program was conducted with SPSS 25.0. Both social scientific and biomedical scientific measures show that TSL-JSM program has most strong effects on enhancing interpersonal relationship skills and allaying level of job stress by levels of increased HRV SDNN and decreased Corisol.

• Journal of The Geomorphological Association of Korea
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• v.25 no.4
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• pp.37-47
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• 2018

Coastal sand dunes have been regarded as natural defenses to protect hinterland from disasters such as storm surge and typhoons. However, many dunes are not well-deserved in South Korea because of imprudent land development or inappropriate measures after coastal erosion. Lately, beach nourishment and dune reinforcement are emphasized as the effective and environmentally sustainable solution for the coastal protection. They are regarded good strategies to keep landscapes for a time, with little side effects. However, there is little knowledge on the construction methods including proper design and time plans for the best results.In addition, the effects of dune reinforcement in the field should be tested.In thisstudy, we performed sand filling in an eroded dune scarp and surveyed topographic changes in the beach-dune system, which is located along Dasa-ri coast, Chungnam Province, South Korea. Using a network RTK-GPS and drone-based aerial photographs, we analyzed the temporal and spatial changes in the area, before and after the reinforcement. As a result, the dune reinforcement seems to be helpful to mitigates the coastal erosion and to prevent the coastline retreat at least for one year.

• Journal of Advanced Navigation Technology
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• v.25 no.1
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• pp.52-59
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• 2021

According to the International Air Transport Association (IATA), 61% of the accidents occurred during the approach and landing phase of the flight, with 16% of the accidents caused by unstable access of the commercial aircraft. It was identified that the pilot's unstable approach and poor manipulation of correction led to accidents by continuing the excessive approach without go-around manuever. The causes of unstable access may vary, including airport approach procedures, pilot error, misplanning, workload, ATC (Air Traffic Contol) congestion, etc. In this study, we use the flight data analysis system to select domestic case airports and aircraft type where unstable approach events occur repeatedly. Through flight data analysis, including main events, airport approach procedures, pilot operations, as well as various environmental factors such as weather and geographical conditions at the airport. It aims to identify and eliminate the tendency of unstable approach events and the causes and risks of them to derive implications for mitigating unstable approach events and for developing navigation safety measures.

• Health Policy and Management
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• v.33 no.3
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• pp.338-354
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• 2023

Background: The influence of social capital on the spread of coronavirus disease 2019 (COVID-19) and related behaviors and perceptions has been recognized during the COVID-19 pandemic. This study aims to analyze the trends in social capital using primary data from the Korean Community Health Survey, which is the only available source in Korea for local-level social capital analysis. It also investigates the relationship between various variables, including social capital, as factors influencing COVID-19-related behaviors and perceptions. Methods: The study analyzed the temporal trends of social capital using raw data from four community health surveys conducted in 2017, 2019, 2020, and 2021. A multilevel analysis was conducted to examine the relationship between social capital and COVID-19-related behaviors and perceptions following the onset of the COVID-19 pandemic in 2020. Results: Social capital consists of trust, bonding social capital, and bridging social capital. Within the trust sub-factor, trust in neighbors (Trust-1) declined after the COVID-19 pandemic, whereas trust in safety and general environment (Trust-2) and trust in medical services and public transportation (Trust-3) increased. Additionally, the gap between municipalities narrowed. COVID-19-related behaviors and perceptions, such as adherence to COVID-19 prevention measures, return to normal activities, and fear of COVID-19, showed improvement in 2021 compared to the previous year. Individual-level trust in neighbors was associated with reduced fear of COVID-19, while community-level trust in neighbors was associated with increased fear of COVID-19. Conclusion: Social capital plays a role in mitigating public health crises, and it is necessary to implement active policies that address the gap in social capital between metropolitan and rural areas. Strengthening risk communication regarding emerging infectious diseases such as COVID-19 is crucial.

• Journal of Preventive Medicine and Public Health
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• v.56 no.6
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• pp.563-572
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• 2023

Objectives: The high readmission rate of patients with chronic obstructive pulmonary disease (COPD) has led to the worldwide establishment of proactive measures for identifying and mitigating readmissions. This study aimed to identify factors associated with readmission, as well as groups particularly vulnerable to readmission that require transitional care services. Methods: To apply transitional care services that are compatible with Korea's circumstances, targeted groups that are particularly vulnerable to readmission should be identified. Therefore, using the National Health Insurance Service's Senior Cohort database, we analyzed data from 4874 patients who were first hospitalized with COPD from 2009 to 2019 to define and analyze readmissions within 30 days after discharge. Logistic regression analysis was performed to determine factors correlated with readmission within 30 days. Results: The likelihood of readmission was associated with older age (for individuals in their 80s vs. those in their 50s: odds ratio [OR], 1.59; 95% confidence interval [CI], 1.19 to 2.12), medical insurance type (for workplace subscribers vs. local subscribers: OR, 0.84; 95% CI, 0.72 to 0.99), type of hospital (those with 300 beds or more vs. fewer beds: OR, 0.77; 95% CI, 0.66 to 0.90), and healthcare organization location (provincial areas vs. the capital area: OR, 1.66; 95% CI, 1.14 to 2.41). Conclusions: Older patients, patients holding a local subscriber insurance qualification, individuals admitted to hospitals with fewer than 300 beds, and those admitted to provincial hospitals are suggested to be higher-priority for transitional care services.

• Korean Journal of Remote Sensing
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• v.39 no.5_4
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• pp.1145-1153
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• 2023

Urban trees play an important role in absorbing carbon dioxide from the atmosphere, improving air quality, mitigating the urban heat island effect, and providing ecosystem services. To effectively manage and conserve urban trees, accurate spatial information on their location, condition, species, and population is needed. In this study, we propose an algorithm that uses a high-resolution urban tree cover map constructed from deep learning approach to separate trees from the urban land surface and accurately detect tree locations through local maximum filtering. Instead of using a uniform filter size, we improved the tree detection performance by selecting the appropriate filter size according to the tree height in consideration of various urban growth environments. The research output, the location and height of individual trees in human settlement over Suwon, will serve as a basis for sustainable management of urban ecosystems and carbon reduction measures.

• Journal of Food Hygiene and Safety
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• v.38 no.5
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• pp.279-286
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• 2023

Rapid and accurate detection of pathogenic bacteria is crucial for various applications, including public health and food safety. However, existing bacteria detection techniques have several drawbacks as they are inconvenient and require time-consuming procedures and complex machinery. Recently, the precision and versatility of CRISPR/Cas system has been leveraged to design biosensors that offer a more efficient and accurate approach to bacterial detection compared to the existing techniques. Significant research has been focused on developing biosensors based on the CRISPR/Cas system which has shown promise in efficiently detecting pathogenic bacteria or virus. In this review, we present a biosensor based on the CRISPR/Cas system that has been specifically developed to overcome these limitations and detect different pathogenic bacteria effectively including Vibrio parahaemolyticus, Salmonella, E. coli O157:H7, and Listeria monocytogenes. This biosensor takes advantage of the CRISPR/Cas system's precision and versatility for more efficiently accurately detecting bacteria compared to the previous techniques. The biosensor has potential to enhance public health and ensure food safety as the biosensor's design can revolutionize method of detecting pathogenic bacteria. It provides a rapid and reliable method for identifying harmful bacteria and it can aid in early intervention and preventive measures, mitigating the risk of bacterial outbreaks and their associated consequences. Further research and development in this area will lead to development of even more advanced biosensors capable of detecting an even broader range of bacterial pathogens, thereby significantly benefiting various industries and helping in safeguard human health

• Journal of Korean Society of Industrial and Systems Engineering
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• v.47 no.2
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• pp.30-39
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• 2024

This study introduces a novel approach for identifying potential failure risks in missile manufacturing by leveraging Quality Inspection Management (QIM) data to address the challenges presented by a dataset comprising 666 variables and data imbalances. The utilization of the SMOTE for data augmentation and Lasso Regression for dimensionality reduction, followed by the application of a Random Forest model, results in a 99.40% accuracy rate in classifying missiles with a high likelihood of failure. Such measures enable the preemptive identification of missiles at a heightened risk of failure, thereby mitigating the risk of field failures and enhancing missile life. The integration of Lasso Regression and Random Forest is employed to pinpoint critical variables and test items that significantly impact failure, with a particular emphasis on variables related to performance and connection resistance. Moreover, the research highlights the potential for broadening the scope of data-driven decision-making within quality control systems, including the refinement of maintenance strategies and the adjustment of control limits for essential test items.