• Korea Journal of Hospital Management
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• v.25 no.4
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• pp.1-12
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• 2020

During the COVID-19 pandemic, most of the western countries with advanced medical technology failed to contain coronavirus. This fact triggered our research question of what factors influence the clinical outcomes like infection rates and case mortality rates. This study aims to identify the determinants of COVID-19 related infection rates and case mortality rates. We considered three sets of independent variables: 1) socio-demographic characteristics; 2) cultural characteristics; 3) healthcare system characteristics. For the analysis, we created an international dataset from diverse sources like World Bank, Worldometers, Hofstede Insight, GHS index etc. The COVID-19 related statistics were retrieved from Aug. 1. Total cases are from 95 countries. We used hierarchical regression method to examine the linear relationship among variables. We found that obesity, uncertainty avoidance, hospital beds per 1,000 made a significant influence on the standardized COVID-19 infection rates. The countries with higher BMI score or higher uncertainty avoidance showed higher infection rates. The standardized COVID-19 infection rates were inversely related to hospital beds per 1,000. In the analysis on the standardized COVID-19 case mortality rates, we found that two cultural characteristics(e.g., individualism, uncertainty avoidance) showed statistically significant influence on the case mortality rates. The healthcare system characteristics did not show any statistically significant relationship with the case mortality rates. The cultural characteristics turn out to be significant factors influencing the clinical outcomes during COVID-19 pandemic. The results imply that the persuasive communication is important to trigger the public commitment to follow preventive measures. The strategy to keep the hospital surge capacity needs to be developed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.12
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• pp.1816-1825
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• 2022

In this paper, we propose a new approach for tracking each human in a surveillance camera network with various resolution cameras. When tracking human on multiple non-overlapping cameras, the traditional appearance features are easily affected by various camera viewing conditions. To overcome this limitation, the proposed system utilizes facial information along with appearance information. In general, human images captured by the surveillance camera are often low resolution, so it is necessary to be able to extract useful features even from low-resolution faces to facilitate tracking. In the proposed tracking scheme, texture-based face descriptor is exploited to extract features from detected face after face frontalization. In addition, when the size of the face captured by the surveillance camera is very small, a super-resolution technique that enlarges the face is also exploited. The experimental results on the public benchmark Dana36 dataset show promising performance of the proposed algorithm.

• Journal of the Korean Society of Systems Engineering
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• v.18 no.2
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• pp.94-107
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• 2022

Accidents prevention and mitigation is the highest priority of nuclear power plant (NPP) operation, particularly in the aftermath of the Fukushima Daiichi accident, which has reignited public anxieties and skepticism regarding nuclear energy usage. To deal with accident scenarios more effectively, operators must have ample and precise information about key safety parameters as well as their future trajectories. This work investigates the potential of machine learning in forecasting NPP response in real-time to provide an additional validation method and help reduce human error, especially in accident situations where operators are under a lot of stress. First, a base-case SGTR simulation is carried out by the best-estimate code RELAP5/MOD3.4 to confirm the validity of the model against results reported in the APR1400 Design Control Document (DCD). Then, uncertainty quantification is performed by coupling RELAP5/MOD3.4 and the statistical tool DAKOTA to generate a large enough dataset for the construction and training of neural-based machine learning (ML) models, namely LSTM, GRU, and hybrid CNN-LSTM. Finally, the accuracy and reliability of these models in forecasting system response are tested by their performance on fresh data. To facilitate and oversee the process of developing the ML models, a Systems Engineering (SE) methodology is used to ensure that the work is consistently in line with the originating mission statement and that the findings obtained at each subsequent phase are valid.

• Journal of Internet of Things and Convergence
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• v.9 no.1
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• pp.77-87
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• 2023

Applying various association rule mining algorithms to the network intrusion detection task involves two critical issues: too large size of generated rule set which is hard to be utilized for IoT systems and hardness of control of false negative/positive rates. In this research, we propose an association rule mining algorithm based on the newly defined measures called coverage and exclusion. Coverage shows how frequently a pattern is discovered among the transactions of a class and exclusion does how frequently a pattern is not discovered in the transactions of the other classes. We compare our algorithm experimentally with the Apriori algorithm which is the most famous algorithm using the public dataset called KDDcup99. Compared to Apriori, the proposed algorithm reduces the resulting rule set size by up to 93.2 percent while keeping accuracy completely. The proposed algorithm also controls perfectly the false negative/positive rates of the generated rules by parameters. Therefore, network analysts can effectively apply the proposed association rule mining to the network intrusion detection task by solving two issues.

• Smart Structures and Systems
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• v.31 no.4
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• pp.325-334
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• 2023

Computer vision-based damage detection enables non-contact, efficient and low-cost bridge health monitoring, which reduces the need for labor-intensive manual inspection or that for a large number of on-site sensing instruments. By leveraging recent semantic segmentation approaches, we can detect regions of critical structural components and identify damages at pixel level on images. However, existing methods perform poorly when detecting small and thin damages (e.g., cracks); the problem is exacerbated by imbalanced samples. To this end, we incorporate domain knowledge to introduce a hierarchical semantic segmentation framework that imposes a hierarchical semantic relationship between component categories and damage types. For instance, certain types of concrete cracks are only present on bridge columns, and therefore the noncolumn region may be masked out when detecting such damages. In this way, the damage detection model focuses on extracting features from relevant structural components and avoid those from irrelevant regions. We also utilize multi-scale augmentation to preserve contextual information of each image, without losing the ability to handle small and/or thin damages. In addition, our framework employs an importance sampling, where images with rare components are sampled more often, to address sample imbalance. We evaluated our framework on a public synthetic dataset that consists of 2,000 railway bridges. Our framework achieves a 0.836 mean intersection over union (IoU) for structural component segmentation and a 0.483 mean IoU for damage segmentation. Our results have in total 5% and 18% improvements for the structural component segmentation and damage segmentation tasks, respectively, compared to the best-performing baseline model.

• Journal of the Korean Society for information Management
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• v.40 no.4
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• pp.167-200
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• 2023

For the record management of administrative information datasets in Korea, it is recommended to utilize SIARD as a transfer specification when transferring administrative information datasets. However, there are many cases where the application of SIARD is not suitable due to the record management unit of administrative information datasets, technical limitations of tools that support SIARD, and the realistic situation of public institutions. In this study, we propose a plan to diversify the transfer specifications of administrative information datasets other than SIARD. In the record management of administrative information datasets, the need to reproduce the user interface associated with the dataset has been discussed but not specifically presented. This study confirms that the user interface is a property to be preserved from the perspective of Significant Properties, proposes a method to effectively reproduce the user interface, and provides an example of actual verification.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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• pp.2627-2642
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• 2023

Parkinson's disease (PD) is a typical, chronic neurodegenerative disease involving the concentration of dopamine, which can disrupt motor activity and cause different degrees of gait disturbance relevant to PD severity in patients. As current clinical PD diagnosis is a complex, time-consuming, and challenging task that relays on physicians' subjective evaluation of visual observations, gait disturbance has been extensively explored to make automatic detection of PD diagnosis and severity rating and provides auxiliary information for physicians' decisions using gait data from various acquisition devices. Among them, wearable sensors have the advantage of flexibility since they do not limit the wearers' activity sphere in this application scenario. In this paper, an attention-based temporal network (ATN) is designed for the time series structure of gait data (vertical ground reaction force signals) from foot sensor systems, to learn the discriminative differences related to PD severity levels hidden in sequential data. The structure of the proposed method is illuminated by Transformer Network for its success in excavating temporal information, containing three modules: a preprocessing module to map intra-moment features, a feature extractor computing complicated gait characteristic of the whole signal sequence in the temporal dimension, and a classifier for the final decision-making about PD severity assessment. The experiment is conducted on the public dataset PDgait of VGRF signals to verify the proposed model's validity and show promising classification performance compared with several existing methods.

• Child Health Nursing Research
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• v.29 no.4
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• pp.271-279
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• 2023

Purpose: Adolescent self-harm is a public health problem. Research suggests a link between adverse childhood experiences (ACEs) and self-destructive behaviors. Few studies, however, have examined the effects of ACEs on self-harm among Asian adolescents. This study explored the association between lifetime ACEs and a history of self-harm among Korean children and adolescents in elementary, middle, and high schools. Methods: A cross-sectional, retrospective medical record review was conducted on a dataset of a national psychiatrist advisory service for school counselors who participated in the Wee Doctor Service from January 1 to December 31, 2020. The data were analyzed using multiple logistic regression to predict self-harm. Results: Student cases (n=171) were referred to psychiatrists by school counselors for remote consultation. Multiple logistic regression analyses revealed that the odds of self-harm were higher among high school students (adjusted odds ratio [aOR]=4.97; 95% confidence interval [CI]=1.94-12.76), those with two or more ACEs (aOR=3.27; 95% CI=1.43-7.47), and those with depression (aOR=3.06; 95% CI=1.32-7.10). Conclusion: The study's findings provide compelling evidence that exposure to ACEs can increase vulnerability to self-harm among Korean students. Students with a history of ACEs and depression, as well as high school students, require increased attention during counseling. School counselors can benefit from incorporating screening assessment tools that include questions related to ACEs and depression. Establishing a systematic referral system to connect students with experts can enhance the likelihood of identifying self-harm tendencies and offering the essential support to prevent self-harm.

• Advanced Industrial SCIence
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• v.2 no.4
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• pp.11-16
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• 2023

The subway is a means of public transportation that plays an important role in the transportation system of modern cities. However, congestion often occurs due to sudden breakdowns and system outages, causing inconvenience. Therefore, in this paper, we conducted a study on failure prediction and prevention using machine learning to efficiently operate the subway system. Using UC Irvine's MetroPT-3 dataset, we built a subway breakdown prediction model using logistic regression. The model predicted the non-failure state with a high accuracy of 0.991. However, precision and recall are relatively low, suggesting the possibility of error in failure prediction. The ROC_AUC value is 0.901, indicating that the model can classify better than random guessing. The constructed model is useful for stable operation of the subway system, but additional research is needed to improve performance. Therefore, in the future, if there is a lot of learning data and the data is well purified, failure can be prevented by pre-inspection through prediction.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.4
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• pp.826-842
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• 2024

As 5G and AI continue to develop, there has been a significant surge in the healthcare industry. The COVID-19 pandemic has posed immense challenges to the global health system. This study proposes an FL-supported edge computing model based on federated learning (FL) for predicting clinical outcomes of COVID-19 patients during hospitalization. The model aims to address the challenges posed by the pandemic, such as the need for sophisticated predictive models, privacy concerns, and the non-IID nature of COVID-19 data. The model utilizes the FATE framework, known for its privacy-preserving technologies, to enhance predictive precision while ensuring data privacy and effectively managing data heterogeneity. The model's ability to generalize across diverse datasets and its adaptability in real-world clinical settings are highlighted by the use of SHAP values, which streamline the training process by identifying influential features, thus reducing computational overhead without compromising predictive precision. The study demonstrates that the proposed model achieves comparable precision to specific machine learning models when dataset sizes are identical and surpasses traditional models when larger training data volumes are employed. The model's performance is further improved when trained on datasets from diverse nodes, leading to superior generalization and overall performance, especially in scenarios with insufficient node features. The integration of FL with edge computing contributes significantly to the reliable prediction of COVID-19 patient outcomes with greater privacy. The research contributes to healthcare technology by providing a practical solution for early intervention and personalized treatment plans, leading to improved patient outcomes and efficient resource allocation during public health crises.