• Journal of Information Technology Applications and Management
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• 제31권1호
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• pp.1-9
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• 2024

Predicting the spread of COVID-19 remains a challenge due to the complexity of the disease and its evolving nature. This study presents an integrated approach using the classic SIR model for infectious diseases, enhanced by the chemical master equation (CME). We employ a Monte Carlo method (SSA) to solve the model, revealing unique aspects of the SARS-CoV-2 virus transmission. The study, a first of its kind in Korea, adopts a step-by-step and complementary approach to model prediction. It starts by analyzing the epidemic's trajectory at local government levels using both basic and stochastic SIR models. These models capture the impact of public health policies on the epidemic's dynamics. Further, the study extends its scope from a single-infected individual model to a more comprehensive model that accounts for multiple infections using the jump SIR prediction model. The practical application of this approach involves applying these layered and complementary SIR models to forecast the course of the COVID-19 epidemic in small to medium-sized local governments, particularly in Gangnam-gu, Seoul. The results from these models are then compared and analyzed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권4호
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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.

• Korean Chemical Engineering Research
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• 제61권1호
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• pp.62-67
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• 2023

반도체, 디스플레이 등 IT(Information Technology) 제품 수요 증가로 관련 산업이 확대되고 있다. 이는 생산설비 증설과 화학물질 사용 증가로 이어지며 화재·폭발의 위험성에도 영향을 미치고 있다. 이러한 위험요인에 대해 정부는 오래전부터 인화성 물질을 제조·사용·취급하는 장소의 사고 예방을 위하여 산업안전보건법 및 KS 기준에 따라 폭발위험 장소로 설정하여 관리토록 하고 있다. 그러나, 폭발위험장소를 설정할 때, 중요한 요소인 환기량을 고려하지 않아 실질적인 폭발분위기 조성 가능성을 예측하기는 쉽지 않다. 이 연구에서는 디스플레이 산업에서 주요 공정인 CVD(Chemical Vapor Deposition) 설비에 SEMI S6 Exhaust Ventilation Test 방법을 적용하여 위험한 설비의 환기 성능을 평가하고, 폭발분위기 조성 가능성을 확인하였다. 그 결과, 가상의 시나리오 내에서 환기 성능이 SEMI S6에서 규정한 기준에 적합하였고, 폭발분위기가 조성될 가능성이 낮음을 확인하였다. 따라서, KS 규격뿐만 아니라 공학적 기법으로 폭발분위기의 형성 여부를 예측한 연구 결과를 통해 합리적이고 경제적인 사고 예방에 도움이 될 것으로 기대된다.