Cheol Young Park

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—High-level fusion of hard and soft information from diverse sensor types still depends heavily on human cognition. This results in a scalability conundrum that current technologies are incapable of solving. Although there is widespread acknowledgement that an HLF framework must support automated knowledge representation and reasoning with uncertainty,(More)
In an increasingly interconnected world information comes from various sources, usually with distinct, sometimes inconsistent semantics. Transforming raw data into high-level information fusion (HLIF) products, such as situation displays, automated decision support , and predictive analysis, relies heavily on human cognition. There is a clear lack of(More)
OBJECTIVE To evaluate whether there is a difference in the association between nonalcoholic fatty liver disease (NAFLD) and incident diabetes based on the presence of impaired fasting glucose. RESEARCH DESIGN AND METHODS A total of 7,849 individuals (5,409 men and 2,440 women) without diabetes, who underwent comprehensive health check-ups annually for 5(More)
BACKGROUND Hypertension is common in patients with type 2 diabetes, affecting up to 60% of patients. The Korean Diabetes Association performed a nationwide survey about prevalence, awareness and control of hypertension among diabetic Koreans. METHODS The current survey included 3,859 diabetic patients recruited from 43 hospitals in Korea. Age, gender,(More)
BACKGROUND This study aims to investigate the discrepancy between clinicians' perceptions and actual achievement rates of low density lipoprotein cholesterol (LDL-C) in Korean patients with diabetes according to updated American Diabetes Association (ADA)/American College of Cardiology Foundation (ACC) recommendations. METHODS This is a multi-center,(More)
BACKGROUND/AIMS The aim was to determine which of three sets of metabolic syndrome (MetS) criteria (International Diabetes Federation [IDF], National Cholesterol Education Program Adult Treatment Panel III [ATP III], and European Group for the Study of Insulin Resistance [EGIR]) best predicts the coronary artery calcification (CAC) score in a(More)