Eunshin Byon

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We examine optimal repair strategies for wind turbines operated under stochastic weather conditions. In-situ sensors installed at wind turbines produce useful information about the physical conditions of the system, allowing wind farm operators to make informed decisions. Based on the information from sensors, our research objective is to derive an optimal(More)
When we consider the limited power of wireless sensors, it is necessary to reduce the dimension of data conveyed between sensors, because high dimensional data transmission requires much power consumption of sensors. For data reduction in a network, in-network data aggregation methods and collaborative compression methods were reported. However, the(More)
In this multi-university collaborative research, we will develop a framework for the dynamic data-driven fault diagnosis of wind turbines which aims at making the wind energy a competitive alternative in the energy market. This new methodology is fundamentally different from the current practice whose performance is limited due to the non-dynamic and(More)
Wind farms provide a source of clean and renewable energy. However, unlike many industries where machines are operated under more or less static conditions, wind turbines suffer from stochastic loading due to the hourly or seasonal variation of wind speed and direction. The stochastic loading of wind turbines makes their degradation or failure prediction(More)
This study develops new adaptive learning methods for a dynamic system where the dependency among variables changes over time. In general, many statistical methods focus on characterizing a system or process with historical data and predicting future observations based on a developed time-invariant model. However, for a nonstationary process with(More)
Non-alcoholic steatohepatitis (NASH) cirrhosis is the fastest growing indication for liver transplantation (LT) in the US. We aimed to determine the temporal trend behind the rise in obesity and NASH-related additions to the LT waitlist in the US and make projections for future NASH burden on the LT waitlist. We used data from the Organ Procurement and(More)