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    Comparison of control algorithms for the blood glucose concentration in a virtual patient with an artificial pancreas
    (Elsevier, 2012) Semizer, E.; Yuceer, M.; Atasoy, I.; Berber, R.
    To obtain the most suitable control algorithm for a wearable artificial pancreas, different control algorithms were compared and tested using a Hovorka model. Model predictive control (MPC), linear and nonlinear model forms, proportional integral derivative control (PID), neural-network-based model predictive control (NN-MPC), nonlinear autoregressive moving average (NARMA-L2) and sequential quadratic programming (SQP) were evaluated using the Hovorka model. Due to the fact that modeling of biomedical processes are very complex, to present the most effective control algorithm, various control strategies were needed to application. In the control algorithms, set point tracking and disturbance rejection were performed. With respect to the rise times of the control algorithms, SQP with optimal control had the shortest time, and NARMA-L2 had the longest time. Because the control algorithm connects the glucose meter and the insulin pump in an artificial pancreas, the rise time is the most important parameter. We propose that optimal control with SQP is the most suitable control algorithm to connect the glucose meter and the insulin pump. (C) 2011 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.
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    AN INTERACTIVE GIS-BASED SOFTWARE FOR DYNAMIC MONITORING OF RIVERS
    (Scibulcom Ltd, 2014) Yetik, M. K.; Yuceer, M.; Karadurmus, E.; Semizer, E.; Calimli, A.; Berber, R.
    Water quality research and development attempts have been the most valuable resources in the sense of model calibration and verification techniques. Due to the fact that current degree of pollution in rivers and importance of the sustainable water resources management, the interactive river monitoring becomes inevitable. Within the scope of river water quality monitoring, Geographical Information Systems (GIS) are gaining widespread acceptance besides this fast and reliable water quality models and parameter estimation techniques are becoming available. However, integrating water quality models with GIS is limited in literature. This study presents an integrated platform on which ArcMap as a GIS and a water quality model in MATLAB are brought together in an interactive and user friendly manner. The software provides a considerable developments in future real time river monitoring and environmental pollution assessment.