Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği

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  • Article
    Citation - WoS: 12
    Citation - Scopus: 14
    A Molecular Communication Perspective on Airborne Pathogen Transmission and Reception Via Droplets Generated by Coughing and Sneezing
    (IEEE, 2021) Güleç, Fatih; Atakan, Barış
    Infectious diseases spread via pathogens such as viruses and bacteria. Airborne pathogen transmission via droplets is an important mode for infectious diseases. In this paper, the spreading mechanism of infectious diseases by airborne pathogen transmission between two humans is modeled with a molecular communication perspective. An end-to-end system model which considers the pathogen-laden cough/sneeze droplets as the input and the infection state of the human as the output is proposed. This model uses the gravity, initial velocity and buoyancy for the propagation of droplets and a receiver model which considers the central part of the human face as the reception interface is proposed. Furthermore, the probability of infection for an uninfected human is derived by modeling the number of propagating droplets as a random process. The numerical results reveal that exposure time affects the probability of infection. In addition, the social distance for a horizontal cough should be at least 1.7 m and the safe coughing angle of a coughing human to infect less people should be less than -25 degrees.
  • Article
    Citation - WoS: 9
    Citation - Scopus: 11
    Neural Network-Based Repetitive Learning Control of Euler Lagrange Systems: an Output Feedback Approach
    (IEEE, 2018) Tatlıcıoğlu, Enver; Çobanoğlu, Necati; Zergeroǧlu, Erkan
    In this letter, position tracking control problem of a class of fully actuated Euler Lagrange (EL) systems is aimed. The reference position vector is considered to be periodic with a known period. Only position measurements are available for control design while velocity measurements are not. Furthermore, the dynamic model of the EL systems has parametric and/or unstructured uncertainties which avoid it to be used as part of the control design. To address these constraints, an output feedback neural network-based repetitive learning control strategy is preferred. Via the design of a dynamic model independent velocity observer, the lack of velocity measurements is addressed. To compensate for the lack of dynamic model knowledge, universal approximation property of neural networks is utilized where an online adaptive update rule is designed for the weight matrix. The functional reconstruction error is dealt with the design of a novel repetitive learning feedforward term. The outcome is a dynamic model independent output feedback neural network-based controller with a repetitive learning feedforward component. The stability of the closed-loop system is investigated via rigorous mathematical tools with which semi-global asymptotic stability is ensured. © 2017 IEEE.