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    The role of multisensor data fusion in neuromuscular control of a sagittal arm with a pair of muscles using actor-critic reinforcement learning method

    , Article Technology and Health Care ; Volume 12, Issue 6 , 2004 , Pages 425-438 ; 09287329 (ISSN) Golkhou, V ; Parnianpour, M ; Lucas, C ; Sharif University of Technology
    IOS Press  2004
    Abstract
    In this study, we consider the role of multisensor data fusion in neuromuscular control using an actor-critic reinforcement learning method. The model we use is a single link system actuated by a pair of muscles that are excited with alpha and gamma signals. Various physiological sensor information such as proprioception, spindle sensors, and Golgi tendon organs have been integrated to achieve an oscillatory movement with variable amplitude and frequency, while achieving a stable movement with minimum metabolic cost and coactivation. The system is highly nonlinear in all its physical and physiological attributes. Transmission delays are included in the afferent and efferent neural paths to... 

    Abnormality detection and monitoring in multi-sensor molecular communication

    , Article IEEE Transactions on Molecular, Biological, and Multi-Scale Communications ; Volume 5, Issue 2 , 2019 , Pages 68-83 ; 23327804 (ISSN) Ghoroghchian, N ; Mirmohseni, M ; Nasiri Kenari, M ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2019
    Abstract
    In this paper, we investigate the problem of detecting and monitoring changes (abnormality) in molecular communication (MC), using the quickest change detection (QCD) schemes. The objective is to watch an environment using a sensor network and make decisions on the time and location of changes based on the received signals from sensors in the fusion center (FC). Such assumptions call for considering spatial and temporal correlations among sensors' transmitting signals. We use the framework of Partially Observable Markov Decision Processes (POMDPs) based on non-homogeneous Markov models. The metric in detection (stopping-time) scenario is to minimize the delay of announcing an abnormality... 

    A robust short-circuit fault diagnosis for high voltage DC power supply based on multisensor data fusion

    , Article 10th International Power Electronics, Drive Systems and Technologies Conference, PEDSTC 2019, 12 February 2019 through 14 February 2019 ; 2019 , Pages 659-664 ; 9781538692547 (ISBN) Ayoubi, R ; Kaboli, S ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2019
    Abstract
    Short-circuit fault (SCF) detection is mandatory in a high voltage DC power supply (HVPS) to prevent fatal damage. The majority of converters employ a single sensor to detect the SCF. This attribute increases the interference vulnerability of the fault detection (FD) system in the presence of noise. Therefore, miss detections and false alarms are possible to occur. Miss detections and false alarms are harmful catastrophes in most applications. A commonly used method to suppress the noise impacts is using a low-bandwidth low-pass filter. However, the use of the low-bandwidth low-pass filter reduces the speed of FD due to the filter delay. This paper proposes a fast FD algorithm based on...