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Dr. Marcus's research interests lie in the areas of control and systems engineering, analysis and control of stochastic systems, Markov decision processes, stochastic and adaptive control, learning, fault detection, and discrete event systems, with applications in manufacturing, acoustics, and communication networks.
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This paper proposes iterative learning fault-tolerant control (ILTFC) in terms of common multi-phase batch processes and then applies it to the injection molding processes.
Based on an equivalent two-dimensional Fornasini-Marchsini model for a batch process in industry, a closed-loop robust iterative learning fault-tolerant guaranteed cost control scheme is proposed for batch processes with actuator failures.
Importantly, these AI technologies are integrated in LDP in a synergistic manner: the diagnosis algorithm is modified to consider the learned fault predictions and the planner is modified to consider the possible diagnoses outputted by the diagnosis algorithm.
The use of sparse-autoencoder to learn fault features improves the classification performance significantly with a small number of training data.
A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox.
In essence, this view assumes that if students aren't learning, the fault lies squarely with their teachers alone.
The frontier for geophysicists "is learning how faults take up [plate] motions at the boundary zones," he says.
The Big Island of Hawai'i: Students visit an active volcano and learn about fault systems.
Next, these are practically applied to nonlinear time series identification, prediction, and fault detection problems resulting in higher mapping accuracy, faster learning speed, higher fault sensitivity, and decision reliability in compare to conventional approaches.
Antifragile systems [10] however are defined as systems that adapt after a fault occurs and are not only resilient but also learn from faults and incidents to improve their delivered service level.
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