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436 results
  • Distributed Finite-Time Fault-Tolerant Containment Control for Multiple Unmanned Aerial Vehicles. [Journal Article]
    IEEE Trans Neural Netw Learn Syst 2019Yu Z, Liu Z, … Su CY
  • This paper investigates the distributed finite-time fault-tolerant containment control problem for multiple unmanned aerial vehicles (multi-UAVs) in the presence of actuator faults and input saturation. The distributed finite-time sliding-mode observer (SMO) is first developed to estimate the reference for each follower UAV. Then, based on the estimated knowledge, the distributed finite-time faul…
  • Parallel entangling operations on a universal ion-trap quantum computer. [Journal Article]
    Nature 2019; 572(7769):368-372Figgatt C, Ostrander A, … Monroe C
  • The circuit model of a quantum computer consists of sequences of gate operations between quantum bits (qubits), drawn from a universal family of discrete operations1. The ability to execute parallel entangling quantum gates offers efficiency gains in numerous quantum circuits2-4, as well as for entire algorithms-such as Shor's factoring algorithm5-and quantum simulations6,7. In circuits such as f…
  • Online sensor validation in sensor networks for bioprocess monitoring using swarm intelligence. [Journal Article]
    Anal Bioanal Chem 2019Brunner V, Klöckner L, … Becker T
  • Sensor faults can impede the functionality of monitoring and control systems for bioprocesses. Hence, suitable systems need to be developed to validate the sensor readings prior to their use in monitoring and control systems. This study presents a novel approach for online validation of sensor readings. The basic idea is to compare the original sensor reading with predictions for this sensor read…
  • Global-and-local-structure-based neural network for fault detection. [Journal Article]
    Neural Netw 2019; 118:43-53Zhao H, Lai Z, Chen Y
  • A novel statistical fault detection method, called the global-and-local-structure-based neural network (GLSNN), is proposed for fault detection. GLSNN is a nonlinear data-driven process monitoring technique through preserving both global and local structures of normal process data. GLSNN is characterized by adaptively training a neural network which takes both the global variance information and …
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