skip to main content

Search for: All records

Creators/Authors contains: "Oishi, Meeko M."

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. We present a data-driven method for computing approximate forward reachable sets using separating kernels in a reproducing kernel Hilbert space. We frame the problem as a support estimation problem, and learn a classifier of the support as an element in a reproducing kernel Hilbert space using a data-driven approach. Kernel methods provide a computationally efficient representation for the classifier that is the solution to a regularized least squares problem. The solution converges almost surely as the sample size increases, and admits known finite sample bounds. This approach is applicable to stochastic systems with arbitrary disturbances and neural network verification problems by treating the network as a dynamical system, or by considering neural network controllers as part of a closed-loop system. We present our technique on several examples, including a spacecraft rendezvous and docking problem, and two nonlinear system benchmarks with neural network controllers.
  2. We present a method for dynamics-driven, user-interface design for a human-automation system via sensor selection. We define the user interface to be the output of a multiple-input-multiple-output (MIMO) linear time-invariant (LTI) system and formulate the design problem as one of selecting an output matrix from a given set of candidate output matrices. Necessary conditions for situation awareness are captured as additional constraints on the selection of the output matrix. These constraints depend on the level of trust the human has in the automation. We show that the resulting user-interface design problem is a combinatorial, set-cardinality minimization problem with set function constraints. We propose tractable algorithms to compute optimal or suboptimal solutions with suboptimality bounds. Our approaches exploit monotonicity and submodularity present in the design problem and rely on constraint programming and submodular maximization. We apply this method to the IEEE 118-bus, to construct correct-by-design interfaces under various operating scenarios.