Bounding overwatch

About: Bounding overwatch is a research topic. Over the lifetime, 966 publications have been published within this topic receiving 15156 citations.

System bounding issues for analysis

Abstract: Defining system boundaries is an important part of analysis. Bounding is a separate process from partitioning or decomposing the problem. System boundaries show what is inside and outside the system whereas partitions manage the complexity of the problem. The authors discuss the tradeoffs between early and late bounding and conclude that the bounding process should be done late or at least repeated late in analysis. Specification of system boundaries improves when as much as possible is known about the problem. Analysis techniques should contain a representation and process to support system bounding. The authors demonstrate system bounding by comparing analyses done with structured analysis (SA) and object-oriented analysis (OOA). SA techniques bound the system at the start of analysis. OOA techniques bound the system either late in analysis or not at all. A boundary identification process for late bounding is presented and demonstrated. >

Performance Enhancements by Bounding Flight

Abstract: Bounding flight is a flight mode consisting of repeated cycles with alternating phases involving flapping and non-flapping. The bounding flight mode is treated as a periodic optimal control problem using an efficient optimization procedure to construct solutions. It is shown that bounding flight yields performance enhancements in the energy expenditure required for flying. In zero wind, bounding flight is superior to steady-state cruise wind at higher speeds. When flying against a headwind, the superiority of bounding flight is enlarged. It is shown that bounding flight requires less energy per range for a greater speed range. Particularly, the minimum energy per range of bounding flight is smaller than that possible with continuous flapping flight.

Automatic Convexity Deduction for Efficient Function’s Range Bounding

Abstract: Reliable bounding of a function’s range is essential for deterministic global optimization, approximation, locating roots of nonlinear equations, and several other computational mathematics areas. Despite years of extensive research in this direction, there is still room for improvement. The traditional and compelling approach to this problem is interval analysis. We show that accounting convexity/concavity can significantly tighten the bounds computed by interval analysis. To make our approach applicable to a broad range of functions, we also develop the techniques for handling nondifferentiable composite functions. Traditional ways to ensure the convexity fail in such cases. Experimental evaluation showed the remarkable potential of the proposed methods.

State-bounding estimation for nonlinear models with multiple measurements

Abstract: A hierarchical state bounding estimation method is presented for nonlinear dynamic systems where different sensors offer several measurements of the same state vector, each of which is subject to unknown but bounded disturbances and is equipped with a local processor. For each sampling time, the proposed algorithm proceeds in two stages. At the prediction stage, an approximating outer-bounding ellipsoid is computed for the reachable set of the nonlinear function of the state vector. At the correction stage, the algorithm works at two levels : Each local processor computes the state estimate and its outer-bounding ellipsoid according to the local measurements given by the corresponding sensor. These ellipsoids are transmitted simultaneously from all local processors to the fusion center which synthesizes them to compute the global state bounding ellipsoid. Then it feeds these data back to all the local processors. This feedback allows the local processors to adjust their results by taking into account the measurements of all the other sensors.