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Bounding overwatch

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


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Journal ArticleDOI
TL;DR: A very efficient contingency analysis method for detecting branch megawatt flow violation is presented, based on the linear incremental-power-flow model and consequently does not consider reactive power.
Abstract: A very efficient contingency analysis method for detecting branch megawatt flow violation is presented. The efficiency stems from the use of a bounding criterion that drastically reduces the number of branch-flow computations and limits checking, and the use of state-of-the-art compensation and sparse matrix/vector methods. The method requires no offline setup, is highly efficient, and can handle contingencies with any time of network topology and load/generation changes. The method is based on the linear incremental-power-flow model and consequently does not consider reactive power. >

137 citations

Proceedings ArticleDOI
12 Dec 2005
TL;DR: In this paper, the estimated domains are represented by zonotopes, a particular polytope defined as the linear image of a unit interval vector (i.e. unit hypercube).
Abstract: A state bounding observer aims at computing some domains which are guaranteed to contain the set of states that are consistent both with the uncertain model and with the uncertain measurements. In this paper, the estimated domains are represented by zonotopes. A zonotope is a particular polytope defined as the linear image of a unit interval vector (i.e. unit hypercube). Some results about the validated integration of ordinary differential equations are used to guarantee the inclusion of sampling errors. The main loop of the observation algorithm consists of a one step prediction with a limitation of the domain complexity and a correction using the measurements. The observer is applied to a Lotka-Volterra predator-prey model.

137 citations

Journal ArticleDOI
TL;DR: A motion planning algorithm is described for bounding over rough terrain with the LittleDog robot, and a feedback controller based on transverse linearization was implemented, and shown in simulation to stabilize perturbations in the presence of noise and time delays.
Abstract: A motion planning algorithm is described for bounding over rough terrain with the LittleDog robot. Unlike walking gaits, bounding is highly dynamic and cannot be planned with quasi-steady approximations. LittleDog is modeled as a planar five-link system, with a 16-dimensional state space; computing a plan over rough terrain in this high-dimensional state space that respects the kinodynamic constraints due to underactuation and motor limits is extremely challenging. Rapidly Exploring Random Trees (RRTs) are known for fast kinematic path planning in high-dimensional configuration spaces in the presence of obstacles, but search efficiency degrades rapidly with the addition of challenging dynamics. A computationally tractable planner for bounding was developed by modifying the RRT algorithm by using: (1) motion primitives to reduce the dimensionality of the problem; (2) Reachability Guidance, which dynamically changes the sampling distribution and distance metric to address differential constraints and discontinuous motion primitive dynamics; and (3) sampling with a Voronoi bias in a lower-dimensional “task space” for bounding. Short trajectories were demonstrated to work on the robot, however open-loop bounding is inherently unstable. A feedback controller based on transverse linearization was implemented, and shown in simulation to stabilize perturbations in the presence of noise and time delays.

136 citations

Posted Content
TL;DR: Wang et al. as discussed by the authors proposed an Efficient Intersection over Union (EIOU) loss, which explicitly measures the discrepancies of three geometric factors in BBR, i.e., the overlap area, the central point and the side length.
Abstract: In object detection, bounding box regression (BBR) is a crucial step that determines the object localization performance. However, we find that most previous loss functions for BBR have two main drawbacks: (i) Both $\ell_n$-norm and IOU-based loss functions are inefficient to depict the objective of BBR, which leads to slow convergence and inaccurate regression results. (ii) Most of the loss functions ignore the imbalance problem in BBR that the large number of anchor boxes which have small overlaps with the target boxes contribute most to the optimization of BBR. To mitigate the adverse effects caused thereby, we perform thorough studies to exploit the potential of BBR losses in this paper. Firstly, an Efficient Intersection over Union (EIOU) loss is proposed, which explicitly measures the discrepancies of three geometric factors in BBR, i.e., the overlap area, the central point and the side length. After that, we state the Effective Example Mining (EEM) problem and propose a regression version of focal loss to make the regression process focus on high-quality anchor boxes. Finally, the above two parts are combined to obtain a new loss function, namely Focal-EIOU loss. Extensive experiments on both synthetic and real datasets are performed. Notable superiorities on both the convergence speed and the localization accuracy can be achieved over other BBR losses.

134 citations

Journal ArticleDOI
TL;DR: Upper and lower bounding distributions for the activity starting- and finishing-time probability distributions are obtained, as well as upper and lower bounds for the expected starting and finishing time of each network activity, and for expected network resource flows.
Abstract: Where the durations of the activities in an acyclic scheduling network are random variables, this paper obtains upper and lower bounding distributions for the activity starting- and finishing-time probability distributions, as well as upper and lower bounds for the expected starting and finishing time of each network activity, and for expected network resource flows. The tightness of the bounds for various networks is examined, and a computational experience with the methods is reported.

132 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023714
20221,629
2021155
202075
201973
201850