Topic
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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TL;DR: This paper explores error bounds for data-driven models under all possible training and testing scenarios drawn from an underlying distribution, and proposes an evaluation implementation based on Rademacher complexity theory that focuses on regression problems and can provide a tighter bound.
Abstract: Data-driven models analyze power grids under incomplete physical information, and their accuracy has been mostly validated empirically using certain training and testing datasets. This paper explores error bounds for data-driven models under all possible training and testing scenarios drawn from an underlying distribution, and proposes an evaluation implementation based on Rademacher complexity theory. We answer critical questions for data-driven models: how much training data is required to guarantee a certain error bound, and how partial physical knowledge can be utilized to reduce the required amount of data. Different from traditional Rademacher complexity that mainly addresses classification problems, our method focuses on regression problems and can provide a tighter bound. Our results are crucial for the evaluation and application of data-driven models in power grid analysis. We demonstrate the proposed method by finding generalization error bounds for two applications, i.e., branch flow linearization and external network equivalent under different degrees of physical knowledge. Results identify how the bounds decrease with additional power grid physical knowledge or more training data.
17 citations
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19 Jul 2007TL;DR: In this paper, a randomized importance sampling scheme that uses the Markov inequality is proposed to compute the probability of evidence even with known error bounds is shown to be NP-hard.
Abstract: Computing the probability of evidence even with known error bounds is NP-hard. In this paper we address this hard problem by settling on an easier problem. We propose an approximation which provides high confidence lower bounds on probability of evidence but does not have any guarantees in terms of relative or absolute error. Our proposed approximation is a randomized importance sampling scheme that uses the Markov inequality. However, a straight-forward application of the Markov inequality may lead to poor lower bounds. We therefore propose several heuristic measures to improve its performance in practice. Empirical evaluation of our scheme with state-of-the-art lower bounding schemes reveals the promise of our approach.
17 citations
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TL;DR: An algorithm for bounding the project duration distribution from below and above in the sense of stochastic convex ordering is presented and can be implemented in O(ms^2) time.
17 citations
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01 Jan 2003TL;DR: It is tried to show, that k-DOP bounding volumes can keep up with the theoretically more efficient oriented bounding boxes (OBBs) in parallel-close-proximity situations.
Abstract: In this paper we reconsider pairwise collision detection for rigid motions using a k-DOP bounding volume hierarchy. This data structure is particularly attractive because it is equally efficient for rigid motions as for arbitrary point motions (deformations). We propose a new efficient realignment algorithm, which produces tighter results compared to all known algorithms. It can be implemented easily in software and in hardware. Using this approach we try to show, that k-DOP bounding volumes can keep up with the theoretically more efficient oriented bounding boxes (OBBs) in parallel-close-proximity situations.
17 citations