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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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01 Apr 2021
TL;DR: In this article, the authors introduce a general approach to bounding the performance of ADP methods in the stochastic setting, based on quantifying certain notions of curvature of string functions; the smaller the curvatures the better the bound.
Abstract: For years, there has been interest in approximation methods for solving dynamic programming problems, because of the inherent complexity in computing optimal solutions characterized by Bellman’s principle of optimality. A wide range of approximate dynamic programming (ADP) methods now exists. It is of great interest to guarantee that the performance of an ADP scheme be at least some known fraction, say $\beta $ , of optimal. This letter introduces a general approach to bounding the performance of ADP methods, in this sense, in the stochastic setting. The approach is based on new results for bounding greedy solutions in string optimization problems, where one has to choose a string (ordered set) of actions to maximize an objective function. This bounding technique is inspired by submodularity theory, but submodularity is not required for establishing bounds. Instead, the bounding is based on quantifying certain notions of curvature of string functions; the smaller the curvatures the better the bound. The key insight is that any ADP scheme is a greedy scheme for some surrogate string objective function that coincides in its optimal solution and value with those of the original optimal control problem. The ADP scheme then yields to the bounding technique mentioned above, and the curvatures of the surrogate objective determine the value $\beta $ of the bound. The surrogate objective and its curvatures depend on the specific ADP.

6 citations

Journal ArticleDOI
TL;DR: In this article , a tube-based model predictive control (MPC) scheme was proposed for state and input-constrained linear systems subject to dynamic uncertainties characterized by dynamic integral quadratic constraints (IQCs).
Abstract: In this work, we propose a tube-based model predictive control (MPC) scheme for state- and input-constrained linear systems subject to dynamic uncertainties characterized by dynamic integral quadratic constraints (IQCs). In particular, we extend the framework of $\rho$ -hard IQCs for exponential stability analysis to external inputs. This result yields that the error between the true uncertain system and the nominal prediction model is bounded by an exponentially stable scalar system. In the proposed tube-based MPC scheme, the state of this error bounding system is predicted along with the nominal model and used as a scaling parameter for the tube size. We prove that this method achieves robust constraint satisfaction and input-to-state stability despite dynamic uncertainties and additive bounded disturbances. A numerical example demonstrates the reduced conservatism of this IQC approach compared to state-of-the-art robust MPC approaches for dynamic uncertainties.

6 citations

Journal ArticleDOI
TL;DR: In this article , the authors present the physics of the hard-disk model, the definition of the pressure and its unbiased estimators, several of which are new, and treat different sampling algorithms and crucial criteria for bounding mixing times in the absence of analytical predictions.
Abstract: We discuss pressure computations for the hard-disk model performed since 1953 and compare them to the results that we obtain with a powerful event-chain Monte Carlo and a massively parallel Metropolis algorithm. Like other simple models in the sciences, such as the Drosophila model of biology, the hard-disk model has needed monumental efforts to be understood. In particular, we argue that the difficulty of estimating the pressure has not been fully realized in the decades-long controversy over the hard-disk phase-transition scenario. We present the physics of the hard-disk model, the definition of the pressure and its unbiased estimators, several of which are new. We further treat different sampling algorithms and crucial criteria for bounding mixing times in the absence of analytical predictions. Our definite results for the pressure, for up to one million disks, may serve as benchmarks for future sampling algorithms. A synopsis of hard-disk pressure data as well as different versions of the sampling algorithms and pressure estimators are made available in an open-source repository.

6 citations

Journal ArticleDOI
TL;DR: Wang et al. as mentioned in this paper proposed an enhanced feature foreground network (EFFNet) that performs both smoke source prediction and detection, which can be applied to efficiently focus on a certain object or a specific region to detect movements or anomalies.
Abstract: Smoke detection in video is a challenging task because of the irregular shape of smoke, its complex motion state, which is affected by temperature, wind and other external factors, and background disturbances. Pixel-based foreground modeling method is a crucial step in many smoke detection systems and can be applied to efficiently focus on a certain object or a specific region to detect movements or anomalies. In video analysis, it is a natural idea to move the focus from the pixel-level foreground to the feature-level foreground. In this paper, the feature foreground is generated by the middle layer of a convolutional neural network (CNN) to guide the temporal modeling process for smoke objects. A novel temporal module called the Feature Foreground Module (FFM) is proposed to boost learning of a smoke temporal representation. Consider the problem of smoke analysis in video, we present a novel unifying approach, named an enhanced feature foreground network (EFFNet), that performs both smoke source prediction and detection. Efficient branch networks are designed in EFFNet, to predict the source mask and bounding boxes of smoke plumes in video. To the best of our knowledge, this is the first paper to study the source of smoke using deep learning methods. Finally, experiments on a realistic smoke dataset and a public dataset show that EFFNet method performs much better than do previous state-of-the-art methods.

6 citations


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