6,138 research outputs found
Toward autonomous exploration in confined underwater environments
Author Posting. © The Author(s), 2015. This is the author's version of the work. It is posted here by permission of John Wiley & Sons for personal use, not for redistribution. The definitive version was published in Journal of Field Robotics 33 (2016): 994-1012, doi:10.1002/rob.21640.In this field note we detail the operations and discuss the results of an experiment conducted
in the unstructured environment of an underwater cave complex, using an autonomous underwater vehicle (AUV). For this experiment the AUV was equipped with two acoustic
sonar to simultaneously map the caves’ horizontal and vertical surfaces. Although the
caves’ spatial complexity required AUV guidance by a diver, this field deployment successfully demonstrates a scan matching algorithm in a simultaneous localization and mapping (SLAM) framework that significantly reduces and bounds the localization error for fully
autonomous navigation. These methods are generalizable for AUV exploration in confined
underwater environments where surfacing or pre-deployment of localization equipment are
not feasible and may provide a useful step toward AUV utilization as a response tool in
confined underwater disaster areas.This research work was partially sponsored by the EU FP7-Projects: Tecniospring-
Marie Curie (TECSPR13-1-0052), MORPH (FP7-ICT-2011-7-288704), Eurofleets2 (FP7-INF-2012-312762),
and the National Science Foundation (OCE-0955674)
Power Grid Behavioral Patterns and Risks of Generalization in Applied Machine Learning
Recent years have seen a rich literature of data-driven approaches designed
for power grid applications. However, insufficient consideration of domain
knowledge can impose a high risk to the practicality of the methods.
Specifically, ignoring the grid-specific spatiotemporal patterns (in load,
generation, and topology, etc.) can lead to outputting infeasible,
unrealizable, or completely meaningless predictions on new inputs. To address
this concern, this paper investigates real-world operational data to provide
insights into power grid behavioral patterns, including the time-varying
topology, load, and generation, as well as the spatial differences (in peak
hours, diverse styles) between individual loads and generations. Then based on
these observations, we evaluate the generalization risks in some existing ML
works causedby ignoring these grid-specific patterns in model design and
training
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Experiments with hybrid Bernstein global optimization algorithm for the OPF problem in power systems
This paper presents an algorithm based on the Bernstein form of polynomials for solving the optimal power flow (OPF) problem in electrical power networks. The proposed algorithm combines local and global optimization methods and is therefore referred to as a `hybrid'
Bernstein algorithm in the context of this work. The proposed algorithm is a branch-and-bound (B&B) procedure wherein a local search method is used to obtain a good upper bound on the global minimum at each branching node. Subsequently, the Bernstein form of polynomials is used to obtain a lower bound on the global minimum. The performance of the proposed algorithm is compared with the previously reported Bernstein algorithm
to demonstrate its effi cacy in terms of the chosen performance metrics. Furthermore, the proposed algorithm is tested by solving the OPF problem for several benchmark IEEE power system examples and its performance is compared with generic global optimization solvers such as BARON and COUENNE. The test results demonstrate that the algorithm HBBB
delivers satisfactory performance in terms of solution optimality.This research is supported by the National Research Foundation, Prime Ministers Office, Singapore, under its CREATE programme
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