Software Adaptation for an Unmanned Undersea Vehicle

Pfeffer, Avi; Wu, Curt; Fry, Gerald W.; Lu, Kenny Zhuo Ming; Marotta, Steve; Reposa, Mike; Shi, Yuan; Kumar, T. K. Satish; Knoblock, Craig A.; Parker, David; Muhammad, Irfan; Novakovic, Chris

doi:10.1109/ms.2018.2886815

Cited by 7 publications

(2 citation statements)

References 5 publications

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“…1 AUV perceives the information of surrounding environment via the visual perception system, providing important guarantee for the realization of autonomous navigation capabilities, target recognition and tracking, autonomous route planning, and coordinated control. 2,3 However, there are a large number of algae, plankton, and impurities in the ocean. And the water body has a strong attenuation effect on natural light.…”

Section: Introductionmentioning

confidence: 99%

Fine reconstruction of underwater images for environmental feature fusion

Zhou

Guo

et al. 2021

International Journal of Advanced Robotic Systems

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Complicated underwater environments, such as occlusion by foreign objects and dim light, causes serious loss of underwater targets feature. Furthermore, underwater ripples cause target deformation, which greatly increases the difficulty of feature extraction. Then, existing image reconstruction models cannot effectively achieve target reconstruction due to insufficient underwater target features, and there is a blurred texture in the reconstructed area. To solve the above problems, a fine reconstruction of underwater images with the target feature missing from the environment feature was proposed. Firstly, the salient features of underwater images are obtained in terms of positive and negative sample learning. Secondly, a layered environmental attention mechanism is proposed to retrieve the relevant local and global features in the context. Finally, a coarse-to-fine image reconstruction model, with gradient penalty constraints, is constructed to obtain the fine restoration results. Contrast experiment between the proposed algorithm and the existing image reconstruction methods has been done in stereo quantitative underwater image data set, real-world underwater image enhancement data set, and underwater image data set, clearly proving that the proposed one is more effective and superior.

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Section: Introductionmentioning

confidence: 99%

Fine reconstruction of underwater images for environmental feature fusion

Zhou

Guo

et al. 2021

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

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“…In this way, we can express the requirement property such as “in the MCPSs, the maximum probability to reach a set of bad states is not more than 0.001”. Some probabilistic model checking tools, such as PRISM [ 6 ] and PAT [ 7 ], have been developed and applied to quantitatively estimate, control, and synthesize the MCPSs, e.g., the mobile service robot [ 8 ], unmanned undersea vehicle [ 9 ], peacemaker [ 10 , 11 , 12 ]. As shown in Figure 1 , verifying or evaluating MCPSs is a huge challenge, which involves hardware, software, communication protocols, and so on.…”

Section: Introductionmentioning

confidence: 99%

Counterexample Generation for Probabilistic Model Checking Micro-Scale Cyber-Physical Systems

Liu

Yang

et al. 2021

Micromachines

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Micro-scale Cyber-Physical Systems (MCPSs) can be automatically and formally estimated by probabilistic model checking, on the level of system model MDPs (Markov Decision Processes) against desired requirements in PCTL (Probabilistic Computation Tree Logic). The counterexamples in probabilistic model checking are witnesses of requirements violation, which can provide the meaningful information for debugging, control, and synthesis of MCPSs. Solving the smallest counterexample for probabilistic model checking MDP has been proven to be an NPC (Non-deterministic Polynomial complete) problem. Although some heuristic methods are designed for this, it is usually difficult to fix the heuristic functions. In this paper, the Genetic algorithm optimized with heuristic, i.e., the heuristic Genetic algorithm, is firstly proposed to generate a counterexample for the probabilistic model checking MDP model of MCPSs. The diagnostic subgraph serves as a compact counterexample, and diagnostic paths of MDP constitute an AND/OR tree for constructing a diagnostic subgraph. Indirect path coding of the Genetic algorithm is used to extend the search range of the state space, and a heuristic crossover operator is used to generate more effective diagnostic paths. A prototype tool based on the probabilistic model checker PAT is developed, and some cases (dynamic power management and some communication protocols) are used to illustrate its feasibility and efficiency.

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A three-valued model abstraction framework for PCTL* stochastic model checking

Liu

Yang

2022

Autom Softw Eng

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Software Adaptation for an Unmanned Undersea Vehicle

Cited by 7 publications

References 5 publications

Fine reconstruction of underwater images for environmental feature fusion

Fine reconstruction of underwater images for environmental feature fusion

Counterexample Generation for Probabilistic Model Checking Micro-Scale Cyber-Physical Systems

A three-valued model abstraction framework for PCTL* stochastic model checking

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