Sensor selection for fault diagnosis in uncertain systems

Jung, Daniel; Dong, Yi; Frisk, Erik; Krysander, Mattias; Biswas, Gautam

doi:10.1080/00207179.2018.1484171

Cited by 17 publications

(7 citation statements)

References 25 publications

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“…While many algorithms exist to solve the SCP for UAV photogrammetry, the base greedy algorithm often serves as the base comparison of other solutions due to leveraging both a quick solve time and a feasible (but not necessarily optimal and a low possibility of being the worst) solution [38,[47][48][49][50][51]. Adjustments to the base algorithm fine-tune the balance between solve-time and the necessary output, but the concept remains the same.…”

Section: A Priori Informationmentioning

confidence: 99%

Large-Scale Reality Modeling of a University Campus Using Combined UAV and Terrestrial Photogrammetry for Historical Preservation and Practical Use

Berrett

Vernon

Beckstrand

et al. 2021

Drones

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Unmanned aerial vehicles (UAV) enable detailed historical preservation of large-scale infrastructure and contribute to cultural heritage preservation, improved maintenance, public relations, and development planning. Aerial and terrestrial photo data coupled with high accuracy GPS create hyper-realistic mesh and texture models, high resolution point clouds, orthophotos, and digital elevation models (DEMs) that preserve a snapshot of history. A case study is presented of the development of a hyper-realistic 3D model that spans the complex 1.7 km2 area of the Brigham Young University campus in Provo, Utah, USA and includes over 75 significant structures. The model leverages photos obtained during the historic COVID-19 pandemic during a mandatory and rare campus closure and details a large scale modeling workflow and best practice data acquisition and processing techniques. The model utilizes 80,384 images and high accuracy GPS surveying points to create a 1.65 trillion-pixel textured structure-from-motion (SfM) model with an average ground sampling distance (GSD) near structures of 0.5 cm and maximum of 4 cm. Separate model segments (31) taken from data gathered between April and August 2020 are combined into one cohesive final model with an average absolute error of 3.3 cm and a full model absolute error of <1 cm (relative accuracies from 0.25 cm to 1.03 cm). Optimized and automated UAV techniques complement the data acquisition of the large-scale model, and opportunities are explored to archive as-is building and campus information to enable historical building preservation, facility maintenance, campus planning, public outreach, 3D-printed miniatures, and the possibility of education through virtual reality (VR) and augmented reality (AR) tours.

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Section: A Priori Informationmentioning

confidence: 99%

Large-Scale Reality Modeling of a University Campus Using Combined UAV and Terrestrial Photogrammetry for Historical Preservation and Practical Use

Berrett

Vernon

Beckstrand

et al. 2021

Drones

View full text Add to dashboard Cite

show abstract

“…Greedy algorithms are simple but effective. Research continues to refine adjustments to the basic greedy heuristic [24,25]. The basic greedy heuristic is explained in Freeman et al [7].…”

Section: Point-grouping Bounds Grouped Pointsmentioning

confidence: 99%

“…Ludington et al [52] takes georeferenced SfM and uses bundle adjustment to model uncertainty in SfM. Also of particular note, Jung et al [24] uses a stochastic greedy algorithm for optimal sensor (non-UAV) placement.…”

Section: Grid Independence Of Cg Parametersmentioning

confidence: 99%

Survey of 8 UAV Set-Covering Algorithms for Terrain Photogrammetry

et al. 2020

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Remote sensing with unmanned aerial vehicles (UAVs) facilitates photogrammetry for environmental and infrastructural monitoring. Models are created with less computational cost by reducing the number of photos required. Optimal camera locations for reducing the number of photos needed for structure-from-motion (SfM) are determined through eight mathematical set-covering algorithms as constrained by solve time. The algorithms examined are: traditional greedy, reverse greedy, carousel greedy (CG), linear programming, particle swarm optimization, simulated annealing, genetic, and ant colony optimization. Coverage and solve time are investigated for these algorithms. CG is the best method for choosing optimal camera locations as it balances number of photos required and time required to calculate camera positions as shown through an analysis similar to a Pareto Front. CG obtains a statistically significant 3.2 fewer cameras per modeled area than base greedy algorithm while requiring just one additional order of magnitude of solve time. For comparison, linear programming is capable of fewer cameras than base greedy but takes at least three orders of magnitude longer to solve. A grid independence study serves as a sensitivity analysis of the CG algorithms α (iteration number) and β (percentage to be recalculated) parameters that adjust traditional greedy heuristics, and a case study at the Rock Canyon collection dike in Provo, UT, USA, compares the results of all eight algorithms and the uniqueness (in terms of percentage comparisons based on location/angle metadata and qualitative visual comparison) of each selected set. Though this specific study uses SfM, the principles could apply to other instruments such as multi-spectral cameras or aerial LiDAR.

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“…To overcome the downsides of the nadir grid approach and obtain better orthogonal views of Pescara del Tronto, a view-planning algorithm delineates optimized photo locations and angles through greedy heuristics [25]. View-planning identifies the best sensor locations for observing an object or site by planning oblique images for optimized SfM [26,27].…”

Section: Optimized View and Path Planning Algorithmsmentioning

confidence: 99%

Sequential Earthquake Damage Assessment Incorporating Optimized sUAV Remote Sensing at Pescara del Tronto

et al. 2019

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A sequence of large earthquakes in central Italy ranging in moment magnitudes (Mw) from 4.2 to 6.5 caused significant damage to many small towns in the area. After each earthquake in 2016 (24 August and 26 October), automated small unmanned aerial vehicles (sUAV) acquired valuable imagery data for post-hazard reconnaissance in the mountain village of Pescara del Tronto, and were applied to 3D reconstruction using Structure-from-Motion (SfM). In July 2018, the site was again monitored to obtain additional imagery data capturing changes since the last visit following the 30 October 2016 Earthquake. A genetic-based mission-planning algorithm that delivers optimal viewpoints and path planning was field tested and reduced the required photos for 3D reconstruction by 9.1%. The optimized 3D model provides a better understanding of the current conditions of the village, when compared to the nadir models, by containing fewer holes on angled surfaces, including an additional 17% surface area, and with a comparable ground-sampling distance (GSD) of ≈2.4 cm/px (≈1.5 cm/px when adjusted for camera pixel density). The resulting three time-lapse models provide valuable metrics for ground motion, progression of damage, resilience of the village, and the recovery progress over a span of two years.

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Sensor selection for fault diagnosis in uncertain systems

Cited by 17 publications

References 25 publications

Large-Scale Reality Modeling of a University Campus Using Combined UAV and Terrestrial Photogrammetry for Historical Preservation and Practical Use

Large-Scale Reality Modeling of a University Campus Using Combined UAV and Terrestrial Photogrammetry for Historical Preservation and Practical Use

Survey of 8 UAV Set-Covering Algorithms for Terrain Photogrammetry

Sequential Earthquake Damage Assessment Incorporating Optimized sUAV Remote Sensing at Pescara del Tronto

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