Bayesian Fill Volume Estimation Based on Point Level Sensor Signals Article Swipe
Johannes Zumsande
,
Karl-Philipp Kortmann
,
Mark Wielitzka
,
Tobias Ortmaier
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1016/j.ifacol.2020.12.1852
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1016/j.ifacol.2020.12.1852
In dry bulk and fluid processing, the composites are usually stored in hoppers, tanks, or other containers. Due to the economic advantages, binary point level sensors, which detect fill level exceeding, are widely used for process monitoring and control. In this paper, we propose different filters for estimating the probability distribution of the fill volume based on a time-variant measurement distribution and a stochastic physical model with white process noise. A filter based on the model prediction with separated measurement update and two Bayesian particle filters are proposed and compared with a simulated ground truth. The performance measures are the root-mean-square error, the precision of the 95 % and 75 % credible intervals, and the average value of the estimated probability density function at the simulated fill volumes.
Related Topics
Concepts
Mean squared error
Volume (thermodynamics)
Particle filter
Probability density function
Noise (video)
Statistics
Bayesian probability
Filter (signal processing)
Mathematics
White noise
Computer science
Algorithm
Kalman filter
Artificial intelligence
Image (mathematics)
Quantum mechanics
Physics
Computer vision
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.ifacol.2020.12.1852
- OA Status
- diamond
- Cited By
- 1
- References
- 22
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3155791903
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W3155791903Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.ifacol.2020.12.1852Digital Object Identifier
- Title
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Bayesian Fill Volume Estimation Based on Point Level Sensor SignalsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2020Year of publication
- Publication date
-
2020-01-01Full publication date if available
- Authors
-
Johannes Zumsande, Karl-Philipp Kortmann, Mark Wielitzka, Tobias OrtmaierList of authors in order
- Landing page
-
https://doi.org/10.1016/j.ifacol.2020.12.1852Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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diamondOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.ifacol.2020.12.1852Direct OA link when available
- Concepts
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Mean squared error, Volume (thermodynamics), Particle filter, Probability density function, Noise (video), Statistics, Bayesian probability, Filter (signal processing), Mathematics, White noise, Computer science, Algorithm, Kalman filter, Artificial intelligence, Image (mathematics), Quantum mechanics, Physics, Computer visionTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2021: 1Per-year citation counts (last 5 years)
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22Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.Due | 17 |
| abstract_inverted_index.The | 95 |
| abstract_inverted_index.and | 3, 37, 61, 81, 88, 108, 113 |
| abstract_inverted_index.are | 8, 31, 86, 98 |
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| abstract_inverted_index.for | 34, 46 |
| abstract_inverted_index.the | 6, 19, 48, 52, 74, 99, 102, 105, 114, 118, 124 |
| abstract_inverted_index.two | 82 |
| abstract_inverted_index.bulk | 2 |
| abstract_inverted_index.fill | 28, 53, 126 |
| abstract_inverted_index.this | 40 |
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| abstract_inverted_index.with | 66, 77, 90 |
| abstract_inverted_index.based | 55, 72 |
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| abstract_inverted_index.level | 24, 29 |
| abstract_inverted_index.model | 65, 75 |
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| abstract_inverted_index.value | 116 |
| abstract_inverted_index.which | 26 |
| abstract_inverted_index.white | 67 |
| abstract_inverted_index.binary | 22 |
| abstract_inverted_index.detect | 27 |
| abstract_inverted_index.error, | 101 |
| abstract_inverted_index.filter | 71 |
| abstract_inverted_index.ground | 93 |
| abstract_inverted_index.noise. | 69 |
| abstract_inverted_index.paper, | 41 |
| abstract_inverted_index.stored | 10 |
| abstract_inverted_index.tanks, | 13 |
| abstract_inverted_index.truth. | 94 |
| abstract_inverted_index.update | 80 |
| abstract_inverted_index.volume | 54 |
| abstract_inverted_index.widely | 32 |
| abstract_inverted_index.average | 115 |
| abstract_inverted_index.density | 121 |
| abstract_inverted_index.filters | 45, 85 |
| abstract_inverted_index.process | 35, 68 |
| abstract_inverted_index.propose | 43 |
| abstract_inverted_index.usually | 9 |
| abstract_inverted_index.Bayesian | 83 |
| abstract_inverted_index.compared | 89 |
| abstract_inverted_index.control. | 38 |
| abstract_inverted_index.credible | 111 |
| abstract_inverted_index.economic | 20 |
| abstract_inverted_index.function | 122 |
| abstract_inverted_index.hoppers, | 12 |
| abstract_inverted_index.measures | 97 |
| abstract_inverted_index.particle | 84 |
| abstract_inverted_index.physical | 64 |
| abstract_inverted_index.proposed | 87 |
| abstract_inverted_index.sensors, | 25 |
| abstract_inverted_index.volumes. | 127 |
| abstract_inverted_index.different | 44 |
| abstract_inverted_index.estimated | 119 |
| abstract_inverted_index.precision | 103 |
| abstract_inverted_index.separated | 78 |
| abstract_inverted_index.simulated | 92, 125 |
| abstract_inverted_index.composites | 7 |
| abstract_inverted_index.estimating | 47 |
| abstract_inverted_index.exceeding, | 30 |
| abstract_inverted_index.intervals, | 112 |
| abstract_inverted_index.monitoring | 36 |
| abstract_inverted_index.prediction | 76 |
| abstract_inverted_index.stochastic | 63 |
| abstract_inverted_index.advantages, | 21 |
| abstract_inverted_index.containers. | 16 |
| abstract_inverted_index.measurement | 59, 79 |
| abstract_inverted_index.performance | 96 |
| abstract_inverted_index.probability | 49, 120 |
| abstract_inverted_index.processing, | 5 |
| abstract_inverted_index.distribution | 50, 60 |
| abstract_inverted_index.time-variant | 58 |
| abstract_inverted_index.root-mean-square | 100 |
| cited_by_percentile_year.max | 93 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5046964061 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I114112103 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/8 |
| sustainable_development_goals[0].score | 0.4000000059604645 |
| sustainable_development_goals[0].display_name | Decent work and economic growth |
| citation_normalized_percentile.value | 0.63831144 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |