An approach based on gamma-ray transmission technique and artificial neural network for accurately measuring the thickness of various materials Article Swipe
Le Thi Ngoc Trang
,
Nguyễn Thị Trúc Linh
,
Trần Thiện Thanh
,
Hoàng Đức Tâm
,
Huỳnh Đình Chương
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.2298/ntrp2402098t
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.2298/ntrp2402098t
This paper presents an approach based on the gamma-ray transmission technique and artificial neural network for accurately measuring the thickness of various materials in flat sheet form. The gamma-ray transmission system comprises a NaI(Tl) scintillation detector coupled with a 137Cs radioactive source. The artificial neural network model predicts the sample thickness through three input features: mass density, linear attenuation coefficient, and ln(R) - where R represents the ratio of areas under the 662 keV peak in spectra acquired from measurements with and without the sample. The artificial neural network model was trained using simulation data generated by MCNP6 code, facilitating the creation of comprehensive datasets covering diverse material types and thickness variations at a low cost. Hyperparameters of the artificial neural network model were defined by several optimization methods, such as hyperband-bayesian, tree-structured Parzen estimator, and random search, to establish an optimal artificial neural network architecture. Subsequently, the optimal artificial neural network model was deployed to predict the thickness of graphite, aluminum, copper, steel, and polymethyl methacrylate sheets, using input data obtained from the experiments. The results showed a good agreement between predicted and reference thicknesses, with a maximum relative deviation of 1.94 % and an average relative deviation of 0.52%.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.2298/ntrp2402098t
- OA Status
- gold
- Cited By
- 4
- References
- 24
- Related Works
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- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4404145586Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.2298/ntrp2402098tDigital Object Identifier
- Title
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An approach based on gamma-ray transmission technique and artificial neural network for accurately measuring the thickness of various materialsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-01-01Full publication date if available
- Authors
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Le Thi Ngoc Trang, Nguyễn Thị Trúc Linh, Trần Thiện Thanh, Hoàng Đức Tâm, Huỳnh Đình ChươngList of authors in order
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https://doi.org/10.2298/ntrp2402098tPublisher landing page
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://doi.org/10.2298/ntrp2402098tDirect OA link when available
- Concepts
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Artificial neural network, Transmission (telecommunications), Computer science, Materials science, Biological system, Artificial intelligence, Telecommunications, BiologyTop concepts (fields/topics) attached by OpenAlex
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4Total citation count in OpenAlex
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2025: 3, 2024: 1Per-year citation counts (last 5 years)
- References (count)
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- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.in | 23, 75 |
| abstract_inverted_index.of | 20, 68, 102, 117, 159, 191, 199 |
| abstract_inverted_index.on | 6 |
| abstract_inverted_index.to | 138, 155 |
| abstract_inverted_index.662 | 72 |
| abstract_inverted_index.The | 27, 42, 85, 175 |
| abstract_inverted_index.and | 11, 60, 81, 109, 135, 164, 183, 194 |
| abstract_inverted_index.for | 15 |
| abstract_inverted_index.keV | 73 |
| abstract_inverted_index.low | 114 |
| abstract_inverted_index.the | 7, 18, 48, 66, 71, 83, 100, 118, 147, 157, 173 |
| abstract_inverted_index.was | 90, 153 |
| abstract_inverted_index.1.94 | 192 |
| abstract_inverted_index.This | 0 |
| abstract_inverted_index.data | 94, 170 |
| abstract_inverted_index.flat | 24 |
| abstract_inverted_index.from | 78, 172 |
| abstract_inverted_index.good | 179 |
| abstract_inverted_index.mass | 55 |
| abstract_inverted_index.peak | 74 |
| abstract_inverted_index.such | 129 |
| abstract_inverted_index.were | 123 |
| abstract_inverted_index.with | 37, 80, 186 |
| abstract_inverted_index.137Cs | 39 |
| abstract_inverted_index.MCNP6 | 97 |
| abstract_inverted_index.areas | 69 |
| abstract_inverted_index.based | 5 |
| abstract_inverted_index.code, | 98 |
| abstract_inverted_index.cost. | 115 |
| abstract_inverted_index.form. | 26 |
| abstract_inverted_index.input | 53, 169 |
| abstract_inverted_index.ln(R) | 61 |
| abstract_inverted_index.model | 46, 89, 122, 152 |
| abstract_inverted_index.paper | 1 |
| abstract_inverted_index.ratio | 67 |
| abstract_inverted_index.sheet | 25 |
| abstract_inverted_index.three | 52 |
| abstract_inverted_index.types | 108 |
| abstract_inverted_index.under | 70 |
| abstract_inverted_index.using | 92, 168 |
| abstract_inverted_index.where | 63 |
| abstract_inverted_index.0.52%. | 200 |
| abstract_inverted_index.Parzen | 133 |
| abstract_inverted_index.linear | 57 |
| abstract_inverted_index.neural | 13, 44, 87, 120, 143, 150 |
| abstract_inverted_index.random | 136 |
| abstract_inverted_index.sample | 49 |
| abstract_inverted_index.showed | 177 |
| abstract_inverted_index.steel, | 163 |
| abstract_inverted_index.system | 30 |
| abstract_inverted_index.NaI(Tl) | 33 |
| abstract_inverted_index.average | 196 |
| abstract_inverted_index.between | 181 |
| abstract_inverted_index.copper, | 162 |
| abstract_inverted_index.coupled | 36 |
| abstract_inverted_index.defined | 124 |
| abstract_inverted_index.diverse | 106 |
| abstract_inverted_index.maximum | 188 |
| abstract_inverted_index.network | 14, 45, 88, 121, 144, 151 |
| abstract_inverted_index.optimal | 141, 148 |
| abstract_inverted_index.predict | 156 |
| abstract_inverted_index.results | 176 |
| abstract_inverted_index.sample. | 84 |
| abstract_inverted_index.search, | 137 |
| abstract_inverted_index.several | 126 |
| abstract_inverted_index.sheets, | 167 |
| abstract_inverted_index.source. | 41 |
| abstract_inverted_index.spectra | 76 |
| abstract_inverted_index.through | 51 |
| abstract_inverted_index.trained | 91 |
| abstract_inverted_index.various | 21 |
| abstract_inverted_index.without | 82 |
| abstract_inverted_index.acquired | 77 |
| abstract_inverted_index.approach | 4 |
| abstract_inverted_index.covering | 105 |
| abstract_inverted_index.creation | 101 |
| abstract_inverted_index.datasets | 104 |
| abstract_inverted_index.density, | 56 |
| abstract_inverted_index.deployed | 154 |
| abstract_inverted_index.detector | 35 |
| abstract_inverted_index.material | 107 |
| abstract_inverted_index.methods, | 128 |
| abstract_inverted_index.obtained | 171 |
| abstract_inverted_index.predicts | 47 |
| abstract_inverted_index.presents | 2 |
| abstract_inverted_index.relative | 189, 197 |
| abstract_inverted_index.agreement | 180 |
| abstract_inverted_index.aluminum, | 161 |
| abstract_inverted_index.comprises | 31 |
| abstract_inverted_index.deviation | 190, 198 |
| abstract_inverted_index.establish | 139 |
| abstract_inverted_index.features: | 54 |
| abstract_inverted_index.gamma-ray | 8, 28 |
| abstract_inverted_index.generated | 95 |
| abstract_inverted_index.graphite, | 160 |
| abstract_inverted_index.materials | 22 |
| abstract_inverted_index.measuring | 17 |
| abstract_inverted_index.predicted | 182 |
| abstract_inverted_index.reference | 184 |
| abstract_inverted_index.technique | 10 |
| abstract_inverted_index.thickness | 19, 50, 110, 158 |
| abstract_inverted_index.accurately | 16 |
| abstract_inverted_index.artificial | 12, 43, 86, 119, 142, 149 |
| abstract_inverted_index.estimator, | 134 |
| abstract_inverted_index.polymethyl | 165 |
| abstract_inverted_index.represents | 65 |
| abstract_inverted_index.simulation | 93 |
| abstract_inverted_index.variations | 111 |
| abstract_inverted_index.attenuation | 58 |
| abstract_inverted_index.radioactive | 40 |
| abstract_inverted_index.coefficient, | 59 |
| abstract_inverted_index.experiments. | 174 |
| abstract_inverted_index.facilitating | 99 |
| abstract_inverted_index.measurements | 79 |
| abstract_inverted_index.methacrylate | 166 |
| abstract_inverted_index.optimization | 127 |
| abstract_inverted_index.thicknesses, | 185 |
| abstract_inverted_index.transmission | 9, 29 |
| abstract_inverted_index.Subsequently, | 146 |
| abstract_inverted_index.architecture. | 145 |
| abstract_inverted_index.comprehensive | 103 |
| abstract_inverted_index.scintillation | 34 |
| abstract_inverted_index.Hyperparameters | 116 |
| abstract_inverted_index.tree-structured | 132 |
| abstract_inverted_index.hyperband-bayesian, | 131 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.7518948 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |