Development of a new approach for rapid identification and classification of uranium ore concentrate powders using textural and spectroscopy signatures
Recently, a concept for a new approach for rapid identification of uranium ore concentrate (UOC) powders using colour, textural and spectroscopy signatures was developed using a sample dataset consisting of 79 industrial uranium ore concentrate powders produced by different production routes at diff...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Fongaro, L. [verfasserIn] Futsæther, C. [verfasserIn] Tomic, O. [verfasserIn] Lande, I.B. [verfasserIn] Kvaal, K. [verfasserIn] Wallenius, M. [verfasserIn] Mayer, K. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Chemometrics and intelligent laboratory systems - Amsterdam [u.a.] : Elsevier Science, 1986, 239 |
|---|---|
| Übergeordnetes Werk: |
volume:239 |
| DOI / URN: |
10.1016/j.chemolab.2023.104858 |
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| Katalog-ID: |
ELV010349847 |
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| 245 | 1 | 0 | |a Development of a new approach for rapid identification and classification of uranium ore concentrate powders using textural and spectroscopy signatures |
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| 520 | |a Recently, a concept for a new approach for rapid identification of uranium ore concentrate (UOC) powders using colour, textural and spectroscopy signatures was developed using a sample dataset consisting of 79 industrial uranium ore concentrate powders produced by different production routes at different facilities. The samples were first grouped into six different predefined colour categories using a colour-based classification model previously developed. In this study, a machine learning approach was used to develop supervised texture- and spectroscopy-based classification models for each colour category of uranium ore concentrates. | ||
| 650 | 4 | |a Nuclear forensics | |
| 650 | 4 | |a Uranium ore concentrates | |
| 650 | 4 | |a Colour analysis | |
| 650 | 4 | |a Image texture analysis | |
| 650 | 4 | |a Hyperspectral image analysis | |
| 650 | 4 | |a Machine learning | |
| 700 | 1 | |a Futsæther, C. |e verfasserin |4 aut | |
| 700 | 1 | |a Tomic, O. |e verfasserin |4 aut | |
| 700 | 1 | |a Lande, I.B. |e verfasserin |4 aut | |
| 700 | 1 | |a Kvaal, K. |e verfasserin |4 aut | |
| 700 | 1 | |a Wallenius, M. |e verfasserin |4 aut | |
| 700 | 1 | |a Mayer, K. |e verfasserin |4 aut | |
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10.1016/j.chemolab.2023.104858 doi (DE-627)ELV010349847 (ELSEVIER)S0169-7439(23)00108-9 DE-627 ger DE-627 rda eng 540 VZ 35.07 bkl 35.05 bkl Fongaro, L. verfasserin (orcid)0000-0001-9267-3525 aut Development of a new approach for rapid identification and classification of uranium ore concentrate powders using textural and spectroscopy signatures 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Recently, a concept for a new approach for rapid identification of uranium ore concentrate (UOC) powders using colour, textural and spectroscopy signatures was developed using a sample dataset consisting of 79 industrial uranium ore concentrate powders produced by different production routes at different facilities. The samples were first grouped into six different predefined colour categories using a colour-based classification model previously developed. In this study, a machine learning approach was used to develop supervised texture- and spectroscopy-based classification models for each colour category of uranium ore concentrates. Nuclear forensics Uranium ore concentrates Colour analysis Image texture analysis Hyperspectral image analysis Machine learning Futsæther, C. verfasserin aut Tomic, O. verfasserin aut Lande, I.B. verfasserin aut Kvaal, K. verfasserin aut Wallenius, M. verfasserin aut Mayer, K. verfasserin aut Enthalten in Chemometrics and intelligent laboratory systems Amsterdam [u.a.] : Elsevier Science, 1986 239 Online-Ressource (DE-627)320603512 (DE-600)2020467-X (DE-576)255554133 0169-7439 nnns volume:239 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 35.07 Chemisches Labor chemische Methoden VZ 35.05 Mathematische Chemie chemische Statistik VZ AR 239 |
| spelling |
10.1016/j.chemolab.2023.104858 doi (DE-627)ELV010349847 (ELSEVIER)S0169-7439(23)00108-9 DE-627 ger DE-627 rda eng 540 VZ 35.07 bkl 35.05 bkl Fongaro, L. verfasserin (orcid)0000-0001-9267-3525 aut Development of a new approach for rapid identification and classification of uranium ore concentrate powders using textural and spectroscopy signatures 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Recently, a concept for a new approach for rapid identification of uranium ore concentrate (UOC) powders using colour, textural and spectroscopy signatures was developed using a sample dataset consisting of 79 industrial uranium ore concentrate powders produced by different production routes at different facilities. The samples were first grouped into six different predefined colour categories using a colour-based classification model previously developed. In this study, a machine learning approach was used to develop supervised texture- and spectroscopy-based classification models for each colour category of uranium ore concentrates. Nuclear forensics Uranium ore concentrates Colour analysis Image texture analysis Hyperspectral image analysis Machine learning Futsæther, C. verfasserin aut Tomic, O. verfasserin aut Lande, I.B. verfasserin aut Kvaal, K. verfasserin aut Wallenius, M. verfasserin aut Mayer, K. verfasserin aut Enthalten in Chemometrics and intelligent laboratory systems Amsterdam [u.a.] : Elsevier Science, 1986 239 Online-Ressource (DE-627)320603512 (DE-600)2020467-X (DE-576)255554133 0169-7439 nnns volume:239 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 35.07 Chemisches Labor chemische Methoden VZ 35.05 Mathematische Chemie chemische Statistik VZ AR 239 |
| allfields_unstemmed |
10.1016/j.chemolab.2023.104858 doi (DE-627)ELV010349847 (ELSEVIER)S0169-7439(23)00108-9 DE-627 ger DE-627 rda eng 540 VZ 35.07 bkl 35.05 bkl Fongaro, L. verfasserin (orcid)0000-0001-9267-3525 aut Development of a new approach for rapid identification and classification of uranium ore concentrate powders using textural and spectroscopy signatures 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Recently, a concept for a new approach for rapid identification of uranium ore concentrate (UOC) powders using colour, textural and spectroscopy signatures was developed using a sample dataset consisting of 79 industrial uranium ore concentrate powders produced by different production routes at different facilities. The samples were first grouped into six different predefined colour categories using a colour-based classification model previously developed. In this study, a machine learning approach was used to develop supervised texture- and spectroscopy-based classification models for each colour category of uranium ore concentrates. Nuclear forensics Uranium ore concentrates Colour analysis Image texture analysis Hyperspectral image analysis Machine learning Futsæther, C. verfasserin aut Tomic, O. verfasserin aut Lande, I.B. verfasserin aut Kvaal, K. verfasserin aut Wallenius, M. verfasserin aut Mayer, K. verfasserin aut Enthalten in Chemometrics and intelligent laboratory systems Amsterdam [u.a.] : Elsevier Science, 1986 239 Online-Ressource (DE-627)320603512 (DE-600)2020467-X (DE-576)255554133 0169-7439 nnns volume:239 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 35.07 Chemisches Labor chemische Methoden VZ 35.05 Mathematische Chemie chemische Statistik VZ AR 239 |
| allfieldsGer |
10.1016/j.chemolab.2023.104858 doi (DE-627)ELV010349847 (ELSEVIER)S0169-7439(23)00108-9 DE-627 ger DE-627 rda eng 540 VZ 35.07 bkl 35.05 bkl Fongaro, L. verfasserin (orcid)0000-0001-9267-3525 aut Development of a new approach for rapid identification and classification of uranium ore concentrate powders using textural and spectroscopy signatures 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Recently, a concept for a new approach for rapid identification of uranium ore concentrate (UOC) powders using colour, textural and spectroscopy signatures was developed using a sample dataset consisting of 79 industrial uranium ore concentrate powders produced by different production routes at different facilities. The samples were first grouped into six different predefined colour categories using a colour-based classification model previously developed. In this study, a machine learning approach was used to develop supervised texture- and spectroscopy-based classification models for each colour category of uranium ore concentrates. Nuclear forensics Uranium ore concentrates Colour analysis Image texture analysis Hyperspectral image analysis Machine learning Futsæther, C. verfasserin aut Tomic, O. verfasserin aut Lande, I.B. verfasserin aut Kvaal, K. verfasserin aut Wallenius, M. verfasserin aut Mayer, K. verfasserin aut Enthalten in Chemometrics and intelligent laboratory systems Amsterdam [u.a.] : Elsevier Science, 1986 239 Online-Ressource (DE-627)320603512 (DE-600)2020467-X (DE-576)255554133 0169-7439 nnns volume:239 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 35.07 Chemisches Labor chemische Methoden VZ 35.05 Mathematische Chemie chemische Statistik VZ AR 239 |
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development of a new approach for rapid identification and classification of uranium ore concentrate powders using textural and spectroscopy signatures |
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Development of a new approach for rapid identification and classification of uranium ore concentrate powders using textural and spectroscopy signatures |
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Recently, a concept for a new approach for rapid identification of uranium ore concentrate (UOC) powders using colour, textural and spectroscopy signatures was developed using a sample dataset consisting of 79 industrial uranium ore concentrate powders produced by different production routes at different facilities. The samples were first grouped into six different predefined colour categories using a colour-based classification model previously developed. In this study, a machine learning approach was used to develop supervised texture- and spectroscopy-based classification models for each colour category of uranium ore concentrates. |
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Recently, a concept for a new approach for rapid identification of uranium ore concentrate (UOC) powders using colour, textural and spectroscopy signatures was developed using a sample dataset consisting of 79 industrial uranium ore concentrate powders produced by different production routes at different facilities. The samples were first grouped into six different predefined colour categories using a colour-based classification model previously developed. In this study, a machine learning approach was used to develop supervised texture- and spectroscopy-based classification models for each colour category of uranium ore concentrates. |
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Recently, a concept for a new approach for rapid identification of uranium ore concentrate (UOC) powders using colour, textural and spectroscopy signatures was developed using a sample dataset consisting of 79 industrial uranium ore concentrate powders produced by different production routes at different facilities. The samples were first grouped into six different predefined colour categories using a colour-based classification model previously developed. In this study, a machine learning approach was used to develop supervised texture- and spectroscopy-based classification models for each colour category of uranium ore concentrates. |
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