Particle Swarm Optimization (PSO) for improving the accuracy of ChemCam LIBS sub-model quantitative method

Abstract Laser-induced breakdown spectroscopy (LIBS) is a powerful tool for qualitative analysis of chemical composition on planetary surface. Specifically, the quantitative compositional analysis method is a significant challenge for LIBS instrument onboard the Mars Science Laboratory (MSL) rover C...
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Gespeichert in:

Autor*in:	Zhang, Li [verfasserIn] Wu, Zhongchen [verfasserIn] Ling, Zongcheng [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Laser-induced breakdown spectroscopy (LIBS) ChemCam Quantitative analysis PLS sub-model Particle Swarm Optimization (PSO)

Übergeordnetes Werk:	Enthalten in: Earth science informatics - Berlin : Springer, 2008, 13(2020), 4 vom: 10. Sept., Seite 1485-1497
Übergeordnetes Werk:	volume:13 ; year:2020 ; number:4 ; day:10 ; month:09 ; pages:1485-1497

Links:	Volltext

DOI / URN:	10.1007/s12145-020-00497-y

Katalog-ID:	SPR042061555

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520			\|a Abstract Laser-induced breakdown spectroscopy (LIBS) is a powerful tool for qualitative analysis of chemical composition on planetary surface. Specifically, the quantitative compositional analysis method is a significant challenge for LIBS instrument onboard the Mars Science Laboratory (MSL) rover Curiosity ChemCam. Partial Least Squares (PLS) sub-model strategy is one of the outstanding multivariate analysis methods for calibration modeling, which is firstly developed by ChemCam science team. However, a troubling key issue is there are many parameters that need to be optimized, which increases the uncertainty of predicting outcomes and is time-consuming. In this study, an automatic parameters selection method based on Particle Swarm Optimization (PSO) tool is introduced. In the process of PSO iteration, RMSE minimization is taken as fitness, and finally the optimal sub-model parameters set is searched. In this way, the authors also get the best PLS latent variables of each sub-model by traversal method. Finally, the PSO PLS sub-model (PSO-PLS-SM) gets significant improvement in accuracy for the expanded Chemcam standards (408). And the RMSE of Si, Al, Ca, Na elements has been reduced by more than 20% relative to the conventional predictions.
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allfields	10.1007/s12145-020-00497-y doi (DE-627)SPR042061555 (SPR)s12145-020-00497-y-e DE-627 ger DE-627 rakwb eng 550 004 ASE 550 ASE Zhang, Li verfasserin aut Particle Swarm Optimization (PSO) for improving the accuracy of ChemCam LIBS sub-model quantitative method 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Laser-induced breakdown spectroscopy (LIBS) is a powerful tool for qualitative analysis of chemical composition on planetary surface. Specifically, the quantitative compositional analysis method is a significant challenge for LIBS instrument onboard the Mars Science Laboratory (MSL) rover Curiosity ChemCam. Partial Least Squares (PLS) sub-model strategy is one of the outstanding multivariate analysis methods for calibration modeling, which is firstly developed by ChemCam science team. However, a troubling key issue is there are many parameters that need to be optimized, which increases the uncertainty of predicting outcomes and is time-consuming. In this study, an automatic parameters selection method based on Particle Swarm Optimization (PSO) tool is introduced. In the process of PSO iteration, RMSE minimization is taken as fitness, and finally the optimal sub-model parameters set is searched. In this way, the authors also get the best PLS latent variables of each sub-model by traversal method. Finally, the PSO PLS sub-model (PSO-PLS-SM) gets significant improvement in accuracy for the expanded Chemcam standards (408). And the RMSE of Si, Al, Ca, Na elements has been reduced by more than 20% relative to the conventional predictions. Laser-induced breakdown spectroscopy (LIBS) (dpeaa)DE-He213 ChemCam (dpeaa)DE-He213 Quantitative analysis (dpeaa)DE-He213 PLS sub-model (dpeaa)DE-He213 Particle Swarm Optimization (PSO) (dpeaa)DE-He213 Wu, Zhongchen verfasserin aut Ling, Zongcheng verfasserin aut Enthalten in Earth science informatics Berlin : Springer, 2008 13(2020), 4 vom: 10. Sept., Seite 1485-1497 (DE-627)565515772 (DE-600)2423990-2 1865-0481 nnns volume:13 year:2020 number:4 day:10 month:09 pages:1485-1497 https://dx.doi.org/10.1007/s12145-020-00497-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GEO SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 13 2020 4 10 09 1485-1497
spelling	10.1007/s12145-020-00497-y doi (DE-627)SPR042061555 (SPR)s12145-020-00497-y-e DE-627 ger DE-627 rakwb eng 550 004 ASE 550 ASE Zhang, Li verfasserin aut Particle Swarm Optimization (PSO) for improving the accuracy of ChemCam LIBS sub-model quantitative method 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Laser-induced breakdown spectroscopy (LIBS) is a powerful tool for qualitative analysis of chemical composition on planetary surface. Specifically, the quantitative compositional analysis method is a significant challenge for LIBS instrument onboard the Mars Science Laboratory (MSL) rover Curiosity ChemCam. Partial Least Squares (PLS) sub-model strategy is one of the outstanding multivariate analysis methods for calibration modeling, which is firstly developed by ChemCam science team. However, a troubling key issue is there are many parameters that need to be optimized, which increases the uncertainty of predicting outcomes and is time-consuming. In this study, an automatic parameters selection method based on Particle Swarm Optimization (PSO) tool is introduced. In the process of PSO iteration, RMSE minimization is taken as fitness, and finally the optimal sub-model parameters set is searched. In this way, the authors also get the best PLS latent variables of each sub-model by traversal method. Finally, the PSO PLS sub-model (PSO-PLS-SM) gets significant improvement in accuracy for the expanded Chemcam standards (408). And the RMSE of Si, Al, Ca, Na elements has been reduced by more than 20% relative to the conventional predictions. Laser-induced breakdown spectroscopy (LIBS) (dpeaa)DE-He213 ChemCam (dpeaa)DE-He213 Quantitative analysis (dpeaa)DE-He213 PLS sub-model (dpeaa)DE-He213 Particle Swarm Optimization (PSO) (dpeaa)DE-He213 Wu, Zhongchen verfasserin aut Ling, Zongcheng verfasserin aut Enthalten in Earth science informatics Berlin : Springer, 2008 13(2020), 4 vom: 10. Sept., Seite 1485-1497 (DE-627)565515772 (DE-600)2423990-2 1865-0481 nnns volume:13 year:2020 number:4 day:10 month:09 pages:1485-1497 https://dx.doi.org/10.1007/s12145-020-00497-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GEO SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 13 2020 4 10 09 1485-1497
allfields_unstemmed	10.1007/s12145-020-00497-y doi (DE-627)SPR042061555 (SPR)s12145-020-00497-y-e DE-627 ger DE-627 rakwb eng 550 004 ASE 550 ASE Zhang, Li verfasserin aut Particle Swarm Optimization (PSO) for improving the accuracy of ChemCam LIBS sub-model quantitative method 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Laser-induced breakdown spectroscopy (LIBS) is a powerful tool for qualitative analysis of chemical composition on planetary surface. Specifically, the quantitative compositional analysis method is a significant challenge for LIBS instrument onboard the Mars Science Laboratory (MSL) rover Curiosity ChemCam. Partial Least Squares (PLS) sub-model strategy is one of the outstanding multivariate analysis methods for calibration modeling, which is firstly developed by ChemCam science team. However, a troubling key issue is there are many parameters that need to be optimized, which increases the uncertainty of predicting outcomes and is time-consuming. In this study, an automatic parameters selection method based on Particle Swarm Optimization (PSO) tool is introduced. In the process of PSO iteration, RMSE minimization is taken as fitness, and finally the optimal sub-model parameters set is searched. In this way, the authors also get the best PLS latent variables of each sub-model by traversal method. Finally, the PSO PLS sub-model (PSO-PLS-SM) gets significant improvement in accuracy for the expanded Chemcam standards (408). And the RMSE of Si, Al, Ca, Na elements has been reduced by more than 20% relative to the conventional predictions. Laser-induced breakdown spectroscopy (LIBS) (dpeaa)DE-He213 ChemCam (dpeaa)DE-He213 Quantitative analysis (dpeaa)DE-He213 PLS sub-model (dpeaa)DE-He213 Particle Swarm Optimization (PSO) (dpeaa)DE-He213 Wu, Zhongchen verfasserin aut Ling, Zongcheng verfasserin aut Enthalten in Earth science informatics Berlin : Springer, 2008 13(2020), 4 vom: 10. Sept., Seite 1485-1497 (DE-627)565515772 (DE-600)2423990-2 1865-0481 nnns volume:13 year:2020 number:4 day:10 month:09 pages:1485-1497 https://dx.doi.org/10.1007/s12145-020-00497-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GEO SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 13 2020 4 10 09 1485-1497
allfieldsGer	10.1007/s12145-020-00497-y doi (DE-627)SPR042061555 (SPR)s12145-020-00497-y-e DE-627 ger DE-627 rakwb eng 550 004 ASE 550 ASE Zhang, Li verfasserin aut Particle Swarm Optimization (PSO) for improving the accuracy of ChemCam LIBS sub-model quantitative method 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Laser-induced breakdown spectroscopy (LIBS) is a powerful tool for qualitative analysis of chemical composition on planetary surface. Specifically, the quantitative compositional analysis method is a significant challenge for LIBS instrument onboard the Mars Science Laboratory (MSL) rover Curiosity ChemCam. Partial Least Squares (PLS) sub-model strategy is one of the outstanding multivariate analysis methods for calibration modeling, which is firstly developed by ChemCam science team. However, a troubling key issue is there are many parameters that need to be optimized, which increases the uncertainty of predicting outcomes and is time-consuming. In this study, an automatic parameters selection method based on Particle Swarm Optimization (PSO) tool is introduced. In the process of PSO iteration, RMSE minimization is taken as fitness, and finally the optimal sub-model parameters set is searched. In this way, the authors also get the best PLS latent variables of each sub-model by traversal method. Finally, the PSO PLS sub-model (PSO-PLS-SM) gets significant improvement in accuracy for the expanded Chemcam standards (408). And the RMSE of Si, Al, Ca, Na elements has been reduced by more than 20% relative to the conventional predictions. Laser-induced breakdown spectroscopy (LIBS) (dpeaa)DE-He213 ChemCam (dpeaa)DE-He213 Quantitative analysis (dpeaa)DE-He213 PLS sub-model (dpeaa)DE-He213 Particle Swarm Optimization (PSO) (dpeaa)DE-He213 Wu, Zhongchen verfasserin aut Ling, Zongcheng verfasserin aut Enthalten in Earth science informatics Berlin : Springer, 2008 13(2020), 4 vom: 10. Sept., Seite 1485-1497 (DE-627)565515772 (DE-600)2423990-2 1865-0481 nnns volume:13 year:2020 number:4 day:10 month:09 pages:1485-1497 https://dx.doi.org/10.1007/s12145-020-00497-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GEO SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 13 2020 4 10 09 1485-1497
allfieldsSound	10.1007/s12145-020-00497-y doi (DE-627)SPR042061555 (SPR)s12145-020-00497-y-e DE-627 ger DE-627 rakwb eng 550 004 ASE 550 ASE Zhang, Li verfasserin aut Particle Swarm Optimization (PSO) for improving the accuracy of ChemCam LIBS sub-model quantitative method 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Laser-induced breakdown spectroscopy (LIBS) is a powerful tool for qualitative analysis of chemical composition on planetary surface. Specifically, the quantitative compositional analysis method is a significant challenge for LIBS instrument onboard the Mars Science Laboratory (MSL) rover Curiosity ChemCam. Partial Least Squares (PLS) sub-model strategy is one of the outstanding multivariate analysis methods for calibration modeling, which is firstly developed by ChemCam science team. However, a troubling key issue is there are many parameters that need to be optimized, which increases the uncertainty of predicting outcomes and is time-consuming. In this study, an automatic parameters selection method based on Particle Swarm Optimization (PSO) tool is introduced. In the process of PSO iteration, RMSE minimization is taken as fitness, and finally the optimal sub-model parameters set is searched. In this way, the authors also get the best PLS latent variables of each sub-model by traversal method. Finally, the PSO PLS sub-model (PSO-PLS-SM) gets significant improvement in accuracy for the expanded Chemcam standards (408). And the RMSE of Si, Al, Ca, Na elements has been reduced by more than 20% relative to the conventional predictions. Laser-induced breakdown spectroscopy (LIBS) (dpeaa)DE-He213 ChemCam (dpeaa)DE-He213 Quantitative analysis (dpeaa)DE-He213 PLS sub-model (dpeaa)DE-He213 Particle Swarm Optimization (PSO) (dpeaa)DE-He213 Wu, Zhongchen verfasserin aut Ling, Zongcheng verfasserin aut Enthalten in Earth science informatics Berlin : Springer, 2008 13(2020), 4 vom: 10. Sept., Seite 1485-1497 (DE-627)565515772 (DE-600)2423990-2 1865-0481 nnns volume:13 year:2020 number:4 day:10 month:09 pages:1485-1497 https://dx.doi.org/10.1007/s12145-020-00497-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GEO SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 13 2020 4 10 09 1485-1497
language	English
source	Enthalten in Earth science informatics 13(2020), 4 vom: 10. Sept., Seite 1485-1497 volume:13 year:2020 number:4 day:10 month:09 pages:1485-1497
sourceStr	Enthalten in Earth science informatics 13(2020), 4 vom: 10. Sept., Seite 1485-1497 volume:13 year:2020 number:4 day:10 month:09 pages:1485-1497
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Laser-induced breakdown spectroscopy (LIBS) ChemCam Quantitative analysis PLS sub-model Particle Swarm Optimization (PSO)
dewey-raw	550
isfreeaccess_bool	false
container_title	Earth science informatics
authorswithroles_txt_mv	Zhang, Li @@aut@@ Wu, Zhongchen @@aut@@ Ling, Zongcheng @@aut@@
publishDateDaySort_date	2020-09-10T00:00:00Z
hierarchy_top_id	565515772
dewey-sort	3550
id	SPR042061555
language_de	englisch
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author	Zhang, Li
spellingShingle	Zhang, Li ddc 550 misc Laser-induced breakdown spectroscopy (LIBS) misc ChemCam misc Quantitative analysis misc PLS sub-model misc Particle Swarm Optimization (PSO) Particle Swarm Optimization (PSO) for improving the accuracy of ChemCam LIBS sub-model quantitative method
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topic_title	550 004 ASE 550 ASE Particle Swarm Optimization (PSO) for improving the accuracy of ChemCam LIBS sub-model quantitative method Laser-induced breakdown spectroscopy (LIBS) (dpeaa)DE-He213 ChemCam (dpeaa)DE-He213 Quantitative analysis (dpeaa)DE-He213 PLS sub-model (dpeaa)DE-He213 Particle Swarm Optimization (PSO) (dpeaa)DE-He213
topic	ddc 550 misc Laser-induced breakdown spectroscopy (LIBS) misc ChemCam misc Quantitative analysis misc PLS sub-model misc Particle Swarm Optimization (PSO)
topic_unstemmed	ddc 550 misc Laser-induced breakdown spectroscopy (LIBS) misc ChemCam misc Quantitative analysis misc PLS sub-model misc Particle Swarm Optimization (PSO)
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title	Particle Swarm Optimization (PSO) for improving the accuracy of ChemCam LIBS sub-model quantitative method
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title_full	Particle Swarm Optimization (PSO) for improving the accuracy of ChemCam LIBS sub-model quantitative method
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author_browse	Zhang, Li Wu, Zhongchen Ling, Zongcheng
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title_sort	particle swarm optimization (pso) for improving the accuracy of chemcam libs sub-model quantitative method
title_auth	Particle Swarm Optimization (PSO) for improving the accuracy of ChemCam LIBS sub-model quantitative method
abstract	Abstract Laser-induced breakdown spectroscopy (LIBS) is a powerful tool for qualitative analysis of chemical composition on planetary surface. Specifically, the quantitative compositional analysis method is a significant challenge for LIBS instrument onboard the Mars Science Laboratory (MSL) rover Curiosity ChemCam. Partial Least Squares (PLS) sub-model strategy is one of the outstanding multivariate analysis methods for calibration modeling, which is firstly developed by ChemCam science team. However, a troubling key issue is there are many parameters that need to be optimized, which increases the uncertainty of predicting outcomes and is time-consuming. In this study, an automatic parameters selection method based on Particle Swarm Optimization (PSO) tool is introduced. In the process of PSO iteration, RMSE minimization is taken as fitness, and finally the optimal sub-model parameters set is searched. In this way, the authors also get the best PLS latent variables of each sub-model by traversal method. Finally, the PSO PLS sub-model (PSO-PLS-SM) gets significant improvement in accuracy for the expanded Chemcam standards (408). And the RMSE of Si, Al, Ca, Na elements has been reduced by more than 20% relative to the conventional predictions.
abstractGer	Abstract Laser-induced breakdown spectroscopy (LIBS) is a powerful tool for qualitative analysis of chemical composition on planetary surface. Specifically, the quantitative compositional analysis method is a significant challenge for LIBS instrument onboard the Mars Science Laboratory (MSL) rover Curiosity ChemCam. Partial Least Squares (PLS) sub-model strategy is one of the outstanding multivariate analysis methods for calibration modeling, which is firstly developed by ChemCam science team. However, a troubling key issue is there are many parameters that need to be optimized, which increases the uncertainty of predicting outcomes and is time-consuming. In this study, an automatic parameters selection method based on Particle Swarm Optimization (PSO) tool is introduced. In the process of PSO iteration, RMSE minimization is taken as fitness, and finally the optimal sub-model parameters set is searched. In this way, the authors also get the best PLS latent variables of each sub-model by traversal method. Finally, the PSO PLS sub-model (PSO-PLS-SM) gets significant improvement in accuracy for the expanded Chemcam standards (408). And the RMSE of Si, Al, Ca, Na elements has been reduced by more than 20% relative to the conventional predictions.
abstract_unstemmed	Abstract Laser-induced breakdown spectroscopy (LIBS) is a powerful tool for qualitative analysis of chemical composition on planetary surface. Specifically, the quantitative compositional analysis method is a significant challenge for LIBS instrument onboard the Mars Science Laboratory (MSL) rover Curiosity ChemCam. Partial Least Squares (PLS) sub-model strategy is one of the outstanding multivariate analysis methods for calibration modeling, which is firstly developed by ChemCam science team. However, a troubling key issue is there are many parameters that need to be optimized, which increases the uncertainty of predicting outcomes and is time-consuming. In this study, an automatic parameters selection method based on Particle Swarm Optimization (PSO) tool is introduced. In the process of PSO iteration, RMSE minimization is taken as fitness, and finally the optimal sub-model parameters set is searched. In this way, the authors also get the best PLS latent variables of each sub-model by traversal method. Finally, the PSO PLS sub-model (PSO-PLS-SM) gets significant improvement in accuracy for the expanded Chemcam standards (408). And the RMSE of Si, Al, Ca, Na elements has been reduced by more than 20% relative to the conventional predictions.
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title_short	Particle Swarm Optimization (PSO) for improving the accuracy of ChemCam LIBS sub-model quantitative method
url	https://dx.doi.org/10.1007/s12145-020-00497-y
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up_date	2024-07-04T00:39:12.958Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR042061555</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220111115107.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201126s2020 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s12145-020-00497-y</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR042061555</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s12145-020-00497-y-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">550</subfield><subfield code="a">004</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">550</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Zhang, Li</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Particle Swarm Optimization (PSO) for improving the accuracy of ChemCam LIBS sub-model quantitative method</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Laser-induced breakdown spectroscopy (LIBS) is a powerful tool for qualitative analysis of chemical composition on planetary surface. Specifically, the quantitative compositional analysis method is a significant challenge for LIBS instrument onboard the Mars Science Laboratory (MSL) rover Curiosity ChemCam. Partial Least Squares (PLS) sub-model strategy is one of the outstanding multivariate analysis methods for calibration modeling, which is firstly developed by ChemCam science team. However, a troubling key issue is there are many parameters that need to be optimized, which increases the uncertainty of predicting outcomes and is time-consuming. In this study, an automatic parameters selection method based on Particle Swarm Optimization (PSO) tool is introduced. In the process of PSO iteration, RMSE minimization is taken as fitness, and finally the optimal sub-model parameters set is searched. In this way, the authors also get the best PLS latent variables of each sub-model by traversal method. Finally, the PSO PLS sub-model (PSO-PLS-SM) gets significant improvement in accuracy for the expanded Chemcam standards (408). 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Particle Swarm Optimization (PSO) for improving the accuracy of ChemCam LIBS sub-model quantitative method

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