Modeling and Predicting the Central Magnetic Flux Density of the Superconducting Solenoid Surrounded with Iron Yoke via SVR
Abstract A novel machine learning method based on support vector regression (SVR) approach, combined with a particle swarm optimization (PSO) algorithm for its parameter optimization, was proposed to predict the magnetic field in the centre of a superconducting solenoid surrounded by a cold iron yok...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Tang, J. L. [verfasserIn] Cai, C. Z. [verfasserIn] Xiao, T. T. [verfasserIn] Huang, S. J. [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2012 |
|---|
| Schlagwörter: |
|---|
| Übergeordnetes Werk: |
Enthalten in: Journal of superconductivity - Dordrecht [u.a.] : Springer Science + Business Media B.V., 1988, 25(2012), 6 vom: 25. März, Seite 1747-1751 |
|---|---|
| Übergeordnetes Werk: |
volume:25 ; year:2012 ; number:6 ; day:25 ; month:03 ; pages:1747-1751 |
| Links: |
|---|
| DOI / URN: |
10.1007/s10948-012-1527-z |
|---|
| Katalog-ID: |
SPR014877287 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | SPR014877287 | ||
| 003 | DE-627 | ||
| 005 | 20220111012847.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 201006s2012 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s10948-012-1527-z |2 doi | |
| 035 | |a (DE-627)SPR014877287 | ||
| 035 | |a (SPR)s10948-012-1527-z-e | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 530 |q ASE |
| 084 | |a 33.74 |2 bkl | ||
| 100 | 1 | |a Tang, J. L. |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Modeling and Predicting the Central Magnetic Flux Density of the Superconducting Solenoid Surrounded with Iron Yoke via SVR |
| 264 | 1 | |c 2012 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 520 | |a Abstract A novel machine learning method based on support vector regression (SVR) approach, combined with a particle swarm optimization (PSO) algorithm for its parameter optimization, was proposed to predict the magnetic field in the centre of a superconducting solenoid surrounded by a cold iron yoke in terms of the geometrical parameters of the yoke. The leave-one-out cross validation (LOOCV) test results of SVR reveal that the prediction ability of the SVR model is greater than that of the conventional multivariate nonlinear regression. The maximum absolute percentage error of 26 samples obtained by SVR did not exceed 0.50% and the statistical mean absolute percentage error was solely 0.05%, which was quite accurate and satisfactory with the requirement of ultraprecision engineering and manufacturing. This investigation provides a clue that the hybrid PSO-SVR approach elaborated in this paper is a promising and practical methodology to precisely design the physical dimension of the iron yoke surrounded around the superconducting solenoid. | ||
| 650 | 4 | |a Cold iron yoke |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Superconducting solenoid |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Magnetic flux density |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Support vector regression |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Particle swarm optimization |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Modeling and predicting |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Cai, C. Z. |e verfasserin |4 aut | |
| 700 | 1 | |a Xiao, T. T. |e verfasserin |4 aut | |
| 700 | 1 | |a Huang, S. J. |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Journal of superconductivity |d Dordrecht [u.a.] : Springer Science + Business Media B.V., 1988 |g 25(2012), 6 vom: 25. März, Seite 1747-1751 |w (DE-627)313651175 |w (DE-600)2000540-4 |x 1572-9605 |7 nnns |
| 773 | 1 | 8 | |g volume:25 |g year:2012 |g number:6 |g day:25 |g month:03 |g pages:1747-1751 |
| 856 | 4 | 0 | |u https://dx.doi.org/10.1007/s10948-012-1527-z |z lizenzpflichtig |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_SPRINGER | ||
| 912 | |a GBV_ILN_20 | ||
| 912 | |a GBV_ILN_22 | ||
| 912 | |a GBV_ILN_23 | ||
| 912 | |a GBV_ILN_24 | ||
| 912 | |a GBV_ILN_31 | ||
| 912 | |a GBV_ILN_32 | ||
| 912 | |a GBV_ILN_39 | ||
| 912 | |a GBV_ILN_40 | ||
| 912 | |a GBV_ILN_60 | ||
| 912 | |a GBV_ILN_62 | ||
| 912 | |a GBV_ILN_69 | ||
| 912 | |a GBV_ILN_70 | ||
| 912 | |a GBV_ILN_73 | ||
| 912 | |a GBV_ILN_74 | ||
| 912 | |a GBV_ILN_90 | ||
| 912 | |a GBV_ILN_95 | ||
| 912 | |a GBV_ILN_100 | ||
| 912 | |a GBV_ILN_101 | ||
| 912 | |a GBV_ILN_105 | ||
| 912 | |a GBV_ILN_110 | ||
| 912 | |a GBV_ILN_120 | ||
| 912 | |a GBV_ILN_138 | ||
| 912 | |a GBV_ILN_152 | ||
| 912 | |a GBV_ILN_161 | ||
| 912 | |a GBV_ILN_171 | ||
| 912 | |a GBV_ILN_187 | ||
| 912 | |a GBV_ILN_224 | ||
| 912 | |a GBV_ILN_250 | ||
| 912 | |a GBV_ILN_281 | ||
| 912 | |a GBV_ILN_285 | ||
| 912 | |a GBV_ILN_293 | ||
| 912 | |a GBV_ILN_370 | ||
| 912 | |a GBV_ILN_602 | ||
| 912 | |a GBV_ILN_702 | ||
| 936 | b | k | |a 33.74 |q ASE |
| 951 | |a AR | ||
| 952 | |d 25 |j 2012 |e 6 |b 25 |c 03 |h 1747-1751 | ||
| author_variant |
j l t jl jlt c z c cz czc t t x tt ttx s j h sj sjh |
|---|---|
| matchkey_str |
article:15729605:2012----::oeignpeitnteetamgeifudniyfhsprodcigoeod |
| hierarchy_sort_str |
2012 |
| bklnumber |
33.74 |
| publishDate |
2012 |
| allfields |
10.1007/s10948-012-1527-z doi (DE-627)SPR014877287 (SPR)s10948-012-1527-z-e DE-627 ger DE-627 rakwb eng 530 ASE 33.74 bkl Tang, J. L. verfasserin aut Modeling and Predicting the Central Magnetic Flux Density of the Superconducting Solenoid Surrounded with Iron Yoke via SVR 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract A novel machine learning method based on support vector regression (SVR) approach, combined with a particle swarm optimization (PSO) algorithm for its parameter optimization, was proposed to predict the magnetic field in the centre of a superconducting solenoid surrounded by a cold iron yoke in terms of the geometrical parameters of the yoke. The leave-one-out cross validation (LOOCV) test results of SVR reveal that the prediction ability of the SVR model is greater than that of the conventional multivariate nonlinear regression. The maximum absolute percentage error of 26 samples obtained by SVR did not exceed 0.50% and the statistical mean absolute percentage error was solely 0.05%, which was quite accurate and satisfactory with the requirement of ultraprecision engineering and manufacturing. This investigation provides a clue that the hybrid PSO-SVR approach elaborated in this paper is a promising and practical methodology to precisely design the physical dimension of the iron yoke surrounded around the superconducting solenoid. Cold iron yoke (dpeaa)DE-He213 Superconducting solenoid (dpeaa)DE-He213 Magnetic flux density (dpeaa)DE-He213 Support vector regression (dpeaa)DE-He213 Particle swarm optimization (dpeaa)DE-He213 Modeling and predicting (dpeaa)DE-He213 Cai, C. Z. verfasserin aut Xiao, T. T. verfasserin aut Huang, S. J. verfasserin aut Enthalten in Journal of superconductivity Dordrecht [u.a.] : Springer Science + Business Media B.V., 1988 25(2012), 6 vom: 25. März, Seite 1747-1751 (DE-627)313651175 (DE-600)2000540-4 1572-9605 nnns volume:25 year:2012 number:6 day:25 month:03 pages:1747-1751 https://dx.doi.org/10.1007/s10948-012-1527-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_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_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 33.74 ASE AR 25 2012 6 25 03 1747-1751 |
| spelling |
10.1007/s10948-012-1527-z doi (DE-627)SPR014877287 (SPR)s10948-012-1527-z-e DE-627 ger DE-627 rakwb eng 530 ASE 33.74 bkl Tang, J. L. verfasserin aut Modeling and Predicting the Central Magnetic Flux Density of the Superconducting Solenoid Surrounded with Iron Yoke via SVR 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract A novel machine learning method based on support vector regression (SVR) approach, combined with a particle swarm optimization (PSO) algorithm for its parameter optimization, was proposed to predict the magnetic field in the centre of a superconducting solenoid surrounded by a cold iron yoke in terms of the geometrical parameters of the yoke. The leave-one-out cross validation (LOOCV) test results of SVR reveal that the prediction ability of the SVR model is greater than that of the conventional multivariate nonlinear regression. The maximum absolute percentage error of 26 samples obtained by SVR did not exceed 0.50% and the statistical mean absolute percentage error was solely 0.05%, which was quite accurate and satisfactory with the requirement of ultraprecision engineering and manufacturing. This investigation provides a clue that the hybrid PSO-SVR approach elaborated in this paper is a promising and practical methodology to precisely design the physical dimension of the iron yoke surrounded around the superconducting solenoid. Cold iron yoke (dpeaa)DE-He213 Superconducting solenoid (dpeaa)DE-He213 Magnetic flux density (dpeaa)DE-He213 Support vector regression (dpeaa)DE-He213 Particle swarm optimization (dpeaa)DE-He213 Modeling and predicting (dpeaa)DE-He213 Cai, C. Z. verfasserin aut Xiao, T. T. verfasserin aut Huang, S. J. verfasserin aut Enthalten in Journal of superconductivity Dordrecht [u.a.] : Springer Science + Business Media B.V., 1988 25(2012), 6 vom: 25. März, Seite 1747-1751 (DE-627)313651175 (DE-600)2000540-4 1572-9605 nnns volume:25 year:2012 number:6 day:25 month:03 pages:1747-1751 https://dx.doi.org/10.1007/s10948-012-1527-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_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_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 33.74 ASE AR 25 2012 6 25 03 1747-1751 |
| allfields_unstemmed |
10.1007/s10948-012-1527-z doi (DE-627)SPR014877287 (SPR)s10948-012-1527-z-e DE-627 ger DE-627 rakwb eng 530 ASE 33.74 bkl Tang, J. L. verfasserin aut Modeling and Predicting the Central Magnetic Flux Density of the Superconducting Solenoid Surrounded with Iron Yoke via SVR 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract A novel machine learning method based on support vector regression (SVR) approach, combined with a particle swarm optimization (PSO) algorithm for its parameter optimization, was proposed to predict the magnetic field in the centre of a superconducting solenoid surrounded by a cold iron yoke in terms of the geometrical parameters of the yoke. The leave-one-out cross validation (LOOCV) test results of SVR reveal that the prediction ability of the SVR model is greater than that of the conventional multivariate nonlinear regression. The maximum absolute percentage error of 26 samples obtained by SVR did not exceed 0.50% and the statistical mean absolute percentage error was solely 0.05%, which was quite accurate and satisfactory with the requirement of ultraprecision engineering and manufacturing. This investigation provides a clue that the hybrid PSO-SVR approach elaborated in this paper is a promising and practical methodology to precisely design the physical dimension of the iron yoke surrounded around the superconducting solenoid. Cold iron yoke (dpeaa)DE-He213 Superconducting solenoid (dpeaa)DE-He213 Magnetic flux density (dpeaa)DE-He213 Support vector regression (dpeaa)DE-He213 Particle swarm optimization (dpeaa)DE-He213 Modeling and predicting (dpeaa)DE-He213 Cai, C. Z. verfasserin aut Xiao, T. T. verfasserin aut Huang, S. J. verfasserin aut Enthalten in Journal of superconductivity Dordrecht [u.a.] : Springer Science + Business Media B.V., 1988 25(2012), 6 vom: 25. März, Seite 1747-1751 (DE-627)313651175 (DE-600)2000540-4 1572-9605 nnns volume:25 year:2012 number:6 day:25 month:03 pages:1747-1751 https://dx.doi.org/10.1007/s10948-012-1527-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_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_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 33.74 ASE AR 25 2012 6 25 03 1747-1751 |
| allfieldsGer |
10.1007/s10948-012-1527-z doi (DE-627)SPR014877287 (SPR)s10948-012-1527-z-e DE-627 ger DE-627 rakwb eng 530 ASE 33.74 bkl Tang, J. L. verfasserin aut Modeling and Predicting the Central Magnetic Flux Density of the Superconducting Solenoid Surrounded with Iron Yoke via SVR 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract A novel machine learning method based on support vector regression (SVR) approach, combined with a particle swarm optimization (PSO) algorithm for its parameter optimization, was proposed to predict the magnetic field in the centre of a superconducting solenoid surrounded by a cold iron yoke in terms of the geometrical parameters of the yoke. The leave-one-out cross validation (LOOCV) test results of SVR reveal that the prediction ability of the SVR model is greater than that of the conventional multivariate nonlinear regression. The maximum absolute percentage error of 26 samples obtained by SVR did not exceed 0.50% and the statistical mean absolute percentage error was solely 0.05%, which was quite accurate and satisfactory with the requirement of ultraprecision engineering and manufacturing. This investigation provides a clue that the hybrid PSO-SVR approach elaborated in this paper is a promising and practical methodology to precisely design the physical dimension of the iron yoke surrounded around the superconducting solenoid. Cold iron yoke (dpeaa)DE-He213 Superconducting solenoid (dpeaa)DE-He213 Magnetic flux density (dpeaa)DE-He213 Support vector regression (dpeaa)DE-He213 Particle swarm optimization (dpeaa)DE-He213 Modeling and predicting (dpeaa)DE-He213 Cai, C. Z. verfasserin aut Xiao, T. T. verfasserin aut Huang, S. J. verfasserin aut Enthalten in Journal of superconductivity Dordrecht [u.a.] : Springer Science + Business Media B.V., 1988 25(2012), 6 vom: 25. März, Seite 1747-1751 (DE-627)313651175 (DE-600)2000540-4 1572-9605 nnns volume:25 year:2012 number:6 day:25 month:03 pages:1747-1751 https://dx.doi.org/10.1007/s10948-012-1527-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_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_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 33.74 ASE AR 25 2012 6 25 03 1747-1751 |
| allfieldsSound |
10.1007/s10948-012-1527-z doi (DE-627)SPR014877287 (SPR)s10948-012-1527-z-e DE-627 ger DE-627 rakwb eng 530 ASE 33.74 bkl Tang, J. L. verfasserin aut Modeling and Predicting the Central Magnetic Flux Density of the Superconducting Solenoid Surrounded with Iron Yoke via SVR 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract A novel machine learning method based on support vector regression (SVR) approach, combined with a particle swarm optimization (PSO) algorithm for its parameter optimization, was proposed to predict the magnetic field in the centre of a superconducting solenoid surrounded by a cold iron yoke in terms of the geometrical parameters of the yoke. The leave-one-out cross validation (LOOCV) test results of SVR reveal that the prediction ability of the SVR model is greater than that of the conventional multivariate nonlinear regression. The maximum absolute percentage error of 26 samples obtained by SVR did not exceed 0.50% and the statistical mean absolute percentage error was solely 0.05%, which was quite accurate and satisfactory with the requirement of ultraprecision engineering and manufacturing. This investigation provides a clue that the hybrid PSO-SVR approach elaborated in this paper is a promising and practical methodology to precisely design the physical dimension of the iron yoke surrounded around the superconducting solenoid. Cold iron yoke (dpeaa)DE-He213 Superconducting solenoid (dpeaa)DE-He213 Magnetic flux density (dpeaa)DE-He213 Support vector regression (dpeaa)DE-He213 Particle swarm optimization (dpeaa)DE-He213 Modeling and predicting (dpeaa)DE-He213 Cai, C. Z. verfasserin aut Xiao, T. T. verfasserin aut Huang, S. J. verfasserin aut Enthalten in Journal of superconductivity Dordrecht [u.a.] : Springer Science + Business Media B.V., 1988 25(2012), 6 vom: 25. März, Seite 1747-1751 (DE-627)313651175 (DE-600)2000540-4 1572-9605 nnns volume:25 year:2012 number:6 day:25 month:03 pages:1747-1751 https://dx.doi.org/10.1007/s10948-012-1527-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_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_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 33.74 ASE AR 25 2012 6 25 03 1747-1751 |
| language |
English |
| source |
Enthalten in Journal of superconductivity 25(2012), 6 vom: 25. März, Seite 1747-1751 volume:25 year:2012 number:6 day:25 month:03 pages:1747-1751 |
| sourceStr |
Enthalten in Journal of superconductivity 25(2012), 6 vom: 25. März, Seite 1747-1751 volume:25 year:2012 number:6 day:25 month:03 pages:1747-1751 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Cold iron yoke Superconducting solenoid Magnetic flux density Support vector regression Particle swarm optimization Modeling and predicting |
| dewey-raw |
530 |
| isfreeaccess_bool |
false |
| container_title |
Journal of superconductivity |
| authorswithroles_txt_mv |
Tang, J. L. @@aut@@ Cai, C. Z. @@aut@@ Xiao, T. T. @@aut@@ Huang, S. J. @@aut@@ |
| publishDateDaySort_date |
2012-03-25T00:00:00Z |
| hierarchy_top_id |
313651175 |
| dewey-sort |
3530 |
| id |
SPR014877287 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR014877287</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220111012847.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201006s2012 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10948-012-1527-z</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR014877287</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s10948-012-1527-z-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">530</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">33.74</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Tang, J. L.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Modeling and Predicting the Central Magnetic Flux Density of the Superconducting Solenoid Surrounded with Iron Yoke via SVR</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2012</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 A novel machine learning method based on support vector regression (SVR) approach, combined with a particle swarm optimization (PSO) algorithm for its parameter optimization, was proposed to predict the magnetic field in the centre of a superconducting solenoid surrounded by a cold iron yoke in terms of the geometrical parameters of the yoke. The leave-one-out cross validation (LOOCV) test results of SVR reveal that the prediction ability of the SVR model is greater than that of the conventional multivariate nonlinear regression. The maximum absolute percentage error of 26 samples obtained by SVR did not exceed 0.50% and the statistical mean absolute percentage error was solely 0.05%, which was quite accurate and satisfactory with the requirement of ultraprecision engineering and manufacturing. This investigation provides a clue that the hybrid PSO-SVR approach elaborated in this paper is a promising and practical methodology to precisely design the physical dimension of the iron yoke surrounded around the superconducting solenoid.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Cold iron yoke</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Superconducting solenoid</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Magnetic flux density</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Support vector regression</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Particle swarm optimization</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Modeling and predicting</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Cai, C. Z.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Xiao, T. T.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Huang, S. J.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Journal of superconductivity</subfield><subfield code="d">Dordrecht [u.a.] : Springer Science + Business Media B.V., 1988</subfield><subfield code="g">25(2012), 6 vom: 25. März, Seite 1747-1751</subfield><subfield code="w">(DE-627)313651175</subfield><subfield code="w">(DE-600)2000540-4</subfield><subfield code="x">1572-9605</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:25</subfield><subfield code="g">year:2012</subfield><subfield code="g">number:6</subfield><subfield code="g">day:25</subfield><subfield code="g">month:03</subfield><subfield code="g">pages:1747-1751</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s10948-012-1527-z</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_32</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_90</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_101</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_120</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_138</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_171</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_187</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_250</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_281</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">33.74</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">25</subfield><subfield code="j">2012</subfield><subfield code="e">6</subfield><subfield code="b">25</subfield><subfield code="c">03</subfield><subfield code="h">1747-1751</subfield></datafield></record></collection>
|
| author |
Tang, J. L. |
| spellingShingle |
Tang, J. L. ddc 530 bkl 33.74 misc Cold iron yoke misc Superconducting solenoid misc Magnetic flux density misc Support vector regression misc Particle swarm optimization misc Modeling and predicting Modeling and Predicting the Central Magnetic Flux Density of the Superconducting Solenoid Surrounded with Iron Yoke via SVR |
| authorStr |
Tang, J. L. |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)313651175 |
| format |
electronic Article |
| dewey-ones |
530 - Physics |
| delete_txt_mv |
keep |
| author_role |
aut aut aut aut |
| collection |
springer |
| remote_str |
true |
| illustrated |
Not Illustrated |
| issn |
1572-9605 |
| topic_title |
530 ASE 33.74 bkl Modeling and Predicting the Central Magnetic Flux Density of the Superconducting Solenoid Surrounded with Iron Yoke via SVR Cold iron yoke (dpeaa)DE-He213 Superconducting solenoid (dpeaa)DE-He213 Magnetic flux density (dpeaa)DE-He213 Support vector regression (dpeaa)DE-He213 Particle swarm optimization (dpeaa)DE-He213 Modeling and predicting (dpeaa)DE-He213 |
| topic |
ddc 530 bkl 33.74 misc Cold iron yoke misc Superconducting solenoid misc Magnetic flux density misc Support vector regression misc Particle swarm optimization misc Modeling and predicting |
| topic_unstemmed |
ddc 530 bkl 33.74 misc Cold iron yoke misc Superconducting solenoid misc Magnetic flux density misc Support vector regression misc Particle swarm optimization misc Modeling and predicting |
| topic_browse |
ddc 530 bkl 33.74 misc Cold iron yoke misc Superconducting solenoid misc Magnetic flux density misc Support vector regression misc Particle swarm optimization misc Modeling and predicting |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
cr |
| hierarchy_parent_title |
Journal of superconductivity |
| hierarchy_parent_id |
313651175 |
| dewey-tens |
530 - Physics |
| hierarchy_top_title |
Journal of superconductivity |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)313651175 (DE-600)2000540-4 |
| title |
Modeling and Predicting the Central Magnetic Flux Density of the Superconducting Solenoid Surrounded with Iron Yoke via SVR |
| ctrlnum |
(DE-627)SPR014877287 (SPR)s10948-012-1527-z-e |
| title_full |
Modeling and Predicting the Central Magnetic Flux Density of the Superconducting Solenoid Surrounded with Iron Yoke via SVR |
| author_sort |
Tang, J. L. |
| journal |
Journal of superconductivity |
| journalStr |
Journal of superconductivity |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
500 - Science |
| recordtype |
marc |
| publishDateSort |
2012 |
| contenttype_str_mv |
txt |
| container_start_page |
1747 |
| author_browse |
Tang, J. L. Cai, C. Z. Xiao, T. T. Huang, S. J. |
| container_volume |
25 |
| class |
530 ASE 33.74 bkl |
| format_se |
Elektronische Aufsätze |
| author-letter |
Tang, J. L. |
| doi_str_mv |
10.1007/s10948-012-1527-z |
| dewey-full |
530 |
| author2-role |
verfasserin |
| title_sort |
modeling and predicting the central magnetic flux density of the superconducting solenoid surrounded with iron yoke via svr |
| title_auth |
Modeling and Predicting the Central Magnetic Flux Density of the Superconducting Solenoid Surrounded with Iron Yoke via SVR |
| abstract |
Abstract A novel machine learning method based on support vector regression (SVR) approach, combined with a particle swarm optimization (PSO) algorithm for its parameter optimization, was proposed to predict the magnetic field in the centre of a superconducting solenoid surrounded by a cold iron yoke in terms of the geometrical parameters of the yoke. The leave-one-out cross validation (LOOCV) test results of SVR reveal that the prediction ability of the SVR model is greater than that of the conventional multivariate nonlinear regression. The maximum absolute percentage error of 26 samples obtained by SVR did not exceed 0.50% and the statistical mean absolute percentage error was solely 0.05%, which was quite accurate and satisfactory with the requirement of ultraprecision engineering and manufacturing. This investigation provides a clue that the hybrid PSO-SVR approach elaborated in this paper is a promising and practical methodology to precisely design the physical dimension of the iron yoke surrounded around the superconducting solenoid. |
| abstractGer |
Abstract A novel machine learning method based on support vector regression (SVR) approach, combined with a particle swarm optimization (PSO) algorithm for its parameter optimization, was proposed to predict the magnetic field in the centre of a superconducting solenoid surrounded by a cold iron yoke in terms of the geometrical parameters of the yoke. The leave-one-out cross validation (LOOCV) test results of SVR reveal that the prediction ability of the SVR model is greater than that of the conventional multivariate nonlinear regression. The maximum absolute percentage error of 26 samples obtained by SVR did not exceed 0.50% and the statistical mean absolute percentage error was solely 0.05%, which was quite accurate and satisfactory with the requirement of ultraprecision engineering and manufacturing. This investigation provides a clue that the hybrid PSO-SVR approach elaborated in this paper is a promising and practical methodology to precisely design the physical dimension of the iron yoke surrounded around the superconducting solenoid. |
| abstract_unstemmed |
Abstract A novel machine learning method based on support vector regression (SVR) approach, combined with a particle swarm optimization (PSO) algorithm for its parameter optimization, was proposed to predict the magnetic field in the centre of a superconducting solenoid surrounded by a cold iron yoke in terms of the geometrical parameters of the yoke. The leave-one-out cross validation (LOOCV) test results of SVR reveal that the prediction ability of the SVR model is greater than that of the conventional multivariate nonlinear regression. The maximum absolute percentage error of 26 samples obtained by SVR did not exceed 0.50% and the statistical mean absolute percentage error was solely 0.05%, which was quite accurate and satisfactory with the requirement of ultraprecision engineering and manufacturing. This investigation provides a clue that the hybrid PSO-SVR approach elaborated in this paper is a promising and practical methodology to precisely design the physical dimension of the iron yoke surrounded around the superconducting solenoid. |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_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_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 |
| container_issue |
6 |
| title_short |
Modeling and Predicting the Central Magnetic Flux Density of the Superconducting Solenoid Surrounded with Iron Yoke via SVR |
| url |
https://dx.doi.org/10.1007/s10948-012-1527-z |
| remote_bool |
true |
| author2 |
Cai, C. Z. Xiao, T. T. Huang, S. J. |
| author2Str |
Cai, C. Z. Xiao, T. T. Huang, S. J. |
| ppnlink |
313651175 |
| mediatype_str_mv |
c |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/s10948-012-1527-z |
| up_date |
2024-07-04T03:21:11.551Z |
| _version_ |
1803617065024618496 |
| 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">SPR014877287</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220111012847.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201006s2012 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10948-012-1527-z</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR014877287</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s10948-012-1527-z-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">530</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">33.74</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Tang, J. L.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Modeling and Predicting the Central Magnetic Flux Density of the Superconducting Solenoid Surrounded with Iron Yoke via SVR</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2012</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 A novel machine learning method based on support vector regression (SVR) approach, combined with a particle swarm optimization (PSO) algorithm for its parameter optimization, was proposed to predict the magnetic field in the centre of a superconducting solenoid surrounded by a cold iron yoke in terms of the geometrical parameters of the yoke. The leave-one-out cross validation (LOOCV) test results of SVR reveal that the prediction ability of the SVR model is greater than that of the conventional multivariate nonlinear regression. The maximum absolute percentage error of 26 samples obtained by SVR did not exceed 0.50% and the statistical mean absolute percentage error was solely 0.05%, which was quite accurate and satisfactory with the requirement of ultraprecision engineering and manufacturing. This investigation provides a clue that the hybrid PSO-SVR approach elaborated in this paper is a promising and practical methodology to precisely design the physical dimension of the iron yoke surrounded around the superconducting solenoid.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Cold iron yoke</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Superconducting solenoid</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Magnetic flux density</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Support vector regression</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Particle swarm optimization</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Modeling and predicting</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Cai, C. Z.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Xiao, T. T.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Huang, S. J.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Journal of superconductivity</subfield><subfield code="d">Dordrecht [u.a.] : Springer Science + Business Media B.V., 1988</subfield><subfield code="g">25(2012), 6 vom: 25. März, Seite 1747-1751</subfield><subfield code="w">(DE-627)313651175</subfield><subfield code="w">(DE-600)2000540-4</subfield><subfield code="x">1572-9605</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:25</subfield><subfield code="g">year:2012</subfield><subfield code="g">number:6</subfield><subfield code="g">day:25</subfield><subfield code="g">month:03</subfield><subfield code="g">pages:1747-1751</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s10948-012-1527-z</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_32</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_90</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_101</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_120</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_138</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_171</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_187</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_250</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_281</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">33.74</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">25</subfield><subfield code="j">2012</subfield><subfield code="e">6</subfield><subfield code="b">25</subfield><subfield code="c">03</subfield><subfield code="h">1747-1751</subfield></datafield></record></collection>
|
| score |
7.4012938 |