Magnetic object recognition with magnetic gradient tensor system heading-line surveys based on kernel extreme learning machine and sparrow search algorithm
•Innovatively proposed a pattern recognition method for the physical properties such as the posture and shape of the magnetic targets with the MGTS single heading-line surveys;•Studies the feature extraction method of MGT route signal, including a) use the magnetization offset sensitivity analysis o...
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| Veröffentlicht in: | Measurement : journal of the International Measurement Confederation Jg. 203; S. 111967 |
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| Sprache: | Englisch |
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15.11.2022
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| ISSN: | 0263-2241, 1873-412X |
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| Abstract | •Innovatively proposed a pattern recognition method for the physical properties such as the posture and shape of the magnetic targets with the MGTS single heading-line surveys;•Studies the feature extraction method of MGT route signal, including a) use the magnetization offset sensitivity analysis of MGT various attribute quantities to screen types of magnetic objects; b) summarize the statistical features of dimensionless and dimensionless time-domain waveforms applicable to different attribute signals;•Optimized the KELM classifier with the sparrow search algorithm (SSA) and have improved the efficiency and accuracy of samples training; designed a target pattern recognition flow based on SSA-KELM for training and learning of MGTS single heading-line surveys data.
We found that single heading-line surveys from magnetic gradient tensor system (MGTS) can be used to realize pattern recognition of magnetic objects, such as shape and posture, which can greatly improve the target detection efficiency compared with the two-dimensional grid measurement. Abandoning complex mathematical process, we measure and learn several training routes in advance, and use kernel extreme learning machine (KELM) and sparrow search algorithm (SSA) to recognize the target. The magnetic gradient tensor and its derived variables are analyzed for the sensitivity of the magnetization direction, and two types of characteristic attributes suitable for the target posture and shape categories are summarized. Time-domain waveform feature extraction from continuously sampled signals helps build datasets with corresponding category labels. Principal component analysis (PCA) is used to reduce feature dimensionality and improve classifier efficiency. Both simulation and experiment dataset have achieved 100% accurate recognition of the target posture and shape categories. |
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| AbstractList | •Innovatively proposed a pattern recognition method for the physical properties such as the posture and shape of the magnetic targets with the MGTS single heading-line surveys;•Studies the feature extraction method of MGT route signal, including a) use the magnetization offset sensitivity analysis of MGT various attribute quantities to screen types of magnetic objects; b) summarize the statistical features of dimensionless and dimensionless time-domain waveforms applicable to different attribute signals;•Optimized the KELM classifier with the sparrow search algorithm (SSA) and have improved the efficiency and accuracy of samples training; designed a target pattern recognition flow based on SSA-KELM for training and learning of MGTS single heading-line surveys data.
We found that single heading-line surveys from magnetic gradient tensor system (MGTS) can be used to realize pattern recognition of magnetic objects, such as shape and posture, which can greatly improve the target detection efficiency compared with the two-dimensional grid measurement. Abandoning complex mathematical process, we measure and learn several training routes in advance, and use kernel extreme learning machine (KELM) and sparrow search algorithm (SSA) to recognize the target. The magnetic gradient tensor and its derived variables are analyzed for the sensitivity of the magnetization direction, and two types of characteristic attributes suitable for the target posture and shape categories are summarized. Time-domain waveform feature extraction from continuously sampled signals helps build datasets with corresponding category labels. Principal component analysis (PCA) is used to reduce feature dimensionality and improve classifier efficiency. Both simulation and experiment dataset have achieved 100% accurate recognition of the target posture and shape categories. |
| ArticleNumber | 111967 |
| Author | Shi, Zhiyong Fan, Hongbo Li, Qingzhu Li, Zhining |
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| CitedBy_id | crossref_primary_10_1016_j_resourpol_2023_104189 crossref_primary_10_1016_j_jmmm_2024_172586 crossref_primary_10_1016_j_saa_2024_124858 crossref_primary_10_1016_j_measurement_2024_114550 crossref_primary_10_1109_TGRS_2024_3405478 crossref_primary_10_1109_TIM_2025_3548822 crossref_primary_10_1016_j_measurement_2025_117819 crossref_primary_10_1109_TGRS_2022_3222799 crossref_primary_10_1515_teme_2023_0116 crossref_primary_10_1007_s10462_023_10549_6 crossref_primary_10_1109_TIM_2025_3568958 |
| Cites_doi | 10.1063/1.5110626 10.1109/TMAG.2019.2914881 10.1071/EG12020 10.1007/s00024-019-02202-7 10.1109/JSEN.2021.3085573 10.1190/1.3493639 10.1080/08123985.2019.1615834 10.1088/1361-6501/ab8dfe 10.1016/j.jmmm.2019.03.066 10.1016/j.measurement.2014.09.045 10.1016/j.neucom.2005.12.126 10.1046/j.1365-2478.2000.00171.x 10.1016/j.ijhydene.2020.12.107 10.1109/TGRS.2011.2164086 10.1016/j.jappgeo.2016.03.022 10.1109/ACCESS.2020.3030676 10.1109/34.824819 10.1016/j.cageo.2009.10.002 10.1046/j.1365-2478.2000.00188.x 10.1093/gji/ggz421 10.1016/0926-9851(94)90022-1 10.1080/21642583.2019.1708830 10.1007/s12559-014-9255-2 10.1002/wics.101 10.1016/j.geoderma.2009.09.008 10.1016/j.knosys.2021.106924 10.1016/j.jappgeo.2003.10.001 |
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| Keywords | Heading-line survey Magnetic target pattern recognition Sparrow search algorithm Magnetic gradient tensor Kernel extreme learning machine |
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| References | Jain, Duin, Mao (b0065) 2000; 22 Turlapaty, Anantharaj, Younan (b0090) 2010; 36 Zhu, Yousefi (b0165) 2021; 46 Gang, Yingtang, Hongbo, Zhining, Guoquan (b0015) 2016; 128 Qingzhu, Zhiyong, Zhining (b0060) 2021; 70 Miller, Singh (b0035) 1994; 32 Fernández, Barrowes, O'Neill (b0085) 2006; 6217 Ehret (b0095) 2010; 160 Zhang, Ma, Lin (b0155) 2015; 59 Beiki (b0140) 2010; 75 Kim (b0030) 2018; 54 Zheng, Fan, Zhang (b0100) 2019; 55 Yin, Zhang (b0040) 2019; 482 Li, Li, Shi (b0050) 2022; 111612 Zhou, Zhang, Chen (b0110) 2020; 8 Zheng, Fan, Yin (b0105) 2019; 9 Abdi, Williams (b0125) 2010; 2 Sheinker, Ginzburg, Salomonski, Dickstein, Frumkis, Kaplan (b0005) April 2012; 50 Clack (b0010) 2012; 43 Li, Shi, Li (b0135) 2020; 31 Snydsman, Aminzadeh, Weil (b0075) 1987; 768 Huang (b0120) 2014; 6 Zhang, Ding (b0160) 2021; 220 Huang, Zhu, Siew (b0070) 2006; 70 Paoletti, Buggi, Pašteka (b0025) 2019; 176 Calderón-Macías, Sen, Stoffa (b0080) 2000; 48 Gerovska, Araúzo-Bravo, Stavrev (b0145) 2004; 55 Xue, Shen (b0115) 2020; 8 Stavrev, Gerovska (b0150) 2000; 48 Wigh, Hansen, Døssing (b0020) 2020; 220 Li, Zhang, Fan (b0045) 2019; 50 Li, Zhiyong, Zhining (b0055) 2021; 21 Qingzhu, Zhining, Yingtang (b0130) 2018; 54 Li (10.1016/j.measurement.2022.111967_b0055) 2021; 21 Calderón-Macías (10.1016/j.measurement.2022.111967_b0080) 2000; 48 Yin (10.1016/j.measurement.2022.111967_b0040) 2019; 482 Ehret (10.1016/j.measurement.2022.111967_b0095) 2010; 160 Qingzhu (10.1016/j.measurement.2022.111967_b0130) 2018; 54 Snydsman (10.1016/j.measurement.2022.111967_b0075) 1987; 768 Xue (10.1016/j.measurement.2022.111967_b0115) 2020; 8 Zheng (10.1016/j.measurement.2022.111967_b0100) 2019; 55 Li (10.1016/j.measurement.2022.111967_b0135) 2020; 31 Kim (10.1016/j.measurement.2022.111967_b0030) 2018; 54 Sheinker (10.1016/j.measurement.2022.111967_b0005) 2012; 50 Miller (10.1016/j.measurement.2022.111967_b0035) 1994; 32 Turlapaty (10.1016/j.measurement.2022.111967_b0090) 2010; 36 Zhang (10.1016/j.measurement.2022.111967_b0155) 2015; 59 Qingzhu (10.1016/j.measurement.2022.111967_b0060) 2021; 70 Stavrev (10.1016/j.measurement.2022.111967_b0150) 2000; 48 Zhang (10.1016/j.measurement.2022.111967_b0160) 2021; 220 Zhou (10.1016/j.measurement.2022.111967_b0110) 2020; 8 Li (10.1016/j.measurement.2022.111967_b0045) 2019; 50 Li (10.1016/j.measurement.2022.111967_b0050) 2022; 111612 Zheng (10.1016/j.measurement.2022.111967_b0105) 2019; 9 Abdi (10.1016/j.measurement.2022.111967_b0125) 2010; 2 Beiki (10.1016/j.measurement.2022.111967_b0140) 2010; 75 Paoletti (10.1016/j.measurement.2022.111967_b0025) 2019; 176 Fernández (10.1016/j.measurement.2022.111967_b0085) 2006; 6217 Zhu (10.1016/j.measurement.2022.111967_b0165) 2021; 46 Gerovska (10.1016/j.measurement.2022.111967_b0145) 2004; 55 Wigh (10.1016/j.measurement.2022.111967_b0020) 2020; 220 Huang (10.1016/j.measurement.2022.111967_b0070) 2006; 70 Huang (10.1016/j.measurement.2022.111967_b0120) 2014; 6 Jain (10.1016/j.measurement.2022.111967_b0065) 2000; 22 Clack (10.1016/j.measurement.2022.111967_b0010) 2012; 43 Gang (10.1016/j.measurement.2022.111967_b0015) 2016; 128 |
| References_xml | – volume: 2 start-page: 433 year: 2010 end-page: 459 ident: b0125 article-title: Principal component analysis[J] publication-title: Wiley Interdiscip. Rev. Comput. Stat. – volume: 46 start-page: 9541 year: 2021 end-page: 9552 ident: b0165 article-title: Optimal parameter identification of PEMFC stacks using adaptive sparrow search algorithm publication-title: Int. J. Hydrogen Energy – volume: 128 start-page: 131 year: 2016 end-page: 139 ident: b0015 article-title: Detection, localization and classification of multiple dipole-like magnetic sources using magnetic gradient tensor data publication-title: J. Appl. Geophys. – volume: 32 start-page: 213 year: 1994 end-page: 217 ident: b0035 article-title: Potential field tilt—a new concept for location of potential field sources[J] publication-title: J. Appl. Geophys. – volume: 8 start-page: 22 year: 2020 end-page: 34 ident: b0115 article-title: A novel swarm intelligence optimization approach: sparrow search algorithm[J] publication-title: Systems Science & Control Engineering – volume: 54 start-page: 4001011 year: 2018 ident: b0130 article-title: Integrated Compensation and Rotation Alignment for Three-Axis Magnetic Sensors Array[J] publication-title: IEEE Trans. Magn. – volume: 176 start-page: 4363 year: 2019 end-page: 4381 ident: b0025 article-title: UXO detection by multiscale potential field methods publication-title: Pure Appl. Geophys. – volume: 21 start-page: 18237 year: 2021 end-page: 18248 ident: b0055 article-title: Magnetic Object Positioning Based on Second-Order Magnetic Gradient Tensor System[J] publication-title: IEEE Sens. J. – volume: 54 start-page: 1 year: 2018 end-page: 5 ident: b0030 article-title: Determination scheme for accurate defect depth in underground pipeline inspection by using magnetic flux leakage sensors publication-title: IEEE Trans. Magn. – volume: 482 start-page: 229 year: 2019 end-page: 238 ident: b0040 article-title: Three-dimensional reconstruction of a small-scale magnetic target from magnetic gradient observations[J] publication-title: J. Magn. Magn. Mater. – volume: 9 year: 2019 ident: b0105 article-title: A method of using geomagnetic anomaly to recognize objects based on HOG and 2D-AVMD[J] publication-title: AIP Adv. – volume: 160 start-page: 111 year: 2010 end-page: 125 ident: b0095 article-title: Pattern recognition of geophysical data[J] publication-title: Geoderma – volume: 50 start-page: 1095 year: April 2012 end-page: 1103 ident: b0005 article-title: Magnetic Anomaly Detection Using High-Order Crossing Method publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 43 start-page: 267 year: 2012 end-page: 282 ident: b0010 article-title: New methods for interpretation of magnetic vector and gradient tensor data I: eigenvector analysis and the normalised source strength[J] publication-title: Explor. Geophys. – volume: 48 start-page: 317 year: 2000 end-page: 340 ident: b0150 article-title: Magnetic field transforms with low sensitivity to the direction of source magnetization and high centricity [J] publication-title: Geophys. Prospect. – volume: 50 start-page: 600 year: 2019 end-page: 612 ident: b0045 article-title: Estimating the location of magnetic sources using magnetic gradient tensor data[J] publication-title: Explor. Geophys. – volume: 220 start-page: 37 year: 2020 end-page: 58 ident: b0020 article-title: Inference of unexploded ordnance (UXO) by probabilistic inversion of magnetic data publication-title: Geophys. J. Int. – volume: 70 start-page: 1010214 year: 2021 ident: b0060 article-title: Preferred Configuration and Detection Limits Estimation of Magnetic Gradient Tensor System[J] publication-title: IEEE Trans. Instrum. Meas. – volume: 6 start-page: 376 year: 2014 end-page: 390 ident: b0120 article-title: An insight into extreme learning machines: random neurons, random features and kernels[J] publication-title: Cognitive Computation – volume: 220 year: 2021 ident: b0160 article-title: A stochastic configuration network based on chaotic sparrow search algorithm publication-title: Knowl.-Based Syst. – volume: 768 start-page: 53 year: 1987 end-page: 60 ident: b0075 article-title: Pattern recognition in geophysical exploration[C]//Pattern Recognition and Acoustical Imaging publication-title: International Society for Optics and Photonics – volume: 36 start-page: 464 year: 2010 end-page: 476 ident: b0090 article-title: A pattern recognition based approach to consistency analysis of geophysical datasets[J] publication-title: Comput. Geosci. – volume: 59 start-page: 73 year: 2015 end-page: 87 ident: b0155 article-title: Fault diagnosis approach for rotating machinery based on dynamic model and computational intelligence[J] publication-title: Measurement – volume: 22 start-page: 4 year: 2000 end-page: 37 ident: b0065 article-title: Statistical pattern recognition: A review[J] publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 55 start-page: 173 year: 2004 end-page: 186 ident: b0145 article-title: Determination of the parameters of compact ferro-metallic objects with transforms of magnitude magnetic anomalies[J] publication-title: J. Appl. Geophys. – volume: 31 year: 2020 ident: b0135 article-title: Magnetic object positioning method based on tensor spacial invariant relations[J] publication-title: Meas. Sci. Technol. – volume: 111612 year: 2022 ident: b0050 article-title: Application of Helbig integrals to magnetic gradient tensor multi-target detection[J] publication-title: Measurement – volume: 70 start-page: 489 year: 2006 end-page: 501 ident: b0070 article-title: Extreme learning machine: theory and applications[J] publication-title: Neurocomputing – volume: 6217 year: 2006 ident: b0085 article-title: Evaluation of SVM classification of metallic objects based on a magnetic-dipole representation[C]//Detection and Remediation Technologies for Mines and Minelike Targets XI publication-title: International Society for Optics and Photonics – volume: 48 start-page: 21 year: 2000 end-page: 47 ident: b0080 article-title: Artificial neural networks for parameter estimation in geophysics [Link][J] publication-title: Geophys. Prospect. – volume: 55 start-page: 1 year: 2019 end-page: 8 ident: b0100 article-title: Magnetic anomaly target recognition based on svd and svms[J] publication-title: IEEE Trans. Magn. – volume: 8 start-page: 187202 year: 2020 end-page: 187207 ident: b0110 article-title: Detection and classification of multi-magnetic targets using mask-RCNN[J] publication-title: IEEE Access – volume: 75 start-page: I59 year: 2010 end-page: I74 ident: b0140 article-title: Analytic signals of gravity gradient tensor and their application to estimate source location publication-title: Geophysics – volume: 9 issue: 7 year: 2019 ident: 10.1016/j.measurement.2022.111967_b0105 article-title: A method of using geomagnetic anomaly to recognize objects based on HOG and 2D-AVMD[J] publication-title: AIP Adv. doi: 10.1063/1.5110626 – volume: 6217 year: 2006 ident: 10.1016/j.measurement.2022.111967_b0085 article-title: Evaluation of SVM classification of metallic objects based on a magnetic-dipole representation[C]//Detection and Remediation Technologies for Mines and Minelike Targets XI publication-title: International Society for Optics and Photonics – volume: 55 start-page: 1 issue: 9 year: 2019 ident: 10.1016/j.measurement.2022.111967_b0100 article-title: Magnetic anomaly target recognition based on svd and svms[J] publication-title: IEEE Trans. Magn. doi: 10.1109/TMAG.2019.2914881 – volume: 43 start-page: 267 issue: 04 year: 2012 ident: 10.1016/j.measurement.2022.111967_b0010 article-title: New methods for interpretation of magnetic vector and gradient tensor data I: eigenvector analysis and the normalised source strength[J] publication-title: Explor. Geophys. doi: 10.1071/EG12020 – volume: 176 start-page: 4363 issue: 10 year: 2019 ident: 10.1016/j.measurement.2022.111967_b0025 article-title: UXO detection by multiscale potential field methods publication-title: Pure Appl. Geophys. doi: 10.1007/s00024-019-02202-7 – volume: 21 start-page: 18237 issue: 16 year: 2021 ident: 10.1016/j.measurement.2022.111967_b0055 article-title: Magnetic Object Positioning Based on Second-Order Magnetic Gradient Tensor System[J] publication-title: IEEE Sens. J. doi: 10.1109/JSEN.2021.3085573 – volume: 75 start-page: I59 issue: 6 year: 2010 ident: 10.1016/j.measurement.2022.111967_b0140 article-title: Analytic signals of gravity gradient tensor and their application to estimate source location publication-title: Geophysics doi: 10.1190/1.3493639 – volume: 50 start-page: 600 issue: 6 year: 2019 ident: 10.1016/j.measurement.2022.111967_b0045 article-title: Estimating the location of magnetic sources using magnetic gradient tensor data[J] publication-title: Explor. Geophys. doi: 10.1080/08123985.2019.1615834 – volume: 70 start-page: 1010214 year: 2021 ident: 10.1016/j.measurement.2022.111967_b0060 article-title: Preferred Configuration and Detection Limits Estimation of Magnetic Gradient Tensor System[J] publication-title: IEEE Trans. Instrum. Meas. – volume: 31 issue: 11 year: 2020 ident: 10.1016/j.measurement.2022.111967_b0135 article-title: Magnetic object positioning method based on tensor spacial invariant relations[J] publication-title: Meas. Sci. Technol. doi: 10.1088/1361-6501/ab8dfe – volume: 482 start-page: 229 year: 2019 ident: 10.1016/j.measurement.2022.111967_b0040 article-title: Three-dimensional reconstruction of a small-scale magnetic target from magnetic gradient observations[J] publication-title: J. Magn. Magn. Mater. doi: 10.1016/j.jmmm.2019.03.066 – volume: 59 start-page: 73 year: 2015 ident: 10.1016/j.measurement.2022.111967_b0155 article-title: Fault diagnosis approach for rotating machinery based on dynamic model and computational intelligence[J] publication-title: Measurement doi: 10.1016/j.measurement.2014.09.045 – volume: 70 start-page: 489 issue: 1–3 year: 2006 ident: 10.1016/j.measurement.2022.111967_b0070 article-title: Extreme learning machine: theory and applications[J] publication-title: Neurocomputing doi: 10.1016/j.neucom.2005.12.126 – volume: 48 start-page: 21 issue: 1 year: 2000 ident: 10.1016/j.measurement.2022.111967_b0080 article-title: Artificial neural networks for parameter estimation in geophysics [Link][J] publication-title: Geophys. Prospect. doi: 10.1046/j.1365-2478.2000.00171.x – volume: 46 start-page: 9541 issue: 14 year: 2021 ident: 10.1016/j.measurement.2022.111967_b0165 article-title: Optimal parameter identification of PEMFC stacks using adaptive sparrow search algorithm publication-title: Int. J. Hydrogen Energy doi: 10.1016/j.ijhydene.2020.12.107 – volume: 50 start-page: 1095 issue: 4 year: 2012 ident: 10.1016/j.measurement.2022.111967_b0005 article-title: Magnetic Anomaly Detection Using High-Order Crossing Method publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2011.2164086 – volume: 128 start-page: 131 year: 2016 ident: 10.1016/j.measurement.2022.111967_b0015 article-title: Detection, localization and classification of multiple dipole-like magnetic sources using magnetic gradient tensor data publication-title: J. Appl. Geophys. doi: 10.1016/j.jappgeo.2016.03.022 – volume: 8 start-page: 187202 year: 2020 ident: 10.1016/j.measurement.2022.111967_b0110 article-title: Detection and classification of multi-magnetic targets using mask-RCNN[J] publication-title: IEEE Access doi: 10.1109/ACCESS.2020.3030676 – volume: 22 start-page: 4 issue: 1 year: 2000 ident: 10.1016/j.measurement.2022.111967_b0065 article-title: Statistical pattern recognition: A review[J] publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/34.824819 – volume: 36 start-page: 464 issue: 4 year: 2010 ident: 10.1016/j.measurement.2022.111967_b0090 article-title: A pattern recognition based approach to consistency analysis of geophysical datasets[J] publication-title: Comput. Geosci. doi: 10.1016/j.cageo.2009.10.002 – volume: 48 start-page: 317 issue: 2 year: 2000 ident: 10.1016/j.measurement.2022.111967_b0150 article-title: Magnetic field transforms with low sensitivity to the direction of source magnetization and high centricity [J] publication-title: Geophys. Prospect. doi: 10.1046/j.1365-2478.2000.00188.x – volume: 220 start-page: 37 issue: 1 year: 2020 ident: 10.1016/j.measurement.2022.111967_b0020 article-title: Inference of unexploded ordnance (UXO) by probabilistic inversion of magnetic data publication-title: Geophys. J. Int. doi: 10.1093/gji/ggz421 – volume: 54 start-page: 1 issue: 11 year: 2018 ident: 10.1016/j.measurement.2022.111967_b0030 article-title: Determination scheme for accurate defect depth in underground pipeline inspection by using magnetic flux leakage sensors publication-title: IEEE Trans. Magn. – volume: 32 start-page: 213 issue: 2–3 year: 1994 ident: 10.1016/j.measurement.2022.111967_b0035 article-title: Potential field tilt—a new concept for location of potential field sources[J] publication-title: J. Appl. Geophys. doi: 10.1016/0926-9851(94)90022-1 – volume: 768 start-page: 53 year: 1987 ident: 10.1016/j.measurement.2022.111967_b0075 article-title: Pattern recognition in geophysical exploration[C]//Pattern Recognition and Acoustical Imaging publication-title: International Society for Optics and Photonics – volume: 8 start-page: 22 issue: 1 year: 2020 ident: 10.1016/j.measurement.2022.111967_b0115 article-title: A novel swarm intelligence optimization approach: sparrow search algorithm[J] publication-title: Systems Science & Control Engineering doi: 10.1080/21642583.2019.1708830 – volume: 6 start-page: 376 issue: 3 year: 2014 ident: 10.1016/j.measurement.2022.111967_b0120 article-title: An insight into extreme learning machines: random neurons, random features and kernels[J] publication-title: Cognitive Computation doi: 10.1007/s12559-014-9255-2 – volume: 2 start-page: 433 issue: 4 year: 2010 ident: 10.1016/j.measurement.2022.111967_b0125 article-title: Principal component analysis[J] publication-title: Wiley Interdiscip. Rev. Comput. Stat. doi: 10.1002/wics.101 – volume: 54 start-page: 4001011 issue: 10 year: 2018 ident: 10.1016/j.measurement.2022.111967_b0130 article-title: Integrated Compensation and Rotation Alignment for Three-Axis Magnetic Sensors Array[J] publication-title: IEEE Trans. Magn. – volume: 111612 year: 2022 ident: 10.1016/j.measurement.2022.111967_b0050 article-title: Application of Helbig integrals to magnetic gradient tensor multi-target detection[J] publication-title: Measurement – volume: 160 start-page: 111 issue: 1 year: 2010 ident: 10.1016/j.measurement.2022.111967_b0095 article-title: Pattern recognition of geophysical data[J] publication-title: Geoderma doi: 10.1016/j.geoderma.2009.09.008 – volume: 220 year: 2021 ident: 10.1016/j.measurement.2022.111967_b0160 article-title: A stochastic configuration network based on chaotic sparrow search algorithm publication-title: Knowl.-Based Syst. doi: 10.1016/j.knosys.2021.106924 – volume: 55 start-page: 173 issue: 3–4 year: 2004 ident: 10.1016/j.measurement.2022.111967_b0145 article-title: Determination of the parameters of compact ferro-metallic objects with transforms of magnitude magnetic anomalies[J] publication-title: J. Appl. Geophys. doi: 10.1016/j.jappgeo.2003.10.001 |
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| SubjectTerms | Heading-line survey Kernel extreme learning machine Magnetic gradient tensor Magnetic target pattern recognition Sparrow search algorithm |
| Title | Magnetic object recognition with magnetic gradient tensor system heading-line surveys based on kernel extreme learning machine and sparrow search algorithm |
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