Artificial intelligence for determining systematic effects of laser scanners

Abstract Artificial intelligence is interpreted by a machine learning algorithm. Its realization is applied for a two-dimensional grid of points and depends on six parameters which determine the limits of loops. The outer loop defines the width of the grid, the most inner loop the number of scans, which result from the three-dimensional coordinate system for the %$x_i%$-, %$y_i%$-, %$z_i%$-coordinates of a laser scanner. The %$y_i%$-coordinates approximate the distances measured by the laser scanner. The minimal standard deviations of the measurements distorted by systematic effects for the %$y_i%$-coordinates are computed by the Monte Carlo estimate of Sect. 6. The minimum of these minimal standard deviations is found in the grid of points by the machine learning algorithm and used to judge the outcome. Two results are given in Sect. 7. They differ by the widths of the grid and show that only for precise applications the systematic effects of the laser scanner have to be taken care of. Instead of assuming a standard deviation for the systematic effects from prior information as mentioned in Sect. 1, the %$x_i%$-, %$y_i%$-, %$z_i%$-coordinates are repeatedly measured by the laser scanner. However, there are too few repetitions to fulfill the conditions of the multivariate model of Sect. 2 for all measured coordinates. The variances of the measurements plus systematic effects computed by the Monte Carlo estimate of Sect. 6 can therefore be obtained for a restricted number of points only. This number is computed by random variates. For two numbers, the variations of the standard deviations of the %$y_i%$-coordinates, the variations of the standard deviations of the %$x_i%$-, %$y_i%$-, %$z_i%$-coordinates from the multivariate model, the variations of the standard deviations of the systematic effects and the variations of the confidence intervals are presented. The repeated measurements define time series whose auto- and cross-correlation functions are applied as correlations for the systematic effects of the measurements. The ergodicity of the time series is shown. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Koch, Karl-Rudolf [verfasserIn] Brockmann, Jan Martin

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Machine learning algorithm Repeated measurement Confidence interval Autocovariance and cross-covariance function Probability density function Monte Carlo method

Anmerkung:	© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Übergeordnetes Werk:	Enthalten in: GEM - Berlin : Springer, 2010, 10(2019), 1 vom: 20. Jan.
Übergeordnetes Werk:	volume:10 ; year:2019 ; number:1 ; day:20 ; month:01

Links:	Volltext

DOI / URN:	10.1007/s13137-019-0122-x

Katalog-ID:	SPR030620309

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520			\|a Abstract Artificial intelligence is interpreted by a machine learning algorithm. Its realization is applied for a two-dimensional grid of points and depends on six parameters which determine the limits of loops. The outer loop defines the width of the grid, the most inner loop the number of scans, which result from the three-dimensional coordinate system for the %$x_i%$-, %$y_i%$-, %$z_i%$-coordinates of a laser scanner. The %$y_i%$-coordinates approximate the distances measured by the laser scanner. The minimal standard deviations of the measurements distorted by systematic effects for the %$y_i%$-coordinates are computed by the Monte Carlo estimate of Sect. 6. The minimum of these minimal standard deviations is found in the grid of points by the machine learning algorithm and used to judge the outcome. Two results are given in Sect. 7. They differ by the widths of the grid and show that only for precise applications the systematic effects of the laser scanner have to be taken care of. Instead of assuming a standard deviation for the systematic effects from prior information as mentioned in Sect. 1, the %$x_i%$-, %$y_i%$-, %$z_i%$-coordinates are repeatedly measured by the laser scanner. However, there are too few repetitions to fulfill the conditions of the multivariate model of Sect. 2 for all measured coordinates. The variances of the measurements plus systematic effects computed by the Monte Carlo estimate of Sect. 6 can therefore be obtained for a restricted number of points only. This number is computed by random variates. For two numbers, the variations of the standard deviations of the %$y_i%$-coordinates, the variations of the standard deviations of the %$x_i%$-, %$y_i%$-, %$z_i%$-coordinates from the multivariate model, the variations of the standard deviations of the systematic effects and the variations of the confidence intervals are presented. The repeated measurements define time series whose auto- and cross-correlation functions are applied as correlations for the systematic effects of the measurements. The ergodicity of the time series is shown.
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matchkey_str	article:18692680:2019----::riiilnelgneodtriigytmtcfe
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publishDate	2019
allfields	10.1007/s13137-019-0122-x doi (DE-627)SPR030620309 (SPR)s13137-019-0122-x-e DE-627 ger DE-627 rakwb eng Koch, Karl-Rudolf verfasserin (orcid)0000-0001-5810-1531 aut Artificial intelligence for determining systematic effects of laser scanners 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2019 Abstract Artificial intelligence is interpreted by a machine learning algorithm. Its realization is applied for a two-dimensional grid of points and depends on six parameters which determine the limits of loops. The outer loop defines the width of the grid, the most inner loop the number of scans, which result from the three-dimensional coordinate system for the %$x_i%$-, %$y_i%$-, %$z_i%$-coordinates of a laser scanner. The %$y_i%$-coordinates approximate the distances measured by the laser scanner. The minimal standard deviations of the measurements distorted by systematic effects for the %$y_i%$-coordinates are computed by the Monte Carlo estimate of Sect. 6. The minimum of these minimal standard deviations is found in the grid of points by the machine learning algorithm and used to judge the outcome. Two results are given in Sect. 7. They differ by the widths of the grid and show that only for precise applications the systematic effects of the laser scanner have to be taken care of. Instead of assuming a standard deviation for the systematic effects from prior information as mentioned in Sect. 1, the %$x_i%$-, %$y_i%$-, %$z_i%$-coordinates are repeatedly measured by the laser scanner. However, there are too few repetitions to fulfill the conditions of the multivariate model of Sect. 2 for all measured coordinates. The variances of the measurements plus systematic effects computed by the Monte Carlo estimate of Sect. 6 can therefore be obtained for a restricted number of points only. This number is computed by random variates. For two numbers, the variations of the standard deviations of the %$y_i%$-coordinates, the variations of the standard deviations of the %$x_i%$-, %$y_i%$-, %$z_i%$-coordinates from the multivariate model, the variations of the standard deviations of the systematic effects and the variations of the confidence intervals are presented. The repeated measurements define time series whose auto- and cross-correlation functions are applied as correlations for the systematic effects of the measurements. The ergodicity of the time series is shown. Machine learning algorithm (dpeaa)DE-He213 Repeated measurement (dpeaa)DE-He213 Confidence interval (dpeaa)DE-He213 Autocovariance and cross-covariance function (dpeaa)DE-He213 Probability density function (dpeaa)DE-He213 Monte Carlo method (dpeaa)DE-He213 Brockmann, Jan Martin aut Enthalten in GEM Berlin : Springer, 2010 10(2019), 1 vom: 20. Jan. (DE-627)631499296 (DE-600)2564274-1 1869-2680 nnns volume:10 year:2019 number:1 day:20 month:01 https://dx.doi.org/10.1007/s13137-019-0122-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 10 2019 1 20 01
spelling	10.1007/s13137-019-0122-x doi (DE-627)SPR030620309 (SPR)s13137-019-0122-x-e DE-627 ger DE-627 rakwb eng Koch, Karl-Rudolf verfasserin (orcid)0000-0001-5810-1531 aut Artificial intelligence for determining systematic effects of laser scanners 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2019 Abstract Artificial intelligence is interpreted by a machine learning algorithm. Its realization is applied for a two-dimensional grid of points and depends on six parameters which determine the limits of loops. The outer loop defines the width of the grid, the most inner loop the number of scans, which result from the three-dimensional coordinate system for the %$x_i%$-, %$y_i%$-, %$z_i%$-coordinates of a laser scanner. The %$y_i%$-coordinates approximate the distances measured by the laser scanner. The minimal standard deviations of the measurements distorted by systematic effects for the %$y_i%$-coordinates are computed by the Monte Carlo estimate of Sect. 6. The minimum of these minimal standard deviations is found in the grid of points by the machine learning algorithm and used to judge the outcome. Two results are given in Sect. 7. They differ by the widths of the grid and show that only for precise applications the systematic effects of the laser scanner have to be taken care of. Instead of assuming a standard deviation for the systematic effects from prior information as mentioned in Sect. 1, the %$x_i%$-, %$y_i%$-, %$z_i%$-coordinates are repeatedly measured by the laser scanner. However, there are too few repetitions to fulfill the conditions of the multivariate model of Sect. 2 for all measured coordinates. The variances of the measurements plus systematic effects computed by the Monte Carlo estimate of Sect. 6 can therefore be obtained for a restricted number of points only. This number is computed by random variates. For two numbers, the variations of the standard deviations of the %$y_i%$-coordinates, the variations of the standard deviations of the %$x_i%$-, %$y_i%$-, %$z_i%$-coordinates from the multivariate model, the variations of the standard deviations of the systematic effects and the variations of the confidence intervals are presented. The repeated measurements define time series whose auto- and cross-correlation functions are applied as correlations for the systematic effects of the measurements. The ergodicity of the time series is shown. Machine learning algorithm (dpeaa)DE-He213 Repeated measurement (dpeaa)DE-He213 Confidence interval (dpeaa)DE-He213 Autocovariance and cross-covariance function (dpeaa)DE-He213 Probability density function (dpeaa)DE-He213 Monte Carlo method (dpeaa)DE-He213 Brockmann, Jan Martin aut Enthalten in GEM Berlin : Springer, 2010 10(2019), 1 vom: 20. Jan. (DE-627)631499296 (DE-600)2564274-1 1869-2680 nnns volume:10 year:2019 number:1 day:20 month:01 https://dx.doi.org/10.1007/s13137-019-0122-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 10 2019 1 20 01
allfields_unstemmed	10.1007/s13137-019-0122-x doi (DE-627)SPR030620309 (SPR)s13137-019-0122-x-e DE-627 ger DE-627 rakwb eng Koch, Karl-Rudolf verfasserin (orcid)0000-0001-5810-1531 aut Artificial intelligence for determining systematic effects of laser scanners 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2019 Abstract Artificial intelligence is interpreted by a machine learning algorithm. Its realization is applied for a two-dimensional grid of points and depends on six parameters which determine the limits of loops. The outer loop defines the width of the grid, the most inner loop the number of scans, which result from the three-dimensional coordinate system for the %$x_i%$-, %$y_i%$-, %$z_i%$-coordinates of a laser scanner. The %$y_i%$-coordinates approximate the distances measured by the laser scanner. The minimal standard deviations of the measurements distorted by systematic effects for the %$y_i%$-coordinates are computed by the Monte Carlo estimate of Sect. 6. The minimum of these minimal standard deviations is found in the grid of points by the machine learning algorithm and used to judge the outcome. Two results are given in Sect. 7. They differ by the widths of the grid and show that only for precise applications the systematic effects of the laser scanner have to be taken care of. Instead of assuming a standard deviation for the systematic effects from prior information as mentioned in Sect. 1, the %$x_i%$-, %$y_i%$-, %$z_i%$-coordinates are repeatedly measured by the laser scanner. However, there are too few repetitions to fulfill the conditions of the multivariate model of Sect. 2 for all measured coordinates. The variances of the measurements plus systematic effects computed by the Monte Carlo estimate of Sect. 6 can therefore be obtained for a restricted number of points only. This number is computed by random variates. For two numbers, the variations of the standard deviations of the %$y_i%$-coordinates, the variations of the standard deviations of the %$x_i%$-, %$y_i%$-, %$z_i%$-coordinates from the multivariate model, the variations of the standard deviations of the systematic effects and the variations of the confidence intervals are presented. The repeated measurements define time series whose auto- and cross-correlation functions are applied as correlations for the systematic effects of the measurements. The ergodicity of the time series is shown. Machine learning algorithm (dpeaa)DE-He213 Repeated measurement (dpeaa)DE-He213 Confidence interval (dpeaa)DE-He213 Autocovariance and cross-covariance function (dpeaa)DE-He213 Probability density function (dpeaa)DE-He213 Monte Carlo method (dpeaa)DE-He213 Brockmann, Jan Martin aut Enthalten in GEM Berlin : Springer, 2010 10(2019), 1 vom: 20. Jan. (DE-627)631499296 (DE-600)2564274-1 1869-2680 nnns volume:10 year:2019 number:1 day:20 month:01 https://dx.doi.org/10.1007/s13137-019-0122-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 10 2019 1 20 01
allfieldsGer	10.1007/s13137-019-0122-x doi (DE-627)SPR030620309 (SPR)s13137-019-0122-x-e DE-627 ger DE-627 rakwb eng Koch, Karl-Rudolf verfasserin (orcid)0000-0001-5810-1531 aut Artificial intelligence for determining systematic effects of laser scanners 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2019 Abstract Artificial intelligence is interpreted by a machine learning algorithm. Its realization is applied for a two-dimensional grid of points and depends on six parameters which determine the limits of loops. The outer loop defines the width of the grid, the most inner loop the number of scans, which result from the three-dimensional coordinate system for the %$x_i%$-, %$y_i%$-, %$z_i%$-coordinates of a laser scanner. The %$y_i%$-coordinates approximate the distances measured by the laser scanner. The minimal standard deviations of the measurements distorted by systematic effects for the %$y_i%$-coordinates are computed by the Monte Carlo estimate of Sect. 6. The minimum of these minimal standard deviations is found in the grid of points by the machine learning algorithm and used to judge the outcome. Two results are given in Sect. 7. They differ by the widths of the grid and show that only for precise applications the systematic effects of the laser scanner have to be taken care of. Instead of assuming a standard deviation for the systematic effects from prior information as mentioned in Sect. 1, the %$x_i%$-, %$y_i%$-, %$z_i%$-coordinates are repeatedly measured by the laser scanner. However, there are too few repetitions to fulfill the conditions of the multivariate model of Sect. 2 for all measured coordinates. The variances of the measurements plus systematic effects computed by the Monte Carlo estimate of Sect. 6 can therefore be obtained for a restricted number of points only. This number is computed by random variates. For two numbers, the variations of the standard deviations of the %$y_i%$-coordinates, the variations of the standard deviations of the %$x_i%$-, %$y_i%$-, %$z_i%$-coordinates from the multivariate model, the variations of the standard deviations of the systematic effects and the variations of the confidence intervals are presented. The repeated measurements define time series whose auto- and cross-correlation functions are applied as correlations for the systematic effects of the measurements. The ergodicity of the time series is shown. Machine learning algorithm (dpeaa)DE-He213 Repeated measurement (dpeaa)DE-He213 Confidence interval (dpeaa)DE-He213 Autocovariance and cross-covariance function (dpeaa)DE-He213 Probability density function (dpeaa)DE-He213 Monte Carlo method (dpeaa)DE-He213 Brockmann, Jan Martin aut Enthalten in GEM Berlin : Springer, 2010 10(2019), 1 vom: 20. Jan. (DE-627)631499296 (DE-600)2564274-1 1869-2680 nnns volume:10 year:2019 number:1 day:20 month:01 https://dx.doi.org/10.1007/s13137-019-0122-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 10 2019 1 20 01
allfieldsSound	10.1007/s13137-019-0122-x doi (DE-627)SPR030620309 (SPR)s13137-019-0122-x-e DE-627 ger DE-627 rakwb eng Koch, Karl-Rudolf verfasserin (orcid)0000-0001-5810-1531 aut Artificial intelligence for determining systematic effects of laser scanners 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2019 Abstract Artificial intelligence is interpreted by a machine learning algorithm. Its realization is applied for a two-dimensional grid of points and depends on six parameters which determine the limits of loops. The outer loop defines the width of the grid, the most inner loop the number of scans, which result from the three-dimensional coordinate system for the %$x_i%$-, %$y_i%$-, %$z_i%$-coordinates of a laser scanner. The %$y_i%$-coordinates approximate the distances measured by the laser scanner. The minimal standard deviations of the measurements distorted by systematic effects for the %$y_i%$-coordinates are computed by the Monte Carlo estimate of Sect. 6. The minimum of these minimal standard deviations is found in the grid of points by the machine learning algorithm and used to judge the outcome. Two results are given in Sect. 7. They differ by the widths of the grid and show that only for precise applications the systematic effects of the laser scanner have to be taken care of. Instead of assuming a standard deviation for the systematic effects from prior information as mentioned in Sect. 1, the %$x_i%$-, %$y_i%$-, %$z_i%$-coordinates are repeatedly measured by the laser scanner. However, there are too few repetitions to fulfill the conditions of the multivariate model of Sect. 2 for all measured coordinates. The variances of the measurements plus systematic effects computed by the Monte Carlo estimate of Sect. 6 can therefore be obtained for a restricted number of points only. This number is computed by random variates. For two numbers, the variations of the standard deviations of the %$y_i%$-coordinates, the variations of the standard deviations of the %$x_i%$-, %$y_i%$-, %$z_i%$-coordinates from the multivariate model, the variations of the standard deviations of the systematic effects and the variations of the confidence intervals are presented. The repeated measurements define time series whose auto- and cross-correlation functions are applied as correlations for the systematic effects of the measurements. The ergodicity of the time series is shown. Machine learning algorithm (dpeaa)DE-He213 Repeated measurement (dpeaa)DE-He213 Confidence interval (dpeaa)DE-He213 Autocovariance and cross-covariance function (dpeaa)DE-He213 Probability density function (dpeaa)DE-He213 Monte Carlo method (dpeaa)DE-He213 Brockmann, Jan Martin aut Enthalten in GEM Berlin : Springer, 2010 10(2019), 1 vom: 20. Jan. (DE-627)631499296 (DE-600)2564274-1 1869-2680 nnns volume:10 year:2019 number:1 day:20 month:01 https://dx.doi.org/10.1007/s13137-019-0122-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 10 2019 1 20 01
language	English
source	Enthalten in GEM 10(2019), 1 vom: 20. Jan. volume:10 year:2019 number:1 day:20 month:01
sourceStr	Enthalten in GEM 10(2019), 1 vom: 20. Jan. volume:10 year:2019 number:1 day:20 month:01
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Machine learning algorithm Repeated measurement Confidence interval Autocovariance and cross-covariance function Probability density function Monte Carlo method
isfreeaccess_bool	false
container_title	GEM
authorswithroles_txt_mv	Koch, Karl-Rudolf @@aut@@ Brockmann, Jan Martin @@aut@@
publishDateDaySort_date	2019-01-20T00:00:00Z
hierarchy_top_id	631499296
id	SPR030620309
language_de	englisch
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author	Koch, Karl-Rudolf
spellingShingle	Koch, Karl-Rudolf misc Machine learning algorithm misc Repeated measurement misc Confidence interval misc Autocovariance and cross-covariance function misc Probability density function misc Monte Carlo method Artificial intelligence for determining systematic effects of laser scanners
authorStr	Koch, Karl-Rudolf
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topic_title	Artificial intelligence for determining systematic effects of laser scanners Machine learning algorithm (dpeaa)DE-He213 Repeated measurement (dpeaa)DE-He213 Confidence interval (dpeaa)DE-He213 Autocovariance and cross-covariance function (dpeaa)DE-He213 Probability density function (dpeaa)DE-He213 Monte Carlo method (dpeaa)DE-He213
topic	misc Machine learning algorithm misc Repeated measurement misc Confidence interval misc Autocovariance and cross-covariance function misc Probability density function misc Monte Carlo method
topic_unstemmed	misc Machine learning algorithm misc Repeated measurement misc Confidence interval misc Autocovariance and cross-covariance function misc Probability density function misc Monte Carlo method
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title	Artificial intelligence for determining systematic effects of laser scanners
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title_full	Artificial intelligence for determining systematic effects of laser scanners
author_sort	Koch, Karl-Rudolf
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author_browse	Koch, Karl-Rudolf Brockmann, Jan Martin
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title_sort	artificial intelligence for determining systematic effects of laser scanners
title_auth	Artificial intelligence for determining systematic effects of laser scanners
abstract	Abstract Artificial intelligence is interpreted by a machine learning algorithm. Its realization is applied for a two-dimensional grid of points and depends on six parameters which determine the limits of loops. The outer loop defines the width of the grid, the most inner loop the number of scans, which result from the three-dimensional coordinate system for the %$x_i%$-, %$y_i%$-, %$z_i%$-coordinates of a laser scanner. The %$y_i%$-coordinates approximate the distances measured by the laser scanner. The minimal standard deviations of the measurements distorted by systematic effects for the %$y_i%$-coordinates are computed by the Monte Carlo estimate of Sect. 6. The minimum of these minimal standard deviations is found in the grid of points by the machine learning algorithm and used to judge the outcome. Two results are given in Sect. 7. They differ by the widths of the grid and show that only for precise applications the systematic effects of the laser scanner have to be taken care of. Instead of assuming a standard deviation for the systematic effects from prior information as mentioned in Sect. 1, the %$x_i%$-, %$y_i%$-, %$z_i%$-coordinates are repeatedly measured by the laser scanner. However, there are too few repetitions to fulfill the conditions of the multivariate model of Sect. 2 for all measured coordinates. The variances of the measurements plus systematic effects computed by the Monte Carlo estimate of Sect. 6 can therefore be obtained for a restricted number of points only. This number is computed by random variates. For two numbers, the variations of the standard deviations of the %$y_i%$-coordinates, the variations of the standard deviations of the %$x_i%$-, %$y_i%$-, %$z_i%$-coordinates from the multivariate model, the variations of the standard deviations of the systematic effects and the variations of the confidence intervals are presented. The repeated measurements define time series whose auto- and cross-correlation functions are applied as correlations for the systematic effects of the measurements. The ergodicity of the time series is shown. © Springer-Verlag GmbH Germany, part of Springer Nature 2019
abstractGer	Abstract Artificial intelligence is interpreted by a machine learning algorithm. Its realization is applied for a two-dimensional grid of points and depends on six parameters which determine the limits of loops. The outer loop defines the width of the grid, the most inner loop the number of scans, which result from the three-dimensional coordinate system for the %$x_i%$-, %$y_i%$-, %$z_i%$-coordinates of a laser scanner. The %$y_i%$-coordinates approximate the distances measured by the laser scanner. The minimal standard deviations of the measurements distorted by systematic effects for the %$y_i%$-coordinates are computed by the Monte Carlo estimate of Sect. 6. The minimum of these minimal standard deviations is found in the grid of points by the machine learning algorithm and used to judge the outcome. Two results are given in Sect. 7. They differ by the widths of the grid and show that only for precise applications the systematic effects of the laser scanner have to be taken care of. Instead of assuming a standard deviation for the systematic effects from prior information as mentioned in Sect. 1, the %$x_i%$-, %$y_i%$-, %$z_i%$-coordinates are repeatedly measured by the laser scanner. However, there are too few repetitions to fulfill the conditions of the multivariate model of Sect. 2 for all measured coordinates. The variances of the measurements plus systematic effects computed by the Monte Carlo estimate of Sect. 6 can therefore be obtained for a restricted number of points only. This number is computed by random variates. For two numbers, the variations of the standard deviations of the %$y_i%$-coordinates, the variations of the standard deviations of the %$x_i%$-, %$y_i%$-, %$z_i%$-coordinates from the multivariate model, the variations of the standard deviations of the systematic effects and the variations of the confidence intervals are presented. The repeated measurements define time series whose auto- and cross-correlation functions are applied as correlations for the systematic effects of the measurements. The ergodicity of the time series is shown. © Springer-Verlag GmbH Germany, part of Springer Nature 2019
abstract_unstemmed	Abstract Artificial intelligence is interpreted by a machine learning algorithm. Its realization is applied for a two-dimensional grid of points and depends on six parameters which determine the limits of loops. The outer loop defines the width of the grid, the most inner loop the number of scans, which result from the three-dimensional coordinate system for the %$x_i%$-, %$y_i%$-, %$z_i%$-coordinates of a laser scanner. The %$y_i%$-coordinates approximate the distances measured by the laser scanner. The minimal standard deviations of the measurements distorted by systematic effects for the %$y_i%$-coordinates are computed by the Monte Carlo estimate of Sect. 6. The minimum of these minimal standard deviations is found in the grid of points by the machine learning algorithm and used to judge the outcome. Two results are given in Sect. 7. They differ by the widths of the grid and show that only for precise applications the systematic effects of the laser scanner have to be taken care of. Instead of assuming a standard deviation for the systematic effects from prior information as mentioned in Sect. 1, the %$x_i%$-, %$y_i%$-, %$z_i%$-coordinates are repeatedly measured by the laser scanner. However, there are too few repetitions to fulfill the conditions of the multivariate model of Sect. 2 for all measured coordinates. The variances of the measurements plus systematic effects computed by the Monte Carlo estimate of Sect. 6 can therefore be obtained for a restricted number of points only. This number is computed by random variates. For two numbers, the variations of the standard deviations of the %$y_i%$-coordinates, the variations of the standard deviations of the %$x_i%$-, %$y_i%$-, %$z_i%$-coordinates from the multivariate model, the variations of the standard deviations of the systematic effects and the variations of the confidence intervals are presented. The repeated measurements define time series whose auto- and cross-correlation functions are applied as correlations for the systematic effects of the measurements. The ergodicity of the time series is shown. © Springer-Verlag GmbH Germany, part of Springer Nature 2019
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title_short	Artificial intelligence for determining systematic effects of laser scanners
url	https://dx.doi.org/10.1007/s13137-019-0122-x
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author2	Brockmann, Jan Martin
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doi_str	10.1007/s13137-019-0122-x
up_date	2024-07-03T19:10:47.814Z
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Artificial intelligence for determining systematic effects of laser scanners

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