Design of artistic graphic symbols based on intelligent guidance marking system

Abstract This paper presents an in-depth study and analysis of the design of the symbolization of art graphics through a linear regression algorithm. The improved method first selects a suitable range of learning rate and lets the learning rate transform in the range of values according to the chara...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Guo, Yongqiang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Linear regression algorithm Art graphics Symbolic design

Anmerkung:	© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022

Übergeordnetes Werk:	Enthalten in: Neural computing & applications - London : Springer, 1993, 35(2022), 6 vom: 13. März, Seite 4255-4266
Übergeordnetes Werk:	volume:35 ; year:2022 ; number:6 ; day:13 ; month:03 ; pages:4255-4266

Links:	Volltext

DOI / URN:	10.1007/s00521-022-07088-6

Katalog-ID:	SPR049176269

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allfields	10.1007/s00521-022-07088-6 doi (DE-627)SPR049176269 (SPR)s00521-022-07088-6-e DE-627 ger DE-627 rakwb eng Guo, Yongqiang verfasserin (orcid)0000-0002-3649-1409 aut Design of artistic graphic symbols based on intelligent guidance marking system 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 Abstract This paper presents an in-depth study and analysis of the design of the symbolization of art graphics through a linear regression algorithm. The improved method first selects a suitable range of learning rate and lets the learning rate transform in the range of values according to the characteristics of cosine function transformation, so that the learning rate gradually decreases at different rates in different periods of training to achieve the effect of a faster convergence of logistic regression loss function and better convergence value and improve the training efficiency and classification accuracy of logistic regression algorithm. At the same time, the modules of data reordering and regularization penalty method are added to make the logistic regression algorithm model have better generalization ability. Based on the interrelationship between guidance signage system, graphic symbols, and emotional expression, we focus on the emotional characteristics and presentation of graphic symbols and explore their emotional transmission in the guidance signage system, thus the impact on human cognitive behavior. We will summarize the significance of the emotional expression of graphic symbols in the guidance signage system. Graphic symbols as the soul of the guidance signage system, are also the most impactful visual elements, their rich expression, presenting a unique visual aesthetic, more in line with the aesthetic interests of modern people. It conveys information, plays a guiding function at the same time, establishes a sense of place, creates a specific emotional atmosphere to convey a certain emotional experience; the message is more deeply rooted. Linear regression algorithm (dpeaa)DE-He213 Art graphics (dpeaa)DE-He213 Symbolic design (dpeaa)DE-He213 Enthalten in Neural computing & applications London : Springer, 1993 35(2022), 6 vom: 13. März, Seite 4255-4266 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:35 year:2022 number:6 day:13 month:03 pages:4255-4266 https://dx.doi.org/10.1007/s00521-022-07088-6 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_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_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 35 2022 6 13 03 4255-4266
spelling	10.1007/s00521-022-07088-6 doi (DE-627)SPR049176269 (SPR)s00521-022-07088-6-e DE-627 ger DE-627 rakwb eng Guo, Yongqiang verfasserin (orcid)0000-0002-3649-1409 aut Design of artistic graphic symbols based on intelligent guidance marking system 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 Abstract This paper presents an in-depth study and analysis of the design of the symbolization of art graphics through a linear regression algorithm. The improved method first selects a suitable range of learning rate and lets the learning rate transform in the range of values according to the characteristics of cosine function transformation, so that the learning rate gradually decreases at different rates in different periods of training to achieve the effect of a faster convergence of logistic regression loss function and better convergence value and improve the training efficiency and classification accuracy of logistic regression algorithm. At the same time, the modules of data reordering and regularization penalty method are added to make the logistic regression algorithm model have better generalization ability. Based on the interrelationship between guidance signage system, graphic symbols, and emotional expression, we focus on the emotional characteristics and presentation of graphic symbols and explore their emotional transmission in the guidance signage system, thus the impact on human cognitive behavior. We will summarize the significance of the emotional expression of graphic symbols in the guidance signage system. Graphic symbols as the soul of the guidance signage system, are also the most impactful visual elements, their rich expression, presenting a unique visual aesthetic, more in line with the aesthetic interests of modern people. It conveys information, plays a guiding function at the same time, establishes a sense of place, creates a specific emotional atmosphere to convey a certain emotional experience; the message is more deeply rooted. Linear regression algorithm (dpeaa)DE-He213 Art graphics (dpeaa)DE-He213 Symbolic design (dpeaa)DE-He213 Enthalten in Neural computing & applications London : Springer, 1993 35(2022), 6 vom: 13. März, Seite 4255-4266 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:35 year:2022 number:6 day:13 month:03 pages:4255-4266 https://dx.doi.org/10.1007/s00521-022-07088-6 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_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_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 35 2022 6 13 03 4255-4266
allfields_unstemmed	10.1007/s00521-022-07088-6 doi (DE-627)SPR049176269 (SPR)s00521-022-07088-6-e DE-627 ger DE-627 rakwb eng Guo, Yongqiang verfasserin (orcid)0000-0002-3649-1409 aut Design of artistic graphic symbols based on intelligent guidance marking system 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 Abstract This paper presents an in-depth study and analysis of the design of the symbolization of art graphics through a linear regression algorithm. The improved method first selects a suitable range of learning rate and lets the learning rate transform in the range of values according to the characteristics of cosine function transformation, so that the learning rate gradually decreases at different rates in different periods of training to achieve the effect of a faster convergence of logistic regression loss function and better convergence value and improve the training efficiency and classification accuracy of logistic regression algorithm. At the same time, the modules of data reordering and regularization penalty method are added to make the logistic regression algorithm model have better generalization ability. Based on the interrelationship between guidance signage system, graphic symbols, and emotional expression, we focus on the emotional characteristics and presentation of graphic symbols and explore their emotional transmission in the guidance signage system, thus the impact on human cognitive behavior. We will summarize the significance of the emotional expression of graphic symbols in the guidance signage system. Graphic symbols as the soul of the guidance signage system, are also the most impactful visual elements, their rich expression, presenting a unique visual aesthetic, more in line with the aesthetic interests of modern people. It conveys information, plays a guiding function at the same time, establishes a sense of place, creates a specific emotional atmosphere to convey a certain emotional experience; the message is more deeply rooted. Linear regression algorithm (dpeaa)DE-He213 Art graphics (dpeaa)DE-He213 Symbolic design (dpeaa)DE-He213 Enthalten in Neural computing & applications London : Springer, 1993 35(2022), 6 vom: 13. März, Seite 4255-4266 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:35 year:2022 number:6 day:13 month:03 pages:4255-4266 https://dx.doi.org/10.1007/s00521-022-07088-6 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_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_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 35 2022 6 13 03 4255-4266
allfieldsGer	10.1007/s00521-022-07088-6 doi (DE-627)SPR049176269 (SPR)s00521-022-07088-6-e DE-627 ger DE-627 rakwb eng Guo, Yongqiang verfasserin (orcid)0000-0002-3649-1409 aut Design of artistic graphic symbols based on intelligent guidance marking system 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 Abstract This paper presents an in-depth study and analysis of the design of the symbolization of art graphics through a linear regression algorithm. The improved method first selects a suitable range of learning rate and lets the learning rate transform in the range of values according to the characteristics of cosine function transformation, so that the learning rate gradually decreases at different rates in different periods of training to achieve the effect of a faster convergence of logistic regression loss function and better convergence value and improve the training efficiency and classification accuracy of logistic regression algorithm. At the same time, the modules of data reordering and regularization penalty method are added to make the logistic regression algorithm model have better generalization ability. Based on the interrelationship between guidance signage system, graphic symbols, and emotional expression, we focus on the emotional characteristics and presentation of graphic symbols and explore their emotional transmission in the guidance signage system, thus the impact on human cognitive behavior. We will summarize the significance of the emotional expression of graphic symbols in the guidance signage system. Graphic symbols as the soul of the guidance signage system, are also the most impactful visual elements, their rich expression, presenting a unique visual aesthetic, more in line with the aesthetic interests of modern people. It conveys information, plays a guiding function at the same time, establishes a sense of place, creates a specific emotional atmosphere to convey a certain emotional experience; the message is more deeply rooted. Linear regression algorithm (dpeaa)DE-He213 Art graphics (dpeaa)DE-He213 Symbolic design (dpeaa)DE-He213 Enthalten in Neural computing & applications London : Springer, 1993 35(2022), 6 vom: 13. März, Seite 4255-4266 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:35 year:2022 number:6 day:13 month:03 pages:4255-4266 https://dx.doi.org/10.1007/s00521-022-07088-6 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_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_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 35 2022 6 13 03 4255-4266
allfieldsSound	10.1007/s00521-022-07088-6 doi (DE-627)SPR049176269 (SPR)s00521-022-07088-6-e DE-627 ger DE-627 rakwb eng Guo, Yongqiang verfasserin (orcid)0000-0002-3649-1409 aut Design of artistic graphic symbols based on intelligent guidance marking system 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 Abstract This paper presents an in-depth study and analysis of the design of the symbolization of art graphics through a linear regression algorithm. The improved method first selects a suitable range of learning rate and lets the learning rate transform in the range of values according to the characteristics of cosine function transformation, so that the learning rate gradually decreases at different rates in different periods of training to achieve the effect of a faster convergence of logistic regression loss function and better convergence value and improve the training efficiency and classification accuracy of logistic regression algorithm. At the same time, the modules of data reordering and regularization penalty method are added to make the logistic regression algorithm model have better generalization ability. Based on the interrelationship between guidance signage system, graphic symbols, and emotional expression, we focus on the emotional characteristics and presentation of graphic symbols and explore their emotional transmission in the guidance signage system, thus the impact on human cognitive behavior. We will summarize the significance of the emotional expression of graphic symbols in the guidance signage system. Graphic symbols as the soul of the guidance signage system, are also the most impactful visual elements, their rich expression, presenting a unique visual aesthetic, more in line with the aesthetic interests of modern people. It conveys information, plays a guiding function at the same time, establishes a sense of place, creates a specific emotional atmosphere to convey a certain emotional experience; the message is more deeply rooted. Linear regression algorithm (dpeaa)DE-He213 Art graphics (dpeaa)DE-He213 Symbolic design (dpeaa)DE-He213 Enthalten in Neural computing & applications London : Springer, 1993 35(2022), 6 vom: 13. März, Seite 4255-4266 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:35 year:2022 number:6 day:13 month:03 pages:4255-4266 https://dx.doi.org/10.1007/s00521-022-07088-6 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_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_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 35 2022 6 13 03 4255-4266
language	English
source	Enthalten in Neural computing & applications 35(2022), 6 vom: 13. März, Seite 4255-4266 volume:35 year:2022 number:6 day:13 month:03 pages:4255-4266
sourceStr	Enthalten in Neural computing & applications 35(2022), 6 vom: 13. März, Seite 4255-4266 volume:35 year:2022 number:6 day:13 month:03 pages:4255-4266
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Linear regression algorithm Art graphics Symbolic design
isfreeaccess_bool	false
container_title	Neural computing & applications
authorswithroles_txt_mv	Guo, Yongqiang @@aut@@
publishDateDaySort_date	2022-03-13T00:00:00Z
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id	SPR049176269
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author	Guo, Yongqiang
spellingShingle	Guo, Yongqiang misc Linear regression algorithm misc Art graphics misc Symbolic design Design of artistic graphic symbols based on intelligent guidance marking system
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topic_title	Design of artistic graphic symbols based on intelligent guidance marking system Linear regression algorithm (dpeaa)DE-He213 Art graphics (dpeaa)DE-He213 Symbolic design (dpeaa)DE-He213
topic	misc Linear regression algorithm misc Art graphics misc Symbolic design
topic_unstemmed	misc Linear regression algorithm misc Art graphics misc Symbolic design
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title	Design of artistic graphic symbols based on intelligent guidance marking system
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title_full	Design of artistic graphic symbols based on intelligent guidance marking system
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title_sort	design of artistic graphic symbols based on intelligent guidance marking system
title_auth	Design of artistic graphic symbols based on intelligent guidance marking system
abstract	Abstract This paper presents an in-depth study and analysis of the design of the symbolization of art graphics through a linear regression algorithm. The improved method first selects a suitable range of learning rate and lets the learning rate transform in the range of values according to the characteristics of cosine function transformation, so that the learning rate gradually decreases at different rates in different periods of training to achieve the effect of a faster convergence of logistic regression loss function and better convergence value and improve the training efficiency and classification accuracy of logistic regression algorithm. At the same time, the modules of data reordering and regularization penalty method are added to make the logistic regression algorithm model have better generalization ability. Based on the interrelationship between guidance signage system, graphic symbols, and emotional expression, we focus on the emotional characteristics and presentation of graphic symbols and explore their emotional transmission in the guidance signage system, thus the impact on human cognitive behavior. We will summarize the significance of the emotional expression of graphic symbols in the guidance signage system. Graphic symbols as the soul of the guidance signage system, are also the most impactful visual elements, their rich expression, presenting a unique visual aesthetic, more in line with the aesthetic interests of modern people. It conveys information, plays a guiding function at the same time, establishes a sense of place, creates a specific emotional atmosphere to convey a certain emotional experience; the message is more deeply rooted. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022
abstractGer	Abstract This paper presents an in-depth study and analysis of the design of the symbolization of art graphics through a linear regression algorithm. The improved method first selects a suitable range of learning rate and lets the learning rate transform in the range of values according to the characteristics of cosine function transformation, so that the learning rate gradually decreases at different rates in different periods of training to achieve the effect of a faster convergence of logistic regression loss function and better convergence value and improve the training efficiency and classification accuracy of logistic regression algorithm. At the same time, the modules of data reordering and regularization penalty method are added to make the logistic regression algorithm model have better generalization ability. Based on the interrelationship between guidance signage system, graphic symbols, and emotional expression, we focus on the emotional characteristics and presentation of graphic symbols and explore their emotional transmission in the guidance signage system, thus the impact on human cognitive behavior. We will summarize the significance of the emotional expression of graphic symbols in the guidance signage system. Graphic symbols as the soul of the guidance signage system, are also the most impactful visual elements, their rich expression, presenting a unique visual aesthetic, more in line with the aesthetic interests of modern people. It conveys information, plays a guiding function at the same time, establishes a sense of place, creates a specific emotional atmosphere to convey a certain emotional experience; the message is more deeply rooted. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022
abstract_unstemmed	Abstract This paper presents an in-depth study and analysis of the design of the symbolization of art graphics through a linear regression algorithm. The improved method first selects a suitable range of learning rate and lets the learning rate transform in the range of values according to the characteristics of cosine function transformation, so that the learning rate gradually decreases at different rates in different periods of training to achieve the effect of a faster convergence of logistic regression loss function and better convergence value and improve the training efficiency and classification accuracy of logistic regression algorithm. At the same time, the modules of data reordering and regularization penalty method are added to make the logistic regression algorithm model have better generalization ability. Based on the interrelationship between guidance signage system, graphic symbols, and emotional expression, we focus on the emotional characteristics and presentation of graphic symbols and explore their emotional transmission in the guidance signage system, thus the impact on human cognitive behavior. We will summarize the significance of the emotional expression of graphic symbols in the guidance signage system. Graphic symbols as the soul of the guidance signage system, are also the most impactful visual elements, their rich expression, presenting a unique visual aesthetic, more in line with the aesthetic interests of modern people. It conveys information, plays a guiding function at the same time, establishes a sense of place, creates a specific emotional atmosphere to convey a certain emotional experience; the message is more deeply rooted. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022
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container_issue	6
title_short	Design of artistic graphic symbols based on intelligent guidance marking system
url	https://dx.doi.org/10.1007/s00521-022-07088-6
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doi_str	10.1007/s00521-022-07088-6
up_date	2024-07-03T23:41:17.878Z
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Design of artistic graphic symbols based on intelligent guidance marking system

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