Sustainable Recognition Methods of Modeling Design Features of Light and Micro Vehicle-Mounted UAV: Based on Support Vector Regression and Kano Model

In the era of Industry 4.0, intelligent algorithms provide an effective way to make design methods more sustainable through mining people’s demands continuously, especially in the field of evaluating and predicting the user preferences of phasic or interim design schemes. Vehicle-mounted unmanned ae...
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Gespeichert in:

Autor*in:	Hao Yang [verfasserIn] Yunxiang Huo [verfasserIn] Ruoyu Jia [verfasserIn] Feng Sha [verfasserIn] Naiqi Hu [verfasserIn] Linglan Yu [verfasserIn] Yueran Wang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	sustainable innovation UAV prediction model SVR Kano model design elements

Übergeordnetes Werk:	In: Sustainability - MDPI AG, 2009, 14(2022), 13, p 8210
Übergeordnetes Werk:	volume:14 ; year:2022 ; number:13, p 8210

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/su14138210

Katalog-ID:	DOAJ024713023

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allfieldsSound	10.3390/su14138210 doi (DE-627)DOAJ024713023 (DE-599)DOAJ7191d8c17636463ea9b00d7378881f9f DE-627 ger DE-627 rakwb eng TD194-195 TJ807-830 GE1-350 Hao Yang verfasserin aut Sustainable Recognition Methods of Modeling Design Features of Light and Micro Vehicle-Mounted UAV: Based on Support Vector Regression and Kano Model 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In the era of Industry 4.0, intelligent algorithms provide an effective way to make design methods more sustainable through mining people’s demands continuously, especially in the field of evaluating and predicting the user preferences of phasic or interim design schemes. Vehicle-mounted unmanned aerial vehicles (UAVs) are of significance in mobility experience and traffic surveys. However, as a new type of product, UAVs lack general rules in modeling design and the process of development decision making presents some fuzzy characteristics, which make the evolution and iteration of modeling design more complex. Based on the theories of Kansei Engineering, this study utilized support vector regression (SVR) to establish a correlation model between design factors and preference degree. Because the perceptual evaluation knowledge is fuzzy and uncertain, the paper applied cross-validation and grid search methods to find the optimal parameters. The parameters of the SVR model were adjusted to meet the need for stable learning and for endurance of the noise from subjective experience data to improve the prediction effect and generalization ability. In addition, by means of the Kano model, the customers’ cognition of demand types was quantified to obtain the prioritization of UAV modeling design elements, as well as to compare with the preference scores to validate the feasibility of this research. It was found that the SVR model proposed in the study could effectively predict user preference (R<sup<2</sup< = 0.763, RMSE = 0.057). For the UAVs with a higher preference score, the modeling characteristics were consistent with the attractive, one-dimensional or must-be quality elements in the results of the Kano model, which verified the reliability of the study. The conclusion is expected to provide a sustainable design method for vehicle-mounted UAVs commonly used in citizen travel and outdoor activities. sustainable innovation UAV prediction model SVR Kano model design elements Environmental effects of industries and plants Renewable energy sources Environmental sciences Yunxiang Huo verfasserin aut Ruoyu Jia verfasserin aut Feng Sha verfasserin aut Naiqi Hu verfasserin aut Linglan Yu verfasserin aut Yueran Wang verfasserin aut In Sustainability MDPI AG, 2009 14(2022), 13, p 8210 (DE-627)610604120 (DE-600)2518383-7 20711050 nnns volume:14 year:2022 number:13, p 8210 https://doi.org/10.3390/su14138210 kostenfrei https://doaj.org/article/7191d8c17636463ea9b00d7378881f9f kostenfrei https://www.mdpi.com/2071-1050/14/13/8210 kostenfrei https://doaj.org/toc/2071-1050 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 14 2022 13, p 8210
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callnumber-first	T - Technology
author	Hao Yang
spellingShingle	Hao Yang misc TD194-195 misc TJ807-830 misc GE1-350 misc sustainable innovation misc UAV misc prediction model misc SVR misc Kano model misc design elements misc Environmental effects of industries and plants misc Renewable energy sources misc Environmental sciences Sustainable Recognition Methods of Modeling Design Features of Light and Micro Vehicle-Mounted UAV: Based on Support Vector Regression and Kano Model
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topic_title	TD194-195 TJ807-830 GE1-350 Sustainable Recognition Methods of Modeling Design Features of Light and Micro Vehicle-Mounted UAV: Based on Support Vector Regression and Kano Model sustainable innovation UAV prediction model SVR Kano model design elements
topic	misc TD194-195 misc TJ807-830 misc GE1-350 misc sustainable innovation misc UAV misc prediction model misc SVR misc Kano model misc design elements misc Environmental effects of industries and plants misc Renewable energy sources misc Environmental sciences
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title	Sustainable Recognition Methods of Modeling Design Features of Light and Micro Vehicle-Mounted UAV: Based on Support Vector Regression and Kano Model
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title_full	Sustainable Recognition Methods of Modeling Design Features of Light and Micro Vehicle-Mounted UAV: Based on Support Vector Regression and Kano Model
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title_sort	sustainable recognition methods of modeling design features of light and micro vehicle-mounted uav: based on support vector regression and kano model
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title_auth	Sustainable Recognition Methods of Modeling Design Features of Light and Micro Vehicle-Mounted UAV: Based on Support Vector Regression and Kano Model
abstract	In the era of Industry 4.0, intelligent algorithms provide an effective way to make design methods more sustainable through mining people’s demands continuously, especially in the field of evaluating and predicting the user preferences of phasic or interim design schemes. Vehicle-mounted unmanned aerial vehicles (UAVs) are of significance in mobility experience and traffic surveys. However, as a new type of product, UAVs lack general rules in modeling design and the process of development decision making presents some fuzzy characteristics, which make the evolution and iteration of modeling design more complex. Based on the theories of Kansei Engineering, this study utilized support vector regression (SVR) to establish a correlation model between design factors and preference degree. Because the perceptual evaluation knowledge is fuzzy and uncertain, the paper applied cross-validation and grid search methods to find the optimal parameters. The parameters of the SVR model were adjusted to meet the need for stable learning and for endurance of the noise from subjective experience data to improve the prediction effect and generalization ability. In addition, by means of the Kano model, the customers’ cognition of demand types was quantified to obtain the prioritization of UAV modeling design elements, as well as to compare with the preference scores to validate the feasibility of this research. It was found that the SVR model proposed in the study could effectively predict user preference (R<sup<2</sup< = 0.763, RMSE = 0.057). For the UAVs with a higher preference score, the modeling characteristics were consistent with the attractive, one-dimensional or must-be quality elements in the results of the Kano model, which verified the reliability of the study. The conclusion is expected to provide a sustainable design method for vehicle-mounted UAVs commonly used in citizen travel and outdoor activities.
abstractGer	In the era of Industry 4.0, intelligent algorithms provide an effective way to make design methods more sustainable through mining people’s demands continuously, especially in the field of evaluating and predicting the user preferences of phasic or interim design schemes. Vehicle-mounted unmanned aerial vehicles (UAVs) are of significance in mobility experience and traffic surveys. However, as a new type of product, UAVs lack general rules in modeling design and the process of development decision making presents some fuzzy characteristics, which make the evolution and iteration of modeling design more complex. Based on the theories of Kansei Engineering, this study utilized support vector regression (SVR) to establish a correlation model between design factors and preference degree. Because the perceptual evaluation knowledge is fuzzy and uncertain, the paper applied cross-validation and grid search methods to find the optimal parameters. The parameters of the SVR model were adjusted to meet the need for stable learning and for endurance of the noise from subjective experience data to improve the prediction effect and generalization ability. In addition, by means of the Kano model, the customers’ cognition of demand types was quantified to obtain the prioritization of UAV modeling design elements, as well as to compare with the preference scores to validate the feasibility of this research. It was found that the SVR model proposed in the study could effectively predict user preference (R<sup<2</sup< = 0.763, RMSE = 0.057). For the UAVs with a higher preference score, the modeling characteristics were consistent with the attractive, one-dimensional or must-be quality elements in the results of the Kano model, which verified the reliability of the study. The conclusion is expected to provide a sustainable design method for vehicle-mounted UAVs commonly used in citizen travel and outdoor activities.
abstract_unstemmed	In the era of Industry 4.0, intelligent algorithms provide an effective way to make design methods more sustainable through mining people’s demands continuously, especially in the field of evaluating and predicting the user preferences of phasic or interim design schemes. Vehicle-mounted unmanned aerial vehicles (UAVs) are of significance in mobility experience and traffic surveys. However, as a new type of product, UAVs lack general rules in modeling design and the process of development decision making presents some fuzzy characteristics, which make the evolution and iteration of modeling design more complex. Based on the theories of Kansei Engineering, this study utilized support vector regression (SVR) to establish a correlation model between design factors and preference degree. Because the perceptual evaluation knowledge is fuzzy and uncertain, the paper applied cross-validation and grid search methods to find the optimal parameters. The parameters of the SVR model were adjusted to meet the need for stable learning and for endurance of the noise from subjective experience data to improve the prediction effect and generalization ability. In addition, by means of the Kano model, the customers’ cognition of demand types was quantified to obtain the prioritization of UAV modeling design elements, as well as to compare with the preference scores to validate the feasibility of this research. It was found that the SVR model proposed in the study could effectively predict user preference (R<sup<2</sup< = 0.763, RMSE = 0.057). For the UAVs with a higher preference score, the modeling characteristics were consistent with the attractive, one-dimensional or must-be quality elements in the results of the Kano model, which verified the reliability of the study. The conclusion is expected to provide a sustainable design method for vehicle-mounted UAVs commonly used in citizen travel and outdoor activities.
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container_issue	13, p 8210
title_short	Sustainable Recognition Methods of Modeling Design Features of Light and Micro Vehicle-Mounted UAV: Based on Support Vector Regression and Kano Model
url	https://doi.org/10.3390/su14138210 https://doaj.org/article/7191d8c17636463ea9b00d7378881f9f https://www.mdpi.com/2071-1050/14/13/8210 https://doaj.org/toc/2071-1050
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Vehicle-mounted unmanned aerial vehicles (UAVs) are of significance in mobility experience and traffic surveys. However, as a new type of product, UAVs lack general rules in modeling design and the process of development decision making presents some fuzzy characteristics, which make the evolution and iteration of modeling design more complex. Based on the theories of Kansei Engineering, this study utilized support vector regression (SVR) to establish a correlation model between design factors and preference degree. Because the perceptual evaluation knowledge is fuzzy and uncertain, the paper applied cross-validation and grid search methods to find the optimal parameters. The parameters of the SVR model were adjusted to meet the need for stable learning and for endurance of the noise from subjective experience data to improve the prediction effect and generalization ability. In addition, by means of the Kano model, the customers’ cognition of demand types was quantified to obtain the prioritization of UAV modeling design elements, as well as to compare with the preference scores to validate the feasibility of this research. It was found that the SVR model proposed in the study could effectively predict user preference (R<sup<2</sup< = 0.763, RMSE = 0.057). For the UAVs with a higher preference score, the modeling characteristics were consistent with the attractive, one-dimensional or must-be quality elements in the results of the Kano model, which verified the reliability of the study. The conclusion is expected to provide a sustainable design method for vehicle-mounted UAVs commonly used in citizen travel and outdoor activities.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">sustainable innovation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">UAV</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">prediction model</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">SVR</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Kano model</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">design elements</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Environmental effects of industries and plants</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Renewable energy sources</subfield></datafield><datafield tag="653" ind1=" " 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Sustainable Recognition Methods of Modeling Design Features of Light and Micro Vehicle-Mounted UAV: Based on Support Vector Regression and Kano Model

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