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Ein Vergleich von Datenanalysemethoden für eine Affective Engineering Methode
Susan, Gretchen Zöller; Tina, Schröppel; Sandro, Wartzack
Type:
Year:
2017
Editor:
Dieter Krause, Kristin Paetzold, Sandro Wartzack
Author:
Series:
DfX
Institution:
FAU Erlangen-Nürnberg
Section:
User-Centered Design
Page(s):
287-298
ISBN:
978-3-946094-20-3-24
Abstract:
Affective Engineering (AE) is an engineering genre that deals with users’ subjective value creation in technical product design. Therein, quantitative instruments to map subjective quality criteria are dominant. ACADE is such an instrument that focuses on a long-term alignment of product design impres-sions to the subjective needs of users. Due to its quantitative backbone, sev-eral mathematical analysis methods seem convenient, whereas their specific benefits and drawbacks are not yet clear in the AE context. Therefore, anal-yses of nonlinear regression, artificial neural networks, fuzzy logic systems and hybrids are examined under the aspect of ACADE applicability. Different quality indicators unveil characteristics which the designer may use to mine their potential for future AE analyses.
Keywords:
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