Abstract

Design concept evaluation is a key process in the new product development process with a significant impact on the product’s success and total cost over its life cycle. This paper is motivated by two limitations of the state-of-the-art in concept evaluation: (1) the amount and diversity of user feedback and insights utilized by existing concept evaluation methods such as quality function deployment are limited. (2) Subjective concept evaluation methods require significant manual effort which in turn may limit the number of concepts considered for evaluation. A deep multimodal design evaluation (DMDE) model is proposed in this paper to bridge these gaps by providing designers with an accurate and scalable prediction of new concepts’ overall and attribute-level desirability based on large-scale user reviews on existing designs. The attribute-level sentiment intensities of users are first extracted and aggregated from online reviews. A multimodal deep regression model is then developed to predict the overall and attribute-level sentiment values based on the features extracted from orthographic product images via a fine-tuned ResNet-50 model and from product descriptions via a fine-tuned bidirectional encoder representations from transformer model and aggregated using a novel self-attention-based fusion model. The DMDE model adds a data-driven, user-centered loop within the concept development process to better inform the concept evaluation process. Numerical experiments on a large dataset from an online footwear store indicate a promising performance by the DMDE model with 0.001 MSE loss and over 99.1% accuracy.

References

1.
Takeuchi
,
H.
, and
Nonaka
,
I.
,
1986
, “
The New Product Development Game
,”
Harvard Business Rev.
,
64
(
1
), pp.
137
146
.
2.
Osborn
,
A.
,
1953
,
Applied Imagination
,
Scribner’s
,
New York
.
3.
Yilmaz
,
S.
,
Seifert
,
C.
,
Daly
,
S. R.
, and
Gonzalez
,
R.
,
2016
, “
Design Heuristics in Innovative Products
,”
J. Mech. Des.
,
138
(
7
), p.
071102
.
4.
Crismond
,
D. P.
, and
Adams
,
R. S.
,
2012
, “
The Informed Design Teaching and Learning Matrix
,”
J. Eng. Educ.
,
101
(
4
), p.
738
.
5.
Forbes
,
H.
, and
Schaefer
,
D.
,
2018
, “
Crowdsourcing in Product Development: Current State and Future Research Directions
,”
Proceedings of the DESIGN 2018 15th International Design Conference
,
Dubrovnik, Croatia
,
May 21–24
, pp.
579
588
.
6.
Mumford
,
M. D.
,
Feldman
,
J. M.
,
Hein
,
M. B.
, and
Nagao
,
D. J.
,
2001
, “
Tradeoffs Between Ideas and Structure: Individual Versus Group Performance in Creative Problem Solving
,”
J. Creat. Behav.
,
35
(
1
), pp.
1
23
.
7.
Linsey
,
J. S.
,
Clauss
,
E. F.
,
Kurtoglu
,
T.
,
Murphy
,
J. T.
,
Wood
,
K. L.
, and
Markman
,
A. B.
,
2011
, “
An Experimental Study of Group Idea Generation Techniques: Understanding the Roles of Idea Representation and Viewing Methods
,”
ASME J. Mech. Des.
,
133
(
3
), p.
031008
.
8.
Yilmaz
,
S.
,
Daly
,
S. R.
,
Seifert
,
C. M.
, and
Gonzalez
,
R.
,
2016
, “
Evidence-Based Design Heuristics for Idea Generation
,”
Des. Stud.
,
46
(
C
), pp.
95
124
.
9.
Simonton
,
D. K.
,
1990
,
Psychology, Science, and History: An Introduction to Historiometry
,
Yale University Press
,
New Haven, CT
.
10.
Daly
,
S. R.
,
Seifert
,
C. M.
,
Yilmaz
,
S.
, and
Gonzalez
,
R.
,
2016
, “
Comparing Ideation Techniques for Beginning Designers
,”
ASME J. Mech. Des.
,
138
(
10
), p.
101108
.
11.
Han
,
J.
,
Shi
,
F.
,
Chen
,
L.
, and
Childs
,
P. R.
,
2018
, “
The Combinator—A Computer-Based Tool for Creative Idea Generation Based on a Simulation Approach
,”
Des. Sci.
,
4
(
11
), pp.
1
34
.
12.
Gerhard
,
P.
, and
Karl-Heinrich
,
G.
,
1984
,
Engineering Design: A Systematic Approach
,
Springer
,
Berlin, Germany
.
13.
Howard
,
T. J.
,
Culley
,
S.
, and
Dekoninck
,
E. A.
,
2011
, “
Reuse of Ideas and Concepts for Creative Stimuli in Engineering Design
,”
J. Eng. Des.
,
22
(
8
), pp.
565
581
.
14.
Gray
,
C. M.
,
McKilligan
,
S.
,
Daly
,
S. R.
,
Seifert
,
C. M.
, and
Gonzalez
,
R.
,
2019
, “
Using Creative Exhaustion to Foster Idea Generation
,”
Int. J. Technol. Des. Educ.
,
29
(
1
), pp.
177
195
.
15.
Shidpour
,
H.
,
Da Cunha
,
C.
, and
Bernard
,
A.
,
2016
, “
Group Multi-Criteria Design Concept Evaluation Using Combined Rough Set Theory and Fuzzy Set Theory
,”
Expert Syst. Appl.
,
64
(
C
), pp.
633
644
.
16.
Pugh
,
S.
, and
Clausing
,
D.
,
1996
,
Creating Innovtive Products Using Total Design: The Living Legacy of Stuart Pugh
,
Addison-Wesley Longman Publishing Co Inc.
,
Boston, MA
.
17.
Tsai
,
H.-C.
, and
Hsiao
,
S.-W.
,
2004
, “
Evaluation of Alternatives for Product Customization Using Fuzzy Logic
,”
Inform. Sci.
,
158
(
10
), pp.
233
262
.
18.
Huang
,
H.-Z.
,
Bo
,
R.
, and
Chen
,
W.
,
2006
, “
An Integrated Computational Intelligence Approach to Product Concept Generation and Evaluation
,”
Mech. Mach. Theory
,
41
(
5
), pp.
567
583
.
19.
Huang
,
H.-Z.
,
Liu
,
Y.
,
Li
,
Y.
,
Xue
,
L.
, and
Wang
,
Z.
,
2013
, “
New Evaluation Methods for Conceptual Design Selection Using Computational Intelligence Techniques
,”
J. Mech. Sci. Technol.
,
27
(
3
), pp.
733
746
.
20.
Ulrich
,
K. T.
,
2003
,
Product Design and Development
,
Tata McGraw-Hill Education
,
New York
.
21.
Dym
,
C. L.
,
Wood
,
W. H.
, and
Scott
,
M. J.
,
2002
, “
Rank Ordering Engineering Designs: Pairwise Comparison Charts and Borda Counts
,”
Res. Eng. Des.
,
13
(
4
), pp.
236
242
.
22.
Frey
,
D. D.
,
Herder
,
P. M.
,
Wijnia
,
Y.
,
Subrahmanian
,
E.
,
Katsikopoulos
,
K.
, and
Clausing
,
D. P.
,
2009
, “
The Pugh Controlled Convergence Method: Model-Based Evaluation and Implications for Design Theory
,”
Res. Eng. Des.
,
20
(
1
), pp.
41
58
.
23.
Scott
,
M. J.
, and
Antonsson
,
E. K.
,
1998
, “
Aggregation Functions for Engineering Design Trade-offs
,”
Fuzzy Sets Syst.
,
99
(
3
), pp.
253
264
.
24.
King
,
A. M.
, and
Sivaloganathan
,
S.
,
1999
, “
Development of a Methodology for Concept Selection in Flexible Design Strategies
,”
J. Eng. Des.
,
10
(
4
), pp.
329
349
.
25.
Thurston
,
D.
, and
Carnahan
,
J.
,
1992
, “
Fuzzy Ratings and Utility Analysis in Preliminary Design Evaluation of Multiple Attributes
,”
ASME J. Mech. Des.
,
114
(
4
), pp.
648
658
.
26.
Wang
,
J.
,
2001
, “
Ranking Engineering Design Concepts Using a Fuzzy Outranking Preference Model
,”
Fuzzy Sets Syst.
,
119
(
1
), pp.
161
170
.
27.
Ayag*
,
Z.
,
2005
, “
An Integrated Approach to Evaluating Conceptual Design Alternatives in a New Product Development Environment
,”
Int. J. Prod. Res.
,
43
(
4
), pp.
687
713
.
28.
Papadakis
,
V. M.
, and
Barwise
,
P.
,
2002
, “
How Much Do CEOs and Top Managers Matter in Strategic Decision-Making?
,”
Br. J. Manage.
,
13
(
1
), pp.
83
95
.
29.
Scott
,
M. J.
,
2002
, “
Quantifying Certainty in Design Decisions: Examining AHP
,”
International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
,
Montreal, Quebec, Canada
,
Sept. 29–Oct. 2
, Vol. 3624, pp.
219
229
.
30.
Malekly
,
H.
,
Mousavi
,
S. M.
, and
Hashemi
,
H.
,
2010
, “
A Fuzzy Integrated Methodology for Evaluating Conceptual Bridge Design
,”
Expert Syst. Appl.
,
37
(
7
), pp.
4910
4920
.
31.
Ayağ
,
Z.
, and
Özdem[idot]r
,
R.
,
2007
, “
An Analytic Network Process-Based Approach to Concept Evaluation in a New Product Development Environment
,”
J. Eng. Des.
,
18
(
3
), pp.
209
226
.
32.
Huang
,
H.-Z.
,
Li
,
Y.
,
Liu
,
W.
,
Liu
,
Y.
, and
Wang
,
Z.
,
2011
, “
Evaluation and Decision of Products Conceptual Design Schemes Based on Customer Requirements
,”
J. Mech. Sci. Technol.
,
25
(
9
), pp.
2413
2425
.
33.
Ramanujan
,
D.
,
Nawal
,
Y.
,
Reid
,
T.
, and
Ramani
,
K.
,
2015
, “
Informing Early Design Via Crowd-Based Co-creation
,”
International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
,
Boston, MA
,
Aug. 2–5
, Vol. 57175, American Society of Mechanical Engineers, p. V007T06A043..
34.
Griffin
,
A.
, and
Hauser
,
J. R.
,
1993
, “
The Voice of the Customer
,”
Market. Sci.
,
12
(
1
), pp.
1
27
.
35.
Cooper
,
R. G.
,
2008
, “
Perspective: The Stage-gate® Idea-to-Launch Process–Update, What’s New, and Nexgen Systems
,”
J. Prod. Innov. Manage.
,
25
(
3
), pp.
213
232
.
36.
Zheng
,
J.
, and
Jakiela
,
M. J.
,
2009
, “
An Investigation of the Productivity Difference in Mechanical Embodiment Design Between Face-to-Face and Threaded Online Collaboration
,”
Proceedings of the ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
,
San Diego, CA
,
Aug. 30–Sept. 2
, Vol. 48999, pp.
1173
1182
.
37.
Burnap
,
A.
,
Hartley
,
J.
,
Pan
,
Y.
,
Gonzalez
,
R.
, and
Papalambros
,
P. Y.
,
2016
, “
Balancing Design Freedom and Brand Recognition in the Evolution of Automotive Brand Styling
,”
Des. Sci.
,
2
(
9
), pp.
1
28
.
38.
Zhang
,
Z.
, and
Chu
,
X.
,
2009
, “
A New Integrated Decision-Making Approach for Design Alternative Selection for Supporting Complex Product Development
,”
Int. J. Comput. Int. Manuf.
,
22
(
3
), pp.
179
198
.
39.
Sa’Ed
,
M. S.
, and
Al-Harris
,
M. Y.
,
2014
, “
New Product Concept Selection: An Integrated Approach Using Data Envelopment Analysis (DEA) and Conjoint Analysis (CA)
,”
Int. J. Eng. Technol.
,
3
(
1
), p.
44
.
40.
Feyzioglu
,
O.
, and
Buyukozkan
,
G.
,
2006
, “
Evaluation of New Product Development Projects Using Artificial Intelligence and Fuzzy Logic
,”
International Conference on Knowledge Mining and Computer Science
,
Las Vegas, NV
,
June 26–29
, Vol. 11, pp.
183
189
.
41.
Gosnell
,
C. A.
, and
Miller
,
S. R.
,
2016
, “
But Is It Creative? Delineating the Impact of Expertise and Concept Ratings on Creative Concept Selection
,”
ASME J. Mech. Des.
,
138
(
2
), p.
021101
.
42.
Toh
,
C. A.
, and
Miller
,
S. R.
,
2015
, “
How Engineering Teams Select Design Concepts: A View Through the Lens of Creativity
,”
Des. Stud.
,
38
(
C
), pp.
111
138
.
43.
Nikander
,
J. B.
,
Liikkanen
,
L. A.
, and
Laakso
,
M.
,
2014
, “
The Preference Effect in Design Concept Evaluation
,”
Des. Stud.
,
35
(
5
), pp.
473
499
.
44.
Hauser
,
J. R.
, and
Clausing
,
D.
,
1988
,
The House of Quality
, Harvard Business Review.
45.
Liedtka
,
J.
,
2015
, “
Perspective: Linking Design Thinking With Innovation Outcomes Through Cognitive Bias Reduction
,”
J. Prod. Innov. Manage.
,
32
(
6
), pp.
925
938
.
46.
Ulrich
,
K.
, and
Eppinger
,
S.
,
2016
,
Product Design and Development
,
McGraw-Hill Education
,
New York
.
47.
Suryadi
,
D.
, and
Kim
,
H. M.
,
2019
, “
A Data-Driven Methodology to Construct Customer Choice Sets Using Online Data and Customer Reviews
,”
ASME J. Mech. Des.
,
141
(
11
), p.
111103
.
48.
Joung
,
J.
, and
Kim
,
H. M.
,
2021
, “
Approach for Importance–Performance Analysis of Product Attributes From Online Reviews
,”
ASME J. Mech. Des.
,
143
(
8
), p.
081705
.
49.
Zhang
,
L.
,
Wang
,
S.
, and
Liu
,
B.
,
2018
, “
Deep Learning for Sentiment Analysis: A Survey
,”
Wiley Interdiscipl. Rev.: Data Mining Knowledge Discov.
,
8
(
4
), p.
e1253
.
50.
Tang
,
H.
,
Tan
,
S.
, and
Cheng
,
X.
,
2009
, “
A Survey on Sentiment Detection of Reviews
,”
Expert Syst. Appl.
,
36
(
7
), pp.
10760
10773
.
51.
Liu
,
B.
,
2010
, “Sentiment Analysis and Subjectivity,”
Handbook of Natural Language Processing
, 2nd ed.,
N.
Indurkhya
and
F. J.
Damerau
, eds, Vol.
2
, pp.
627
666
.
52.
He
,
K.
,
Zhang
,
X.
,
Ren
,
S.
, and
Sun
,
J.
,
2016
, “
Deep Residual Learning for Image Recognition
,”
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
,
Las Vegas, NV
,
June 27–30
, pp.
770
778
.
53.
Deng
,
J.
,
Dong
,
W.
,
Socher
,
R.
,
Li
,
L.-J.
,
Li
,
K.
, and
Fei-Fei
,
L.
,
2009
, “
ImageNet: A Large-Scale Hierarchical Image Database
,”
IEEE Conference on Computer Vision and Pattern Recognition
,
Miami, FL
,
June 20–25
, pp.
248
255
.
54.
Devlin
,
J.
,
Chang
,
M.-W.
,
Lee
,
K.
, and
Toutanova
,
K.
,
2018
, “
BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding
,”
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, {NAACL-HLT} 2019
,
Minneapolis, MN
,
June 2–7
.
55.
Verganti
,
R.
,
2008
, “
Design, Meanings, and Radical Innovation: A Metamodel and a Research Agenda
,”
J. Prod. Innov. Manage.
,
25
(
5
), pp.
436
456
.
56.
Zhu
,
G.-N.
,
Hu
,
J.
,
Qi
,
J.
,
Gu
,
C.-C.
, and
Peng
,
Y.-H.
,
2015
, “
An Integrated AHP and Vikor for Design Concept Evaluation Based on Rough Number
,”
Adv. Eng. Inform.
,
29
(
3
), pp.
408
418
.
57.
Toh
,
C. A.
, and
Miller
,
S. R.
,
2016
, “
Choosing Creativity: The Role of Individual Risk and Ambiguity Aversion on Creative Concept Selection in Engineering Design
,”
Res. Eng. Des.
,
27
(
3
), pp.
195
219
.
58.
Calantone
,
R. J.
,
Di Benedetto
,
C. A.
, and
Schmidt
,
J. B.
,
1999
, “
Using the Analytic Hierarchy Process in New Product Screening
,”
J. Prod. Innov. Manage.: Inter. Public Product Dev. Manage. Assoc.
,
16
(
1
), pp.
65
76
.
59.
Lin
,
M.-C.
,
Wang
,
C.-C.
,
Chen
,
M.-S.
, and
Chang
,
C. A.
,
2008
, “
Using AHP and Topsis Approaches in Customer-Driven Product Design Process
,”
Comput. Ind.
,
59
(
1
), pp.
17
31
.
60.
Takai
,
S.
, and
Ishii
,
K.
,
2006
, “
Integrating Target Costing Into Perception-Based Concept Evaluation of Complex and Large-Scale Systems Using Simultaneously Decomposed QFD
,”
ASME J. Mech. Des
,
128
(
6
), pp.
1186
1195
.
61.
Besharati
,
B.
,
Azarm
,
S.
, and
Kannan
,
P.
,
2006
, “
A Decision Support System for Product Design Selection: A Generalized Purchase Modeling Approach
,”
Decision Support Syst.
,
42
(
1
), pp.
333
350
.
62.
Ayağ
,
Z.
,
2005
, “
A Fuzzy AHP-Based Simulation Approach to Concept Evaluation in a NPD Environment
,”
IIE Trans.
,
37
(
9
), pp.
827
842
.
63.
Ayağ
,
Z.
, and
Özdemir
,
R. G.
,
2009
, “
A Hybrid Approach to Concept Selection Through Fuzzy Analytic Network Process
,”
Comput. Ind. Eng.
,
56
(
1
), pp.
368
379
.
64.
Vanegas
,
L.
, and
Labib
,
A.
,
2001
, “
Application of New Fuzzy-Weighted Average (NFWA) Method to Engineering Design Evaluation
,”
Int. J. Prod. Res.
,
39
(
6
), pp.
1147
1162
.
65.
Vanegas
,
L. V.
, and
Labib
,
A. W.
,
2005
, “
Fuzzy Approaches to Evaluation in Engineering Design
,”
ASME J. Mech. Des.
,
127
(
1
), pp.
24
33
.
66.
Chin
,
K.-S.
,
Yang
,
J.-B.
,
Guo
,
M.
, and
Lam
,
J. P.-K.
,
2009
, “
An Evidential-Reasoning-Interval-Based Method for New Product Design Assessment
,”
IEEE Trans. Eng. Manage.
,
56
(
1
), pp.
142
156
.
67.
Zhai
,
L.-Y.
,
Khoo
,
L.-P.
, and
Zhong
,
Z.-W.
,
2009
, “
Design Concept Evaluation in Product Development Using Rough Sets and Grey Relation Analysis
,”
Expert Syst. Appl.
,
36
(
3
), pp.
7072
7079
.
68.
Li
,
Y.
,
Tang
,
J.
,
Luo
,
X.
, and
Xu
,
J.
,
2009
, “
An Integrated Method of Rough Set, Kano’s Model and AHP for Rating Customer Requirements’ Final Importance
,”
Expert Syst. Appl.
,
36
(
3
), pp.
7045
7053
.
69.
Aydogan
,
E. K.
,
2011
, “
Performance Measurement Model for Turkish Aviation Firms Using the Rough-AHP and Topsis Methods Under Fuzzy Environment
,”
Expert Syst. Appl.
,
38
(
4
), pp.
3992
3998
.
70.
Zou
,
Z.
,
Tseng
,
T.-L. B.
,
Sohn
,
H.
,
Song
,
G.
, and
Gutierrez
,
R.
,
2011
, “
A Rough Set Based Approach to Distributor Selection in Supply Chain Management
,”
Expert Syst. Appl.
,
38
(
1
), pp.
106
115
.
71.
Ashour
,
O. M.
, and
Kremer
,
G. E. O.
,
2013
, “
A Simulation Analysis of the Impact of Fahp–maut Triage Algorithm on the Emergency Department Performance Measures
,”
Expert Syst. Appl.
,
40
(
1
), pp.
177
187
.
72.
Jimenez
,
A.
,
Mateos
,
A.
, and
Sabio
,
P.
,
2013
, “
Dominance Intensity Measure Within Fuzzy Weight Oriented Maut: An Application
,”
Omega
,
41
(
2
), pp.
397
405
.
73.
Kilic
,
H. S.
,
Zaim
,
S.
, and
Delen
,
D.
,
2015
, “
Selecting “the Best” ERP System for SMES Using a Combination of ANP and Promethee Methods
,”
Expert Syst. Appl.
,
42
(
5
), pp.
2343
2352
.
74.
Vetschera
,
R.
, and
De Almeida
,
A. T.
,
2012
, “
A Promethee-Based Approach to Portfolio Selection Problems
,”
Comput. Oper. Res.
,
39
(
5
), pp.
1010
1020
.
75.
Song
,
W.
,
Ming
,
X.
, and
Wu
,
Z.
,
2013
, “
An Integrated Rough Number-Based Approach to Design Concept Evaluation Under Subjective Environments
,”
J. Eng. Des.
,
24
(
5
), pp.
320
341
.
76.
Ayağ
,
Z.
,
2016
, “
An Integrated Approach to Concept Evaluation in a New Product Development
,”
J. Intell. Manuf.
,
27
(
5
), pp.
991
1005
.
77.
Zhang
,
Z.-J.
,
Gong
,
L.
,
Jin
,
Y.
,
Xie
,
J.
, and
Hao
,
J.
,
2017
, “
A Quantitative Approach to Design Alternative Evaluation Based on Data-Driven Performance Prediction
,”
Adv. Eng. Inform.
,
32
(
C
), pp.
52
65
.
78.
Cortes
,
C.
, and
Vapnik
,
V.
,
1995
, “
Support-Vector Networks
,”
Mach. Learn.
,
20
(
3
), pp.
273
297
.
79.
Shieh
,
M.-D.
, and
Yang
,
C.-C.
,
2008
, “
Classification Model for Product Form Design Using Fuzzy Support Vector Machines
,”
Comput. Ind. Eng.
,
55
(
1
), pp.
150
164
.
80.
Yang
,
C.-C.
, and
Shieh
,
M.-D.
,
2010
, “
A Support Vector Regression Based Prediction Model of Affective Responses for Product Form Design
,”
Comput. Ind. Eng.
,
59
(
4
), pp.
682
689
.
81.
Yang
,
C.-C.
,
2011
, “
Constructing a Hybrid Kansei Engineering System Based on Multiple Affective Responses: Application to Product Form Design
,”
Comput. Ind. Eng.
,
60
(
4
), pp.
760
768
.
82.
Hsiao
,
S.-W.
, and
Huang
,
H.-C.
,
2002
, “
A Neural Network Based Approach for Product Form Design
,”
Des. Stud.
,
23
(
1
), pp.
67
84
.
83.
Hsiao
,
S.-W.
, and
Tsai
,
H.-C.
,
2005
, “
Applying a Hybrid Approach Based on Fuzzy Neural Network and Genetic Algorithm to Product Form Design
,”
Int. J. Ind. Ergon.
,
35
(
5
), pp.
411
428
.
84.
Roy
,
P.
,
Mahapatra
,
G.
,
Rani
,
P.
,
Pandey
,
S.
, and
Dey
,
K.
,
2014
, “
Robust Feedforward and Recurrent Neural Network Based Dynamic Weighted Combination Models for Software Reliability Prediction
,”
Appl. Soft. Comput.
,
22
(
C
), pp.
629
637
.
85.
Morente-Molinera
,
J.
,
Pérez
,
I.
,
Ureña
,
M.
, and
Herrera-Viedma
,
E.
,
2016
, “
Creating Knowledge Databases for Storing and Sharing People Knowledge Automatically Using Group Decision Making and Fuzzy Ontologies
,”
Inform. Sci.
,
328
(
C
), pp.
418
434
.
86.
Lourenzutti
,
R.
, and
Krohling
,
R. A.
,
2016
, “
A Generalized Topsis Method for Group Decision Making With Heterogeneous Information in a Dynamic Environment
,”
Inform. Sci.
,
330
(
C
), pp.
1
18
.
87.
Zhang
,
X.
,
Ge
,
B.
,
Jiang
,
J.
, and
Tan
,
Y.
,
2016
, “
Consensus Building in Group Decision Making Based on Multiplicative Consistency With Incomplete Reciprocal Preference Relations
,”
Knowled.-Based Syst.
,
106
(
5
), pp.
96
104
.
88.
Cabrerizo
,
F. J.
,
Moreno
,
J. M.
,
Pérez
,
I. J.
, and
Herrera-Viedma
,
E.
,
2010
, “
Analyzing Consensus Approaches in Fuzzy Group Decision Making: Advantages and Drawbacks
,”
Soft Comput.
,
14
(
5
), pp.
451
463
.
89.
Pérez
,
I. J.
,
Cabrerizo
,
F. J.
,
Alonso
,
S.
, and
Herrera-Viedma
,
E.
,
2013
, “
A New Consensus Model for Group Decision Making Problems With Non-homogeneous Experts
,”
IEEE Trans. Syst. Man Cybernet.: Syst.
,
44
(
4
), pp.
494
498
.
90.
Ma
,
H.
,
Chu
,
X.
,
Xue
,
D.
, and
Chen
,
D.
,
2017
, “
A Systematic Decision Making Approach for Product Conceptual Design Based on Fuzzy Morphological Matrix
,”
Expert Syst. Appl.
,
81
(
C
), pp.
444
456
.
91.
Zheng
,
H.
,
Feng
,
Y.
,
Gao
,
Y.
, and
Tan
,
J.
,
2018
, “
A Robust Predicted Performance Analysis Approach for Data-Driven Product Development in the Industrial Internet of Things
,”
Sensors
,
18
(
9
), p.
2871
.
92.
Liu
,
W.
,
Tan
,
R.
,
Cao
,
G.
,
Zhang
,
Z.
,
Huang
,
S.
, and
Liu
,
L.
,
2019
, “
A Proposed Radicality Evaluation Method for Design Ideas at Conceptual Design Stage
,”
Comput. Ind. Eng.
,
132
(
C
), pp.
141
152
.
93.
Kang
,
W.-C.
,
Fang
,
C.
,
Wang
,
Z.
, and
McAuley
,
J.
,
2017
, “
Visually-Aware Fashion Recommendation and Design With Generative Image Models
,”
2017 IEEE International Conference on Data Mining (ICDM)
,
New Orleans, LA
,
Nov. 18–21
, IEEE, pp.
207
216
.
94.
Yuan
,
C.
, and
Moghaddam
,
M.
,
2020
, “
Attribute-Aware Generative Design With Generative Adversarial Networks
,”
IEEE Access
,
8
(
C
), p.
190710
.
95.
Al-Halah
,
Z.
,
Stiefelhagen
,
R.
, and
Grauman
,
K.
,
2017
, “
Fashion Forward: Forecasting Visual Style in Fashion
,”
Proceedings of the IEEE International Conference on Computer Vision
,
Venice, Italy
,
Oct. 22–29
, pp.
388
397
.
96.
Han
,
Y.
, and
Moghaddam
,
M.
,
2021
, “
Analysis of Sentiment Expressions for User-Centered Design
,”
Expert Syst. Appl.
,
171
(
C
), p.
114604
.
97.
El Dehaibi
,
N.
,
Goodman
,
N. D.
, and
MacDonald
,
E. F.
,
2019
, “
Extracting Customer Perceptions of Product Sustainability From Online Reviews
,”
ASME J. Mech. Des.
,
141
(
12
), p.
121103
.
98.
Fang
,
H.
,
Gupta
,
S.
,
Iandola
,
F.
,
Srivastava
,
R. K.
,
Deng
,
L.
,
Dollár
,
P.
,
Gao
,
J.
,
He
,
X.
,
Mitchell
,
M.
,
Platt
,
J. C.
,
Zitnick
,
C. L.
, and
Zweig
,
G.
,
2015
, “
From Captions to Visual Concepts and Back
,”
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
,
Boston, MA
,
June 7–12
, pp.
1473
1482
.
99.
Vinyals
,
O.
,
Toshev
,
A.
,
Bengio
,
S.
, and
Erhan
,
D.
,
2015
, “
Show and Tell: A Neural Image Caption Generator
,”
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
,
Boston, MA
,
June 7–12
, pp.
3156
3164
.
100.
Gregor
,
K.
,
Danihelka
,
I.
,
Graves
,
A.
,
Rezende
,
D.
, and
Wierstra
,
D.
,
2015
, “
Draw: A Recurrent Neural Network for Image Generation
,”
Proceedings of the 32nd International Conference on Machine Learning
,
Lille, France
,
July 6–11
, PMLR, pp.
1462
1471
.
101.
Huang
,
J.
, and
Kingsbury
,
B.
,
2013
, “
Audio-Visual Deep Learning for Noise Robust Speech Recognition
,”
2013 IEEE International Conference on Acoustics, Speech and Signal Processing
,
Vancouver, Canada
,
May 26–31
, pp.
7596
7599
.
102.
Lai
,
S.
,
Xu
,
L.
,
Liu
,
K.
, and
Zhao
,
J.
,
2015
, “
Recurrent Convolutional Neural Networks for Text Classification
,”
Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence
,
Austin, TX
,
Jan. 25–30
, AAAI Press, pp.
2267
2273
.
103.
Audebert
,
N.
,
Herold
,
C.
,
Slimani
,
K.
, and
Vidal
,
C.
,
2019
, “
Multimodal Deep Networks for Text and Image-Based Document Classification
,”
Joint European Conference on Machine Learning and Knowledge Discovery in Databases
,
Würzburg, Germany
,
Sept. 16–20
, Springer, pp.
427
443
.
104.
Yang
,
X.
,
Yumer
,
E.
,
Asente
,
P.
,
Kraley
,
M.
,
Kifer
,
D.
, and
Lee Giles
,
C.
,
2017
, “
Learning to Extract Semantic Structure From Documents Using Multimodal Fully Convolutional Neural Networks
,”
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
,
Honolulu, HI
,
July 21–26
, pp.
5315
5324
.
105.
Xu
,
Y.
,
Li
,
M.
,
Cui
,
L.
,
Huang
,
S.
,
Wei
,
F.
, and
Zhou
,
M.
,
2020
, “
Layoutlm: Pre-training of Text and Layout for Document Image Understanding
,”
Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
,
Virtual Event, CA
,
July 6–10
.
106.
Ngiam
,
J.
,
Khosla
,
A.
,
Kim
,
M.
,
Nam
,
J.
,
Lee
,
H.
, and
Ng
,
A. Y.
,
2011
, “
Multimodal Deep Learning
,”
Proceedings of the 28th International Conference on International Conference on Machine Learning
,
Bellevue, WA
,
June 28–July 2
, pp.
689
696
.
107.
Lynch
,
C.
,
Aryafar
,
K.
, and
Attenberg
,
J.
,
2016
, “
Images Don’t Lie: Transferring Deep Visual Semantic Features to Large-Scale Multimodal Learning to Rank
,”
Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
,
San Francisco, CA
,
Aug. 13–17
, pp.
541
548
.
108.
Kiros
,
R.
,
Salakhutdinov
,
R.
, and
Zemel
,
R.
,
2014
, “
Multimodal Neural Language Models
,”
International Conference on Machine Learning
,
Beijing, China
,
June 21–26
, PMLR, pp.
595
603
.
109.
Gong
,
Y.
,
Wang
,
L.
,
Hodosh
,
M.
,
Hockenmaier
,
J.
, and
Lazebnik
,
S.
,
2014
, “
Improving Image-Sentence Embeddings Using Large Weakly Annotated Photo Collections
,”
European Conference on Computer Vision
,
Zurich, Switzerland
,
Sept. 6–12, Springer
, pp.
529
545
.
110.
Mikolov
,
T.
,
Sutskever
,
I.
,
Chen
,
K.
,
Corrado
,
G. S.
,
Dean
,
J.
,
Burges
,
C. J. C.
,
Bottou
,
L.
,
Welling
,
M.
,
Ghahramani
,
Z.
, and
Weinberger
,
K. Q.
,
2013
, “
Distributed Representations of Words and Phrases and Their Compositionality
,”
Advances in Neural Information Processing Systems
,
Lake Tahoe, NV
,
Dec. 5–10
.
111.
Vaswani
,
A.
,
Shazeer
,
N.
,
Parmar
,
N.
,
Uszkoreit
,
J.
,
Jones
,
L.
,
Gomez
,
A. N.
,
Kaiser
,
L.
, and
Polosukhin
,
I.
,
2017
, “
Attention Is All You Need
,”
31st Conference on Neural Information Processing System
,
Long Beach, NY
,
Dec. 4–9
.
112.
Wolf
,
T.
,
Debut
,
L.
,
Sanh
,
V.
,
Chaumond
,
J.
,
Delangue
,
C.
,
Moi
,
A.
,
Cistac
,
P.
,
Rault
,
T.
,
Louf
,
R.
,
Funtowicz
,
M.
,
Davison
,
J.
,
Shleifer
,
S.
,
von Platen
,
P.
,
Ma
,
C.
,
Jernite
,
Y.
,
Plu
,
J.
,
Xu
,
C.
,
Scao
,
T. L.
,
Gugger
,
S.
,
Drame
,
M.
,
Lhoest
,
Q.
, and
Rush
,
A. M.
,
2020
, “
Transformers: State-of-the-Art Natural Language Processing
,”
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, Association for Computational Linguistics
,
Virtually
,
Nov. 16–20
, pp.
38
45
.
113.
Hori
,
C.
,
Hori
,
T.
,
Lee
,
T.-Y.
,
Zhang
,
Z.
,
Harsham
,
B.
,
Hershey
,
J. R.
,
Marks
,
T. K.
, and
Sumi
,
K.
,
2017
, “
Attention-Based Multimodal Fusion for Video Description
,”
Proceedings of the IEEE International Conference on Computer Vision
,
Venice, Italy
,
Oct. 22–29
, pp.
4193
4202
.
114.
Kerroumi
,
M.
,
Sayem
,
O.
, and
Shabou
,
A.
,
2021
, “
Visualwordgrid: Information Extraction From Scanned Documents Using a Multimodal Approach
,”
ICDAR Workshops 2021
,
Lausanne, Switzerland
,
Sept. 5–7
.
115.
McLachlan
,
G. J.
,
Do
,
K. -A.
, and
Ambroise
,
C.
,
2004
,
Analyzing Microarray Gene Expression Data
,
Wiley
,
Hoboken, NJ
.
116.
Paszke
,
A.
,
Gross
,
S.
,
Massa
,
F.
,
Lerer
,
A.
,
Bradbury
,
J.
,
Chanan
,
G.
,
Killeen
,
T.
,
Lin
,
Z.
,
Gimelshein
,
N.
, and
Antiga
,
L.
,
2019
, “
Pytorch: An Imperative Style, High-Performance Deep Learning Library
,”
Conference on Neural Information Processing Systems
,
Vancouver, Canada
,
Dec. 8–14
.
117.
Kingma
,
D. P.
, and
Ba
,
J.
,
2015
, “
Adam: A Method for Stochastic Optimization
,”
International Conference for Learning Representations
,
San Diego, CA
,
May 7–9
.
118.
Oh
,
S.
,
Jung
,
Y.
,
Kim
,
S.
,
Lee
,
I.
, and
Kang
,
N.
,
2019
, “
Deep Generative Design: Integration of Topology Optimization and Generative Models
,”
ASME J. Mech. Des.
,
141
(
11
), p.
111405
.
119.
Shu
,
D.
,
Cunningham
,
J.
,
Stump
,
G.
,
Miller
,
S. W.
,
Yukish
,
M. A.
,
Simpson
,
T. W.
, and
Tucker
,
C. S.
,
2020
, “
3d Design Using Generative Adversarial Networks and Physics-Based Validation
,”
ASME J. Mech. Des.
,
142
(
7
), p.
071701
.
120.
Zhang
,
Z.
,
Liu
,
L.
,
Wei
,
W.
,
Tao
,
F.
,
Li
,
T.
, and
Liu
,
A.
,
2017
, “
A Systematic Function Recommendation Process for Data-Driven Product and Service Design
,”
ASME J. Mech. Des.
,
139
(
11
), p.
111404
.
121.
Goodfellow
,
I. J.
,
Pouget-Abadie
,
J.
,
Mirza
,
M.
,
Xu
,
B.
,
Warde-Farley
,
D.
,
Ozair
,
S.
,
Courville
,
A.
, and
Bengio
,
Y.
,
2014
, “
Generative Adversarial Nets
,”
Proceedings of the 27th International Conference on Neural Information Processing Systems
,
Montreal, Canada
,
Dec. 8–13
. https://arxiv.org/abs/1406.2661
122.
Yang
,
Z.
,
Li
,
X.
,
Catherine Brinson
,
L.
,
Choudhary
,
A. N.
,
Chen
,
W.
, and
Agrawal
,
A.
,
2018
, “
Microstructural Materials Design Via Deep Adversarial Learning Methodology
,”
ASME J. Mech. Des.
,
140
(
11
), p.
111416
.
You do not currently have access to this content.