TY - GEN
T1 - Intelligent computing for vehicle form design
T2 - 4th International Conference on Internet of Vehicles, IOV 2017
AU - Zheng, Feng
AU - Wei, Chun Chun
AU - Lin, Yang Cheng
AU - Du, Juan
AU - Yao, Jiacheng
N1 - Funding Information:
Acknowledgement. This research was, in part, supported by the Ministry of Science and Technology, Taiwan under Grant MOST105-2221-E-006-264.
Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - In this paper, one of the most commonly used artificial intelligence techniques, i.e. neural networks (NNs), due to its effective learning ability, is utilized to develop NN models that can build a design decision support system for facilitating the vehicle form design process and matching specific needs. The sand making machine is chosen as an empirical example because it is the main equipment for the mining machinery. However, product designers only pay attention to its structure and/or functions when they design it. Consequently, the design decision support system built in this paper can be an important reference for product designers’ work, which can examine the design optimization on product elements and help them do the best choice as they design a new vehicle product. The result shows that the NN technique is promising to help product designers design a new sand making machine that best meets specific needs.
AB - In this paper, one of the most commonly used artificial intelligence techniques, i.e. neural networks (NNs), due to its effective learning ability, is utilized to develop NN models that can build a design decision support system for facilitating the vehicle form design process and matching specific needs. The sand making machine is chosen as an empirical example because it is the main equipment for the mining machinery. However, product designers only pay attention to its structure and/or functions when they design it. Consequently, the design decision support system built in this paper can be an important reference for product designers’ work, which can examine the design optimization on product elements and help them do the best choice as they design a new vehicle product. The result shows that the NN technique is promising to help product designers design a new sand making machine that best meets specific needs.
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U2 - 10.1007/978-3-319-72329-7_14
DO - 10.1007/978-3-319-72329-7_14
M3 - Conference contribution
AN - SCOPUS:85036461730
SN - 9783319723280
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 154
EP - 161
BT - Internet of Vehicles
A2 - Peng, Sheng-Lung
A2 - Lee, Guan-Ling
A2 - Hsu, Ching-Hsien
A2 - Klette, Reinhard
PB - Springer Verlag
Y2 - 22 November 2017 through 25 November 2017
ER -