TY - GEN
T1 - Generating information-diffusion-based virtual samples to improve small data set prediction for ceramic powder
T2 - 3rd International Conference on Applied Computing and Information Technology and 2nd International Conference on Computational Science and Intelligence, ACIT-CSI 2015
AU - Chen, Hung Yu
AU - Li, Der Chiang
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/11/23
Y1 - 2015/11/23
N2 - A multi-layer ceramic capacitor is a widely used passive component in modern electronics. However, most passive component manufacturers have to undertake pilot runs after receiving a batch of the key component, ceramic powder, to confirm the dielectric constant because of its low stability among batches. It takes at least two weeks from the pilot runs to mass production. In order to reduce the costs, one effective way is to predict the dielectric constant with experiential data. Although neural networks are widely applied to implement this task, the learning models are usually built with a great amount of training data, which is difficult to collect in the early stages of a manufacturing system. Therefore, this paper on the basis of the information-diffusion concept generates more training samples to help improve the prediction. The results reveal that the proposed method can rapidly help develop a model of production with limited data.
AB - A multi-layer ceramic capacitor is a widely used passive component in modern electronics. However, most passive component manufacturers have to undertake pilot runs after receiving a batch of the key component, ceramic powder, to confirm the dielectric constant because of its low stability among batches. It takes at least two weeks from the pilot runs to mass production. In order to reduce the costs, one effective way is to predict the dielectric constant with experiential data. Although neural networks are widely applied to implement this task, the learning models are usually built with a great amount of training data, which is difficult to collect in the early stages of a manufacturing system. Therefore, this paper on the basis of the information-diffusion concept generates more training samples to help improve the prediction. The results reveal that the proposed method can rapidly help develop a model of production with limited data.
UR - http://www.scopus.com/inward/record.url?scp=84962753926&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84962753926&partnerID=8YFLogxK
U2 - 10.1109/ACIT-CSI.2015.71
DO - 10.1109/ACIT-CSI.2015.71
M3 - Conference contribution
AN - SCOPUS:84962753926
T3 - Proceedings - 3rd International Conference on Applied Computing and Information Technology and 2nd International Conference on Computational Science and Intelligence, ACIT-CSI 2015
SP - 374
EP - 378
BT - Proceedings - 3rd International Conference on Applied Computing and Information Technology and 2nd International Conference on Computational Science and Intelligence, ACIT-CSI 2015
A2 - Tsuchida, Kensei
A2 - Ishii, Naohiro
A2 - Goto, Takaaki
A2 - Takahashi, Satoshi
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 12 July 2015 through 16 July 2015
ER -