default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDComputers in Industry, Volume 112
Volume 112, November 2019
- Juan Jose Rubio, Takahiro Kashiwa, Teera Laiteerapong, Wenlong Deng, Kohei Nagai, Sergio Escalera, Kotaro Nakayama, Yutaka Matsuo, Helmut Prendinger:
Multi-class structural damage segmentation using fully convolutional networks. - Patrick Dallasega, Rafael A. Rojas, Giulia Bruno, Erwin Rauch
:
An agile scheduling and control approach in ETO construction supply chains. - Rui Miao, Yuntian Gao, Liang Ge, Zihang Jiang, Jie Zhang:
Online defect recognition of narrow overlap weld based on two-stage recognition model combining continuous wavelet transform and convolutional neural network. - Katia Lupinetti, Franca Giannini, Marina Monti, Jean-Philippe Pernot:
Content-based multi-criteria similarity assessment of CAD assembly models. - Spencer Breiner, Albert T. Jones, Eswaran Subrahmanian:
Categorical models for process planning. - Kerman López de Calle, Susana Ferreiro, Aitor Arnaiz, Basilio Sierra:
Dynamic condition monitoring method based on dimensionality reduction techniques for data-limited industrial environments. - Celia Garrido-Hidalgo, Teresa Olivares, F. Javier Ramírez, Luis Roda-Sanchez:
An end-to-end Internet of Things solution for Reverse Supply Chain Management in Industry 4.0.
- Lin Zhang, Longfei Zhou, Lei Ren, Yuanjun Laili:
Modeling and simulation in intelligent manufacturing.
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
last updated on 2025-01-08 20:20 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
