Editorial for the Special Issue on Industrial Machine Learning Applications-Reference-Cited by-同舟云学术

Editorial for the Special Issue on Industrial Machine Learning Applications

Published:2023-12-14 Issue:12 Volume:9 Page:278
ISSN:2313-433X
Container-title:Journal of Imaging
language:en
Short-container-title:J. Imaging

Author:

Rota Paolo¹^ORCID,Lopez Miguel Angel Guevara²^ORCID,Setti Francesco³^ORCID

Affiliation:

1. Center for Mind and Brain (CIMeC), Department of Engineering and Computer Science, University of Trento, 38122 Trento, Italy

2. Department of Systems and Computing, Setubal School of Technology, Polytechnic Institute of Setubal, Campus do IPS, Estefanilha, 2910-761 Setubal, Portugal

3. Department of Computer Science, University of Verona, 37134 Verona, Italy

Abstract

In the rapidly evolving field of industrial machine learning, this Special Issue on Industrial Machine Learning Applications aims to shed light on the innovative strides made toward more intelligent, more efficient, and adaptive industrial processes [...]

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Computer Graphics and Computer-Aided Design,Computer Vision and Pattern Recognition,Radiology, Nuclear Medicine and imaging

Link

https://www.mdpi.com/2313-433X/9/12/278/pdf

Reference5 articles.

1. Papadaki, A., and Pateraki, M. (2023). 6D Object Localization in Car-Assembly Industrial Environment. J. Imaging, 9.

2. Antunes, A., Ferreira, B., Marques, N., and Carriço, N. (2023). Hyperparameter Optimization of a Convolutional Neural Network Model for Pipe Burst Location in Water Distribution Networks. J. Imaging, 9.

3. Schwendemann, S., and Sikora, A. (2023). Transfer-Learning-Based Estimation of the Remaining Useful Life of Heterogeneous Bearing Types Using Low-Frequency Accelerometers. J. Imaging, 9.

4. Tsamos, A., Evsevleev, S., Fioresi, R., Faglioni, F., and Bruno, G. (2023). Synthetic Data Generation for Automatic Segmentation of X-ray Computed Tomography Reconstructions of Complex Microstructures. J. Imaging, 9.

5. Wang, P., Li, C., Yang, Q., Fu, L., Yu, F., Min, L., Guo, D., and Li, X. (2022). Environment Understanding Algorithm for Substation Inspection Robot Based on Improved DeepLab V3+. J. Imaging, 8.