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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA)
San-Payo, G., Ferreira, J., Santos, P. & Martins, A. (2020). Machine learning for quality control system. Journal of Ambient Intelligence and Humanized Computing. 11 (11), 4491-4500
Exportar Referência (IEEE)
Gonçalo et al.,  "Machine learning for quality control system", in Journal of Ambient Intelligence and Humanized Computing, vol. 11, no. 11, pp. 4491-4500, 2020
Exportar BibTeX
	author = "San-Payo, G. and Ferreira, J. and Santos, P. and Martins, A.",
	title = "Machine learning for quality control system",
	journal = "Journal of Ambient Intelligence and Humanized Computing",
	year = "2020",
	volume = "11",
	number = "11",
	doi = "10.1007/s12652-019-01640-4",
	pages = "4491-4500",
	url = "https://www.springer.com/journal/12652"
Exportar RIS
TI  - Machine learning for quality control system
T2  - Journal of Ambient Intelligence and Humanized Computing
VL  - 11
IS  - 11
AU  - San-Payo, G.
AU  - Ferreira, J.
AU  - Santos, P.
AU  - Martins, A.
PY  - 2020
SP  - 4491-4500
SN  - 1868-5137
DO  - 10.1007/s12652-019-01640-4
UR  - https://www.springer.com/journal/12652
AB  - In this work, we propose and develop a classification model to be used in a quality control system for clothing manufacturing using machine learning algorithms. The system consists of using pictures taken through mobile devices to detect defects on production objects. In this work, a defect can be a missing component or a wrong component in a production object. Therefore, the function of the system is to classify the components that compose a production object through the use of a classification model. As a manufacturing business progresses, new objects are created, thus, the classification model must be able to learn the new classes without losing previous knowledge. However, most classification algorithms do not support an increase of classes, these need to be trained from scratch with all . Thus. In this work, we make use of an incremental learning algorithm to tackle this problem. This algorithm classifies features extracted from pictures of the production objects using a convolutional neural network (CNN), which have proven to be very successful in image classification problems. We apply the current developed approach to a process in clothing manufacturing. Therefore, the production objects correspond to clothing items
ER  -