This page has only limited features, please log in for full access.
Master student in Advanced Computing Systems at Lucian Blaga University of Sibiu
This paper studies Long Short-Term Memory as a component of an adaptive assembly assistance system suggesting the next manufacturing step. The final goal is an assistive system able to help the inexperienced workers in their training stage or even experienced workers who prefer such support in their manufacturing activity. In contrast with the earlier analyzed context-based techniques, Long Short-Term Memory can be applied in unknown scenarios. The evaluation was performed on the data collected previously in an experiment with 68 participants assembling as target product a customizable modular tablet. We are interested in identifying the most accurate method of next assembly step prediction. The results show that the prediction based on Long Short-Term Memory is better fitted to new (previously unseen) data.
Stefan-Alexandru Precup; Arpad Gellert; Alexandru Dorobantiu; Constantin-Bala Zamfirescu. Assembly Process Modeling Through Long Short-Term Memory. Communications in Computer and Information Science 2021, 28 -39.
AMA StyleStefan-Alexandru Precup, Arpad Gellert, Alexandru Dorobantiu, Constantin-Bala Zamfirescu. Assembly Process Modeling Through Long Short-Term Memory. Communications in Computer and Information Science. 2021; ():28-39.
Chicago/Turabian StyleStefan-Alexandru Precup; Arpad Gellert; Alexandru Dorobantiu; Constantin-Bala Zamfirescu. 2021. "Assembly Process Modeling Through Long Short-Term Memory." Communications in Computer and Information Science , no. : 28-39.
Industrial assistive systems result from a multidisciplinary effort that integrates IoT (and Industrial IoT), Cognetics, and Artificial Intelligence. This paper evaluates the Prediction by Partial Matching algorithm as a component of an assembly assistance system that supports factory workers, by providing choices for the next manufacturing step. The evaluation of the proposed method was performed on datasets collected within an experiment involving trainees and experienced workers. The goal is to find out which method best suits the datasets in order to be integrated afterwards into our context-aware assistance system. The obtained results show that the Prediction by Partial Matching method presents a significant improvement with respect to the existing Markov predictors.
Arpad Gellert; Stefan-Alexandru Precup; Bogdan-Constantin Pirvu; Ugo Fiore; Constantin-Bala Zamfirescu; Francesco Palmieri. An Empirical Evaluation of Prediction by Partial Matching in Assembly Assistance Systems. Applied Sciences 2021, 11, 3278 .
AMA StyleArpad Gellert, Stefan-Alexandru Precup, Bogdan-Constantin Pirvu, Ugo Fiore, Constantin-Bala Zamfirescu, Francesco Palmieri. An Empirical Evaluation of Prediction by Partial Matching in Assembly Assistance Systems. Applied Sciences. 2021; 11 (7):3278.
Chicago/Turabian StyleArpad Gellert; Stefan-Alexandru Precup; Bogdan-Constantin Pirvu; Ugo Fiore; Constantin-Bala Zamfirescu; Francesco Palmieri. 2021. "An Empirical Evaluation of Prediction by Partial Matching in Assembly Assistance Systems." Applied Sciences 11, no. 7: 3278.