DSpace Collection:
http://hdl.handle.net/10261/31566
2024-03-19T05:46:13ZData-Driven Insights on Time-to-Failure of Electromechanical Manufacturing Devices: A Procedure and Case Study
http://hdl.handle.net/10261/350295
Título: Data-Driven Insights on Time-to-Failure of Electromechanical Manufacturing Devices: A Procedure and Case Study
Autor: Castaño, Fernando; Cruz, Yarens J.; Villalonga, Alberto; Haber, Rodolfo E
Resumen: Nowadays, there is a fresh push towards putting more attention on sustainability issues without affecting productivity as main target in industrial cyberphysical systems. In this direction, this article proposes a procedure and presents a data-driven insight method in order to predict the remaining useful life and to classify faults by a condition base-monitoring. Therefore, by using a framework that combines both outputs, a maintenance stop can be scheduled near to the failure, thus improving its sustainability, without affecting productivity. A fuzzy decision-making strategy supported on generated insights is developed in order to extend the useful life of electromechanical devices. A case study is presented in order to assess the proposed methodology using a dataset of bearing faults. Experimental results and its comparison with previous reported works corroborate a good trade-off solution offered by the proposed procedure considering productivity and sustainability for bearing faults detection.2024-03-13T12:54:03ZVISUALLY-INDUCED MOTOR IMAGERY EFFECTS ON MOTOR ADAPTATION TO REVERSE STEERING CYCLING. A RANDOMIZED CONTROLLED TRIAL
http://hdl.handle.net/10261/350279
Título: VISUALLY-INDUCED MOTOR IMAGERY EFFECTS ON MOTOR ADAPTATION TO REVERSE STEERING CYCLING. A RANDOMIZED CONTROLLED TRIAL
Autor: Muñoz Garcia, Daniel; Serrano, J. Ignacio; Ferrer-Pena, Raul; d'Eudeville, Victor; Brero, Marta; Boisson, Maxime; Castillo, M. Dolores del
Resumen: Purpose: First, testing an intervention of neuromodulation based on motor imagery and action observa-tion as a promoter of motor adaptation of a complex motor task involving balance. Second, determining what prior balance factors can affect the motor adaptation task. Methods: A double-blind randomized controlled trial was performed. Forty-eight healthy subjects were recruited. The balance of all participants during gait and standing was assessed before adapting to the complex, multi-limb motor task of riding an inverse steering bicycle (ISB). Two interventions were carried out interleaved among trials of adaptation to the motor task: the experimental group (n = 24) was asked to perform neuromodulation (EN) by watching first-person ISB riding through immersive VR glasses and, simultaneously, mentally mimicking the movements. The control group (CG) was asked to watch a slideshow video of steady landscape images. Results: The results showed that the EN group did not improve the motor adaptation rate and induced higher adaptation times with respect to the CG. However, while the motor adaptation success showed a significant dependence on the prior proprioceptive participation in balance in the CG, the EN group did not present any relationship between the prior balance profile and motor adaptation outcome. Conclusions: Results point to a benefit of the visually guided neuromodulation for the motor adaptation of the subjects with low participation of proprioception in balance. Moreover, the results from the control group would allow to disclose prognostic factors about the success of the motor adaptation, and also prescription criteria for the proposed neuromodulation based on the balance profile.2024-03-13T12:30:59ZAutomatic Video-Oculography System for Detection of Minimal Hepatic Encephalopathy Using Machine Learning Tools
http://hdl.handle.net/10261/350208
Título: Automatic Video-Oculography System for Detection of Minimal Hepatic Encephalopathy Using Machine Learning Tools
Autor: Calvo Córdoba, Alberto; García Cena, Cecilia E.; Montoliu, Carmina
Resumen: This article presents an automatic gaze-tracker system to assist in the detection of minimal hepatic encephalopathy by analyzing eye movements with machine learning tools. To record eye movements, we used video-oculography technology and developed automatic feature-extraction software as well as a machine learning algorithm to assist clinicians in the diagnosis. In order to validate the procedure, we selected a sample ((Formula presented.)) of cirrhotic patients. Approximately half of them were diagnosed with minimal hepatic encephalopathy (MHE), a common neurological impairment in patients with liver disease. By using the actual gold standard, the Psychometric Hepatic Encephalopathy Score battery, PHES, patients were classified into two groups: cirrhotic patients with MHE and those without MHE. Eye movement tests were carried out on all participants. Using classical statistical concepts, we analyzed the significance of 150 eye movement features, and the most relevant (p-values ≤ 0.05) were selected for training machine learning algorithms. To summarize, while the PHES battery is a time-consuming exploration (between 25–40 min per patient), requiring expert training and not amenable to longitudinal analysis, the automatic video oculography is a simple test that takes between 7 and 10 min per patient and has a sensitivity and a specificity of 93%.2024-03-13T09:39:33ZUse of Finite Elements in the Training of a Neural Network for the Modeling of a Soft Robot
http://hdl.handle.net/10261/347856
Título: Use of Finite Elements in the Training of a Neural Network for the Modeling of a Soft Robot
Autor: Terrile, Silvia; López, Andrea; Barrientos, Antonio
Resumen: Soft bioinspired manipulators have a theoretically infinite number of degrees of freedom, providing considerable advantages. However, their control is very complex, making it challenging to model the elastic elements that define their structure. Finite elements (FEA) can provide a model with sufficient accuracy but are inadequate for real-time use. In this context, Machine Learning (ML) is postulated as an option, both for robot modeling and for its control, but it requires a very high number of experiments to train the model. A linked combination of both options (FEA and ML) can be an approach to the solution. This work presents the implementation of a real robot made up of three flexible modules and actuated with SMA (shape memory alloy) springs, the development of its model through finite elements, its use to adjust a neural network, and the results obtained.2024-02-21T12:04:58Z