| dc.contributor.advisor | Ruizo, Luis | |
| dc.contributor.advisor | Rizo-Domínguez, Luis | |
| dc.contributor.author | Rodríguez-García, Francisco J. | |
| dc.date.accessioned | 2021-08-27T17:29:47Z | |
| dc.date.accessioned | 2023-03-10T16:53:35Z | |
| dc.date.available | 2021-08-27T17:29:47Z | |
| dc.date.available | 2023-03-10T16:53:35Z | |
| dc.date.issued | 2021-08 | |
| dc.identifier.citation | Rodríguez-García, F. J. (2021). A Novel SVM Voltage Supervisor. Trabajo de obtención de grado, Especialidad en Sistemas Embebidos. Tlaquepaque, Jalisco: ITESO. | es_MX |
| dc.identifier.uri | https://hdl.handle.net/20.500.12032/68877 | |
| dc.description | Voltage supervisors ensure that an electronic system is turned off whenever the rail voltage drops below the threshold value. Common implementations rely on hard decisions to assert the system’s reset; however, these schemes lack flexibility in configuration. In this paper, we introduce a soft-decision system using a machine learning algorithm called support vector machine (SVM). The proposed monitoring system’s software is built on top of scikit-learn SVM libraries and experimentation was conducted in the Raspberry Pi 4 platform. Confusion matrix for the SVM model shows that the system will perform well on new and training data. Overall, the resulting system is configurable and, unlike other implementations, it can be trained in online and offline modes. | es_MX |
| dc.description.sponsorship | ITESO, A. C. | es |
| dc.language.iso | eng | es_MX |
| dc.publisher | ITESO | es_MX |
| dc.rights.uri | http://quijote.biblio.iteso.mx/licencias/CC-BY-NC-2.5-MX.pdf | es_MX |
| dc.subject | Support Vector Machine | es_MX |
| dc.subject | Raspberry Pi | es_MX |
| dc.subject | Voltage Monitoring | es_MX |
| dc.title | A Novel SVM Voltage Supervisor | es_MX |
| dc.type | info:eu-repo/semantics/academicSpecialization | es_MX |
