Pub Date : 2020-12-20DOI: 10.1109/STA50679.2020.9329342
C. Khomsi, M. Bouzid, G. Champenois, K. Jelassi
Thid paper deals with the improvement of the quality of the grid current which is affected by nonlinear load supplied by single-phase photovoltaic system connected to the grid. Thus, different perturbations are introduced by nonlinear load such as harmonic distortion and power factor deterioration, which lead to the degradation of the quality of the current of the PV system. To eliminate these disturbances, this work consists on the use of the inverter connected to the grid as a shunt active power filter. Therefore, based on an effective control method of the PV inverter which uses an original algorithm useful to determine the disturbing current, the quality of the grid current is strongly improved by cancelling the different disturbances injected by the nonlinear load. The effectiveness of the suggested algorithm to improve the quality of the current is verified by simulation and validated experimentally through an experimental platform.
{"title":"Improvement of the current quality of a single-phase photovoltaic system connected to the grid","authors":"C. Khomsi, M. Bouzid, G. Champenois, K. Jelassi","doi":"10.1109/STA50679.2020.9329342","DOIUrl":"https://doi.org/10.1109/STA50679.2020.9329342","url":null,"abstract":"Thid paper deals with the improvement of the quality of the grid current which is affected by nonlinear load supplied by single-phase photovoltaic system connected to the grid. Thus, different perturbations are introduced by nonlinear load such as harmonic distortion and power factor deterioration, which lead to the degradation of the quality of the current of the PV system. To eliminate these disturbances, this work consists on the use of the inverter connected to the grid as a shunt active power filter. Therefore, based on an effective control method of the PV inverter which uses an original algorithm useful to determine the disturbing current, the quality of the grid current is strongly improved by cancelling the different disturbances injected by the nonlinear load. The effectiveness of the suggested algorithm to improve the quality of the current is verified by simulation and validated experimentally through an experimental platform.","PeriodicalId":158545,"journal":{"name":"2020 20th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131874716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-20DOI: 10.1109/STA50679.2020.9329321
Dhouha Sagga, Amira Echtioui, R. Khemakhem, M. Ghorbel
Epilepsy is one of the chronic neural conditions branded by an excessive and uncontrolled electrical explosion in the brain; it appears as seizures. This anomaly affects almost 1percent of the world. For this reason, seizure detection has become a subject of interest in the last decade, to perform these analyzes, study characteristics of brain activity, furthermore neurological disorders, and especially epileptic seizures electroencephalography (EEG) is used. Diverse scientific methods have been used to reliably detect epileptic seizures due to EEG Signals. In this analysis, deep learning based on CNN 1D convolutional neural networks were developed, as used as models for DL, VGGNET, and ResNet. The tests were conducted using standard data sets. The proposed method was exercised on 23 subjects of the CHBMIT dataset, which successfully achieved an average accuracy of 97.60% and 97.32% respectively for ResNet and VGGNET. The results obtained suggest the effectiveness of the use of ResNet in epileptic seizure detection.
{"title":"Epileptic Seizure Detection using EEG Signals based on 1D-CNN Approach","authors":"Dhouha Sagga, Amira Echtioui, R. Khemakhem, M. Ghorbel","doi":"10.1109/STA50679.2020.9329321","DOIUrl":"https://doi.org/10.1109/STA50679.2020.9329321","url":null,"abstract":"Epilepsy is one of the chronic neural conditions branded by an excessive and uncontrolled electrical explosion in the brain; it appears as seizures. This anomaly affects almost 1percent of the world. For this reason, seizure detection has become a subject of interest in the last decade, to perform these analyzes, study characteristics of brain activity, furthermore neurological disorders, and especially epileptic seizures electroencephalography (EEG) is used. Diverse scientific methods have been used to reliably detect epileptic seizures due to EEG Signals. In this analysis, deep learning based on CNN 1D convolutional neural networks were developed, as used as models for DL, VGGNET, and ResNet. The tests were conducted using standard data sets. The proposed method was exercised on 23 subjects of the CHBMIT dataset, which successfully achieved an average accuracy of 97.60% and 97.32% respectively for ResNet and VGGNET. The results obtained suggest the effectiveness of the use of ResNet in epileptic seizure detection.","PeriodicalId":158545,"journal":{"name":"2020 20th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126690523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-20DOI: 10.1109/STA50679.2020.9329336
A. Atitallah, I. Abid
This paper presents an efficient High-Level Synthesis (HLS) hardware architecture for a floating-point implementation of the AVMF filter using SQRT function. The Vivado HLS tool was used to develop several solutions by adding incrementally various directives to the C code. The validation of our HLS design was realized on the Zedboard platform using a Hardware/Software (HW/SW) environment to estimate the performance. The simulation results prove that the HW/SW design was improved by more than 75% in terms of processing time compared to the SW solution with same image quality.
{"title":"An efficient FPGA implementation of AVMF filter using High-Level Synthesis","authors":"A. Atitallah, I. Abid","doi":"10.1109/STA50679.2020.9329336","DOIUrl":"https://doi.org/10.1109/STA50679.2020.9329336","url":null,"abstract":"This paper presents an efficient High-Level Synthesis (HLS) hardware architecture for a floating-point implementation of the AVMF filter using SQRT function. The Vivado HLS tool was used to develop several solutions by adding incrementally various directives to the C code. The validation of our HLS design was realized on the Zedboard platform using a Hardware/Software (HW/SW) environment to estimate the performance. The simulation results prove that the HW/SW design was improved by more than 75% in terms of processing time compared to the SW solution with same image quality.","PeriodicalId":158545,"journal":{"name":"2020 20th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129495476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-20DOI: 10.1109/STA50679.2020.9329340
H. Mhamdi, A. Zouinkhi, H. Sakli
The rapid development of the Internet of Things (IoT) is affecting several sectors, in particular the Vehicular Ad-Hoc Network (VANET). The link between the IoT and VANET is giving rise to the Internet of Vehicles (10V). With the IoV,vehicles are able to communicate with each other and with other connected objects. To ensure the exchange of information flows between the different entities, autonomy, speed and the decentralized concept must be taken into account. Blockchain technology is a distributed register technology. It is the solution to overcome these challenges and to guarantee vehicular communication. This research work deals primarily with this new technology and its applications in IoV.We then present a multi-agent model of vehicle communication based on Blockchain technology and smart contracts.
{"title":"Multi-agents system of vehicle services based on Blockchain","authors":"H. Mhamdi, A. Zouinkhi, H. Sakli","doi":"10.1109/STA50679.2020.9329340","DOIUrl":"https://doi.org/10.1109/STA50679.2020.9329340","url":null,"abstract":"The rapid development of the Internet of Things (IoT) is affecting several sectors, in particular the Vehicular Ad-Hoc Network (VANET). The link between the IoT and VANET is giving rise to the Internet of Vehicles (10V). With the IoV,vehicles are able to communicate with each other and with other connected objects. To ensure the exchange of information flows between the different entities, autonomy, speed and the decentralized concept must be taken into account. Blockchain technology is a distributed register technology. It is the solution to overcome these challenges and to guarantee vehicular communication. This research work deals primarily with this new technology and its applications in IoV.We then present a multi-agent model of vehicle communication based on Blockchain technology and smart contracts.","PeriodicalId":158545,"journal":{"name":"2020 20th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129616931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2005-08-10DOI: 10.1080/01431160500104392
A. Olmos-Ortíz, Janice García-Quiroz, E. Avila, F. Caldiño-Soto, A. Halhali, F. Larrea, L. Díaz
Cover
封面
{"title":"Cover","authors":"A. Olmos-Ortíz, Janice García-Quiroz, E. Avila, F. Caldiño-Soto, A. Halhali, F. Larrea, L. Díaz","doi":"10.1080/01431160500104392","DOIUrl":"https://doi.org/10.1080/01431160500104392","url":null,"abstract":"Cover","PeriodicalId":158545,"journal":{"name":"2020 20th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133692600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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