Pub Date : 2021-03-22DOI: 10.1109/SSD52085.2021.9429473
José Manuel Lozano Domínguez, F. Al-Tam, T. M. Sanguino, N. Correia
Cities are transforming into smart areas thanks to several key technologies involving artificial intelligence (AI), 5G or big data aimed at improving the lives of their inhabitants with new services (e.g., transport systems, including road safety). In this field, the paper describes how to improve vehicle detection through several machine learning techniques applied to smart crosswalks. As a main advantage, this approach avoids readjusting labels in classic fuzzy classifiers that typically depends on the system location and road conditions. To address this, various AI methods were evaluated with data taken from real traffic pertaining to roads in Spain and Portugal. The machine learning techniques were random forest (RF), extremely randomized trees (extra-tree), deep reinforcement learning (DRL), time series forecasting (TSF), multi-layer perceptron (MLP), k-nearest neighbor (KNN) and logistic regression (LR). The results were validated through a receiver operating characteristic (ROC) analysis, obtaining the best performance in RF with a true positive rate (TPR) of 96.82%, false positive rate (FPR) of 1.73% and accuracy (ACC) of 97.85%. This was followed by DRL and TSF, while MLP and LR presented the worst outcomes.
{"title":"Vehicle Detection System for Smart Crosswalks Using Sensors and Machine Learning","authors":"José Manuel Lozano Domínguez, F. Al-Tam, T. M. Sanguino, N. Correia","doi":"10.1109/SSD52085.2021.9429473","DOIUrl":"https://doi.org/10.1109/SSD52085.2021.9429473","url":null,"abstract":"Cities are transforming into smart areas thanks to several key technologies involving artificial intelligence (AI), 5G or big data aimed at improving the lives of their inhabitants with new services (e.g., transport systems, including road safety). In this field, the paper describes how to improve vehicle detection through several machine learning techniques applied to smart crosswalks. As a main advantage, this approach avoids readjusting labels in classic fuzzy classifiers that typically depends on the system location and road conditions. To address this, various AI methods were evaluated with data taken from real traffic pertaining to roads in Spain and Portugal. The machine learning techniques were random forest (RF), extremely randomized trees (extra-tree), deep reinforcement learning (DRL), time series forecasting (TSF), multi-layer perceptron (MLP), k-nearest neighbor (KNN) and logistic regression (LR). The results were validated through a receiver operating characteristic (ROC) analysis, obtaining the best performance in RF with a true positive rate (TPR) of 96.82%, false positive rate (FPR) of 1.73% and accuracy (ACC) of 97.85%. This was followed by DRL and TSF, while MLP and LR presented the worst outcomes.","PeriodicalId":6799,"journal":{"name":"2021 18th International Multi-Conference on Systems, Signals & Devices (SSD)","volume":"34 1","pages":"984-991"},"PeriodicalIF":0.0,"publicationDate":"2021-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77433621","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 : 2021-03-22DOI: 10.1109/SSD52085.2021.9429437
J. Ghaeb, Malek Alkayyali
In this work, a Particle Swarm Optimization (PSO) technique is proposed to determine the optimal firing angles of the Thyristor-Controlled Reactor (TCR) to regulate the voltage of the electrical power system. A 500 km-length electrical power system is considered. The transmission line is modeled by three pi-section networks each represents a distance of 500/3 km. The mathematical model of the electrical power system is derived and used by the PSO algorithm to find the required TCR firing angles for voltage regulation. Different test cases have been conducted to assess and validate the proposed PSO technique capabilities. The results have revealed the ability of the proposed PSO technique to regulate the load voltage efficiently to average load voltage change equals 0.271%.
{"title":"An Optimization Technique for Voltage Regulation in Electrical Power Systems","authors":"J. Ghaeb, Malek Alkayyali","doi":"10.1109/SSD52085.2021.9429437","DOIUrl":"https://doi.org/10.1109/SSD52085.2021.9429437","url":null,"abstract":"In this work, a Particle Swarm Optimization (PSO) technique is proposed to determine the optimal firing angles of the Thyristor-Controlled Reactor (TCR) to regulate the voltage of the electrical power system. A 500 km-length electrical power system is considered. The transmission line is modeled by three pi-section networks each represents a distance of 500/3 km. The mathematical model of the electrical power system is derived and used by the PSO algorithm to find the required TCR firing angles for voltage regulation. Different test cases have been conducted to assess and validate the proposed PSO technique capabilities. The results have revealed the ability of the proposed PSO technique to regulate the load voltage efficiently to average load voltage change equals 0.271%.","PeriodicalId":6799,"journal":{"name":"2021 18th International Multi-Conference on Systems, Signals & Devices (SSD)","volume":"84 2","pages":"934-941"},"PeriodicalIF":0.0,"publicationDate":"2021-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91467696","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 : 2021-03-22DOI: 10.1109/SSD52085.2021.9429511
Hiba Hellara, Rim Barioul, S. Sahnoun, A. Fakhfakh, O. Kanoun
This paper proposes a comparative of binary swarm optimization based wrappers for ElectroMyography (EMG) feature selection. Time-domain and frequency-domain features are extracted from two EMG channels to evaluate the effect of each of them according to the accuracy and computational costs. Six binary algorithms are used in this study namely Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Moth-Flame Optimization (MFO), Salp Swarm Algorithm (SSA), Bat Algorithm (BA), and Particle Swarm Optimization (PSO) in the domain of machine learning for feature selection and classification. Results prove that time-domain features are enough to give satisfying classification accuracy, WOA is giving the best average classification accuracy of 80.15% but needs more execution time. Compared with others, SSA is the best algorithm according to the number of selected features, execution time, and fitness function 78.25% as accuracy.
{"title":"Comparative of Swarm Intelligence based Wrappers for sEMG Signals Feature Selection","authors":"Hiba Hellara, Rim Barioul, S. Sahnoun, A. Fakhfakh, O. Kanoun","doi":"10.1109/SSD52085.2021.9429511","DOIUrl":"https://doi.org/10.1109/SSD52085.2021.9429511","url":null,"abstract":"This paper proposes a comparative of binary swarm optimization based wrappers for ElectroMyography (EMG) feature selection. Time-domain and frequency-domain features are extracted from two EMG channels to evaluate the effect of each of them according to the accuracy and computational costs. Six binary algorithms are used in this study namely Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Moth-Flame Optimization (MFO), Salp Swarm Algorithm (SSA), Bat Algorithm (BA), and Particle Swarm Optimization (PSO) in the domain of machine learning for feature selection and classification. Results prove that time-domain features are enough to give satisfying classification accuracy, WOA is giving the best average classification accuracy of 80.15% but needs more execution time. Compared with others, SSA is the best algorithm according to the number of selected features, execution time, and fitness function 78.25% as accuracy.","PeriodicalId":6799,"journal":{"name":"2021 18th International Multi-Conference on Systems, Signals & Devices (SSD)","volume":"16 1","pages":"829-834"},"PeriodicalIF":0.0,"publicationDate":"2021-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86152242","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 : 2021-03-22DOI: 10.1109/SSD52085.2021.9429457
Tony Castillo-Calzadilla, Simon Fessler, C. E. Borges, C. M. Andonegui
This article analysis the possibility of achieving a Positive Energy District (PED), i.e. a district that generates more energy than it consumes. The paper presents a simulation-based analysis (MATLAB-Simulink environment) of an urban unit composed by 6 buildings, 6 streetlights and an electric vehicle (EV) charger. The PED is analysed with respect to the electrical energy generation and consumption evaluating a set of monthly and yearly energy profiles representative of the North of Spain (Bilbao). On the one hand, the monthly analysis is conducted by simulating the solar photovoltaic (PV) generation, an energy storage system (ESS), and the interconnection with the utility grid. For this analysis, we consider standard lights (regular bulbs) and standard isolation of buildings (certified as $A, B, C$ or $D$). The result is that only summer months (April, May, June, July, and August) present a positive energy balance. For the annual analysis, eight scenarios are defined in which different interventions are simulated, such as the upgrade of insulation profiles, replacement of standard luminaires with LEDs, inclusion of PV rooftops, etc. The positivity of the district is achieved when energy efficiency of buildings was high (with $B$ certification) and no EV was included (652.50 kWh positive balance), or very high (buildings labelled as $A$) and the EV was considered (2882.91 kWh positive balance).
{"title":"Urban district modelling simulation-based analysis: under which scenarios can we achieve a Positive Energy District?","authors":"Tony Castillo-Calzadilla, Simon Fessler, C. E. Borges, C. M. Andonegui","doi":"10.1109/SSD52085.2021.9429457","DOIUrl":"https://doi.org/10.1109/SSD52085.2021.9429457","url":null,"abstract":"This article analysis the possibility of achieving a Positive Energy District (PED), i.e. a district that generates more energy than it consumes. The paper presents a simulation-based analysis (MATLAB-Simulink environment) of an urban unit composed by 6 buildings, 6 streetlights and an electric vehicle (EV) charger. The PED is analysed with respect to the electrical energy generation and consumption evaluating a set of monthly and yearly energy profiles representative of the North of Spain (Bilbao). On the one hand, the monthly analysis is conducted by simulating the solar photovoltaic (PV) generation, an energy storage system (ESS), and the interconnection with the utility grid. For this analysis, we consider standard lights (regular bulbs) and standard isolation of buildings (certified as $A, B, C$ or $D$). The result is that only summer months (April, May, June, July, and August) present a positive energy balance. For the annual analysis, eight scenarios are defined in which different interventions are simulated, such as the upgrade of insulation profiles, replacement of standard luminaires with LEDs, inclusion of PV rooftops, etc. The positivity of the district is achieved when energy efficiency of buildings was high (with $B$ certification) and no EV was included (652.50 kWh positive balance), or very high (buildings labelled as $A$) and the EV was considered (2882.91 kWh positive balance).","PeriodicalId":6799,"journal":{"name":"2021 18th International Multi-Conference on Systems, Signals & Devices (SSD)","volume":"15 1","pages":"1107-1114"},"PeriodicalIF":0.0,"publicationDate":"2021-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85750924","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 : 2021-03-22DOI: 10.1109/SSD52085.2021.9429416
Noureddaher Zaidi, A. Khedher, M. Jemli
In this paper, we present a hybrid fault diagnosis approach dedicated for PV generators via current-voltage and power-voltage curves analysis. It is mainly based on two signature generators: a fuzzy logic classifier and a based threshold conventional one. The two classifiers act on the available data by considering three thresholds which are made according to the generator PV typical parameters and with a total conformity with the standards. The proposed fault diagnosis approach requires only the available measured variables and is able to identify the most frequently met faults related to shading, temperature, leakage current and increased series resistance losses. The addressed PV generators are based on one diode model. The overall results have proved the proposed methodology capability in PV faults detection and identification.
{"title":"A Hybrid fault diagnosis approach for PV generators based on I-V and P-V characteristics analysis","authors":"Noureddaher Zaidi, A. Khedher, M. Jemli","doi":"10.1109/SSD52085.2021.9429416","DOIUrl":"https://doi.org/10.1109/SSD52085.2021.9429416","url":null,"abstract":"In this paper, we present a hybrid fault diagnosis approach dedicated for PV generators via current-voltage and power-voltage curves analysis. It is mainly based on two signature generators: a fuzzy logic classifier and a based threshold conventional one. The two classifiers act on the available data by considering three thresholds which are made according to the generator PV typical parameters and with a total conformity with the standards. The proposed fault diagnosis approach requires only the available measured variables and is able to identify the most frequently met faults related to shading, temperature, leakage current and increased series resistance losses. The addressed PV generators are based on one diode model. The overall results have proved the proposed methodology capability in PV faults detection and identification.","PeriodicalId":6799,"journal":{"name":"2021 18th International Multi-Conference on Systems, Signals & Devices (SSD)","volume":"56 1","pages":"97-102"},"PeriodicalIF":0.0,"publicationDate":"2021-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81372101","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 : 2021-03-22DOI: 10.1109/SSD52085.2021.9429525
Amal Sellami, Leila Nasraoui, L. N. Atallah
In this paper, we propose a multi-stage localization technique that enables to determine the position of a Target User Equipment (T-UE) with harsh channel conditions through the assistance of neighboring User Equipments (UEs). The proposed approach allows a reduced-complexity localization by minimizing the search space through multistage treatment. A neighbor discovery processing is first performed to identify the two nearest neighboring UEs among a set of UEs in the vicinity. The so selected neighbors are used as anchors (A-UE). Then, based on the signal strength, the distances separating the two A-UEs to the T-UE are determined. The distance estimates are used to plot two potential solutions of the Angle of Arrival (AoA) of T-UE. To distinguish the correct AoA estimate, oriented beamforming is performed around short arcs centered on the two AoA candidates. The user's position is then deduced based on the estimates of the AoA and the distance to the A-UEs. Our approach exploits the capabilities of neighbor discovery and oriented beamforming to provide an accurate position estimate for UEs experiencing harsh channel conditions that render direct localization at the base station difficult.
{"title":"Neighbor-Assisted Localization for Massive MIMO 5G Systems","authors":"Amal Sellami, Leila Nasraoui, L. N. Atallah","doi":"10.1109/SSD52085.2021.9429525","DOIUrl":"https://doi.org/10.1109/SSD52085.2021.9429525","url":null,"abstract":"In this paper, we propose a multi-stage localization technique that enables to determine the position of a Target User Equipment (T-UE) with harsh channel conditions through the assistance of neighboring User Equipments (UEs). The proposed approach allows a reduced-complexity localization by minimizing the search space through multistage treatment. A neighbor discovery processing is first performed to identify the two nearest neighboring UEs among a set of UEs in the vicinity. The so selected neighbors are used as anchors (A-UE). Then, based on the signal strength, the distances separating the two A-UEs to the T-UE are determined. The distance estimates are used to plot two potential solutions of the Angle of Arrival (AoA) of T-UE. To distinguish the correct AoA estimate, oriented beamforming is performed around short arcs centered on the two AoA candidates. The user's position is then deduced based on the estimates of the AoA and the distance to the A-UEs. Our approach exploits the capabilities of neighbor discovery and oriented beamforming to provide an accurate position estimate for UEs experiencing harsh channel conditions that render direct localization at the base station difficult.","PeriodicalId":6799,"journal":{"name":"2021 18th International Multi-Conference on Systems, Signals & Devices (SSD)","volume":"44 1","pages":"503-509"},"PeriodicalIF":0.0,"publicationDate":"2021-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83949585","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 : 2021-03-22DOI: 10.1109/SSD52085.2021.9429425
Abdul-Wahid A. Saif, Mujahed Al Dhaifallah, Turki Bin Muhaya, Omer Majeed
This paper intends to present an educational attempt to build a self-managed robot in which it can generate power for itself using renewable, solar cell, incorporating the maximum power point tacking theory and the use of soft computing (Artificial Intelligence) to derive itself. Such devices are expected to replace many labor work jobs in the next decades. This report covers the hardware part of the robot, the software part, further analysis using Fuzzy Logic and Neural Networks, and some proposed industrial applications.
{"title":"Intelligent Robot Powered by Solar Energy","authors":"Abdul-Wahid A. Saif, Mujahed Al Dhaifallah, Turki Bin Muhaya, Omer Majeed","doi":"10.1109/SSD52085.2021.9429425","DOIUrl":"https://doi.org/10.1109/SSD52085.2021.9429425","url":null,"abstract":"This paper intends to present an educational attempt to build a self-managed robot in which it can generate power for itself using renewable, solar cell, incorporating the maximum power point tacking theory and the use of soft computing (Artificial Intelligence) to derive itself. Such devices are expected to replace many labor work jobs in the next decades. This report covers the hardware part of the robot, the software part, further analysis using Fuzzy Logic and Neural Networks, and some proposed industrial applications.","PeriodicalId":6799,"journal":{"name":"2021 18th International Multi-Conference on Systems, Signals & Devices (SSD)","volume":"75 1","pages":"194-200"},"PeriodicalIF":0.0,"publicationDate":"2021-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80664434","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 : 2021-03-22DOI: 10.1109/SSD52085.2021.9429301
K. Aloui, A. Guizani, M. Hammadi, M. Haddar, T. Soriano
Swarm engineering is a systematic application of scientific and technical knowledge to specify requirements, model, design, realize, verify, validate, operate and maintain a swarm intelligence system. In swarm robotics, there is not a well-structured methodology until today for developing robotic swarm systems. Several researchers have developed steps to design swarm robots but these steps are still incomplete. In this paper, we focus on the functional architecture of the swarm robots where we propose a top-down approach to ensure consistency and continuity from requirement level to behavioral level up to the functional and structural levels. This approach is based on the Model-Based Systems Engineering method (MBSE) using the Systems Modeling Language (SysML) where we present the allocations between the functions of each swarm member and the overall swarm behaviors. Then, we will be interested in the architecture of Robot Operating System (ROS) of a swarm behavior where we identify the allocations between the component and the functions of a robot.
{"title":"A Top Down Approach to Ensure the Continuity of the Different Design Levels of Swarm Robots","authors":"K. Aloui, A. Guizani, M. Hammadi, M. Haddar, T. Soriano","doi":"10.1109/SSD52085.2021.9429301","DOIUrl":"https://doi.org/10.1109/SSD52085.2021.9429301","url":null,"abstract":"Swarm engineering is a systematic application of scientific and technical knowledge to specify requirements, model, design, realize, verify, validate, operate and maintain a swarm intelligence system. In swarm robotics, there is not a well-structured methodology until today for developing robotic swarm systems. Several researchers have developed steps to design swarm robots but these steps are still incomplete. In this paper, we focus on the functional architecture of the swarm robots where we propose a top-down approach to ensure consistency and continuity from requirement level to behavioral level up to the functional and structural levels. This approach is based on the Model-Based Systems Engineering method (MBSE) using the Systems Modeling Language (SysML) where we present the allocations between the functions of each swarm member and the overall swarm behaviors. Then, we will be interested in the architecture of Robot Operating System (ROS) of a swarm behavior where we identify the allocations between the component and the functions of a robot.","PeriodicalId":6799,"journal":{"name":"2021 18th International Multi-Conference on Systems, Signals & Devices (SSD)","volume":"38 1","pages":"1438-1445"},"PeriodicalIF":0.0,"publicationDate":"2021-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82931572","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 : 2021-03-22DOI: 10.1109/SSD52085.2021.9429430
Israa Y. AbuShawish, S. Mahmoud
This work presents the design of a CMOS bio-medical amplifier using $mathrm{T} Omega$ pseudo-resistor based on MOS transistors operating in the weak inversion region. The pseudo-resistor is utilized as a feedback element in the first and second stage of the two-stages bio-medical amplifiers in the portable bio-detection system. Analytical analysis and simulations in LT-spice using 130 nm CMOS technology are performed to validate the realization of the extremely high resistance which can be reached to over tens of $mathrm{T} Omega$. The simulation of the two-stages bio-medical amplifier based pseudo-resistor are carried out in LT-spice using ± 0.6 V supply. The LT-spice simulation results are confirming with both the results obtained by the MATLAB and the analytical analysis of the pseudo-resistor.
{"title":"CMOS Bio-medical Amplifier based on Tera-Ohm Pseudo-resistor for Bio-detection System","authors":"Israa Y. AbuShawish, S. Mahmoud","doi":"10.1109/SSD52085.2021.9429430","DOIUrl":"https://doi.org/10.1109/SSD52085.2021.9429430","url":null,"abstract":"This work presents the design of a CMOS bio-medical amplifier using $mathrm{T} Omega$ pseudo-resistor based on MOS transistors operating in the weak inversion region. The pseudo-resistor is utilized as a feedback element in the first and second stage of the two-stages bio-medical amplifiers in the portable bio-detection system. Analytical analysis and simulations in LT-spice using 130 nm CMOS technology are performed to validate the realization of the extremely high resistance which can be reached to over tens of $mathrm{T} Omega$. The simulation of the two-stages bio-medical amplifier based pseudo-resistor are carried out in LT-spice using ± 0.6 V supply. The LT-spice simulation results are confirming with both the results obtained by the MATLAB and the analytical analysis of the pseudo-resistor.","PeriodicalId":6799,"journal":{"name":"2021 18th International Multi-Conference on Systems, Signals & Devices (SSD)","volume":"29 1","pages":"403-408"},"PeriodicalIF":0.0,"publicationDate":"2021-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89852235","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 : 2021-03-22DOI: 10.1109/SSD52085.2021.9429412
Noura Ben Moussa, M. Chetoui, M. Amairi
This paper proposes a new multi-input-single-output (MISO) system identification methods with fractional models in the errors-in-variables context. The developed methods are based on the instrumental variables and use the Higher-Order Statistics (HOS), such as the third-order cumulants, to obtain an unbiased estimate. Two different cases are established : the first supposes that the fractional orders of the single input-single-output (SISO) systems decomposing the MISO system are known a priori and only their linear coefficients are estimated. In the second case, the fractional orders are optimized along with linear coefficients. A Monte Carlo simulations are used, in a numerical example, to analyze the consistency of the developed estimators.
{"title":"MISO fractional systems identification with fractional models in the EIV context","authors":"Noura Ben Moussa, M. Chetoui, M. Amairi","doi":"10.1109/SSD52085.2021.9429412","DOIUrl":"https://doi.org/10.1109/SSD52085.2021.9429412","url":null,"abstract":"This paper proposes a new multi-input-single-output (MISO) system identification methods with fractional models in the errors-in-variables context. The developed methods are based on the instrumental variables and use the Higher-Order Statistics (HOS), such as the third-order cumulants, to obtain an unbiased estimate. Two different cases are established : the first supposes that the fractional orders of the single input-single-output (SISO) systems decomposing the MISO system are known a priori and only their linear coefficients are estimated. In the second case, the fractional orders are optimized along with linear coefficients. A Monte Carlo simulations are used, in a numerical example, to analyze the consistency of the developed estimators.","PeriodicalId":6799,"journal":{"name":"2021 18th International Multi-Conference on Systems, Signals & Devices (SSD)","volume":"41 1","pages":"942-947"},"PeriodicalIF":0.0,"publicationDate":"2021-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90154187","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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