Pub Date : 2022-10-06DOI: 10.1109/ICOA55659.2022.9934351
Abdellah Haddad, B. A. El Majd, D. Bennis
In this note we discuss the experiment part of the paper “PINet: A Deep Learning Approach to Extract Topological Persistence Images”, where Som et al. trained a base classification model called AlexNet on Cifar10 dataset to get an accuracy of 80%. Then, they concatenated the PIs features with AlexNet base features and trained the model once again to get an accuracy of around 81%. Here we give a slight modification of the PI-Net architecture. Namely, we add two dense layers at the end of the model, the first one has 1024 neurons with ReLu activation and the last one has 10 neurons with Softmax activation, and then we use it as a base classification model on Cifar10 dataset. This enables us to reach an accuracy of 82%.
{"title":"On PI-Net deep learning model for classification of images","authors":"Abdellah Haddad, B. A. El Majd, D. Bennis","doi":"10.1109/ICOA55659.2022.9934351","DOIUrl":"https://doi.org/10.1109/ICOA55659.2022.9934351","url":null,"abstract":"In this note we discuss the experiment part of the paper “PINet: A Deep Learning Approach to Extract Topological Persistence Images”, where Som et al. trained a base classification model called AlexNet on Cifar10 dataset to get an accuracy of 80%. Then, they concatenated the PIs features with AlexNet base features and trained the model once again to get an accuracy of around 81%. Here we give a slight modification of the PI-Net architecture. Namely, we add two dense layers at the end of the model, the first one has 1024 neurons with ReLu activation and the last one has 10 neurons with Softmax activation, and then we use it as a base classification model on Cifar10 dataset. This enables us to reach an accuracy of 82%.","PeriodicalId":345017,"journal":{"name":"2022 8th International Conference on Optimization and Applications (ICOA)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125248760","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 : 2022-10-06DOI: 10.1109/ICOA55659.2022.9934158
A. Farah, Ech-Chatebi Jihane
Enterprise Resource Planning (ERP) is the most popular and successful IT solution, newly used in organizations to exchange the information among different business entities, to improve and maximize productivity. The system is expensive, time consumer and complicated to implement and manage. The challenges of ERP implementation have caused a high rate of failure based on the stories of numerous organizations that have deployed the solution. ERP brings together data from all business functions, giving the entire organization a broader perspective. They can control the whole company by monitoring purchasing, requests, ordering, finished products in stock and other business-critical information needed for management. Successfully deployed enterprise resource planning systems can deliver significant strategic, operational, and informational benefits to the organizations involved, saving resources and time.
{"title":"The importance of enterprise resource planning (ERP) in the optimisation of the small and medium enterprise's ressources in Morocco","authors":"A. Farah, Ech-Chatebi Jihane","doi":"10.1109/ICOA55659.2022.9934158","DOIUrl":"https://doi.org/10.1109/ICOA55659.2022.9934158","url":null,"abstract":"Enterprise Resource Planning (ERP) is the most popular and successful IT solution, newly used in organizations to exchange the information among different business entities, to improve and maximize productivity. The system is expensive, time consumer and complicated to implement and manage. The challenges of ERP implementation have caused a high rate of failure based on the stories of numerous organizations that have deployed the solution. ERP brings together data from all business functions, giving the entire organization a broader perspective. They can control the whole company by monitoring purchasing, requests, ordering, finished products in stock and other business-critical information needed for management. Successfully deployed enterprise resource planning systems can deliver significant strategic, operational, and informational benefits to the organizations involved, saving resources and time.","PeriodicalId":345017,"journal":{"name":"2022 8th International Conference on Optimization and Applications (ICOA)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114358029","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 : 2022-10-06DOI: 10.1109/ICOA55659.2022.9934625
K. Bouzaâchane, E. E. El Guarmah
In order to meet the security needs that are becoming more and more important with the economic advances, the development of physical or biometric access control systems is constantly growing. Several biometric modalities can be used and each one presents a particular interest, according to the targeted application. Within the framework of our study paper, we have realized a facial recognition system based on the EfficientDet model following the architecture of a deep neural network. The facial recognition process is divided into several steps, namely: face detection in each image, face normalization, facial feature extraction, classification and decision. The training and evaluation of the system were done on the database: Casia-web face. As Casia-web Face is unlabelled, we have developed an algorithm using the open source deep learning framework Mxnet to convert the images into binary format, reduce their size and give each image an identifier. Finally, the optimization of the system has been done using Root Mean Squared Propagation (RMSProp) and the Shard shuffling optimizers.
{"title":"Applying Face Recognition in Video Surveillance for Security Systems","authors":"K. Bouzaâchane, E. E. El Guarmah","doi":"10.1109/ICOA55659.2022.9934625","DOIUrl":"https://doi.org/10.1109/ICOA55659.2022.9934625","url":null,"abstract":"In order to meet the security needs that are becoming more and more important with the economic advances, the development of physical or biometric access control systems is constantly growing. Several biometric modalities can be used and each one presents a particular interest, according to the targeted application. Within the framework of our study paper, we have realized a facial recognition system based on the EfficientDet model following the architecture of a deep neural network. The facial recognition process is divided into several steps, namely: face detection in each image, face normalization, facial feature extraction, classification and decision. The training and evaluation of the system were done on the database: Casia-web face. As Casia-web Face is unlabelled, we have developed an algorithm using the open source deep learning framework Mxnet to convert the images into binary format, reduce their size and give each image an identifier. Finally, the optimization of the system has been done using Root Mean Squared Propagation (RMSProp) and the Shard shuffling optimizers.","PeriodicalId":345017,"journal":{"name":"2022 8th International Conference on Optimization and Applications (ICOA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126281597","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 : 2022-10-06DOI: 10.1109/ICOA55659.2022.9934485
M. Atzemourt, Z. Hachkar, Y. Chihab, A. Farchi
This paper presents a beamforming method based on a binary genetic algorithm. Widely known for their ability to increase the performance of antenna arrays, beamforming techniques are expected to play a major role in 5G systems. For reaching low peaks side lobe level (PSLL) in antenna design, it is necessary to optimize the amplitude weights of a linear antenna array. By optimizing the amplitude weight of the array's elements, a method for achieving a low side lobe level is explored. Utilized is a binary genetic algorithm with single point crossover and roulette wheel selection. The minimum SLL (minimize side lobe levels) for the radiation pattern is the cost function that is employed. The convergence of the optimization algorithm is demonstrated by simulations under Matlab environment and, its utility is shown in getting a desired antenna beam pattern, BGA converges before 50 generations in all the considered cases, We have seen that the level of the secondary lobes reaches nearly −30 dB for all the networks. We also note that the more the size of the antenna array increases, the more the number of iterations necessary for convergence is high.
{"title":"Beamforming Optimization by Binary Genetic Algorithm","authors":"M. Atzemourt, Z. Hachkar, Y. Chihab, A. Farchi","doi":"10.1109/ICOA55659.2022.9934485","DOIUrl":"https://doi.org/10.1109/ICOA55659.2022.9934485","url":null,"abstract":"This paper presents a beamforming method based on a binary genetic algorithm. Widely known for their ability to increase the performance of antenna arrays, beamforming techniques are expected to play a major role in 5G systems. For reaching low peaks side lobe level (PSLL) in antenna design, it is necessary to optimize the amplitude weights of a linear antenna array. By optimizing the amplitude weight of the array's elements, a method for achieving a low side lobe level is explored. Utilized is a binary genetic algorithm with single point crossover and roulette wheel selection. The minimum SLL (minimize side lobe levels) for the radiation pattern is the cost function that is employed. The convergence of the optimization algorithm is demonstrated by simulations under Matlab environment and, its utility is shown in getting a desired antenna beam pattern, BGA converges before 50 generations in all the considered cases, We have seen that the level of the secondary lobes reaches nearly −30 dB for all the networks. We also note that the more the size of the antenna array increases, the more the number of iterations necessary for convergence is high.","PeriodicalId":345017,"journal":{"name":"2022 8th International Conference on Optimization and Applications (ICOA)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125046273","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 : 2022-10-06DOI: 10.1109/ICOA55659.2022.9934631
Abdalrahman Alblwi, K. Barner
Automatic object segmentation based on artificial neural networks is a critical task in an array of real-world applications. Localizing and region segmentation is of particular interest, although typical approaches rely on complex networks and/or human interactions. Therefore, various complex networks suffer from suboptimal segmentation due to inaccurate feature extraction. This paper introduces a Multi-Gated Nested Network (MGN-net) that provides precise segmentation performance by capturing relevant contextual information via a channel gating mechanism. Results utilize challenging biomedical image databases, featuring MRI Brain and Chest X-ray images, are presented. The results show that the MGN-net approach subjectively and objectively performs favorably compared to multiple state-of-the-art methods, such as the U2-net and U-net networks.
{"title":"Optimizing Feature Representation via A Nested Network for Object Segmentation","authors":"Abdalrahman Alblwi, K. Barner","doi":"10.1109/ICOA55659.2022.9934631","DOIUrl":"https://doi.org/10.1109/ICOA55659.2022.9934631","url":null,"abstract":"Automatic object segmentation based on artificial neural networks is a critical task in an array of real-world applications. Localizing and region segmentation is of particular interest, although typical approaches rely on complex networks and/or human interactions. Therefore, various complex networks suffer from suboptimal segmentation due to inaccurate feature extraction. This paper introduces a Multi-Gated Nested Network (MGN-net) that provides precise segmentation performance by capturing relevant contextual information via a channel gating mechanism. Results utilize challenging biomedical image databases, featuring MRI Brain and Chest X-ray images, are presented. The results show that the MGN-net approach subjectively and objectively performs favorably compared to multiple state-of-the-art methods, such as the U2-net and U-net networks.","PeriodicalId":345017,"journal":{"name":"2022 8th International Conference on Optimization and Applications (ICOA)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121980861","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 : 2022-10-06DOI: 10.1109/ICOA55659.2022.9934180
E. Osmani, M. Haddou, N. Bensalem
We present a family of smoothing methods to solve nonlinear complementarity problems (NCPs) involving $mathcal{P}_{0}$-function. Several regularization or approximation techniques like Fisher-Burmeister's method, interior-point methods (IPMs) approaches, or smoothing methods already exist. All the corresponding methods solve a sequence of nonlinear systems of equations and depend on parameters that are difficult to drive to zero. The main novelty of our approach is to consider the smoothing parameters as variables that converge by themselves to zero. We do not need any complicated updating strategy, and then obtain nonparametric algorithms. We prove some global and local convergence results and present several numerical experiments, comparisons, that show the efficiency of our approach.
{"title":"A Smooth Approach to the solution of Nonlinear Complementarity Problems involving $mathcal{P}_{0}$-function","authors":"E. Osmani, M. Haddou, N. Bensalem","doi":"10.1109/ICOA55659.2022.9934180","DOIUrl":"https://doi.org/10.1109/ICOA55659.2022.9934180","url":null,"abstract":"We present a family of smoothing methods to solve nonlinear complementarity problems (NCPs) involving $mathcal{P}_{0}$-function. Several regularization or approximation techniques like Fisher-Burmeister's method, interior-point methods (IPMs) approaches, or smoothing methods already exist. All the corresponding methods solve a sequence of nonlinear systems of equations and depend on parameters that are difficult to drive to zero. The main novelty of our approach is to consider the smoothing parameters as variables that converge by themselves to zero. We do not need any complicated updating strategy, and then obtain nonparametric algorithms. We prove some global and local convergence results and present several numerical experiments, comparisons, that show the efficiency of our approach.","PeriodicalId":345017,"journal":{"name":"2022 8th International Conference on Optimization and Applications (ICOA)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124115236","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 : 2022-10-06DOI: 10.1109/ICOA55659.2022.9934731
Elhoucine Ouassam, N. Hmina, B. Bouikhalene, H. Hachimi
In this paper, we propose a novel multiscale approach to modeling the Digital Learning Environment (DLE) by the introduction of a design pattern, which links students, educational organizations, resources, teachers, targeted skills, audience characteristics, constraints, and existing environment. For this purpose, we have to define an organizational structure and adopt management strategies to improve the performance of DLE. This organizational structure is an important element that has to be taken into account to simulate a digital learning environment. To facilitate the design of these simulations, we propose an agent-based methodological framework for this complex system.
{"title":"Agent-based Modeling and Simulation of Digital Learning Environment","authors":"Elhoucine Ouassam, N. Hmina, B. Bouikhalene, H. Hachimi","doi":"10.1109/ICOA55659.2022.9934731","DOIUrl":"https://doi.org/10.1109/ICOA55659.2022.9934731","url":null,"abstract":"In this paper, we propose a novel multiscale approach to modeling the Digital Learning Environment (DLE) by the introduction of a design pattern, which links students, educational organizations, resources, teachers, targeted skills, audience characteristics, constraints, and existing environment. For this purpose, we have to define an organizational structure and adopt management strategies to improve the performance of DLE. This organizational structure is an important element that has to be taken into account to simulate a digital learning environment. To facilitate the design of these simulations, we propose an agent-based methodological framework for this complex system.","PeriodicalId":345017,"journal":{"name":"2022 8th International Conference on Optimization and Applications (ICOA)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129101559","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 : 2022-10-06DOI: 10.1109/ICOA55659.2022.9934146
Khalid Sohaib, Effina Driss, Jouilil Youness
The ultimate goal of this paper is to conduct an exploratory analysis that aims to identify the factors that may influence the residential attractiveness of small cities in Morocco. The contribution was the fruit of the construction of four statistical models (OLS model, Backward regression model, forward stepwise regression, and both ways stepwise regression) aiming to identify the most decisive variables affecting the attractiveness of the population. For this purpose, while aiming to be more exhaustive in the analysis, a diversified battery of socio-economic, spatial, and geographical indicators has been mobilized to conduct structural modeling. The econometrics findings show that the phenomenon of residential attractiveness is quite complex and is subject to the influence of several variables where each has its own effect. However, we have demonstrated that the supply of employment and the development of industrial activity remain the most important factors influencing the territorial attractiveness of small cities in Morocco ($mathrm{p} < 0.001$).
{"title":"An Econometric Analysis of the Determinants of Small's Cities' Attractiveness: Evidence from Moroccan Case","authors":"Khalid Sohaib, Effina Driss, Jouilil Youness","doi":"10.1109/ICOA55659.2022.9934146","DOIUrl":"https://doi.org/10.1109/ICOA55659.2022.9934146","url":null,"abstract":"The ultimate goal of this paper is to conduct an exploratory analysis that aims to identify the factors that may influence the residential attractiveness of small cities in Morocco. The contribution was the fruit of the construction of four statistical models (OLS model, Backward regression model, forward stepwise regression, and both ways stepwise regression) aiming to identify the most decisive variables affecting the attractiveness of the population. For this purpose, while aiming to be more exhaustive in the analysis, a diversified battery of socio-economic, spatial, and geographical indicators has been mobilized to conduct structural modeling. The econometrics findings show that the phenomenon of residential attractiveness is quite complex and is subject to the influence of several variables where each has its own effect. However, we have demonstrated that the supply of employment and the development of industrial activity remain the most important factors influencing the territorial attractiveness of small cities in Morocco ($mathrm{p} < 0.001$).","PeriodicalId":345017,"journal":{"name":"2022 8th International Conference on Optimization and Applications (ICOA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129181154","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 : 2022-10-06DOI: 10.1109/ICOA55659.2022.9934603
Sanaa Lakrouni, Marouane Sebgui, Slimane Bah
In recent years, IoT devices have been widely used in a variety of sectors such as industry, smart farming, and smart homes. Its application requires performing high computational analysis in real-time. The research era of Artificial Intelligence has witnessed an intense development conducted by millions of research and applications that extend from systems recommendation to video/audio surveillance. AI algorithms have been deployed to IoT data to bring intelligent decisions for IoT applications. These numerous data increase the time of the data transition to the cloud, which becomes the bottleneck of the cloud-based architecture. The edge computing technology brings the AI algorithms to the Edge of the network to improve latency, bandwidth, and data privacy, and guarantee the high accuracy of the AI algorithms. Recently Federated learning (FL) is a machine learning technique that distributes the training among edge devices near to the data source in light of increasing privacy and leveraging from the massive data distributed among numerous edge devices. Therefore, in this paper, we introduce recent research that demonstrates the effectiveness of this approach and present the architectures, models, and methods that implement FL with IoT devices.
{"title":"Using AI and IoT at the Edge of the network","authors":"Sanaa Lakrouni, Marouane Sebgui, Slimane Bah","doi":"10.1109/ICOA55659.2022.9934603","DOIUrl":"https://doi.org/10.1109/ICOA55659.2022.9934603","url":null,"abstract":"In recent years, IoT devices have been widely used in a variety of sectors such as industry, smart farming, and smart homes. Its application requires performing high computational analysis in real-time. The research era of Artificial Intelligence has witnessed an intense development conducted by millions of research and applications that extend from systems recommendation to video/audio surveillance. AI algorithms have been deployed to IoT data to bring intelligent decisions for IoT applications. These numerous data increase the time of the data transition to the cloud, which becomes the bottleneck of the cloud-based architecture. The edge computing technology brings the AI algorithms to the Edge of the network to improve latency, bandwidth, and data privacy, and guarantee the high accuracy of the AI algorithms. Recently Federated learning (FL) is a machine learning technique that distributes the training among edge devices near to the data source in light of increasing privacy and leveraging from the massive data distributed among numerous edge devices. Therefore, in this paper, we introduce recent research that demonstrates the effectiveness of this approach and present the architectures, models, and methods that implement FL with IoT devices.","PeriodicalId":345017,"journal":{"name":"2022 8th International Conference on Optimization and Applications (ICOA)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123962790","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 : 2022-10-06DOI: 10.1109/ICOA55659.2022.9934248
Ouassima El qouarti, A. Essadki, Hammadi Laghridat, T. Nasser
In recent years, renewable energies tended to integrate the electricity production grid at different levels, and have become a crucial solution to help mitigate the harmful effect of greenhouse gases and reduce the energy dependence of countries in terms of electricity. Microgrids give a good option to implement these resources in a decentralized manner and expand the electrical grid consistency. In this paper we will model a DC hybrid microgrid combining both Fuel Cell (FC) and Doubly Fed Induction Generator (DFIG) based Wind Turbine (WT) technologies. This microgrid is intended to be capable of guaranteeing the electricity procurement continuity despite of variable load demand and the intermittency aspect of the wind resource. The adopted Power management strategy and controls were emphasized, and the obtained results from MATLAB/Simulink simulation tool support and endorse the presented strategy.
{"title":"Power Management Strategy for a Direct Current Hybrid Microgrid based on Doubly Fed Induction Generator and Fuel Cell","authors":"Ouassima El qouarti, A. Essadki, Hammadi Laghridat, T. Nasser","doi":"10.1109/ICOA55659.2022.9934248","DOIUrl":"https://doi.org/10.1109/ICOA55659.2022.9934248","url":null,"abstract":"In recent years, renewable energies tended to integrate the electricity production grid at different levels, and have become a crucial solution to help mitigate the harmful effect of greenhouse gases and reduce the energy dependence of countries in terms of electricity. Microgrids give a good option to implement these resources in a decentralized manner and expand the electrical grid consistency. In this paper we will model a DC hybrid microgrid combining both Fuel Cell (FC) and Doubly Fed Induction Generator (DFIG) based Wind Turbine (WT) technologies. This microgrid is intended to be capable of guaranteeing the electricity procurement continuity despite of variable load demand and the intermittency aspect of the wind resource. The adopted Power management strategy and controls were emphasized, and the obtained results from MATLAB/Simulink simulation tool support and endorse the presented strategy.","PeriodicalId":345017,"journal":{"name":"2022 8th International Conference on Optimization and Applications (ICOA)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114804938","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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