Smart grids may be characterized as the amalgamation of electrical grids, communication networks, specialized hardware, and computational intelligence (algorithms). This integration aims to oversee, regulate, and coordinate the generation, distribution, storage, and utilization of energy. Indeed, smart grid technologies have the potential to facilitate the distribution of substantial quantities of power generated from renewable sources. For this purpose, a comprehensive modeling approach is employed to simplify and enhance the feasibility of the task. It introduces a highly intricate system where modeling the components and relationships between entities proves challenging. Optimal energy management is necessary in this case. This paper provides a summary of an investigation into the modeling and optimal management of smart grids. In fact, this work allows a discussion of a hybrid system. Then we briefly introduce the domain of conceptual modeling within the enterprise.
{"title":"Synthesis of modeling and optimal management of smart grids","authors":"Jihen El Khaldi, L. Bouslimi, M. Lakhoua","doi":"10.59400/issc.v4i1.464","DOIUrl":"https://doi.org/10.59400/issc.v4i1.464","url":null,"abstract":"Smart grids may be characterized as the amalgamation of electrical grids, communication networks, specialized hardware, and computational intelligence (algorithms). This integration aims to oversee, regulate, and coordinate the generation, distribution, storage, and utilization of energy. Indeed, smart grid technologies have the potential to facilitate the distribution of substantial quantities of power generated from renewable sources. For this purpose, a comprehensive modeling approach is employed to simplify and enhance the feasibility of the task. It introduces a highly intricate system where modeling the components and relationships between entities proves challenging. Optimal energy management is necessary in this case. This paper provides a summary of an investigation into the modeling and optimal management of smart grids. In fact, this work allows a discussion of a hybrid system. Then we briefly introduce the domain of conceptual modeling within the enterprise.","PeriodicalId":503854,"journal":{"name":"Information System and Smart City","volume":"6 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141380524","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}
During recent COVID-19 pandemic across the world, face masks became necessary to stop the spread of infection. This has led to challenges with effective detection and recognition of human faces using the existing face detection systems. This paper proposes a Convolutional Neural Network (CNN) based face mask recognition system, which offers two solutions—recognition of the person wearing face mask and position of face mask i.e., whether the mask is correctly worn or not. The proposed model could play instrumental role of face recognition. In the first stage, with the help of Viola-Jones algorithm, the model detects the position of the face mask. In the second stage, we identify the person with by a modified pre-trained face mask recognition DeepMaskNet model facilitates in identifying the person. The proposed model achieves an accuracy of 94% in detecting the face mask position and 99.96% in identifying the masked person. Lastly, a comparison with the existing models is detailed, proving that the proposed model achieves the highest greater performance.
{"title":"Effective approach of face mask position detection and recognition","authors":"Om Pradyumana Gupta, Arun Prakash Agarwal, Om Pal","doi":"10.59400/issc.v3i1.467","DOIUrl":"https://doi.org/10.59400/issc.v3i1.467","url":null,"abstract":"During recent COVID-19 pandemic across the world, face masks became necessary to stop the spread of infection. This has led to challenges with effective detection and recognition of human faces using the existing face detection systems. This paper proposes a Convolutional Neural Network (CNN) based face mask recognition system, which offers two solutions—recognition of the person wearing face mask and position of face mask i.e., whether the mask is correctly worn or not. The proposed model could play instrumental role of face recognition. In the first stage, with the help of Viola-Jones algorithm, the model detects the position of the face mask. In the second stage, we identify the person with by a modified pre-trained face mask recognition DeepMaskNet model facilitates in identifying the person. The proposed model achieves an accuracy of 94% in detecting the face mask position and 99.96% in identifying the masked person. Lastly, a comparison with the existing models is detailed, proving that the proposed model achieves the highest greater performance.","PeriodicalId":503854,"journal":{"name":"Information System and Smart City","volume":"83 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140368902","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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