2021Journal Article ER5 Auteurs : Lagsaiar, Lamine; Shahrour, Isam; Aljer, Ammar; Soulhi, Aziz Modular Software Architecture for Local Smart Building Servers In: Sensors, vol. 21, no. 17, 2021, ISSN: 1424-8220, (ACL). Abstract | Links @article{s21175810,
title = {Modular Software Architecture for Local Smart Building Servers},
author = {Lamine Lagsaiar and Isam Shahrour and Ammar Aljer and Aziz Soulhi},
url = {https://www.mdpi.com/1424-8220/21/17/5810},
doi = {10.3390/s21175810},
issn = {1424-8220},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {Sensors},
volume = {21},
number = {17},
abstract = {This paper presented the architecture and construction of a novel smart building system that could monitor and control buildings’ use in a safe and optimal way. The system operates on a Raspberry local server, which could be connected via the cloud technology to a central platform. The local system includes nine modules that inter-communicate. The system detects sensor faults, and provides a friendly interface to occupants. The paper presented the software architecture IoT used for the building monitoring and the use of this system for the management of fifteen social housing units during a year. The system allowed the investigation of indoor comfort and both energy and hot water consumptions. Data analysis resulted in the detection of abnormal energy consumptions. The system could be easily used in buildings’ management. It works in a plug-and-play mode.},
note = {ACL},
keywords = {ER5},
pubstate = {published},
tppubtype = {article}
}
This paper presented the architecture and construction of a novel smart building system that could monitor and control buildings’ use in a safe and optimal way. The system operates on a Raspberry local server, which could be connected via the cloud technology to a central platform. The local system includes nine modules that inter-communicate. The system detects sensor faults, and provides a friendly interface to occupants. The paper presented the software architecture IoT used for the building monitoring and the use of this system for the management of fifteen social housing units during a year. The system allowed the investigation of indoor comfort and both energy and hot water consumptions. Data analysis resulted in the detection of abnormal energy consumptions. The system could be easily used in buildings’ management. It works in a plug-and-play mode. |
2021Journal Article ER5 Auteurs : Lagsaiar, Lamine; Shahrour, Isam; Aljer, Ammar; Soulhi, Aziz Use of smart monitoring and users' feedback for to investigate the impact of the indoor environment on learning efficiency In: Environmental Economics and Policy Studies, 2021, (ACL). Links @article{lagsaiar:hal-03526370,
title = {Use of smart monitoring and users' feedback for to investigate the impact of the indoor environment on learning efficiency},
author = {Lamine Lagsaiar and Isam Shahrour and Ammar Aljer and Aziz Soulhi},
url = {https://hal-univ-artois.archives-ouvertes.fr/hal-03526370},
doi = {10.1007/s10018-021-00329-3},
year = {2021},
date = {2021-10-01},
urldate = {2021-10-01},
journal = {Environmental Economics and Policy Studies},
publisher = {Springer},
note = {ACL},
keywords = {ER5},
pubstate = {published},
tppubtype = {article}
}
|
2021Journal Article ER5 Auteurs : Mashhadi, Neda; Shahrour, Isam; Attoue, Nivine; Khattabi, Jamal El; Aljer, Ammar Use of Machine Learning for Leak Detection and Localization in Water Distribution Systems In: Smart Cities, vol. 4, no. 4, pp. 1293–1315, 2021, ISSN: 2624-6511, (ACL). Abstract | Links @article{smartcities4040069,
title = {Use of Machine Learning for Leak Detection and Localization in Water Distribution Systems},
author = {Neda Mashhadi and Isam Shahrour and Nivine Attoue and Jamal El Khattabi and Ammar Aljer},
url = {https://www.mdpi.com/2624-6511/4/4/69},
doi = {10.3390/smartcities4040069},
issn = {2624-6511},
year = {2021},
date = {2021-01-01},
journal = {Smart Cities},
volume = {4},
number = {4},
pages = {1293--1315},
abstract = {This paper presents an investigation of the capacity of machine learning methods (ML) to localize leakage in water distribution systems (WDS). This issue is critical because water leakage causes economic losses, damages to the surrounding infrastructures, and soil contamination. Progress in real-time monitoring of WDS and ML has created new opportunities to develop data-based methods for water leak localization. However, the managers of WDS need recommendations for the selection of the appropriate ML methods as well their practical use for leakage localization. This paper contributes to this issue through an investigation of the capacity of ML methods to localize leakage in WDS. The campus of Lille University was used as support for this research. The paper is presented as follows: First, flow and pressure data were determined using EPANET software; then, the generated data were used to investigate the capacity of six ML methods to localize water leakage. Finally, the results of the investigations were used for leakage localization from offline water flow data. The results showed excellent performance for leakage localization by the artificial neural network, logistic regression, and random forest, but there were low performances for the unsupervised methods because of overlapping clusters.},
note = {ACL},
keywords = {ER5},
pubstate = {published},
tppubtype = {article}
}
This paper presents an investigation of the capacity of machine learning methods (ML) to localize leakage in water distribution systems (WDS). This issue is critical because water leakage causes economic losses, damages to the surrounding infrastructures, and soil contamination. Progress in real-time monitoring of WDS and ML has created new opportunities to develop data-based methods for water leak localization. However, the managers of WDS need recommendations for the selection of the appropriate ML methods as well their practical use for leakage localization. This paper contributes to this issue through an investigation of the capacity of ML methods to localize leakage in WDS. The campus of Lille University was used as support for this research. The paper is presented as follows: First, flow and pressure data were determined using EPANET software; then, the generated data were used to investigate the capacity of six ML methods to localize water leakage. Finally, the results of the investigations were used for leakage localization from offline water flow data. The results showed excellent performance for leakage localization by the artificial neural network, logistic regression, and random forest, but there were low performances for the unsupervised methods because of overlapping clusters. |
2020Book Chapter ER5 Auteurs : Pham, Thi Hai Yen; Shahrour, Isam; Aljer, Ammar; Lepretre, Alain; Pernin, Celine; Ounaies, Sana Smart Monitoring for Urban Biodiversity Preservation In: CIGOS 2019, Innovation for Sustainable Infrastructure, vol. 54, pp. 1123-1128, Springer Singapore, 2020, (OS). Links @inbook{pham:hal-03526398,
title = {Smart Monitoring for Urban Biodiversity Preservation},
author = {Thi Hai Yen Pham and Isam Shahrour and Ammar Aljer and Alain Lepretre and Celine Pernin and Sana Ounaies},
url = {https://hal-univ-artois.archives-ouvertes.fr/hal-03526398},
doi = {10.1007/978-981-15-0802-8_180},
year = {2020},
date = {2020-10-01},
urldate = {2020-10-01},
booktitle = {CIGOS 2019, Innovation for Sustainable Infrastructure},
volume = {54},
pages = {1123-1128},
publisher = {Springer Singapore},
series = {Lecture Notes in Civil Engineering},
note = {OS},
keywords = {ER5},
pubstate = {published},
tppubtype = {inbook}
}
|
2020Journal Article ER5 Auteurs : Shahrour, Isam; Aljer, Ammar Experimental Analysis of the Spatial Variations of Air Pollution in a University Campus In: Biomedical Journal of Scientific & Technical Research, vol. 26, no. 3, 2020, (ACL). Links @article{shahrour:hal-03526391,
title = {Experimental Analysis of the Spatial Variations of Air Pollution in a University Campus},
author = {Isam Shahrour and Ammar Aljer},
url = {https://hal-univ-artois.archives-ouvertes.fr/hal-03526391},
doi = {10.26717/bjstr.2020.26.004341},
year = {2020},
date = {2020-03-01},
urldate = {2020-03-01},
journal = {Biomedical Journal of Scientific & Technical Research},
volume = {26},
number = {3},
publisher = {Biomedical Research Network },
note = {ACL},
keywords = {ER5},
pubstate = {published},
tppubtype = {article}
}
|
2020Book Chapter ER5 Auteurs : Pham, Thi Hai Yen; Shahrour, Isam; Aljer, Ammar; Lepretre, Alain; Pernin, Celine; Ounaies, Sana Smart Monitoring for Urban Biodiversity Preservation In: CIGOS 2019, Innovation for Sustainable Infrastructure, vol. 54, pp. 1123-1128, Springer Singapore, 2020, (OS). Links @inbook{pham:hal-03526398b,
title = {Smart Monitoring for Urban Biodiversity Preservation},
author = {Thi Hai Yen Pham and Isam Shahrour and Ammar Aljer and Alain Lepretre and Celine Pernin and Sana Ounaies},
url = {https://hal-univ-artois.archives-ouvertes.fr/hal-03526398},
doi = {10.1007/978-981-15-0802-8_180},
year = {2020},
date = {2020-10-01},
urldate = {2020-10-01},
booktitle = {CIGOS 2019, Innovation for Sustainable Infrastructure},
volume = {54},
pages = {1123-1128},
publisher = {Springer Singapore},
series = {Lecture Notes in Civil Engineering},
note = {OS},
keywords = {ER5},
pubstate = {published},
tppubtype = {inbook}
}
|
2019Journal Article ER5 Auteurs : Pham, Thi Hai Yen; Shahrour, Isam; Aljer, Ammar; Lepretre, Alain; Pernin, Celine; Ounaies, Sana Smart technology for the protection of urban biodiversity In: MATEC Web of Conferences, vol. 281, pp. 03002, 2019, (ACL). Links @article{pham:hal-03526405,
title = {Smart technology for the protection of urban biodiversity},
author = {Thi Hai Yen Pham and Isam Shahrour and Ammar Aljer and Alain Lepretre and Celine Pernin and Sana Ounaies},
url = {https://hal-univ-artois.archives-ouvertes.fr/hal-03526405},
doi = {10.1051/matecconf/201928103002},
year = {2019},
date = {2019-01-01},
urldate = {2019-01-01},
journal = {MATEC Web of Conferences},
volume = {281},
pages = {03002},
publisher = {EDP sciences},
note = {ACL},
keywords = {ER5},
pubstate = {published},
tppubtype = {article}
}
|
2017Conference ER5 Auteurs : Aljer, Ammar; Loriot, Marine; Shahrour, Isam; Benyahya, Afif Smart system for social housing monitoring 2017 Sensors Networks Smart and Emerging Technologies (SENSET), IEEE, Beirut, France, 2017, (COM). Links @conference{aljer:hal-03526412,
title = {Smart system for social housing monitoring},
author = {Ammar Aljer and Marine Loriot and Isam Shahrour and Afif Benyahya},
url = {https://hal-univ-artois.archives-ouvertes.fr/hal-03526412},
doi = {10.1109/senset.2017.8125057},
year = {2017},
date = {2017-09-01},
urldate = {2017-09-01},
booktitle = {2017 Sensors Networks Smart and Emerging Technologies (SENSET)},
pages = {1-4},
publisher = {IEEE},
address = {Beirut, France},
note = {COM},
keywords = {ER5},
pubstate = {published},
tppubtype = {conference}
}
|
