Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Khosrojerdi, F.; Akhigbe, O.; Gagnon, S.; Ramirez, A.; Richards, G.
Integrating artificial intelligence and analytics in smart grids: a systematic literature review Journal Article
In: International Journal of Energy Sector Management, vol. 16, no. 2, pp. 318–338, 2022.
Abstract | Links | BibTeX | Tags: Advanced Analytics, Automation, Building energy consumption, Data Analytics, Design/methodology/approach, Dynamic energy managements, Electric power system control, Electric power transmission networks, Energy management, Energy management systems, Energy utilization, Extract, Home energy management systems, Information management, Intelligent systems, Project management, quality control, Real time systems, SCADA systems, Smart power grids, Solar buildings, Supervisory control and dataacquisition systems (SCADA), System stability, Systematic literature review, transform and loads, Voltage stability assessment
@article{khosrojerdi_integrating_2022,
title = {Integrating artificial intelligence and analytics in smart grids: a systematic literature review},
author = {F. Khosrojerdi and O. Akhigbe and S. Gagnon and A. Ramirez and G. Richards},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112559126&doi=10.1108%2fIJESM-06-2020-0011&partnerID=40&md5=7052f94c993368405955c1d33d87043c},
doi = {10.1108/IJESM-06-2020-0011},
year = {2022},
date = {2022-01-01},
journal = {International Journal of Energy Sector Management},
volume = {16},
number = {2},
pages = {318–338},
abstract = {Purpose: The purpose of this study is to explore the latest approaches in integrating artificial intelligence and analytics (AIA) in energy smart grid projects. Empirical results are synthesized to highlight their relevance from a technology and project management standpoint, identifying several lessons learned that can be used for planning highly integrated and automated smart grid projects. Design/methodology/approach: A systematic literature review leads to selecting 108 research articles dealing with smart grids and AIA applications. Keywords are based on the following research questions: What is the growth trend in Smart Grid projects using intelligent systems and data analytics? What business value is offered when AI-based methods are applied? How do applications of intelligent systems combine with data analytics? What lessons can be learned for Smart Grid and AIA projects? Findings: The 108 selected articles are classified according to the following four research issues in smart grids project management: AIA integrated applications; AI-focused technologies; analytics-focused technologies; architecture and design methods. A broad set of smart grid functionality is reviewed, seeking to find commonality among several applications, including as follows: dynamic energy management; automation of extract, transform and load for Supervisory Control And Data Acquisition (SCADA) systems data; multi-level representations of data; the relationship between the standard three-phase transforms and modern data analytics; real-time or short-time voltage stability assessment; smart city architecture; home energy management system; building energy consumption; automated fault and disturbance analysis; and power quality control. Originality/value: Given the diversity of issues reviewed, a more capability-focused research agenda is needed to further synthesize empirical findings for AI-based smart grids. Research may converge toward more focus on business rules systems, that may best support smart grid design, proof development, governance and effectiveness. These AIA technologies must be further integrated with smart grid project management methodologies and enable a greater diversity of renewable and non-renewable production sources. © 2021, Emerald Publishing Limited.},
keywords = {Advanced Analytics, Automation, Building energy consumption, Data Analytics, Design/methodology/approach, Dynamic energy managements, Electric power system control, Electric power transmission networks, Energy management, Energy management systems, Energy utilization, Extract, Home energy management systems, Information management, Intelligent systems, Project management, quality control, Real time systems, SCADA systems, Smart power grids, Solar buildings, Supervisory control and dataacquisition systems (SCADA), System stability, Systematic literature review, transform and loads, Voltage stability assessment},
pubstate = {published},
tppubtype = {article}
}
Purpose: The purpose of this study is to explore the latest approaches in integrating artificial intelligence and analytics (AIA) in energy smart grid projects. Empirical results are synthesized to highlight their relevance from a technology and project management standpoint, identifying several lessons learned that can be used for planning highly integrated and automated smart grid projects. Design/methodology/approach: A systematic literature review leads to selecting 108 research articles dealing with smart grids and AIA applications. Keywords are based on the following research questions: What is the growth trend in Smart Grid projects using intelligent systems and data analytics? What business value is offered when AI-based methods are applied? How do applications of intelligent systems combine with data analytics? What lessons can be learned for Smart Grid and AIA projects? Findings: The 108 selected articles are classified according to the following four research issues in smart grids project management: AIA integrated applications; AI-focused technologies; analytics-focused technologies; architecture and design methods. A broad set of smart grid functionality is reviewed, seeking to find commonality among several applications, including as follows: dynamic energy management; automation of extract, transform and load for Supervisory Control And Data Acquisition (SCADA) systems data; multi-level representations of data; the relationship between the standard three-phase transforms and modern data analytics; real-time or short-time voltage stability assessment; smart city architecture; home energy management system; building energy consumption; automated fault and disturbance analysis; and power quality control. Originality/value: Given the diversity of issues reviewed, a more capability-focused research agenda is needed to further synthesize empirical findings for AI-based smart grids. Research may converge toward more focus on business rules systems, that may best support smart grid design, proof development, governance and effectiveness. These AIA technologies must be further integrated with smart grid project management methodologies and enable a greater diversity of renewable and non-renewable production sources. © 2021, Emerald Publishing Limited.
2.
El-Kass, W.; Gagnon, S.; Iglewski, M.
A visual and results-driven rules composition approach for better information extraction Proceedings Article
In: A., Zaremba M. Sasiadek J. Dolgui (Ed.): IFAC-PapersOnLine, pp. 112–117, 2015, ISBN: 24058963 (ISSN), (Issue: 3 Journal Abbreviation: IFAC-PapersOnLine).
Abstract | Links | BibTeX | Tags: Automation, F-score, Flow visualization, Harmonic mean, Information analysis, Information extraction, Information extraction rules, Information retrieval, Rule based, Rule composition, Rules composition, Visual process, Visualization
@inproceedings{el-kass_visual_2015,
title = {A visual and results-driven rules composition approach for better information extraction},
author = {W. El-Kass and S. Gagnon and M. Iglewski},
editor = {Zaremba M. Sasiadek J. Dolgui A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84953876235&doi=10.1016%2fj.ifacol.2015.06.067&partnerID=40&md5=3cd38b1d6b9efc819cd882d181cdda92},
doi = {10.1016/j.ifacol.2015.06.067},
isbn = {24058963 (ISSN)},
year = {2015},
date = {2015-01-01},
booktitle = {IFAC-PapersOnLine},
volume = {28},
pages = {112–117},
abstract = {We present a highly visual process for creating and combining elementary information extraction rules, based on their results, in order to find the rules combination that produces the most accurate information extraction results. A rule's accuracy is determined by its F-Score which is the harmonic mean of the precision and the recall of that rule. Rules are combined using logical OR and AND operators. Running a few hundreds rules combinations over a corpus, in order to determine their accuracies, can take days. Using our approach, millions of rules combinations can be tested and their accuracies (F-Score) can be calculated in few seconds. A prototype was created to demonstrate the effectiveness of our approach. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.},
note = {Issue: 3
Journal Abbreviation: IFAC-PapersOnLine},
keywords = {Automation, F-score, Flow visualization, Harmonic mean, Information analysis, Information extraction, Information extraction rules, Information retrieval, Rule based, Rule composition, Rules composition, Visual process, Visualization},
pubstate = {published},
tppubtype = {inproceedings}
}
We present a highly visual process for creating and combining elementary information extraction rules, based on their results, in order to find the rules combination that produces the most accurate information extraction results. A rule's accuracy is determined by its F-Score which is the harmonic mean of the precision and the recall of that rule. Rules are combined using logical OR and AND operators. Running a few hundreds rules combinations over a corpus, in order to determine their accuracies, can take days. Using our approach, millions of rules combinations can be tested and their accuracies (F-Score) can be calculated in few seconds. A prototype was created to demonstrate the effectiveness of our approach. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
3.
Gagnon, S.; Messaoudi, S.; Charbonneau, A.
In: CORIA 2011: COnference en Recherche d'Information et Applications - Conference on Information Retrieval and Applications, pp. 151–158, 2011, ISSN: 978-235768024-1 (ISBN).
Abstract | Links | BibTeX | Tags: Administrative data processing, Automated Text Classification, Automation, Classification (of information), Domain-specific ontologies, Extensible Business Reporting Language (XBRL), F measure, Financial news, Information retrieval, Ontology, Reuters, Reuters Corpus Volume 1 (RCV1), Text classification, Text processing
@article{gagnon_automated_2011,
title = {Automated Text Classification based on an ontology standard: Application of the Extensible Business Reporting Language (XBRL) to Reuters Corpus Volume 1 (RCV1)},
author = {S. Gagnon and S. Messaoudi and A. Charbonneau},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84869423599&partnerID=40&md5=1d12a436b5acb9f715fd6f1669e37be4},
issn = {978-235768024-1 (ISBN)},
year = {2011},
date = {2011-01-01},
booktitle = {CORIA 2011: COnference en Recherche d'Information et Applications - Conference on Information Retrieval and Applications},
journal = {CORIA 2011: COnference en Recherche d'Information et Applications - Conference on Information Retrieval and Applications},
pages = {151–158},
address = {Avignon},
abstract = {We demonstrate that applying a domain-specific ontology standard significantly improves Automated Text Classification (ATC). We use the Extensible Business Reporting Language (XBRL) to define a standard ontology and compare the performance of an ACT engine (IBM Classification Module v.8.6) against 2 other list of concepts, namely simple and hierarchical. Our sample of financial news is extracted from the Reuters Corpus Volume 1 (RCV1), where 2 experts in finance help us code 1000 of the 45000 news dealing with mergers and acquisitions. We report recall, precision, the F measure, and in addition a hierarchical measure adjusted for classification relevance in parent classes, as well as a more detailed measure evaluating the classification improvements at the level of each text.},
keywords = {Administrative data processing, Automated Text Classification, Automation, Classification (of information), Domain-specific ontologies, Extensible Business Reporting Language (XBRL), F measure, Financial news, Information retrieval, Ontology, Reuters, Reuters Corpus Volume 1 (RCV1), Text classification, Text processing},
pubstate = {published},
tppubtype = {article}
}
We demonstrate that applying a domain-specific ontology standard significantly improves Automated Text Classification (ATC). We use the Extensible Business Reporting Language (XBRL) to define a standard ontology and compare the performance of an ACT engine (IBM Classification Module v.8.6) against 2 other list of concepts, namely simple and hierarchical. Our sample of financial news is extracted from the Reuters Corpus Volume 1 (RCV1), where 2 experts in finance help us code 1000 of the 45000 news dealing with mergers and acquisitions. We report recall, precision, the F measure, and in addition a hierarchical measure adjusted for classification relevance in parent classes, as well as a more detailed measure evaluating the classification improvements at the level of each text.
4.
Allili, M. S.; Ziou, D.
Automatic color-texture image segmentation by using active contours Journal Article
In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4153 LNCS, pp. 495–504, 2006, ISSN: 03029743, (ISBN: 354037597X; 9783540375975 Place: Xi'an Publisher: Springer Verlag).
Abstract | Links | BibTeX | Tags: Active contours, Algorithms, Automatic segmentation, Automation, Boundary localization, Color texture segmentation, Color vision, Image segmentation, Information analysis, Textures
@article{allili_automatic_2006,
title = {Automatic color-texture image segmentation by using active contours},
author = {M. S. Allili and D. Ziou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-33750065117&doi=10.1007%2f11821045_52&partnerID=40&md5=a2eb2582bd6d565ff0c64278e31112a1},
doi = {10.1007/11821045_52},
issn = {03029743},
year = {2006},
date = {2006-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {4153 LNCS},
pages = {495–504},
abstract = {In this paper, we propose a novel method for unsupervised color-texture segmentation. The approach aims at combining color and texture features and active contours to build a fully automatic segmentation algorithm. By fully automatic, we mean the steps of region initialization and calculation of the number of regions are performed automatically by the algorithm. Furthermore, the approach combines boundary and region information for accurate region boundary localization. We validate the approach by examples of synthetic and natural color-texture image segmentation. © Springer-Verlag Berlin Heidelberg 2006.},
note = {ISBN: 354037597X; 9783540375975
Place: Xi'an
Publisher: Springer Verlag},
keywords = {Active contours, Algorithms, Automatic segmentation, Automation, Boundary localization, Color texture segmentation, Color vision, Image segmentation, Information analysis, Textures},
pubstate = {published},
tppubtype = {article}
}
In this paper, we propose a novel method for unsupervised color-texture segmentation. The approach aims at combining color and texture features and active contours to build a fully automatic segmentation algorithm. By fully automatic, we mean the steps of region initialization and calculation of the number of regions are performed automatically by the algorithm. Furthermore, the approach combines boundary and region information for accurate region boundary localization. We validate the approach by examples of synthetic and natural color-texture image segmentation. © Springer-Verlag Berlin Heidelberg 2006.