RT5 Publications: Data Sensitive Application Use-Cases Driven Validation

@article{xie_herrerafernandez_shah_kassem_james_2023,

title = {Learning partial correlation graph for multivariate sensor data and detecting sensor communities in smart buildings},

author = {Xiang Xie and Antonio Manuel Herrera Fernandez and Tejal Shah and Mohamad Kassem and Philip James},

url = {https://www.repository.cam.ac.uk/handle/1810/350199},

doi = {10.17863/CAM.96862},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

publisher = {Apollo - University of Cambridge Repository},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Phengsuwan2020,

title = {An ontology‐based system for discovering landslide‐induced emergencies in electrical grid},

author = {Jedsada Phengsuwan and Tejal Shah and Rui Sun and Philip James and Dhavalkumar Thakker and Rajiv Ranjan},

doi = {10.1002/ett.3899},

issn = {2161-3915},

year  = {2022},

date = {2022-03-00},

urldate = {2022-03-00},

journal = {Trans Emerging Tel Tech},

volume = {33},

number = {3},

publisher = {Wiley},

abstract = {<jats:title>Summary</jats:title><jats:p>Early warning systems (EWS) for electrical grid infrastructure have played a significant role in the efficient management of electricity supply in natural hazard prone areas. Modern EWS rely on scientific methods to analyze a variety of Earth Observation and ancillary data provided by multiple and heterogeneous data sources for the monitoring of electrical grid infrastructure. Furthermore, through cooperation, EWS for natural hazards contribute to monitoring by reporting hazard events that are associated with a particular electrical grid network. Additionally, sophisticated domain knowledge of natural hazards and electrical grid is also required to enable dynamic and timely decision‐making about the management of electrical grid infrastructure in serious hazards. In this paper, we propose a data integration and analytics system that enables an interaction between natural hazard EWS and electrical grid EWS to contribute to electrical grid network monitoring and support decision‐making for electrical grid infrastructure management. We prototype the system using landslides as an example natural hazard for the grid infrastructure monitoring. Essentially, the system consists of background knowledge about landslides as well as information about data sources to facilitate the process of data integration and analysis. Using the knowledge modeled, the prototype system can report the occurrence of landslides and suggest potential data sources for the electrical grid network monitoring.</jats:p>},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Phengsuwan2020b,

title = {An ontology‐based system for discovering landslide‐induced emergencies in electrical grid},

author = {Jedsada Phengsuwan and Tejal Shah and Rui Sun and Philip James and Dhavalkumar Thakker and Rajiv Ranjan},

doi = {10.1002/ett.3899},

issn = {2161-3915},

year  = {2022},

date = {2022-03-00},

urldate = {2022-03-00},

journal = {Trans Emerging Tel Tech},

volume = {33},

number = {3},

publisher = {Wiley},

abstract = {<jats:title>Summary</jats:title><jats:p>Early warning systems (EWS) for electrical grid infrastructure have played a significant role in the efficient management of electricity supply in natural hazard prone areas. Modern EWS rely on scientific methods to analyze a variety of Earth Observation and ancillary data provided by multiple and heterogeneous data sources for the monitoring of electrical grid infrastructure. Furthermore, through cooperation, EWS for natural hazards contribute to monitoring by reporting hazard events that are associated with a particular electrical grid network. Additionally, sophisticated domain knowledge of natural hazards and electrical grid is also required to enable dynamic and timely decision‐making about the management of electrical grid infrastructure in serious hazards. In this paper, we propose a data integration and analytics system that enables an interaction between natural hazard EWS and electrical grid EWS to contribute to electrical grid network monitoring and support decision‐making for electrical grid infrastructure management. We prototype the system using landslides as an example natural hazard for the grid infrastructure monitoring. Essentially, the system consists of background knowledge about landslides as well as information about data sources to facilitate the process of data integration and analysis. Using the knowledge modeled, the prototype system can report the occurrence of landslides and suggest potential data sources for the electrical grid network monitoring.</jats:p>},

keywords = {},

pubstate = {published},

tppubtype = {article}

}