Greg Foliente, University of Melbourne
Prof. Greg Foliente is an Enterprise Professor at the University of Melbourne and the Manager of its Centre for Disaster Management and Public Safety (CDMPS). He leads interdisciplinary and transdisciplinary research, education, consulting and collaboration initiatives that advance innovation in the built environment and urban systems sectors, with a primary focus towards improved sustainability, liveability and resilience. He has an International reputation in diverse areas that include engineering safety and performance assessment under extreme events, quantitative risk analysis and system reliability, disaster mitigation, socio-economic impacts and resilient design, spatial diffusion of technology and innovation, sustainability and more recently, community wellbeing and resilience. He is a strategic leader and facilitator, with a number of internationally recognised achievements and successful projects, including those undertaken with UN agencies, the World Bank, AusAID and various Australian federal and state agencies. He has an extensive and diverse scholarly publications record, has received numerous international honours and awards and appointments in important scientific committees and positions. For further details, see: https://www.linkedin.com/in/foliente/
Presentation: Black Swans, Perfect Storms and Cascading Failures: Emerging R&D challenges in infrastructure systems analysis, disaster resilience and the assessment of socioeconomic impacts of failure in extreme events
Global trends towards higher concentrations of population and economic activities in urban mega-centres have brought increasing complexity and infrastructure interdependencies in the delivery of critical urban services such as energy, water, transport and communication. This presentation identifies critical research and development challenges from the perspective—and for the benefit—of key stakeholders, considering their primary decision goals and context. From this vantage point, the critical evaluation framework is extended to include a classification of disruptions and extreme events and an overview of infrastructure modeling approaches and broader socioeconomic impacts assessment methods. Mapping the range of modeling and assessment methods against different decision contexts, critical gaps in knowledge and tools are identified to support the latter. Deep uncertainties characterize the challenge as each major component in the information and decision-making chain—from the frequency and intensity of a disruptive event, to assessing the first-order and immediate impacts of an infrastructure failure, to estimating the nature, extent and impact of cascading failures—multiplies the uncertainties. The emerging research challenges to deal with these interdependencies and uncertainties are explored.
Neil Gordon, Defence Science and Technology Group
Neil Gordon received a PhD in Statistics from Imperial College London in 1993. He was with the Defence Evaluation and Research Agency in the UK from 1988-2002 working on missile guidance and statistical data processing. In 2002 he moved to the Defence Science and Technology Group in Adelaide, Australia where he is currently head of Data and Information Fusion. In 2014 he became an honorary Professor with the School of Information Technology and Electrical Engineering at the University of Queensland. He is the co-author/co-editor of two books on particle filtering and one on the search for MH370.
Presentation: The search for MH370
On 7th March 2014 Malaysian Airlines flight MH370 from Kuala Lumpur to Beijing lost contact with Air Traffic Control and was subsequently reported missing. An extensive air and sea search was made around the last reported location of the aircraft in the Gulf of Thailand without success. Signals transmitted by the aircraft’s satellite communications terminal to Inmarsat’s 3F1 Indian Ocean Region satellite indicated that the aircraft continued to fly for several hours after loss of contact. In this talk I will describe how nonlinear/non-Gaussian Bayesian time series estimation methods have been used to process the Inmarsat data and produce a probability distribution of MH370 flight paths that defined the search zone in the southern Indian Ocean. I will describe how probabilistic models of aircraft flight dynamics, satellite communication system measurements, environmental effects and radar data were constructed and calibrated. A particle filter based numerical calculation of the aircraft flight path probability distribution will be outlined and the method is demonstrated and validated using data from several previous flights of the accident aircraft. A short book is freely available for download from http://www.springer.com/us/book/9789811003783
Ged Griffin, Victoria Police Australia
Ged Griffin, Inspector with Victoria Police Australia and currently a PhD Candidate at the University of Melbourne where he is researching the next generation of emergency management. He holds a Master of Arts (Police Practice), a Master of Professional Education and Training and a number of subordinate degrees. He has been a police officer for 28 years where he has performed duties in general duties, emergency management, marine policing, criminal investigations, intelligence and counter terrorism operations. He has also performed a wide range of roles in East Timor including duties at the UN Serious Crimes Unit, Victoria Police Contingent Commander and as Liaison Officer supporting the former Victorian Premier the Hon Mr Steve Bracks during his work assisting Xanana Guasmao and the new government of East Timor. He is a member of the Australian Civil Corps and has been appointed as the Team Leader for the Post Disaster Response Team. He is currently an Inspector in the State Emergency Response Coordination Division at the Victoria Police Force.
Presentation: Public Safety Mobile Broadband – To bravely go where no one has gone before
Abstract coming soon.
Yu-Hsing Wang, Department of Civil and Environmental Engineering, and Data-Enabled Scalable Research (DESR) Laboratory, HKUST
Dr. Yu-Hsing Wang received his B.S. and M.S. degrees in Civil Engineering from National Taiwan University and a Ph.D. in Civil Engineering from Georgia Institute of Technology where he received the George F. Sowers Distinguished Graduate Student Award for Ph.D. Students. Currently, he is a Professor at the Department of Civil and Environmental Engineering and director of Data-Enabled Scalable Research (DESR) Laboratory, HKUST. The DESR Lab is a physical Makerspace, specialized in the applications of Geotechnical Internet of Things (Geo-IoT), Big Data Analytics, and Deep Learning on sustainable urban development and city resilience. The DESR Lab is also an open platform for geotechnical academics and practitioners to collaborate and share resources. His research interests include innovative wave-based characterizations of geomaterials (using mechanical and electromagnetic waves), applications of 3D printing techniques on innovation of geotechnical testing devices and sensing techniques, development and applications of Smart Soil Particle sensors (OpenSSP), applications of geotechnical internet of things (GeoIoT), Big Data analytics, and deep learning on geotechnical engineering, health monitoring and predictive maintenance. In 2005, he received the ASTM International Hogentogler Award. In 2008 and 2017, he received the School of Engineering Teaching Award, HKUST. In 2013, he received the Distinguished Alumni Award from the Department of Civil Engineering, National Taiwan University. He has been invited for Keynote and theme lectures in the internal conferences and served as an associated editor and editorial board member in different journals.
Presentation: A Real-time and Long-term Scalable IoT-AI Stack for Natural Hazard Resiliency Assessment and Management of Critical Infrastructure
Lifelines and critical infrastructure will be exposed to higher risks of degradations, damages or failures in the coming decades as unprecedented larger scales of typhoons and extreme precipitations are becoming a norm. Such challenging times call for data-enabled decision making through constant monitoring in order to carry out timely maintenance and upgrade works of the lifelines and critical infrastructure. Predictive maintenance of critical infrastructure relies heavily on large-scale and long-term monitoring, particularly vibrations at different parts of the structural elements. In this talk, we will showcase how we build a realtime, long-term scalable operational IoT stack for low-cost dynamic monitoring with reference to our live landslide monitoring operations in Taiwan and Hong Kong since 2014. Linear scalability is the core design principle of the IoT stack as both cost-effectiveness and performance are the major reasons why we stop short of widespread and continuous dynamic monitoring. We then discuss how we deploy AI – deep learning – on these large-scale dynamic observations pouring in by a second for real-time anomaly detections and classifications. With the entire IoT-AI stack facilitating real-time data discovery, evaluations and disseminations of the dynamic performances of the lifelines and critical infrastructure, efficient decision-making and resource allocation through predictive maintenance is now possible.
Keywords: IoT, AI, critical infrastructure, predictive maintenance, natural hazard, dynamic monitoring