In his previous teaching experience, Amin was a Staff Tutor at the Open University, and prior to that a Senior Lecturer and Course Leader at Leeds Beckett University. Dr Daneshkhah most recent research interest is to employ Deep Gaussian process for image process with applications in processing medical images, satellite images.ĭr Amin Hosseinian-Far holds the position of Senior Lecturer & Deputy Subject Leader in Business Systems and Operations at the University of Northampton. His current research interests are in probabilistic deep learning, in probabilistic risk and reliability analysis of networked infrastructure (EPSRC-UKWIR funded project) uncertainty/sensitivity analysis of complex engineering and environmental systems remote condition monitoring and maintenance for networked infrastructure using advanced dynamic graphical models in the presence of massive heterogeneous information, including on-line data (SCADA and sensor data) expert judgement modelling Big data using a wide range of probabilistic graphical models with applications in environmental risk assessment, reliability analysis, financial modelling, health economics, etc. He uses these tools in risk assessment of chain complex models common in environmental modelling and engineering applications (EPSRC funded project) to generate information about scenario's of interest to the decision makers. Alireza is Bayesian statistician interested in modelling interdependencies of large scale data and simulation of complex systems using the probabilistic methods including graphical models and Gaussian process emulators. Data Science and Computational Intelligence in the Faculty of Engineering, Environment and Computing of Coventry University. Maryam’s research contributions are published in the forms of journal and conference papers, and book chapters.ĭr Alireza Daneshkhah is a Senior Lecturer in Statistics, and course director of M.Sc. She is also the Associate Editor of the International Journal of Strategic Engineering (IJoSE). Dr Farsi is a member of the Institution of Engineering and Technology (IET) and an Fellow of Higher Education Academy (HEA). Her current research work involves studying complex systems simulation using a wide range of computational techniques, Life-Cycle Costing (LCC), system design and flexible manufacturing. She has experience in mathematical and computational modelling of manufacturing processes including dynamic data analysis and visualisation, resources’ utilisation, inventory optimisation, cost analysis, and impact analysis concerning lean principles applications and new technology implementation. Dr Farsi gained her PhD in Nonlinear Structural Mechanics from Imperial College London and her MSc in Structures from City, University of London. She is currently working on different system design and cost engineering projects funded by EPSRC and Innovate UK studying digital technologies, digital twin, automation and digital manufacturing. It is also a valuable asset for graduate students and academics who are looking to identify research gaps and develop their own proposals for further research.ĭr Maryam Farsi is a Research Fellow in Manufacturing Systems Modelling and has over 13 years’ experience in computational model development, data analysis and optimisation and additional experience in complex systems simulation and data visualisation. The book offers an outstanding reference guide for practitioners and researchers in manufacturing, operations research and communications, who are considering digitising some of their assets and related services. ![]() The concepts outlined in the book represents a city together with all of its infrastructure elements, which communicate with each other in a complex manner. Moreover, securing Internet of Things (IoT) which is one of the key enablers of DT’s is discussed in details and from various perspectives. The contributing authors reveal how and why DT technologies that are used for monitoring, visualising, diagnosing and predicting in real-time are vital to cities’ sustainability and efficiency. Hold significant potential for applications in smart cities, by employing a wide range of sensory and data-acquisition systems in various parts of the urban infrastructure. connection with back-end business applications. ![]()
0 Comments
Leave a Reply. |