Crawlers, Bots, Flows, Lambdas, Glues and Autopilots: Applying AI and ML to Radiological Sensor Networks for Safety and Security

Peter Martin, University of Bristol

Driven by step-change advances in cloud computing, the Internet of Things (IoT), and microcontroller technologies, progress in Machine Learning (ML) over the past decade has served to pioneer an increasing number of news technologies; from self-driving (or ‘driverless’) cars to enhanced weather forecasting, and from targeted produce advertising to self-cleaning houses. However, such vast and ever-growing computational intelligence to analyse, interpret, and streamline the potentially vast radiological monitoring dataset that is/could be continually collected using multiple survey ‘nodes’ as part of the UK’s national nuclear safety and security has yet to be applied. Presently, individual detection events are each investigated, as no wider “situational context” to their occurrence is applied – this is hence inefficient, costly and time-consuming as well as blind to small-scale/transient variations (and slow increases in activity) that may otherwise be missed in a large and unwieldy dataset. Work at the University of Bristol has sought to work alongside current academic and industrial collaborations to develop an Artificial Intelligence (AI) and ML system for the enhanced processing and evaluation of “Big Data” derived from such a large (and potentially unlimited) number of mobile (and fixed-position) radiological monitoring devices to yield a more informed detection response, therefore enhancing the UK’s current national radiological surveillance provision.

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