Lessons learned:Symbiotic autonomous robot ecosystem for nuclear environments

Nuclear facilities have a regulatory requirement to measure radiation levels within Post Operational Clean Out(POCO)around nuclear facilities each year,resulting in a trend towards robotic deployments to gain an improved understanding during nuclear decommissioning phases.The UK Nuclear Decommissioning Authority supports the view that human-in-the-loop(HITL)robotic deployments are a solution to improve procedures and reduce risks within radiation characterisation of nuclear sites.The authors present a novel implementation of a Cyber-Physical System(CPS)deployed in an analogue nuclear environment,comprised of a multi-robot(MR)team coordinated by a HITL operator through a digital twin interface.The development of the CPS created efficient partnerships across systems including robots,digital systems and human.This was presented as a multi-staged mission within an inspection scenario for the hetero-geneous Symbiotic Multi-Robot Fleet(SMuRF).Symbiotic interactions were achieved across the SMuRF where robots utilised automated collaborative governance to work together,where a single robot would face challenges in full characterisation of radiation.Key contributions include the demonstration of symbiotic autonomy and query-based learning of an autonomous mission supporting scalable autonomy and autonomy as a service.The coordination of the CPS was a success and displayed further challenges and improvements related to future MR fleets.

SOPA-GA-CNN: Synchronous optimisation of parameters and architectures by genetic algorithms with convolutional neural network blocks for securing Industrial Internet-of-Things

In recent years,deep learning has been applied to a variety of scenarios in Industrial Internet of Things(IIoT),including enhancing the security of IIoT.However,the existing deep learning methods utilised in IIoT security are manually designed by heavily relying on the experience of the designers.The authors have made the first contribution concerning the joint optimisation of neural architecture search and hyper-parameters optimisation for securing IIoT.A novel automated deep learning method called synchronous optimisation of parameters and architectures by GA with CNN blocks(SOPA-GA-CNN)is proposed to synchronously optimise the hyperparameters and block-based architectures in convolutional neural networks(CNNs)by genetic algorithms(GA)for the intrusion detection issue of IIoT.An efficient hybrid encoding strategy and the corresponding GA-based evolutionary operations are designed to characterise and evolve both the hyperparameters,including batch size,learning rate,weight optimiser and weight regularisation,and the architectures,such as the block-based network topology and the parameters of each CNN block.The experimental results on five intrusion detection datasets in IIoT,including secure water treatment,water distribution,Gas Pipeline,Botnet in Internet of Things and Power System Attack Dataset,have demonstrated the superiority of the proposed SOPA-GA-CNN to the state-of-the-art manually designed models and neuron-evolutionary methods in terms of accuracy,precision,recall,F1-score,and the number of parameters of the deep learning models.