RRID: RRID:SCR_008086 , RRID:nlx_56990
Wikidata: Q76473
ISNI: 0000000419369297
FundRef: 501100000739
RRID: RRID:SCR_008086 , RRID:nlx_56990
Wikidata: Q76473
ISNI: 0000000419369297
FundRef: 501100000739
Cable-like structures are used in a variety of engineering applications where their dynamic response is of particular importance. For example, hydrodynamic vortex induced vibration can cause problems in marine risers, tethers and other slender marine structures, which include towed hydrophone arrays. Wave induced vibration can also cause significant levels of vibration in cable-buoy systems and in the civil engineering sector, parametric vibration can cause severe levels of vibration in cable-stayed bridges. Although passive vibration control solutions exist for some of these practical problems, in certain applications they may not provide a sufficient level of vibration control without significant levels of design compromise. For example, fairings designed for towed array cables may reduce vibration at the expense of an increased drag. Therefore, this project will investigate the application of active vibration control technologies to the control of dynamically excited cables under different excitation conditions. The project will consider different methods of actuating the cable to achieve control and, in particular, will investigate a number of different active control strategies. A significant component of the project will focus on experimental testing of the developed control strategies and this will include laboratory tests in both air and underwater. These lab-scale tests will lead to a demonstrator for industrial applications and the potential for larger-scale experimental investigations.
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Activities such as image classification, image segmentation and signal classification, are powerful techniques that have received wide application. Currently they are largely driven by the phenomenon of deep neural networks (DNN) which are now exceeding human performance in some tasks. However, the architecture of a DNN requires significant computation in both inference and especially so in training. Nevertheless, given their capability there is significant interest in researching ways to enable them to be deployed on very low power platforms. The aim of this topic is to demonstrate techniques to undertake neural network inference and/or training on platforms currently employed by the UK MoD, such as unmanned air vehicles, down to the tactical level. In this case the battery life of the platform is currently a major constraint on the mission envelope and it is not currently possible to deploy machine learning (especially deep learning) approaches on these platforms. The task would be to implement and demonstrate ML approaches for one or more of the techniques above (or others chosen in consultation with Dstl) and demonstrate how low the power consumption can be taken, by using novel hardware and/or software. The project will demonstrate how the power consumption has dropped, compared to a standard approach and whether this impacts on the accuracy/efficiency of the approach.
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This project is developing the concept of 'safe operating spaces' for development to prevent misguided actions causing loss or damage to ecosystem services which undermine long-term sustainability. Results will be drawn from an ongoing PFG project in the lower Yangtze basin and another project in Yunnan Province that combine conventional socio-economic records with reconstructed data for ecosystem services to show how close modern regions are to tipping points.
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