We present an inverse methodology for deriving viscoplasticity constitutive model parameters for use in explicit finite element simulations of dynamic processes using functional experiments, i.e., those which provide ...We present an inverse methodology for deriving viscoplasticity constitutive model parameters for use in explicit finite element simulations of dynamic processes using functional experiments, i.e., those which provide value beyond that of constitutive model development. The developed methodology utilises Bayesian optimisation to minimise the error between experimental measurements and numerical simulations performed in LS-DYNA. We demonstrate the optimisation methodology using high hardness armour steels across three types of experiments that induce a wide range of loading conditions: ballistic penetration, rod-on-anvil, and near-field blast deformation. By utilising such a broad range of conditions for the optimisation, the resulting constitutive model parameters are generalised, i.e., applicable across the range of loading conditions encompassed the by those experiments(e.g., stress states, plastic strain magnitudes, strain rates, etc.). Model constants identified using this methodology are demonstrated to provide a generalisable model with superior predictive accuracy than those derived from conventional mechanical characterisation experiments or optimised from a single experimental condition.展开更多
We evaluate an adaptive optimisation methodology,Bayesian optimisation(BO),for designing a minimum weight explosive reactive armour(ERA)for protection against a surrogate medium calibre kinetic energy(KE)long rod proj...We evaluate an adaptive optimisation methodology,Bayesian optimisation(BO),for designing a minimum weight explosive reactive armour(ERA)for protection against a surrogate medium calibre kinetic energy(KE)long rod projectile and surrogate shaped charge(SC)warhead.We perform the optimisation using a conventional BO methodology and compare it with a conventional trial-and-error approach from a human expert.A third approach,utilising a novel human-machine teaming framework for BO is also evaluated.Data for the optimisation is generated using numerical simulations that are demonstrated to provide reasonable qualitative agreement with reference experiments.The human-machine teaming methodology is shown to identify the optimum ERA design in the fewest number of evaluations,outperforming both the stand-alone human and stand-alone BO methodologies.From a design space of almost 1800 configurations the human-machine teaming approach identifies the minimum weight ERA design in 10 samples.展开更多
The Internet of Things(IoT)integrates more than billions of intelligent devices over the globe with the capabilityof communicating with other connected devices with little to no human intervention.IoT enables data agg...The Internet of Things(IoT)integrates more than billions of intelligent devices over the globe with the capabilityof communicating with other connected devices with little to no human intervention.IoT enables data aggregationand analysis on a large scale to improve life quality in many domains.In particular,data collected by IoT containa tremendous amount of information for anomaly detection.The heterogeneous nature of IoT is both a challengeand an opportunity for cybersecurity.Traditional approaches in cybersecurity monitoring often require different kindsof data pre-processing and handling for various data types,which might be problematic for datasets that contain heterogeneousfeatures.However,heterogeneous types of network devices can often capture a more diverse set of signalsthan a single type of device readings,which is particularly useful for anomaly detection.In this paper,we presenta comprehensive study on using ensemble machine learning methods for enhancing IoT cybersecurity via anomalydetection.Rather than using one single machine learning model,ensemble learning combines the predictive powerfrom multiple models,enhancing their predictive accuracy in heterogeneous datasets rather than using one singlemachine learning model.We propose a unified framework with ensemble learning that utilises Bayesian hyperparameteroptimisation to adapt to a network environment that contains multiple IoT sensor readings.Experimentally,weillustrate their high predictive power when compared to traditional methods.展开更多
This paper proposed a framework for muck types identification based on data augmentation-assisted image recognition during shield tunnelling.The muck pictures were collected from the shield monitoring system above the...This paper proposed a framework for muck types identification based on data augmentation-assisted image recognition during shield tunnelling.The muck pictures were collected from the shield monitoring system above the conveyor belt.The data augmentation operations were then used to increase the quality of the original images.Furthermore,the Bayesian optimisation algorithm was employed to adjust the parameters of augmenters and highlight the features of the photos.The deep image recognition algorithms(AlexNet and GoogLeNet)were trained and enhanced by the augmentation images,which were used to establish the muck types identification models and assessed by the evaluation indices.Model efficiency was analysed through the performance and time cost of training and validation processes to select the optimal model for muck types identification.Results showed that the performance of identification models could be highly increased by data augmentation with Bayesian optimisation,and the enhanced GoogLeNet performed the highest efficiency for muck types identification.展开更多
文摘We present an inverse methodology for deriving viscoplasticity constitutive model parameters for use in explicit finite element simulations of dynamic processes using functional experiments, i.e., those which provide value beyond that of constitutive model development. The developed methodology utilises Bayesian optimisation to minimise the error between experimental measurements and numerical simulations performed in LS-DYNA. We demonstrate the optimisation methodology using high hardness armour steels across three types of experiments that induce a wide range of loading conditions: ballistic penetration, rod-on-anvil, and near-field blast deformation. By utilising such a broad range of conditions for the optimisation, the resulting constitutive model parameters are generalised, i.e., applicable across the range of loading conditions encompassed the by those experiments(e.g., stress states, plastic strain magnitudes, strain rates, etc.). Model constants identified using this methodology are demonstrated to provide a generalisable model with superior predictive accuracy than those derived from conventional mechanical characterisation experiments or optimised from a single experimental condition.
文摘We evaluate an adaptive optimisation methodology,Bayesian optimisation(BO),for designing a minimum weight explosive reactive armour(ERA)for protection against a surrogate medium calibre kinetic energy(KE)long rod projectile and surrogate shaped charge(SC)warhead.We perform the optimisation using a conventional BO methodology and compare it with a conventional trial-and-error approach from a human expert.A third approach,utilising a novel human-machine teaming framework for BO is also evaluated.Data for the optimisation is generated using numerical simulations that are demonstrated to provide reasonable qualitative agreement with reference experiments.The human-machine teaming methodology is shown to identify the optimum ERA design in the fewest number of evaluations,outperforming both the stand-alone human and stand-alone BO methodologies.From a design space of almost 1800 configurations the human-machine teaming approach identifies the minimum weight ERA design in 10 samples.
文摘The Internet of Things(IoT)integrates more than billions of intelligent devices over the globe with the capabilityof communicating with other connected devices with little to no human intervention.IoT enables data aggregationand analysis on a large scale to improve life quality in many domains.In particular,data collected by IoT containa tremendous amount of information for anomaly detection.The heterogeneous nature of IoT is both a challengeand an opportunity for cybersecurity.Traditional approaches in cybersecurity monitoring often require different kindsof data pre-processing and handling for various data types,which might be problematic for datasets that contain heterogeneousfeatures.However,heterogeneous types of network devices can often capture a more diverse set of signalsthan a single type of device readings,which is particularly useful for anomaly detection.In this paper,we presenta comprehensive study on using ensemble machine learning methods for enhancing IoT cybersecurity via anomalydetection.Rather than using one single machine learning model,ensemble learning combines the predictive powerfrom multiple models,enhancing their predictive accuracy in heterogeneous datasets rather than using one singlemachine learning model.We propose a unified framework with ensemble learning that utilises Bayesian hyperparameteroptimisation to adapt to a network environment that contains multiple IoT sensor readings.Experimentally,weillustrate their high predictive power when compared to traditional methods.
基金funded by the Guangdong Provincial Basic and Applied Basic Research Fund Committee(2022A1515240073)“The Pearl River Talent Recruitment Program”in 2019(Grant No.2019CX01G338),Guangdong Province,China.
文摘This paper proposed a framework for muck types identification based on data augmentation-assisted image recognition during shield tunnelling.The muck pictures were collected from the shield monitoring system above the conveyor belt.The data augmentation operations were then used to increase the quality of the original images.Furthermore,the Bayesian optimisation algorithm was employed to adjust the parameters of augmenters and highlight the features of the photos.The deep image recognition algorithms(AlexNet and GoogLeNet)were trained and enhanced by the augmentation images,which were used to establish the muck types identification models and assessed by the evaluation indices.Model efficiency was analysed through the performance and time cost of training and validation processes to select the optimal model for muck types identification.Results showed that the performance of identification models could be highly increased by data augmentation with Bayesian optimisation,and the enhanced GoogLeNet performed the highest efficiency for muck types identification.
