Numerical modelling of coastal morphology is a complex and sometimes unrewarding exercise and often not yielding tangible results. Typically, the underlying drivers of morphology are not properly accounted for in nume...Numerical modelling of coastal morphology is a complex and sometimes unrewarding exercise and often not yielding tangible results. Typically, the underlying drivers of morphology are not properly accounted for in numerical models. Such inaccuracies combined with a paucity of validation data create a difficulty for coastal planners/engineers who are required to interpret such morphological models to develop coastal management strategies. This study develops an approach to long term morphological modelling of a barrier beach system that includes the findings of over 10 years of coastal monitoring on a dynamic coastal system. The novel approach to predicting the long term evolution of the area combines a mix of short term hydrodynamic monitoring and long term morphological modelling to predict future changes in a breached barrier system. A coupled wave, wind, hydrodynamic and sediment transport numerical model was used to predict the coastal evolution in the dynamic barrier beach system of Inner Dingle Bay, Co. Kerry, Ireland. The modelling approach utilizes the schematisation of inputs to reflect observed trends. The approach is subject to two stages of validation both quantitative and qualitative. The study highlights the importance of considering all the parameters responsible for driving coastal evolution and the necessity to have long term monitoring results for trend based validation.展开更多
In the study of complex networks （systems）, the scaling phenomenon of flow fluctuations refers to a certain powerlaw between the mean flux （activity） （Fi） of the i-th node and its variance σi as σi α （Fi）α...In the study of complex networks （systems）, the scaling phenomenon of flow fluctuations refers to a certain powerlaw between the mean flux （activity） （Fi） of the i-th node and its variance σi as σi α （Fi）α Such scaling laws are found to be prevalent both in natural and man-made network systems, but the understanding of their origins still remains limited. This paper proposes a non-stationary Poisson process model to give an analytical explanation of the non-universal scaling phenomenon： the exponent α varies between 1/2 and 1 depending on the size of sampling time window and the relative strength of the external/internal driven forces of the systems. The crossover behaviour and the relation of fluctuation scaling with pseudo long range dependence are also accounted for by the model. Numerical experiments show that the proposed model can recover the multi-scaiing phenomenon.展开更多
In this paper we investigate the dynamics of an asymmetric exclusion process on a one-dimensional lattice with long- range hopping and random update via Monte Carlo simulations theoretically. Particles in the model wi...In this paper we investigate the dynamics of an asymmetric exclusion process on a one-dimensional lattice with long- range hopping and random update via Monte Carlo simulations theoretically. Particles in the model will firstly try to hop over successive unoccupied sites with a probability q, which is different from previous exclusion process models. The probability q may represent the random access of particles. Numerical simulations for stationary particle currents, density profiles, and phase diagrams are obtained. There are three possible stationary phases： the low density （LD） phase, high density （HD） phase, and maximal current （MC） in the system, respectively. Interestingly, bulk density in the LD phase tends to zero, while the MC phase is governed by α,β, and q. The HD phase is nearly the same as the normal TASEP, determined by exit rate β. Theoretical analysis is in good agreement with simulation results. The proposed model may provide a better understanding of random interaction dynamics in complex systems.展开更多
Road transport safety has always been paid attention to by the safety production managers of enterprises. In this study, cloud model and analytic hierarchy process were applied to the safety of long-tube trailer trans...Road transport safety has always been paid attention to by the safety production managers of enterprises. In this study, cloud model and analytic hierarchy process were applied to the safety of long-tube trailer transport. The opinions of 30 experts were analyzed, from which 29 key parameters were selected. The study addressed the relevance of the parameters and the possibility of automatic collection and transmission to obtain 12 core risk factors. The macro-safety risk indicator system for long-tube trailers was established based on the identified risk indicators. Finally, a risk assessment model for road transport of long tube trailers consisting of 3 dimensions of likelihood, severity and sensitivity was constructed. This model provides a technical method for strengthening the risk control of road transport of long-tube trailers.展开更多
脱硫过程是具有高度动态非线性和较大延迟时间的复杂工业过程,为了解决烟气脱硫过程的建模问题,设计了注意力机制下的深度长短期记忆(attention mechanism-based long short-term memory,AttLSTM)网络,并基于该网络设计自动编码器,完成...脱硫过程是具有高度动态非线性和较大延迟时间的复杂工业过程,为了解决烟气脱硫过程的建模问题,设计了注意力机制下的深度长短期记忆(attention mechanism-based long short-term memory,AttLSTM)网络,并基于该网络设计自动编码器,完成脱硫过程异常点的检测。该文首次提出使用AttLSTM网络自编码器对脱硫过程进行离群点检测,并且该网络模型同样首次应用于脱硫过程的辨识任务中。从更深的意义上讲,该文尝试使用深度学习模型对复杂系统进行辨识,所建立的AttLSTM网络之前未出现在系统辨识领域,该网络的出现可以丰富辨识模型的选择,同时为人工智能技术在系统辨识领域和控制领域的应用与推广提供参考。实验结果表明,相比于之前文献出现的脱硫过程建模方法,所提方法在不同性能指标上均具有更好的表现,由此可以证明深度AttLSTM网络在脱硫场景下的有效性。展开更多
由于电池荷电状态(state of charge,SOC)无法直接测量,且传统的SOC估算方法精度低。为了提升锂离子电池SOC估算精度,对比了不同深度学习网络模型应用于SOC估算的效果,并提出了一种基于DRSN-CW-LSTM网络的锂离子电池SOC估算方法。该方法...由于电池荷电状态(state of charge,SOC)无法直接测量,且传统的SOC估算方法精度低。为了提升锂离子电池SOC估算精度,对比了不同深度学习网络模型应用于SOC估算的效果,并提出了一种基于DRSN-CW-LSTM网络的锂离子电池SOC估算方法。该方法基于长短期记忆网络(long-short-term memory,LSTM)和逐通道不同阈值的深度残差收缩网络(deep residual shrinkage networks with channel-wise thresholds,DRSN-CW),利用锂离子电池电压、电流、温度、容量等数据信息在深度残差收缩网路中进行特征提取,通过LSTM进一步拟合时间序列数据趋势,实现锂离子电池在使用周期内SOC的预测。在DRSN-CW网络的残差收缩模块中可以实现自适应噪声数据处理功能,消除锂离子电池数据流质量对SOC预测的负面影响。利用锂电池公共数据集训练所提出的网络,对比了3种神经网络模型在该两组数据集上的预测效果。实验结果表明,所提出的深度学习模型在两组公开数据集上的MAE和RMSE均值都控制在5%以内,相比其他3种深度学习模型有更好的抗噪性能和预测性能,且估算精度高。展开更多
文摘Numerical modelling of coastal morphology is a complex and sometimes unrewarding exercise and often not yielding tangible results. Typically, the underlying drivers of morphology are not properly accounted for in numerical models. Such inaccuracies combined with a paucity of validation data create a difficulty for coastal planners/engineers who are required to interpret such morphological models to develop coastal management strategies. This study develops an approach to long term morphological modelling of a barrier beach system that includes the findings of over 10 years of coastal monitoring on a dynamic coastal system. The novel approach to predicting the long term evolution of the area combines a mix of short term hydrodynamic monitoring and long term morphological modelling to predict future changes in a breached barrier system. A coupled wave, wind, hydrodynamic and sediment transport numerical model was used to predict the coastal evolution in the dynamic barrier beach system of Inner Dingle Bay, Co. Kerry, Ireland. The modelling approach utilizes the schematisation of inputs to reflect observed trends. The approach is subject to two stages of validation both quantitative and qualitative. The study highlights the importance of considering all the parameters responsible for driving coastal evolution and the necessity to have long term monitoring results for trend based validation.
基金Project supported in part by National Basic Research Program of China (973 Project) (Grant No 2006CB705506)Hi-Tech Research and Development Program of China (863 Project) (Grant No 2007AA11Z222)National Natural Science Foundation of China (Grant Nos 60721003 and 60774034)
文摘In the study of complex networks （systems）, the scaling phenomenon of flow fluctuations refers to a certain powerlaw between the mean flux （activity） （Fi） of the i-th node and its variance σi as σi α （Fi）α Such scaling laws are found to be prevalent both in natural and man-made network systems, but the understanding of their origins still remains limited. This paper proposes a non-stationary Poisson process model to give an analytical explanation of the non-universal scaling phenomenon： the exponent α varies between 1/2 and 1 depending on the size of sampling time window and the relative strength of the external/internal driven forces of the systems. The crossover behaviour and the relation of fluctuation scaling with pseudo long range dependence are also accounted for by the model. Numerical experiments show that the proposed model can recover the multi-scaiing phenomenon.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.41274109 and 11104022)the Fund for Sichuan Youth Science and Technology Innovation Research Team(Grant No.2011JTD0013)the Creative Team Program of Chengdu University of Technology
文摘In this paper we investigate the dynamics of an asymmetric exclusion process on a one-dimensional lattice with long- range hopping and random update via Monte Carlo simulations theoretically. Particles in the model will firstly try to hop over successive unoccupied sites with a probability q, which is different from previous exclusion process models. The probability q may represent the random access of particles. Numerical simulations for stationary particle currents, density profiles, and phase diagrams are obtained. There are three possible stationary phases： the low density （LD） phase, high density （HD） phase, and maximal current （MC） in the system, respectively. Interestingly, bulk density in the LD phase tends to zero, while the MC phase is governed by α,β, and q. The HD phase is nearly the same as the normal TASEP, determined by exit rate β. Theoretical analysis is in good agreement with simulation results. The proposed model may provide a better understanding of random interaction dynamics in complex systems.
文摘Road transport safety has always been paid attention to by the safety production managers of enterprises. In this study, cloud model and analytic hierarchy process were applied to the safety of long-tube trailer transport. The opinions of 30 experts were analyzed, from which 29 key parameters were selected. The study addressed the relevance of the parameters and the possibility of automatic collection and transmission to obtain 12 core risk factors. The macro-safety risk indicator system for long-tube trailers was established based on the identified risk indicators. Finally, a risk assessment model for road transport of long tube trailers consisting of 3 dimensions of likelihood, severity and sensitivity was constructed. This model provides a technical method for strengthening the risk control of road transport of long-tube trailers.
文摘脱硫过程是具有高度动态非线性和较大延迟时间的复杂工业过程,为了解决烟气脱硫过程的建模问题,设计了注意力机制下的深度长短期记忆(attention mechanism-based long short-term memory,AttLSTM)网络,并基于该网络设计自动编码器,完成脱硫过程异常点的检测。该文首次提出使用AttLSTM网络自编码器对脱硫过程进行离群点检测,并且该网络模型同样首次应用于脱硫过程的辨识任务中。从更深的意义上讲,该文尝试使用深度学习模型对复杂系统进行辨识,所建立的AttLSTM网络之前未出现在系统辨识领域,该网络的出现可以丰富辨识模型的选择,同时为人工智能技术在系统辨识领域和控制领域的应用与推广提供参考。实验结果表明,相比于之前文献出现的脱硫过程建模方法,所提方法在不同性能指标上均具有更好的表现,由此可以证明深度AttLSTM网络在脱硫场景下的有效性。
文摘由于电池荷电状态(state of charge,SOC)无法直接测量,且传统的SOC估算方法精度低。为了提升锂离子电池SOC估算精度,对比了不同深度学习网络模型应用于SOC估算的效果,并提出了一种基于DRSN-CW-LSTM网络的锂离子电池SOC估算方法。该方法基于长短期记忆网络(long-short-term memory,LSTM)和逐通道不同阈值的深度残差收缩网络(deep residual shrinkage networks with channel-wise thresholds,DRSN-CW),利用锂离子电池电压、电流、温度、容量等数据信息在深度残差收缩网路中进行特征提取,通过LSTM进一步拟合时间序列数据趋势,实现锂离子电池在使用周期内SOC的预测。在DRSN-CW网络的残差收缩模块中可以实现自适应噪声数据处理功能,消除锂离子电池数据流质量对SOC预测的负面影响。利用锂电池公共数据集训练所提出的网络,对比了3种神经网络模型在该两组数据集上的预测效果。实验结果表明,所提出的深度学习模型在两组公开数据集上的MAE和RMSE均值都控制在5%以内,相比其他3种深度学习模型有更好的抗噪性能和预测性能,且估算精度高。
