Road traffic flow forecasting provides critical information for the operational management of road mobility challenges, and models are used to generate the forecast. This paper uses a random process to present a novel...Road traffic flow forecasting provides critical information for the operational management of road mobility challenges, and models are used to generate the forecast. This paper uses a random process to present a novel traffic modelling framework for aggregate traffic on urban roads. The main idea is that road traffic flow is random, even for the recurrent flow, such as rush hour traffic, which is predisposed to congestion. Therefore, the structure of the aggregate traffic flow model for urban roads should correlate well with the essential variables of the observed random dynamics of the traffic flow phenomena. The novelty of this paper is the developed framework, based on the Poisson process, the kinematics of urban road traffic flow, and the intermediate modelling approach, which were combined to formulate the model. Empirical data from an urban road in Ghana was used to explore the model’s fidelity. The results show that the distribution from the model correlates well with that of the empirical traffic, providing a strong validation of the new framework and instilling confidence in its potential for significantly improved forecasts and, hence, a more hopeful outlook for real-world traffic management.展开更多
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
Many of the important questions facing farming systems in the world today require long-term studies to provide meaningful information and answers. A long-term agronomic experiment (LTAE) should (1) have long-term obje...Many of the important questions facing farming systems in the world today require long-term studies to provide meaningful information and answers. A long-term agronomic experiment (LTAE) should (1) have long-term objectives; (2) study important soil processes or ecological processes; and (3) be related to the productivity and sustainability of systems. A well established LTAE can provide both insights into how the system operates and foresight into where the system goes. The prerequisites for setting up a LTAE are the secured land, continuous funding and dedicated scientists. A number of principles must be considered carefully when establishing a LTAE, (1) the site must be representative of large areas; (2) the treatments should be simple, but focusing on the big questions; (3) the plots should be large enough to allow subsequent modification of the experiment if this becomes necessary; (4) crop rotations should minimise, wherever possible, the risk of build-up of pests and diseases, and rotational phase should be considered in a rotational experiment; (5) a clearly defined experimental protocol should be developed to ensure data collected is scientifically valid and statistically analysable, but with flexibility to allow essential changes; (6) soil samples, possibly plant samples, should be achieved to provide better answer to the original questions when new, perhaps more accurate analytical techniques are developed, or answer new research questions that were not considered in the original design. The MASTER experiment in Australia was used as a case study to demonstrate how these principles are implemented in practice.展开更多
针对化学工业过程中存在的强非线性和时变特性等问题,提出了一种基于牛顿-拉夫逊优化算法(Newton-Raphson based optimizer,NRBO)驱动的堆叠长短期记忆网络(stacked long short-term memory network,SLSTM)的运行状态评价方法。该方法...针对化学工业过程中存在的强非线性和时变特性等问题,提出了一种基于牛顿-拉夫逊优化算法(Newton-Raphson based optimizer,NRBO)驱动的堆叠长短期记忆网络(stacked long short-term memory network,SLSTM)的运行状态评价方法。该方法通过堆叠多层LSTM网络并引入Dropout层,增强了时序数据的表达能力。同时利用NRBO算法的二阶导数优化特性,有效提高了模型的收敛速度和分类精度,避免了传统LSTM评价方法在高维参数空间中易陷入局部最优的问题。在Tennessee Eastman(TE)过程的实验验证中,所提方法的预测准确率达到了99.31%,显著优于其他几种对比方法。针对非优状态,提出了基于主元分析和组套索正则化贡献(principal component analysis and group lasso regularization contribution,PCA-GLC)相结合的非优因素识别方法,该方法能够有效识别关键变量,减少误判和干扰,为工业过程的实时调整提供准确依据。在TE过程的实验验证中,所提方法相对于基于PCA的图贡献法,对关键变量的识别更加准确,并且降低了其他变量对结果的干扰。展开更多
文摘Road traffic flow forecasting provides critical information for the operational management of road mobility challenges, and models are used to generate the forecast. This paper uses a random process to present a novel traffic modelling framework for aggregate traffic on urban roads. The main idea is that road traffic flow is random, even for the recurrent flow, such as rush hour traffic, which is predisposed to congestion. Therefore, the structure of the aggregate traffic flow model for urban roads should correlate well with the essential variables of the observed random dynamics of the traffic flow phenomena. The novelty of this paper is the developed framework, based on the Poisson process, the kinematics of urban road traffic flow, and the intermediate modelling approach, which were combined to formulate the model. Empirical data from an urban road in Ghana was used to explore the model’s fidelity. The results show that the distribution from the model correlates well with that of the empirical traffic, providing a strong validation of the new framework and instilling confidence in its potential for significantly improved forecasts and, hence, a more hopeful outlook for real-world traffic management.
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supported by the CAS/SAFEA International Partnership Program for Creative Research Teams (CXTD-Z2005-2-4)
文摘Many of the important questions facing farming systems in the world today require long-term studies to provide meaningful information and answers. A long-term agronomic experiment (LTAE) should (1) have long-term objectives; (2) study important soil processes or ecological processes; and (3) be related to the productivity and sustainability of systems. A well established LTAE can provide both insights into how the system operates and foresight into where the system goes. The prerequisites for setting up a LTAE are the secured land, continuous funding and dedicated scientists. A number of principles must be considered carefully when establishing a LTAE, (1) the site must be representative of large areas; (2) the treatments should be simple, but focusing on the big questions; (3) the plots should be large enough to allow subsequent modification of the experiment if this becomes necessary; (4) crop rotations should minimise, wherever possible, the risk of build-up of pests and diseases, and rotational phase should be considered in a rotational experiment; (5) a clearly defined experimental protocol should be developed to ensure data collected is scientifically valid and statistically analysable, but with flexibility to allow essential changes; (6) soil samples, possibly plant samples, should be achieved to provide better answer to the original questions when new, perhaps more accurate analytical techniques are developed, or answer new research questions that were not considered in the original design. The MASTER experiment in Australia was used as a case study to demonstrate how these principles are implemented in practice.
文摘针对化学工业过程中存在的强非线性和时变特性等问题,提出了一种基于牛顿-拉夫逊优化算法(Newton-Raphson based optimizer,NRBO)驱动的堆叠长短期记忆网络(stacked long short-term memory network,SLSTM)的运行状态评价方法。该方法通过堆叠多层LSTM网络并引入Dropout层,增强了时序数据的表达能力。同时利用NRBO算法的二阶导数优化特性,有效提高了模型的收敛速度和分类精度,避免了传统LSTM评价方法在高维参数空间中易陷入局部最优的问题。在Tennessee Eastman(TE)过程的实验验证中,所提方法的预测准确率达到了99.31%,显著优于其他几种对比方法。针对非优状态,提出了基于主元分析和组套索正则化贡献(principal component analysis and group lasso regularization contribution,PCA-GLC)相结合的非优因素识别方法,该方法能够有效识别关键变量,减少误判和干扰,为工业过程的实时调整提供准确依据。在TE过程的实验验证中,所提方法相对于基于PCA的图贡献法,对关键变量的识别更加准确,并且降低了其他变量对结果的干扰。
