The time-driven activity-based costing has received extensive attention from scholars both at home and abroad in recent years,which has been applied to the calculation of manufacturing operations and the cost of produ...The time-driven activity-based costing has received extensive attention from scholars both at home and abroad in recent years,which has been applied to the calculation of manufacturing operations and the cost of products.However, this approach is rarely introduced into the service sector.As to the hospitality industry, the profitability of the customer usually plays a decisive role in the business process.Therefore, this article takes the hotel service industry as the research point and allocates the costs of resources of each departments to customers according to time drivers.The focus of this paper is to calculate the costs of customers ,and then analyze the profitability of customers in order to take the appropriate marketing strategies to improve the hotel service industry Drofitabilitv.展开更多
The classical discrete element approach(DEM)based on Newtonian dynamics can be divided into two major groups,event-driven methods(EDM)and timedriven methods(TDM).Generally speaking,TDM simulations are suited for cases...The classical discrete element approach(DEM)based on Newtonian dynamics can be divided into two major groups,event-driven methods(EDM)and timedriven methods(TDM).Generally speaking,TDM simulations are suited for cases with high volume fractions where there are collisions between multiple objects.EDM simulations are suited for cases with low volume fractions from the viewpoint of CPU time.A method combining EDM and TDM called Hybrid Algorithm of event-driven and time-driven methods(HAET)is presented in this paper.The HAET method employs TDM for the areas with high volume fractions and EDM for the remaining areas with low volume fractions.It can decrease the CPU time for simulating granular flows with strongly non-uniform volume fractions.In addition,a modified EDM algorithm using a constant time as the lower time step limit is presented.Finally,an example is presented to demonstrate the hybrid algorithm.展开更多
A coupled numerical method for the direct numerical simulation of particle-fluid systems is formulated and implemented, resolving an order of magnitude smaller than particle size. The particle motion is described by t...A coupled numerical method for the direct numerical simulation of particle-fluid systems is formulated and implemented, resolving an order of magnitude smaller than particle size. The particle motion is described by the time-driven hard-sphere model, while the hydrodynamic equations governing fluid flow are solved by the lattice Boltzmann method （LBM）, Particle-fluid coupling is realized by an immersed boundary method （IBM）, which considers the effect of boundary on surrounding fluid as a restoring force added to the governing equations of the fluid. The proposed scheme is validated in the classical flow-around-cylinder simulations, and preliminary application of this scheme to fluidization is reported, demonstrating it to be a promising computational strategy for better understanding complex behavior in particle-fluid systems.展开更多
既有谐波源建模方法在应用于内部拓扑未知与机理不明场景时,难以有效兼顾谐波源的频域稳态特征与动态时变特征,致使所构建模型的准确性与鲁棒性难以提升。为此,该文提出一种基于时-频特征联合提取的谐波源数据驱动建模方法。首先,根据...既有谐波源建模方法在应用于内部拓扑未知与机理不明场景时,难以有效兼顾谐波源的频域稳态特征与动态时变特征,致使所构建模型的准确性与鲁棒性难以提升。为此,该文提出一种基于时-频特征联合提取的谐波源数据驱动建模方法。首先,根据谐波源历史电压电流的频域分量确定谐波源的主导谐波频次;其次,构建谐波源稳态电压时-频特征矩阵并对其进行伪彩色编码以实现特征升维;最后,将动态时变信号与稳态特征分量彩色图组合输入所构建的多重卷积神经网络(multiple convolutional neural network,MCNN)与双向长短时记忆网络(bi-directional long short term memory network,BiLSTM)组合模型,构建反映谐波源动态时变特征与频域稳态特征的电压-电流映射关系。经仿真与实测数据验证,相较于其他数据驱动建模方法,所提方法不仅在单一谐波源建模场景下具有明显的优势,在复杂多谐波源场景下也具备较高的准确率与较强的鲁棒性。展开更多
鲁棒优化作为应对风电等新能源出力不确定性的重要工具,广泛应用于微电网优化调度中。传统的不确定集不够紧凑,无法准确刻画风电不确定性,同时不确定集包围的数据中可能存在部分异常值,导致调度结果过于保守。针对上述问题,提出了一种...鲁棒优化作为应对风电等新能源出力不确定性的重要工具,广泛应用于微电网优化调度中。传统的不确定集不够紧凑,无法准确刻画风电不确定性,同时不确定集包围的数据中可能存在部分异常值,导致调度结果过于保守。针对上述问题,提出了一种基于数据驱动不确定集的微电网两阶段鲁棒优化调度方法。首先,通过风电历史数据构建条件正态Copula(conditional normal copula,CNC)模型,再将日前风电预测值输入CNC模型生成次日风电功率样本。然后,通过支持向量聚类(support vector clustering,SVC)和维度分解构建考虑风电时间相关性的数据驱动不确定集。该不确定集可更为准确地刻画风电不确定性,并将风电数据中的异常值排除在外,从而在降低鲁棒优化保守性的同时具备异常值抵抗性。其次,提出了基于上述不确定集的两阶段鲁棒优化调度模型,并采用列约束生成(column and constraint generation,C&CG)算法求解。最后通过仿真证明了相较传统不确定集,本文构建的不确定集保守性更低,同时对风电数据异常值具有良好的抵抗性。展开更多
文摘The time-driven activity-based costing has received extensive attention from scholars both at home and abroad in recent years,which has been applied to the calculation of manufacturing operations and the cost of products.However, this approach is rarely introduced into the service sector.As to the hospitality industry, the profitability of the customer usually plays a decisive role in the business process.Therefore, this article takes the hotel service industry as the research point and allocates the costs of resources of each departments to customers according to time drivers.The focus of this paper is to calculate the costs of customers ,and then analyze the profitability of customers in order to take the appropriate marketing strategies to improve the hotel service industry Drofitabilitv.
基金supported by a grant from Department of Energy and Process Engineering,Norwegian University of Science and Technology,Institute for Energy Technology(IFE)and SINTEF through the FACE(Multiphase Flow Assurance Innovation Center)project.
文摘The classical discrete element approach(DEM)based on Newtonian dynamics can be divided into two major groups,event-driven methods(EDM)and timedriven methods(TDM).Generally speaking,TDM simulations are suited for cases with high volume fractions where there are collisions between multiple objects.EDM simulations are suited for cases with low volume fractions from the viewpoint of CPU time.A method combining EDM and TDM called Hybrid Algorithm of event-driven and time-driven methods(HAET)is presented in this paper.The HAET method employs TDM for the areas with high volume fractions and EDM for the remaining areas with low volume fractions.It can decrease the CPU time for simulating granular flows with strongly non-uniform volume fractions.In addition,a modified EDM algorithm using a constant time as the lower time step limit is presented.Finally,an example is presented to demonstrate the hybrid algorithm.
基金sponsored by Ministry of Finance under the grant ZDYZ2008-2National Key Science and Technology Project under the grant 2008ZX05014-003-006HZthe Chinese Academy of Sciences under the grant KGCX2-YW-124
文摘A coupled numerical method for the direct numerical simulation of particle-fluid systems is formulated and implemented, resolving an order of magnitude smaller than particle size. The particle motion is described by the time-driven hard-sphere model, while the hydrodynamic equations governing fluid flow are solved by the lattice Boltzmann method （LBM）, Particle-fluid coupling is realized by an immersed boundary method （IBM）, which considers the effect of boundary on surrounding fluid as a restoring force added to the governing equations of the fluid. The proposed scheme is validated in the classical flow-around-cylinder simulations, and preliminary application of this scheme to fluidization is reported, demonstrating it to be a promising computational strategy for better understanding complex behavior in particle-fluid systems.
文摘既有谐波源建模方法在应用于内部拓扑未知与机理不明场景时,难以有效兼顾谐波源的频域稳态特征与动态时变特征,致使所构建模型的准确性与鲁棒性难以提升。为此,该文提出一种基于时-频特征联合提取的谐波源数据驱动建模方法。首先,根据谐波源历史电压电流的频域分量确定谐波源的主导谐波频次;其次,构建谐波源稳态电压时-频特征矩阵并对其进行伪彩色编码以实现特征升维;最后,将动态时变信号与稳态特征分量彩色图组合输入所构建的多重卷积神经网络(multiple convolutional neural network,MCNN)与双向长短时记忆网络(bi-directional long short term memory network,BiLSTM)组合模型,构建反映谐波源动态时变特征与频域稳态特征的电压-电流映射关系。经仿真与实测数据验证,相较于其他数据驱动建模方法,所提方法不仅在单一谐波源建模场景下具有明显的优势,在复杂多谐波源场景下也具备较高的准确率与较强的鲁棒性。
文摘鲁棒优化作为应对风电等新能源出力不确定性的重要工具,广泛应用于微电网优化调度中。传统的不确定集不够紧凑,无法准确刻画风电不确定性,同时不确定集包围的数据中可能存在部分异常值,导致调度结果过于保守。针对上述问题,提出了一种基于数据驱动不确定集的微电网两阶段鲁棒优化调度方法。首先,通过风电历史数据构建条件正态Copula(conditional normal copula,CNC)模型,再将日前风电预测值输入CNC模型生成次日风电功率样本。然后,通过支持向量聚类(support vector clustering,SVC)和维度分解构建考虑风电时间相关性的数据驱动不确定集。该不确定集可更为准确地刻画风电不确定性,并将风电数据中的异常值排除在外,从而在降低鲁棒优化保守性的同时具备异常值抵抗性。其次,提出了基于上述不确定集的两阶段鲁棒优化调度模型,并采用列约束生成(column and constraint generation,C&CG)算法求解。最后通过仿真证明了相较传统不确定集,本文构建的不确定集保守性更低,同时对风电数据异常值具有良好的抵抗性。
