A hybrid GSI （Grid-point Statistical Interpolation）-ETKF （Ensemble Transform Kalman Filter） data assimila- tion system has been recently developed for the WRF （Weather Research and Forecasting） model and tested ...A hybrid GSI （Grid-point Statistical Interpolation）-ETKF （Ensemble Transform Kalman Filter） data assimila- tion system has been recently developed for the WRF （Weather Research and Forecasting） model and tested with simu- lated observations for tropical cyclone （TC） forecast. This system is based on the existing GSI but with ensemble back- ground information incorporated. As a follow-up, this work extends the new system to assimilate real observations to further understand the hybrid scheme. As a first effort to explore the system with real observations, relatively coarse grid resolution （27 km） is used. A case study of typhoon Muifa （2011） is performed to assimilate real observations in- cluding conventional in-situ and satellite data. The hybrid system with flow-dependent ensemble eovariance shows sig- nificant improvements with respect to track forecast compared to the standard GSI system which in theory is three di- mensional variational analysis （3DVAR）. By comparing the analyses, analysis increments and forecasts, the hybrid sys- tem is found to be potentially able to recognize the existence of TC vortex, adjust its position systematically, better de- scribe the asymmetric structure of typhoon Muifa and maintain the dynamic and thermodynamic balance in typhoon ini- tial field. In addition, a cold-start hybrid approach by using the global ensembles to provide flow-dependent error is test- ed and similar results are revealed with those from cycled GSI-ETKF approach.展开更多
Decision rules mining is an important issue in machine learning and data mining.However,most proposed algorithms mine categorical data at single level,and these rules are not easily understandable and really useful fo...Decision rules mining is an important issue in machine learning and data mining.However,most proposed algorithms mine categorical data at single level,and these rules are not easily understandable and really useful for users.Thus,a new approach to hierarchical decision rules mining is provided in this paper,in which similarity direction measure is introduced to deal with hybrid data.This approach can mine hierarchical decision rules by adjusting similarity measure parameters and the level of concept hierarchy trees.展开更多
A timely trend in gear transmission involves the replacement of steel with polymers.Nevertheless,the absence of fundamental durability data for polymer gears impedes their reliable application during power transmissio...A timely trend in gear transmission involves the replacement of steel with polymers.Nevertheless,the absence of fundamental durability data for polymer gears impedes their reliable application during power transmission.The expensive and time-consuming gear fatigue experiments make it impossible to rely merely on experimental data.In this study,a strategy for contact fatigue life prediction of polymer gears via an experimental-simulated hybrid data-driven model is presented.The hybrid data are established with a certain mixture ratio of experimental and simulation data and are augmented by the conditional tabular generative adversarial network(CTAB-GAN)algorithm.This specific algorithm was combined with the extreme gradient boosting(XGBoost)algorithm to predict the contact fatigue life of gears made from different polymer materials,with the prediction accuracy controlled within a 3-fold scatter band.Moreover,an empirical predictive formula for contact fatigue life was developed.The hybrid data-driven model,which merges experimental and simulated data,allows for efficient estimation of fatigue life and material selection strategies,generating insight into the anti-fatigue design of polymer gears.展开更多
The futures trading market is an important part of the financial markets and soybeans are one of the most strategically important crops in the world.How to predict soybean future price is a challenging topic being stu...The futures trading market is an important part of the financial markets and soybeans are one of the most strategically important crops in the world.How to predict soybean future price is a challenging topic being studied by many researchers.This paper proposes a novel hybrid soybean future price prediction model which includes two stages of data preprocessing and deep learning prediction.In the data preprocessing stage,futures price series are decomposed into subsequences using the ICEEMDAN(improved complete ensemble empirical mode decomposition with adaptive noise)method.The Lempel-Ziv complexity determination method was then used to identify and reconstruct high-frequency subsequences.Finally,the high frequency component is decomposed secondarily using variational mode decomposition optimized by beluga whale optimization algorithm.In the deep learning prediction stage,a deep extreme learning machine optimized by the sparrow search algorithm was used to obtain the prediction results of all subseries and reconstructs them to obtain the final soybean future price prediction results.Based on the experimental results of soybean future price markets in China,Italy,and the United States,it was found that the hybrid method proposed provides superior performance in terms of prediction accuracy and robustness.展开更多
Based on the GRAPES（Global/Regional Assimilation and Prediction System） regional ensemble prediction system and 3DVAR（three-dimensional variational） data assimilation system,which are implemented operationally at ...Based on the GRAPES（Global/Regional Assimilation and Prediction System） regional ensemble prediction system and 3DVAR（three-dimensional variational） data assimilation system,which are implemented operationally at the Numerical Weather Prediction Center of the China Meteorological Administration,an ensemble-based 3DVAR（En-3DVAR） hybrid data assimilation system for GRAPES-Meso（the regional mesoscale numerical prediction system of GRAPES） was developed by using the extended control variable technique to implement a hybrid background error covariance that combines the climatological covariance and ensemble-estimated covariance.Considering the problems of the ensemble-based data assimilation part of the system,including the reduction in the degree of geostrophic balance between variables,and the non-smooth analysis increment and its obviously smaller size compared with the 3DVAR data assimilation,corresponding measures were taken to optimize and ameliorate the system.Accordingly,a single pressure observation ensemble-based data assimilation experiment was conducted to ensure that the ensemble-based data assimilation part of the system is correct and reasonable.A number of localization-scale sensitivity tests of the ensemble-based data assimilation were also conducted to determine the most appropriate localization scale.Then,a number of hybrid data assimilation experiments were carried out.The results showed that it was most appropriate to set the weight factor of the ensemble-estimated covariance in the experiments to be 0.8.Compared with the 3DVAR data assimilation,the geopotential height forecast of the hybrid data assimilation experiments improved very little,but the wind forecast improved slightly at each forecast time,especially over 300 hPa.Overall,the hybrid data assimilation demonstrates some advantages over the3 DVAR data assimilation.展开更多
An 8×10 GHz receiver optical sub-assembly （ROSA） consisting of an 8-channel arrayed waveguide grating （AWG） and an 8-channel PIN photodetector （PD） array is designed and fabricated based on silica hybrid in...An 8×10 GHz receiver optical sub-assembly （ROSA） consisting of an 8-channel arrayed waveguide grating （AWG） and an 8-channel PIN photodetector （PD） array is designed and fabricated based on silica hybrid integration technology. Multimode output waveguides in the silica AWG with 2% refractive index difference are used to obtain fiat-top spectra. The output waveguide facet is polished to 45° bevel to change the light propagation direction into the mesa-type PIN PD, which simplifies the packaging process. The experimentM results show that the single channel I dB bandwidth of AWG ranges from 2.12nm to 3.06nm, the ROSA responsivity ranges from 0.097 A/W to 0.158A/W, and the 3dB bandwidth is up to 11 GHz. It is promising to be applied in the eight-lane WDM transmission system in data center interconnection.展开更多
Under Type-Ⅱ progressively hybrid censoring, this paper discusses statistical inference and optimal design on stepstress partially accelerated life test for hybrid system in presence of masked data. It is assumed tha...Under Type-Ⅱ progressively hybrid censoring, this paper discusses statistical inference and optimal design on stepstress partially accelerated life test for hybrid system in presence of masked data. It is assumed that the lifetime of the component in hybrid systems follows independent and identical modified Weibull distributions. The maximum likelihood estimations(MLEs)of the unknown parameters, acceleration factor and reliability indexes are derived by using the Newton-Raphson algorithm. The asymptotic variance-covariance matrix and the approximate confidence intervals are obtained based on normal approximation to the asymptotic distribution of MLEs of model parameters. Moreover,two bootstrap confidence intervals are constructed by using the parametric bootstrap method. The optimal time of changing stress levels is determined under D-optimality and A-optimality criteria.Finally, the Monte Carlo simulation study is carried out to illustrate the proposed procedures.展开更多
<span style="font-family:Verdana;">Develop</span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;&qu...<span style="font-family:Verdana;">Develop</span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">ment</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> of renewable energy (RE) and mitigation of carbon dioxide, as the two largest climate action initiatives are the most challenging factors for new generation green data center (GDC). Reduction of conventional electricity consumption as well as cost of electricity (COE) with preferred quality</span></span></span><span><span><span style="font-family:;" "=""> </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">of service (QoS) has been recognized as the interesting research topic in Information and Communication Technology (ICT) sector. Moreover, it becomes challenging to design a large-scale sustainable GDC with standalone RE supply. This paper gives spotlight on hybrid energy supply solution for the GDC to reduce grid electricity usage and minimum net system cost. The proposed framework includes RE source such as solar photovoltaic, wind turbine and non-renewable energy sources as Disel Generator (DG) and Battery. A hybrid optimization model is designed using HOMER software for cost assessment and energy evaluation to validate the effectiveness of the suggested scheme focusing on eco-friendly implication.</span></span></span>展开更多
基于WRF模式构建了Hybrid En SRF-En3DVar同化系统,该系统使用En SRF方案直接更新集合扰动。利用构建的同化系统针对台风"桑美"分别进行集合协方差权重敏感性试验和同化雷达不同观测资料的敏感性试验。集合协方差权重敏感性...基于WRF模式构建了Hybrid En SRF-En3DVar同化系统,该系统使用En SRF方案直接更新集合扰动。利用构建的同化系统针对台风"桑美"分别进行集合协方差权重敏感性试验和同化雷达不同观测资料的敏感性试验。集合协方差权重敏感性试验发现:当集合协方差权重分别为0.25、0.5和0.75时,同化效果优于3DVar试验,其中0.75的集合协方差权重试验得到了分析场的最优估计;当集合协方差权重为1.0时,分析场最差。同化雷达不同观测资料的敏感性试验表明,联合同化雷达径向风及反射率能有效改善大气湿度场和风场,但对风场的改善效果不如仅同化雷达径向风好。将En SRF集合扰动更新方案与扰动观测方案综合分析发现,扰动观测方案集合离散度较小,计算代价大,En SRF方案优于扰动观测方案。展开更多
利用WRF(Weather research and forecasting)模式及模式模拟的资料,采用Hybrid ETKF-3DVAR(ensemble transform Kalman filter-three-dimensional variational data assimilation)方法同化模拟雷达观测资料。该混合同化方法将集合转换...利用WRF(Weather research and forecasting)模式及模式模拟的资料,采用Hybrid ETKF-3DVAR(ensemble transform Kalman filter-three-dimensional variational data assimilation)方法同化模拟雷达观测资料。该混合同化方法将集合转换卡尔曼滤波(ensemble transform Kalman filter)得到的集合样本扰动通过转换矩阵直接作用到背景场上,利用顺序滤波的思想得到分析扰动场;然后通过增加额外控制变量的方式把"流依赖"的集合协方差信息引入到变分目标函数中去,在3DVAR框架基础下与观测数据进行融合,从而给出分析场的最优估计。试验结果表明,Hybrid ETKF-3DVAR同化方法相比传统3DVAR可以提供更为准确的分析场,Hybrid方法雷达资料初始化模拟的台风涡旋结构与位置比3DVAR更加接近"真实场",对台风路径预报也有明显改进。通过对比Hybrid S试验与Hybrid F试验发现,Hybrid的正效果主要来源于混合背景误差协方差中的"流依赖"信息,集合平均场代替确定性背景场带来的效果并不显著。展开更多
为有效提升配电网韧性,提出了一种基于数据-模型混合驱动的多类型移动应急资源优化调度方法。首先,考虑到交通道路状态动态变化对移动储能车(mobile energy storage system,MESS)和应急抢修队(repair crew,RC)策略的影响,构建了以电力-...为有效提升配电网韧性,提出了一种基于数据-模型混合驱动的多类型移动应急资源优化调度方法。首先,考虑到交通道路状态动态变化对移动储能车(mobile energy storage system,MESS)和应急抢修队(repair crew,RC)策略的影响,构建了以电力-交通耦合网总损失成本最小为目标的多类型移动应急资源随机优化调度模型。然后,为了实时准确地求解MESS和RC最优路由和调度策略,提出了一种数据-模型混合驱动方法对所构建的复杂非线性随机优化模型进行求解。在数据驱动部分提出一种图注意力网络多智能体强化学习算法,以求解考虑交通网道路修复时间和移动应急资源邻接关系动态变化等不确定因素的MESS和RC最优路由策略。所提算法有效结合多种改进策略和优先经验回放策略以提高算法的采样效率和训练效果。在模型驱动部分采用二阶锥松弛和大M法将多类型移动应急资源优化调度问题构建为混合整数二阶锥规划模型以求解可再生能源出力和配电网负荷变化影响下MESS和RC最优调度策略。最后,在2个不同规模的电力-交通耦合网中验证所提方法的有效性、泛化能力和可拓展能力。展开更多
基金Project for Public Welfare(Meteorology)of China(GYHY201206006)973 Program(2013CB430305)+2 种基金National Natural Science Foundation of China(41575107)Project of Shanghai Meteorological Bureau(YJ201401)Key Project of Science and Technology Commission of Shanghai Municipality(13231203300)
文摘A hybrid GSI （Grid-point Statistical Interpolation）-ETKF （Ensemble Transform Kalman Filter） data assimila- tion system has been recently developed for the WRF （Weather Research and Forecasting） model and tested with simu- lated observations for tropical cyclone （TC） forecast. This system is based on the existing GSI but with ensemble back- ground information incorporated. As a follow-up, this work extends the new system to assimilate real observations to further understand the hybrid scheme. As a first effort to explore the system with real observations, relatively coarse grid resolution （27 km） is used. A case study of typhoon Muifa （2011） is performed to assimilate real observations in- cluding conventional in-situ and satellite data. The hybrid system with flow-dependent ensemble eovariance shows sig- nificant improvements with respect to track forecast compared to the standard GSI system which in theory is three di- mensional variational analysis （3DVAR）. By comparing the analyses, analysis increments and forecasts, the hybrid sys- tem is found to be potentially able to recognize the existence of TC vortex, adjust its position systematically, better de- scribe the asymmetric structure of typhoon Muifa and maintain the dynamic and thermodynamic balance in typhoon ini- tial field. In addition, a cold-start hybrid approach by using the global ensembles to provide flow-dependent error is test- ed and similar results are revealed with those from cycled GSI-ETKF approach.
基金The research was supported by the National Natural Science Foundation of China under grant No:60775036, 60970061the Higher Education Nature Science Research Fund Project of Jiangsu Province under grant No: 09KJD520004.
文摘Decision rules mining is an important issue in machine learning and data mining.However,most proposed algorithms mine categorical data at single level,and these rules are not easily understandable and really useful for users.Thus,a new approach to hierarchical decision rules mining is provided in this paper,in which similarity direction measure is introduced to deal with hybrid data.This approach can mine hierarchical decision rules by adjusting similarity measure parameters and the level of concept hierarchy trees.
基金supported by the National Natural Science Foundation of China(Nos.52275050 and 52175041).
文摘A timely trend in gear transmission involves the replacement of steel with polymers.Nevertheless,the absence of fundamental durability data for polymer gears impedes their reliable application during power transmission.The expensive and time-consuming gear fatigue experiments make it impossible to rely merely on experimental data.In this study,a strategy for contact fatigue life prediction of polymer gears via an experimental-simulated hybrid data-driven model is presented.The hybrid data are established with a certain mixture ratio of experimental and simulation data and are augmented by the conditional tabular generative adversarial network(CTAB-GAN)algorithm.This specific algorithm was combined with the extreme gradient boosting(XGBoost)algorithm to predict the contact fatigue life of gears made from different polymer materials,with the prediction accuracy controlled within a 3-fold scatter band.Moreover,an empirical predictive formula for contact fatigue life was developed.The hybrid data-driven model,which merges experimental and simulated data,allows for efficient estimation of fatigue life and material selection strategies,generating insight into the anti-fatigue design of polymer gears.
基金fully supported by the National Natural Science Foundation of China(52072412)。
文摘The futures trading market is an important part of the financial markets and soybeans are one of the most strategically important crops in the world.How to predict soybean future price is a challenging topic being studied by many researchers.This paper proposes a novel hybrid soybean future price prediction model which includes two stages of data preprocessing and deep learning prediction.In the data preprocessing stage,futures price series are decomposed into subsequences using the ICEEMDAN(improved complete ensemble empirical mode decomposition with adaptive noise)method.The Lempel-Ziv complexity determination method was then used to identify and reconstruct high-frequency subsequences.Finally,the high frequency component is decomposed secondarily using variational mode decomposition optimized by beluga whale optimization algorithm.In the deep learning prediction stage,a deep extreme learning machine optimized by the sparrow search algorithm was used to obtain the prediction results of all subseries and reconstructs them to obtain the final soybean future price prediction results.Based on the experimental results of soybean future price markets in China,Italy,and the United States,it was found that the hybrid method proposed provides superior performance in terms of prediction accuracy and robustness.
基金Supported by the National Natural Science Foundation of China(91437113 and 41275111)China Meteorological Administration Special Public Welfare Research Fund(GYHY201506005)
文摘Based on the GRAPES（Global/Regional Assimilation and Prediction System） regional ensemble prediction system and 3DVAR（three-dimensional variational） data assimilation system,which are implemented operationally at the Numerical Weather Prediction Center of the China Meteorological Administration,an ensemble-based 3DVAR（En-3DVAR） hybrid data assimilation system for GRAPES-Meso（the regional mesoscale numerical prediction system of GRAPES） was developed by using the extended control variable technique to implement a hybrid background error covariance that combines the climatological covariance and ensemble-estimated covariance.Considering the problems of the ensemble-based data assimilation part of the system,including the reduction in the degree of geostrophic balance between variables,and the non-smooth analysis increment and its obviously smaller size compared with the 3DVAR data assimilation,corresponding measures were taken to optimize and ameliorate the system.Accordingly,a single pressure observation ensemble-based data assimilation experiment was conducted to ensure that the ensemble-based data assimilation part of the system is correct and reasonable.A number of localization-scale sensitivity tests of the ensemble-based data assimilation were also conducted to determine the most appropriate localization scale.Then,a number of hybrid data assimilation experiments were carried out.The results showed that it was most appropriate to set the weight factor of the ensemble-estimated covariance in the experiments to be 0.8.Compared with the 3DVAR data assimilation,the geopotential height forecast of the hybrid data assimilation experiments improved very little,but the wind forecast improved slightly at each forecast time,especially over 300 hPa.Overall,the hybrid data assimilation demonstrates some advantages over the3 DVAR data assimilation.
基金Supported by the National High Technology Research and Development Program of China under Grant No 2015AA016902the National Natural Science Foundation of China under Grant Nos 61435013 and 61405188the K.C.Wong Education Foundation
文摘An 8×10 GHz receiver optical sub-assembly （ROSA） consisting of an 8-channel arrayed waveguide grating （AWG） and an 8-channel PIN photodetector （PD） array is designed and fabricated based on silica hybrid integration technology. Multimode output waveguides in the silica AWG with 2% refractive index difference are used to obtain fiat-top spectra. The output waveguide facet is polished to 45° bevel to change the light propagation direction into the mesa-type PIN PD, which simplifies the packaging process. The experimentM results show that the single channel I dB bandwidth of AWG ranges from 2.12nm to 3.06nm, the ROSA responsivity ranges from 0.097 A/W to 0.158A/W, and the 3dB bandwidth is up to 11 GHz. It is promising to be applied in the eight-lane WDM transmission system in data center interconnection.
基金supported by the National Natural Science Foundation of China(71401134 71571144+1 种基金 71171164)the Program of International Cooperation and Exchanges in Science and Technology Funded by Shaanxi Province(2016KW-033)
文摘Under Type-Ⅱ progressively hybrid censoring, this paper discusses statistical inference and optimal design on stepstress partially accelerated life test for hybrid system in presence of masked data. It is assumed that the lifetime of the component in hybrid systems follows independent and identical modified Weibull distributions. The maximum likelihood estimations(MLEs)of the unknown parameters, acceleration factor and reliability indexes are derived by using the Newton-Raphson algorithm. The asymptotic variance-covariance matrix and the approximate confidence intervals are obtained based on normal approximation to the asymptotic distribution of MLEs of model parameters. Moreover,two bootstrap confidence intervals are constructed by using the parametric bootstrap method. The optimal time of changing stress levels is determined under D-optimality and A-optimality criteria.Finally, the Monte Carlo simulation study is carried out to illustrate the proposed procedures.
文摘<span style="font-family:Verdana;">Develop</span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">ment</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> of renewable energy (RE) and mitigation of carbon dioxide, as the two largest climate action initiatives are the most challenging factors for new generation green data center (GDC). Reduction of conventional electricity consumption as well as cost of electricity (COE) with preferred quality</span></span></span><span><span><span style="font-family:;" "=""> </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">of service (QoS) has been recognized as the interesting research topic in Information and Communication Technology (ICT) sector. Moreover, it becomes challenging to design a large-scale sustainable GDC with standalone RE supply. This paper gives spotlight on hybrid energy supply solution for the GDC to reduce grid electricity usage and minimum net system cost. The proposed framework includes RE source such as solar photovoltaic, wind turbine and non-renewable energy sources as Disel Generator (DG) and Battery. A hybrid optimization model is designed using HOMER software for cost assessment and energy evaluation to validate the effectiveness of the suggested scheme focusing on eco-friendly implication.</span></span></span>
文摘为有效提升配电网韧性,提出了一种基于数据-模型混合驱动的多类型移动应急资源优化调度方法。首先,考虑到交通道路状态动态变化对移动储能车(mobile energy storage system,MESS)和应急抢修队(repair crew,RC)策略的影响,构建了以电力-交通耦合网总损失成本最小为目标的多类型移动应急资源随机优化调度模型。然后,为了实时准确地求解MESS和RC最优路由和调度策略,提出了一种数据-模型混合驱动方法对所构建的复杂非线性随机优化模型进行求解。在数据驱动部分提出一种图注意力网络多智能体强化学习算法,以求解考虑交通网道路修复时间和移动应急资源邻接关系动态变化等不确定因素的MESS和RC最优路由策略。所提算法有效结合多种改进策略和优先经验回放策略以提高算法的采样效率和训练效果。在模型驱动部分采用二阶锥松弛和大M法将多类型移动应急资源优化调度问题构建为混合整数二阶锥规划模型以求解可再生能源出力和配电网负荷变化影响下MESS和RC最优调度策略。最后,在2个不同规模的电力-交通耦合网中验证所提方法的有效性、泛化能力和可拓展能力。
