Recently, the heat and electricity integrated energy system (HE-IES) has become a hot topic in both industry and academia. In the HE- IES, the potential flexibility of the buildings' thermal loads can be exploited...Recently, the heat and electricity integrated energy system (HE-IES) has become a hot topic in both industry and academia. In the HE- IES, the potential flexibility of the buildings' thermal loads can be exploited to relax the heat power balance constraints and consequently allow a more flexible operation of the combined heat and power units. In this paper, model-driven and data-driven techniques are combined to quantify the demand flexibility of the buildings' thermal loads in a non-instructive way. First, the explicit analytical equivalent thermal parameter (ETP) model of the aggregated buildings is developed. The heat transfer coefficient (k) and thermal inertia coefficient (C) of the ETP model are designated to measure the potential demand flexibility. Second, the Particle Swarm Optimization optimized Radial Basis Function neural network (PSO-RBF) is used to identify the relationship between the values of k and C and the meteorological factors. To obtain the training data, an innovative two-stage regression method based on the adaptive temporal resolution is proposed to extract k and C values from the historical thermal load data. Finally, a flexible thermal load model is built based on the predictions of the meteorological factors, which can be conveniently incorporated into the online dispatch of the HE-IES. A comprehensive simulation environment is designed to verify the accuracy and availability of the proposed technique.展开更多
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
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
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
<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
Cloud storage is widely used by large companies to store vast amounts of data and files,offering flexibility,financial savings,and security.However,information shoplifting poses significant threats,potentially leading...Cloud storage is widely used by large companies to store vast amounts of data and files,offering flexibility,financial savings,and security.However,information shoplifting poses significant threats,potentially leading to poor performance and privacy breaches.Blockchain-based cognitive computing can help protect and maintain information security and privacy in cloud platforms,ensuring businesses can focus on business development.To ensure data security in cloud platforms,this research proposed a blockchain-based Hybridized Data Driven Cognitive Computing(HD2C)model.However,the proposed HD2C framework addresses breaches of the privacy information of mixed participants of the Internet of Things(IoT)in the cloud.HD2C is developed by combining Federated Learning(FL)with a Blockchain consensus algorithm to connect smart contracts with Proof of Authority.The“Data Island”problem can be solved by FL’s emphasis on privacy and lightning-fast processing,while Blockchain provides a decentralized incentive structure that is impervious to poisoning.FL with Blockchain allows quick consensus through smart member selection and verification.The HD2C paradigm significantly improves the computational processing efficiency of intelligent manufacturing.Extensive analysis results derived from IIoT datasets confirm HD2C superiority.When compared to other consensus algorithms,the Blockchain PoA’s foundational cost is significant.The accuracy and memory utilization evaluation results predict the total benefits of the system.In comparison to the values 0.004 and 0.04,the value of 0.4 achieves good accuracy.According to the experiment results,the number of transactions per second has minimal impact on memory requirements.The findings of this study resulted in the development of a brand-new IIoT framework based on blockchain technology.展开更多
The majority of spatial data reveal some degree of spatial dependence. The term “spatial dependence” refers to the tendency for phenomena to be more similar when they occur close together than when they occur far ap...The majority of spatial data reveal some degree of spatial dependence. The term “spatial dependence” refers to the tendency for phenomena to be more similar when they occur close together than when they occur far apart in space. This property is ignored in machine learning (ML) for spatial domains of application. Most classical machine learning algorithms are generally inappropriate unless modified in some way to account for it. In this study, we proposed an approach that aimed to improve a ML model to detect the dependence without incorporating any spatial features in the learning process. To detect this dependence while also improving performance, a hybrid model was used based on two representative algorithms. In addition, cross-validation method was used to make the model stable. Furthermore, global moran’s I and local moran were used to capture the spatial dependence in the residuals. The results show that the HM has significant with a R2 of 99.91% performance compared to RBFNN and RF that have 74.22% and 82.26% as R2 respectively. With lower errors, the HM was able to achieve an average test error of 0.033% and a positive global moran’s of 0.12. We concluded that as the R2 value increases, the models become weaker in terms of capturing the dependence.展开更多
A novel quantum memory scheme is proposed for quantum data buses in scalable quantum computers by using adjustable interaction. Our investigation focuses on a hybrid quantum system including coupled flux qubits and a ...A novel quantum memory scheme is proposed for quantum data buses in scalable quantum computers by using adjustable interaction. Our investigation focuses on a hybrid quantum system including coupled flux qubits and a nitrogen–vacancy center ensemble. In our scheme, the transmission and storage（retrieval） of quantum state are performed in two separated steps, which can be controlled by adjusting the coupling strength between the computing unit and the quantum memory. The scheme can be used not only to reduce the time of quantum state transmission, but also to increase the robustness of the system with respect to detuning caused by magnetic noises. In comparison with the previous memory scheme, about 80% of the transmission time is saved. Moreover, it is exemplified that in our scheme the fidelity could achieve 0.99 even when there exists detuning, while the one in the previous scheme is 0.75.展开更多
Aiming at the limitations of traditional thermal model and intelligent model, a new hybrid model is established for soft sensing of the molten steel temperature in LF. Firstly, a thermal model based on energy conserva...Aiming at the limitations of traditional thermal model and intelligent model, a new hybrid model is established for soft sensing of the molten steel temperature in LF. Firstly, a thermal model based on energy conservation is described; and then, an improved intelligent model based on process data is presented by ensemble ELM （extreme learning machine） for predicting the molten steel temperature in LF. Secondly, the self-adaptive data fusion is pro- posed as a hybrid modeling method to combine the thermal model with the intelligent model. The new hybrid model could complement mutual advantage of two models by combination. It can overcome the shortcoming of parameters obtained on-line hardly in a thermal model and the disadvantage of lacking the analysis of ladle furnace metallurgical process in an intelligent model. The new hybrid model is applied to a 300 t LF in Baoshan Iron and Steel Co Ltd for predicting the molten steel temperature. The experiments demonstrate that the hybrid model has good generalization performance and high accuracy.展开更多
This study examines the performance of coupling the deterministic four-dimensional variational assimilation system （4DVAR） with an ensemble Kalman filter （EnKF） to produce a superior hybrid approach for data assim...This study examines the performance of coupling the deterministic four-dimensional variational assimilation system （4DVAR） with an ensemble Kalman filter （EnKF） to produce a superior hybrid approach for data assimilation. The coupled assimilation scheme （E4DVAR） benefits from using the state-dependent uncertainty provided by EnKF while taking advantage of 4DVAR in preventing filter divergence： the 4DVAR analysis produces posterior maximum likelihood solutions through minimization of a cost function about which the ensemble perturbations are transformed, and the resulting ensemble analysis can be propagated forward both for the next assimilation cycle and as a basis for ensemble forecasting. The feasibility and effectiveness of this coupled approach are demonstrated in an idealized model with simulated observations. It is found that the E4DVAR is capable of outperforming both 4DVAR and the EnKF under both perfect- and imperfect-model scenarios. The performance of the coupled scheme is also less sensitive to either the ensemble size or the assimilation window length than those for standard EnKF or 4DVAR implementations.展开更多
Hybrid power supply system consists of a number of independent and different sources of electrical energy with different operating times during different seasons and with energy storage system. Deployment of a hybrid ...Hybrid power supply system consists of a number of independent and different sources of electrical energy with different operating times during different seasons and with energy storage system. Deployment of a hybrid power system is expected in places outside the normal distribution network. For the further research and improvements it is necessary to know in detail the power flow from various sources to the load or to storage battery depending on different seasons. The paper presents data analysis results computed by application developed for detailed analysis of power flows within hybrid power system. Developed application analyses the data from the monitoring system. Data has been acquired and recorded within last year. This data is visualized as power flows in the individual hybrid power system circuits. Together with electrical power the effectiveness and performance parameters of rectifier and DC/AC converter are evaluated. The paper describes achieved results and needs for further improvements of such solution.展开更多
Environmental systems including our atmosphere oceans, biological… etc. can be modeled by mathematical equations to estimate their states. These equations can be solved with numerical methods. Initial and boundary co...Environmental systems including our atmosphere oceans, biological… etc. can be modeled by mathematical equations to estimate their states. These equations can be solved with numerical methods. Initial and boundary conditions are needed for such of these numerical methods. Predication and simulations for different case studies are major sources for the great importance of these models. Satellite data from different wide ranges of sensors provide observations that indicate system state. So both numerical models and satellite data provide estimation of system states, and between the different estimations it is required the best estimate for system state. Assimilation of observations in numerical weather models with data assimilation techniques provide an improved estimate of system states. In this work, highlights on the mathematical perspective for data assimilation methods are introduced. Least square estimation techniques are introduced because it is considered the basic mathematical building block for data assimilation methods. Stochastic version of least square is included to handle the error in both model and observation. Then the three and four dimensional variational assimilation 3dvar and 4dvar respectively will be handled. Kalman filters and its derivatives Extended, (KF, EKF, ENKF) and hybrid filters are introduced.展开更多
基金supported by the National Natural Science Foundation of China(52107072)International Cooperation and Exchange of NSFC(51861145406).
文摘Recently, the heat and electricity integrated energy system (HE-IES) has become a hot topic in both industry and academia. In the HE- IES, the potential flexibility of the buildings' thermal loads can be exploited to relax the heat power balance constraints and consequently allow a more flexible operation of the combined heat and power units. In this paper, model-driven and data-driven techniques are combined to quantify the demand flexibility of the buildings' thermal loads in a non-instructive way. First, the explicit analytical equivalent thermal parameter (ETP) model of the aggregated buildings is developed. The heat transfer coefficient (k) and thermal inertia coefficient (C) of the ETP model are designated to measure the potential demand flexibility. Second, the Particle Swarm Optimization optimized Radial Basis Function neural network (PSO-RBF) is used to identify the relationship between the values of k and C and the meteorological factors. To obtain the training data, an innovative two-stage regression method based on the adaptive temporal resolution is proposed to extract k and C values from the historical thermal load data. Finally, a flexible thermal load model is built based on the predictions of the meteorological factors, which can be conveniently incorporated into the online dispatch of the HE-IES. A comprehensive simulation environment is designed to verify the accuracy and availability of the proposed technique.
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.
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.
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Cloud storage is widely used by large companies to store vast amounts of data and files,offering flexibility,financial savings,and security.However,information shoplifting poses significant threats,potentially leading to poor performance and privacy breaches.Blockchain-based cognitive computing can help protect and maintain information security and privacy in cloud platforms,ensuring businesses can focus on business development.To ensure data security in cloud platforms,this research proposed a blockchain-based Hybridized Data Driven Cognitive Computing(HD2C)model.However,the proposed HD2C framework addresses breaches of the privacy information of mixed participants of the Internet of Things(IoT)in the cloud.HD2C is developed by combining Federated Learning(FL)with a Blockchain consensus algorithm to connect smart contracts with Proof of Authority.The“Data Island”problem can be solved by FL’s emphasis on privacy and lightning-fast processing,while Blockchain provides a decentralized incentive structure that is impervious to poisoning.FL with Blockchain allows quick consensus through smart member selection and verification.The HD2C paradigm significantly improves the computational processing efficiency of intelligent manufacturing.Extensive analysis results derived from IIoT datasets confirm HD2C superiority.When compared to other consensus algorithms,the Blockchain PoA’s foundational cost is significant.The accuracy and memory utilization evaluation results predict the total benefits of the system.In comparison to the values 0.004 and 0.04,the value of 0.4 achieves good accuracy.According to the experiment results,the number of transactions per second has minimal impact on memory requirements.The findings of this study resulted in the development of a brand-new IIoT framework based on blockchain technology.
文摘The majority of spatial data reveal some degree of spatial dependence. The term “spatial dependence” refers to the tendency for phenomena to be more similar when they occur close together than when they occur far apart in space. This property is ignored in machine learning (ML) for spatial domains of application. Most classical machine learning algorithms are generally inappropriate unless modified in some way to account for it. In this study, we proposed an approach that aimed to improve a ML model to detect the dependence without incorporating any spatial features in the learning process. To detect this dependence while also improving performance, a hybrid model was used based on two representative algorithms. In addition, cross-validation method was used to make the model stable. Furthermore, global moran’s I and local moran were used to capture the spatial dependence in the residuals. The results show that the HM has significant with a R2 of 99.91% performance compared to RBFNN and RF that have 74.22% and 82.26% as R2 respectively. With lower errors, the HM was able to achieve an average test error of 0.033% and a positive global moran’s of 0.12. We concluded that as the R2 value increases, the models become weaker in terms of capturing the dependence.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.61673389,61273202,61134008,and 11404113)
文摘A novel quantum memory scheme is proposed for quantum data buses in scalable quantum computers by using adjustable interaction. Our investigation focuses on a hybrid quantum system including coupled flux qubits and a nitrogen–vacancy center ensemble. In our scheme, the transmission and storage（retrieval） of quantum state are performed in two separated steps, which can be controlled by adjusting the coupling strength between the computing unit and the quantum memory. The scheme can be used not only to reduce the time of quantum state transmission, but also to increase the robustness of the system with respect to detuning caused by magnetic noises. In comparison with the previous memory scheme, about 80% of the transmission time is saved. Moreover, it is exemplified that in our scheme the fidelity could achieve 0.99 even when there exists detuning, while the one in the previous scheme is 0.75.
基金Item Sponsored by National Natural Science Foundation of China (50474086,60843007)
文摘Aiming at the limitations of traditional thermal model and intelligent model, a new hybrid model is established for soft sensing of the molten steel temperature in LF. Firstly, a thermal model based on energy conservation is described; and then, an improved intelligent model based on process data is presented by ensemble ELM （extreme learning machine） for predicting the molten steel temperature in LF. Secondly, the self-adaptive data fusion is pro- posed as a hybrid modeling method to combine the thermal model with the intelligent model. The new hybrid model could complement mutual advantage of two models by combination. It can overcome the shortcoming of parameters obtained on-line hardly in a thermal model and the disadvantage of lacking the analysis of ladle furnace metallurgical process in an intelligent model. The new hybrid model is applied to a 300 t LF in Baoshan Iron and Steel Co Ltd for predicting the molten steel temperature. The experiments demonstrate that the hybrid model has good generalization performance and high accuracy.
基金sponsored by the U.S. National Science Foundation (Grant No.ATM0205599)the U.S. Offce of Navy Research under Grant N000140410471Dr. James A. Hansen was partially supported by US Offce of Naval Research (Grant No. N00014-06-1-0500)
文摘This study examines the performance of coupling the deterministic four-dimensional variational assimilation system （4DVAR） with an ensemble Kalman filter （EnKF） to produce a superior hybrid approach for data assimilation. The coupled assimilation scheme （E4DVAR） benefits from using the state-dependent uncertainty provided by EnKF while taking advantage of 4DVAR in preventing filter divergence： the 4DVAR analysis produces posterior maximum likelihood solutions through minimization of a cost function about which the ensemble perturbations are transformed, and the resulting ensemble analysis can be propagated forward both for the next assimilation cycle and as a basis for ensemble forecasting. The feasibility and effectiveness of this coupled approach are demonstrated in an idealized model with simulated observations. It is found that the E4DVAR is capable of outperforming both 4DVAR and the EnKF under both perfect- and imperfect-model scenarios. The performance of the coupled scheme is also less sensitive to either the ensemble size or the assimilation window length than those for standard EnKF or 4DVAR implementations.
文摘Hybrid power supply system consists of a number of independent and different sources of electrical energy with different operating times during different seasons and with energy storage system. Deployment of a hybrid power system is expected in places outside the normal distribution network. For the further research and improvements it is necessary to know in detail the power flow from various sources to the load or to storage battery depending on different seasons. The paper presents data analysis results computed by application developed for detailed analysis of power flows within hybrid power system. Developed application analyses the data from the monitoring system. Data has been acquired and recorded within last year. This data is visualized as power flows in the individual hybrid power system circuits. Together with electrical power the effectiveness and performance parameters of rectifier and DC/AC converter are evaluated. The paper describes achieved results and needs for further improvements of such solution.
文摘Environmental systems including our atmosphere oceans, biological… etc. can be modeled by mathematical equations to estimate their states. These equations can be solved with numerical methods. Initial and boundary conditions are needed for such of these numerical methods. Predication and simulations for different case studies are major sources for the great importance of these models. Satellite data from different wide ranges of sensors provide observations that indicate system state. So both numerical models and satellite data provide estimation of system states, and between the different estimations it is required the best estimate for system state. Assimilation of observations in numerical weather models with data assimilation techniques provide an improved estimate of system states. In this work, highlights on the mathematical perspective for data assimilation methods are introduced. Least square estimation techniques are introduced because it is considered the basic mathematical building block for data assimilation methods. Stochastic version of least square is included to handle the error in both model and observation. Then the three and four dimensional variational assimilation 3dvar and 4dvar respectively will be handled. Kalman filters and its derivatives Extended, (KF, EKF, ENKF) and hybrid filters are introduced.
