We address several load shedding techniques over sliding window joins. We first construct a dual window architectural model including aux-windows and join-windows, and build statistics on aux-windows. With the statist...We address several load shedding techniques over sliding window joins. We first construct a dual window architectural model including aux-windows and join-windows, and build statistics on aux-windows. With the statistics, we develop an effective load shedding strategy producing maximum subset join outputs. In order to accelerate the load shedding process, binary indexed trees have been utilized to reduce the cost on shedding evaluation. When streams have high arrival rates, we propose an approach incorporating front-shedding and rear-shedding, and find an optimal trade-off between them. As for the scenarios of variable speed ratio, we develop a plan reallocating CPU resources and dynamically resizing the windows. In addition, we prove that load shedding is not affected during the process of reallocation. Both synthetic and real data are used in our experiments, and the results show the promise of our strategies.展开更多
As the penetration ratio of wind power in active distribution networks continues to increase,the system exhibits some characteristics such as randomness and volatility.Fast and accurate short-term wind power predictio...As the penetration ratio of wind power in active distribution networks continues to increase,the system exhibits some characteristics such as randomness and volatility.Fast and accurate short-term wind power prediction is essential for algorithms like scheduling and optimization control.Based on the spatio-temporal features of Numerical Weather Prediction(NWP)data,it proposes the WVMD_DSN(Whale Optimization Algorithm,Variational Mode Decomposition,Dual Stream Network)model.The model first applies Pearson correlation coefficient(PCC)to choose some NWP features with strong correlation to wind power to form the feature set.Then,it decomposes the feature set using Variational Mode Decomposition(VMD)to eliminate the nonstationarity and obtains Intrinsic Mode Functions(IMFs).Here Whale Optimization Algorithm(WOA)is applied to optimise the key parameters of VMD,namely the number of mode components K and penalty factor a.Finally,incorporating attention mechanism(AM),Squeeze-Excitation Network(SENet),and Bidirectional Gated Recurrent Unit(BiGRU),it constructs the dual-stream network(DSN)for short-term wind power prediction.Comparative experiments demonstrate that the WVMD_DSN model outperforms existing baseline algorithms and exhibits good generalization performance.The relevant code is available at https://github.com/ruanyuyuan/Wind-power-forecast.git(accessed on 20 August 2024).展开更多
The assembly of monomicelles along onedimension(1D)to construct tubular or fibrous mesostructures is greatly desired but still challenging.Herein,we have demonstrated a facile strategy to synthesize 1D bimodal mesopor...The assembly of monomicelles along onedimension(1D)to construct tubular or fibrous mesostructures is greatly desired but still challenging.Herein,we have demonstrated a facile strategy to synthesize 1D bimodal mesoporous metal oxides(e.g.,WO_(3),WO_(3)/Pd,WO_(3)/Pd Cu,TiO_(2),and ZrO_(2))nanofibers(NFs)through assembling the organic-inorganic composite monomicelles in a beam stream generated via an electrospinning technique.This facile and repeatable methodology relies on the preparation of copolymer@metal-complex monomicelles in an anisotropic solution and oriented assembly of them in the beam stream by the selective evaporation of solvent.WO_(3)and its derivatives are chosen as the demo,which show a uniform continuous fibrous structure with dual mesopore sizes(~4.0 and 7.6 nm)and large surface area(~93.1 m^(2)g^(-1)).Benefitting from the unique textual structure,gas sensors made by Pd-decorated mesoporous WO_(3)NFs display outstanding comprehensive sensing performance to ethylbenzene,including a high sensitivity(52.5),an ultralow detection limit(50 ppb),and fast response/recovery kinetics(11/16 s)as well as an outstanding selectivity,which render them promising for rapid environmental monitoring.展开更多
基金This work is supported by the National Natural Science Foundation of China under Grant Nos. 60473074, 60573089 and National Grand Fundamental Research 973 Program of China under Grant No. 2006CB303103.
文摘We address several load shedding techniques over sliding window joins. We first construct a dual window architectural model including aux-windows and join-windows, and build statistics on aux-windows. With the statistics, we develop an effective load shedding strategy producing maximum subset join outputs. In order to accelerate the load shedding process, binary indexed trees have been utilized to reduce the cost on shedding evaluation. When streams have high arrival rates, we propose an approach incorporating front-shedding and rear-shedding, and find an optimal trade-off between them. As for the scenarios of variable speed ratio, we develop a plan reallocating CPU resources and dynamically resizing the windows. In addition, we prove that load shedding is not affected during the process of reallocation. Both synthetic and real data are used in our experiments, and the results show the promise of our strategies.
基金the Science and Technology Project of State Grid Corporation of China under Grant 5400-202117142A-0-0-00the National Natural Science Foundation of China under Grant 62372242.
文摘As the penetration ratio of wind power in active distribution networks continues to increase,the system exhibits some characteristics such as randomness and volatility.Fast and accurate short-term wind power prediction is essential for algorithms like scheduling and optimization control.Based on the spatio-temporal features of Numerical Weather Prediction(NWP)data,it proposes the WVMD_DSN(Whale Optimization Algorithm,Variational Mode Decomposition,Dual Stream Network)model.The model first applies Pearson correlation coefficient(PCC)to choose some NWP features with strong correlation to wind power to form the feature set.Then,it decomposes the feature set using Variational Mode Decomposition(VMD)to eliminate the nonstationarity and obtains Intrinsic Mode Functions(IMFs).Here Whale Optimization Algorithm(WOA)is applied to optimise the key parameters of VMD,namely the number of mode components K and penalty factor a.Finally,incorporating attention mechanism(AM),Squeeze-Excitation Network(SENet),and Bidirectional Gated Recurrent Unit(BiGRU),it constructs the dual-stream network(DSN)for short-term wind power prediction.Comparative experiments demonstrate that the WVMD_DSN model outperforms existing baseline algorithms and exhibits good generalization performance.The relevant code is available at https://github.com/ruanyuyuan/Wind-power-forecast.git(accessed on 20 August 2024).
基金supported by the Innovation Program of Shanghai Municipal Education Commission(2021-01-0700-03-E00109)the National Natural Science Foundation of China(51822202 and 51772050)+9 种基金the Science and Technology Commission of Shanghai Municipality(19520713200)Shanghai Sailing Program(20YF1400500)Shanghai Rising-Star Program(18QA1400100)the Key Basic Research Program of Science and Technology Commission of Shanghai Municipality(20JC1415300)Shanghai Natural Science Foundation(20ZR1401500)Shanghai Scientific and Technological Innovation Project(19JC1410400)the Fundamental Research Funds for the Central Universities(2232020D-02)the Youth Top-notch Talent Support Program of ShanghaiDHU Distinguished Young Professor Programfinancial support from Australian Research Council through an ARC Future Fellowship(FT180100387).
文摘The assembly of monomicelles along onedimension(1D)to construct tubular or fibrous mesostructures is greatly desired but still challenging.Herein,we have demonstrated a facile strategy to synthesize 1D bimodal mesoporous metal oxides(e.g.,WO_(3),WO_(3)/Pd,WO_(3)/Pd Cu,TiO_(2),and ZrO_(2))nanofibers(NFs)through assembling the organic-inorganic composite monomicelles in a beam stream generated via an electrospinning technique.This facile and repeatable methodology relies on the preparation of copolymer@metal-complex monomicelles in an anisotropic solution and oriented assembly of them in the beam stream by the selective evaporation of solvent.WO_(3)and its derivatives are chosen as the demo,which show a uniform continuous fibrous structure with dual mesopore sizes(~4.0 and 7.6 nm)and large surface area(~93.1 m^(2)g^(-1)).Benefitting from the unique textual structure,gas sensors made by Pd-decorated mesoporous WO_(3)NFs display outstanding comprehensive sensing performance to ethylbenzene,including a high sensitivity(52.5),an ultralow detection limit(50 ppb),and fast response/recovery kinetics(11/16 s)as well as an outstanding selectivity,which render them promising for rapid environmental monitoring.
