Accurate positioning is one of the essential requirements for numerous applications of remote sensing data,especially in the event of a noisy or unreliable satellite signal.Toward this end,we present a novel framework...Accurate positioning is one of the essential requirements for numerous applications of remote sensing data,especially in the event of a noisy or unreliable satellite signal.Toward this end,we present a novel framework for aircraft geo-localization in a large range that only requires a downward-facing monocular camera,an altimeter,a compass,and an open-source Vector Map(VMAP).The algorithm combines the matching and particle filter methods.Shape vector and correlation between two building contour vectors are defined,and a coarse-to-fine building vector matching(CFBVM)method is proposed in the matching stage,for which the original matching results are described by the Gaussian mixture model(GMM).Subsequently,an improved resampling strategy is designed to reduce computing expenses with a huge number of initial particles,and a credibility indicator is designed to avoid location mistakes in the particle filter stage.An experimental evaluation of the approach based on flight data is provided.On a flight at a height of 0.2 km over a flight distance of 2 km,the aircraft is geo-localized in a reference map of 11,025 km~2using 0.09 km~2aerial images without any prior information.The absolute localization error is less than 10 m.展开更多
Power demand prediction for buildings at a large scale is required for power grid operation.The bottom-up prediction method using physics-based models is popular,but has some limitations such as a heavy workload on mo...Power demand prediction for buildings at a large scale is required for power grid operation.The bottom-up prediction method using physics-based models is popular,but has some limitations such as a heavy workload on model creation and long computing time.Top-down methods based on data driven models are fast,but less accurate.Considering the similarity of power demand patterns of single buildings and the superiority of generative adversarial network(GAN),this paper proposes a new method(E-GAN),which combines a physics-based model(EnergyPlus)and a data-driven model(GAN),to predict the daily power demand for buildings at a large scale.The new E-GAN method selects a small number of typical buildings and utilizes EnergyPlus models to predict their power demands.Utilizing the prediction for those typical buildings,the GAN then is adopted to forecast the power demands of a large number of buildings.To verify the proposed method,the E-GAN is used to predict 24-hour power demands for a set of residential buildings.The results show that(1)4.3%of physics-based models in each building category are required to ensure the prediction accuracy;(2)compared with the physics-based model,the E-GAN can predict power demand accurately with only 5%error(measured by mean absolute percentage error,MAPE)while using only approximately 9%of the computing time;and(3)compared with data-driven models(e.g.,support vector regression,extreme learning machine,and polynomial regression model),E-GAN demonstrates at least 60%reduction in prediction error measured by MAPE.展开更多
文摘Accurate positioning is one of the essential requirements for numerous applications of remote sensing data,especially in the event of a noisy or unreliable satellite signal.Toward this end,we present a novel framework for aircraft geo-localization in a large range that only requires a downward-facing monocular camera,an altimeter,a compass,and an open-source Vector Map(VMAP).The algorithm combines the matching and particle filter methods.Shape vector and correlation between two building contour vectors are defined,and a coarse-to-fine building vector matching(CFBVM)method is proposed in the matching stage,for which the original matching results are described by the Gaussian mixture model(GMM).Subsequently,an improved resampling strategy is designed to reduce computing expenses with a huge number of initial particles,and a credibility indicator is designed to avoid location mistakes in the particle filter stage.An experimental evaluation of the approach based on flight data is provided.On a flight at a height of 0.2 km over a flight distance of 2 km,the aircraft is geo-localized in a reference map of 11,025 km~2using 0.09 km~2aerial images without any prior information.The absolute localization error is less than 10 m.
基金The Chinese team is supported by the National Natural Science Foundation of China(62076150,62173216,61903226)the Taishan Scholar Project of Shandong Province(TSQN201812092)+2 种基金the Key Research and Development Program of Shandong Province(2019GGX101072,2019JZZY010115)the Youth Innovation Technology Project of Higher School in Shandong Province(2019KJN005)the Key Research and Development Program of Shandong Province(2019JZZY010115)。
文摘Power demand prediction for buildings at a large scale is required for power grid operation.The bottom-up prediction method using physics-based models is popular,but has some limitations such as a heavy workload on model creation and long computing time.Top-down methods based on data driven models are fast,but less accurate.Considering the similarity of power demand patterns of single buildings and the superiority of generative adversarial network(GAN),this paper proposes a new method(E-GAN),which combines a physics-based model(EnergyPlus)and a data-driven model(GAN),to predict the daily power demand for buildings at a large scale.The new E-GAN method selects a small number of typical buildings and utilizes EnergyPlus models to predict their power demands.Utilizing the prediction for those typical buildings,the GAN then is adopted to forecast the power demands of a large number of buildings.To verify the proposed method,the E-GAN is used to predict 24-hour power demands for a set of residential buildings.The results show that(1)4.3%of physics-based models in each building category are required to ensure the prediction accuracy;(2)compared with the physics-based model,the E-GAN can predict power demand accurately with only 5%error(measured by mean absolute percentage error,MAPE)while using only approximately 9%of the computing time;and(3)compared with data-driven models(e.g.,support vector regression,extreme learning machine,and polynomial regression model),E-GAN demonstrates at least 60%reduction in prediction error measured by MAPE.
