Based on the observation data of 24-hour cumulative precipitation from 92 ground meteorological observation stations in Jiangxi province from March to July during 2015-2016 and the high-resolution numerical forecast d...Based on the observation data of 24-hour cumulative precipitation from 92 ground meteorological observation stations in Jiangxi province from March to July during 2015-2016 and the high-resolution numerical forecast data of precipitation predicted within 24-72 h by the European Centre for Medium-Range Weather Forecasts( ECMWF),the Gamma function was used as the fitting function of probability distribution of cumulative precipitation to match the probability of predicted and observed precipitation. Moreover,the change of forecast score before and after the correction was tested. The results showed that the predicted values of heavy precipitation based on ECMWF model were smaller than the observed values,while the predicted values of light precipitation were larger than the observed values. The probability matching correction method could be used to effectively correct systematic errors of model forecast,and the correction effect of all grades of precipitation( especially for rainstorm) was good.The shorter the period of validity was,the better the correction effect was. The correction method has a good application effect in the interpretation of model precipitation products,and can provide better security services for agricultural production.展开更多
The Lomax distribution is an important member in the distribution family.In this paper,we systematically develop an objective Bayesian analysis of data from a Lomax distribution.Noninformative priors,including probabi...The Lomax distribution is an important member in the distribution family.In this paper,we systematically develop an objective Bayesian analysis of data from a Lomax distribution.Noninformative priors,including probability matching priors,the maximal data information(MDI)prior,Jeffreys prior and reference priors,are derived.The propriety of the posterior under each prior is subsequently validated.It is revealed that the MDI prior and one of the reference priors yield improper posteriors,and the other reference prior is a second-order probability matching prior.A simulation study is conducted to assess the frequentist performance of the proposed Bayesian approach.Finally,this approach along with the bootstrap method is applied to a real data set.展开更多
With the fast development of software defined network(SDN),numerous researches have been conducted for maximizing the performance of SDN.Currently,flow tables are utilized in OpenFlows witch for routing.Due to the spa...With the fast development of software defined network(SDN),numerous researches have been conducted for maximizing the performance of SDN.Currently,flow tables are utilized in OpenFlows witch for routing.Due to the space limitation of flow table and switch capacity,various issues exist in dealing with the flows.The existing schemes typically employ reactive approach such that the selection of evicted entries occurs when timeout or table miss occurs.In this paper a proactive approach is proposed based on the prediction of the probability of matching of the entries.Here eviction occurs proactively when the utilization of flow table exceeds a threshold,and the flow entry of the lowest matching probability is evicted.The matching probability is estimated using hidden Markov model(HMM).Computersimulation reveals that it significantly enhances the prediction accuracy and decreases the number of table misses compared to the standard Hard timeout scheme and Flow master scheme.展开更多
基金Supported by the Special Project for Forecasters of China Meteorological Administration(CMAYBY2016-038)
文摘Based on the observation data of 24-hour cumulative precipitation from 92 ground meteorological observation stations in Jiangxi province from March to July during 2015-2016 and the high-resolution numerical forecast data of precipitation predicted within 24-72 h by the European Centre for Medium-Range Weather Forecasts( ECMWF),the Gamma function was used as the fitting function of probability distribution of cumulative precipitation to match the probability of predicted and observed precipitation. Moreover,the change of forecast score before and after the correction was tested. The results showed that the predicted values of heavy precipitation based on ECMWF model were smaller than the observed values,while the predicted values of light precipitation were larger than the observed values. The probability matching correction method could be used to effectively correct systematic errors of model forecast,and the correction effect of all grades of precipitation( especially for rainstorm) was good.The shorter the period of validity was,the better the correction effect was. The correction method has a good application effect in the interpretation of model precipitation products,and can provide better security services for agricultural production.
基金the National Social Science Foundation of China(Grant No.21BTJ034).
文摘The Lomax distribution is an important member in the distribution family.In this paper,we systematically develop an objective Bayesian analysis of data from a Lomax distribution.Noninformative priors,including probability matching priors,the maximal data information(MDI)prior,Jeffreys prior and reference priors,are derived.The propriety of the posterior under each prior is subsequently validated.It is revealed that the MDI prior and one of the reference priors yield improper posteriors,and the other reference prior is a second-order probability matching prior.A simulation study is conducted to assess the frequentist performance of the proposed Bayesian approach.Finally,this approach along with the bootstrap method is applied to a real data set.
基金This work was partly supported by the Institute for Information&communications Technology Promotion(IITP)grant funded by the Korea government(MSIT)(2016-0-00133,Research on Edge computing via collective intelligence of hyper connection IoT nodes)Korea,under the National Program for Excellence in SW supervised by the IITP(Institute for Information&communications Technology Promotion)(2015-0-00914)+1 种基金Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education,Science and Technology(2016R1A6A3A11931385,Research of key technologies based on software defined wireless sensor network for realtime public safety service,2017R1A2B2009095,Research on SDN-based WSN Supporting Real-time Stream Data Processing and Multiconnectivity)the second Brain Korea 21 PLUS project.
文摘With the fast development of software defined network(SDN),numerous researches have been conducted for maximizing the performance of SDN.Currently,flow tables are utilized in OpenFlows witch for routing.Due to the space limitation of flow table and switch capacity,various issues exist in dealing with the flows.The existing schemes typically employ reactive approach such that the selection of evicted entries occurs when timeout or table miss occurs.In this paper a proactive approach is proposed based on the prediction of the probability of matching of the entries.Here eviction occurs proactively when the utilization of flow table exceeds a threshold,and the flow entry of the lowest matching probability is evicted.The matching probability is estimated using hidden Markov model(HMM).Computersimulation reveals that it significantly enhances the prediction accuracy and decreases the number of table misses compared to the standard Hard timeout scheme and Flow master scheme.