How to deal with uncertainty is crucial in exact concept mapping between ontologies. This paper presents a new framework on modeling uncertainty in ontologies based on bayesian networks （BN）. In our approach, ontolo...How to deal with uncertainty is crucial in exact concept mapping between ontologies. This paper presents a new framework on modeling uncertainty in ontologies based on bayesian networks （BN）. In our approach, ontology Web language （OWL） is extended to add probabilistie markups for attaching probability information, the source and target ontol ogies （expressed by patulous OWL） are translated into hayesian networks （BNs）, the mapping between the two ontologies can be digged out by constructing the conditional probability tables （CPTs） of the BN using a improved algorithm named I-IPFP based on iterative proportional fitting procedure （IPFP）. The basic idea of this framework and algorithm are validated by positive results from computer experiments.展开更多
This study aims to reveal the impacts of three important uncertainty issues in landslide susceptibility prediction(LSP),namely the spatial resolution,proportion of model training and testing datasets and selection of ...This study aims to reveal the impacts of three important uncertainty issues in landslide susceptibility prediction(LSP),namely the spatial resolution,proportion of model training and testing datasets and selection of machine learning models.Taking Yanchang County of China as example,the landslide inventory and 12 important conditioning factors were acquired.The frequency ratios of each conditioning factor were calculated under five spatial resolutions(15,30,60,90 and 120 m).Landslide and non-landslide samples obtained under each spatial resolution were further divided into five proportions of training and testing datasets(9:1,8:2,7:3,6:4 and 5:5),and four typical machine learning models were applied for LSP modelling.The results demonstrated that different spatial resolution and training and testing dataset proportions induce basically similar influences on the modeling uncertainty.With a decrease in the spatial resolution from 15 m to 120 m and a change in the proportions of the training and testing datasets from 9:1 to 5:5,the modelling accuracy gradually decreased,while the mean values of predicted landslide susceptibility indexes increased and their standard deviations decreased.The sensitivities of the three uncertainty issues to LSP modeling were,in order,the spatial resolution,the choice of machine learning model and the proportions of training/testing datasets.展开更多
使用内充气正比计数管测量放射性气体活度是放射性气体活度绝对测量的最主要方法之一.基于Geant4(GEometry ANd Tracking 4)的模拟数据,研究了内充气正比计数法测量活度的过程中,壁效应和小脉冲幅度的修正方法本身对最终活度测量的不确...使用内充气正比计数管测量放射性气体活度是放射性气体活度绝对测量的最主要方法之一.基于Geant4(GEometry ANd Tracking 4)的模拟数据,研究了内充气正比计数法测量活度的过程中,壁效应和小脉冲幅度的修正方法本身对最终活度测量的不确定度的影响.根据模拟结果,得到如下结论:为达到0.3％以下的不确定度,3H与85Kr的测量下阈应分别低于0.2 keV和0.6 keV;壁效应对3H的影响较小,对85Kr的影响较大,但其结果可通过改变气体压强的方法修正到0.3％以内的不确定度.展开更多
基金Supported by the National Natural Science Foun-dation of China (60403027)
文摘How to deal with uncertainty is crucial in exact concept mapping between ontologies. This paper presents a new framework on modeling uncertainty in ontologies based on bayesian networks （BN）. In our approach, ontology Web language （OWL） is extended to add probabilistie markups for attaching probability information, the source and target ontol ogies （expressed by patulous OWL） are translated into hayesian networks （BNs）, the mapping between the two ontologies can be digged out by constructing the conditional probability tables （CPTs） of the BN using a improved algorithm named I-IPFP based on iterative proportional fitting procedure （IPFP）. The basic idea of this framework and algorithm are validated by positive results from computer experiments.
基金This research is funded by the National Natural Science Foundation of China(41807285,41762020,51879127 and 51769014E)Natural Science Foundation of Hebei Province(D2022202005).
文摘This study aims to reveal the impacts of three important uncertainty issues in landslide susceptibility prediction(LSP),namely the spatial resolution,proportion of model training and testing datasets and selection of machine learning models.Taking Yanchang County of China as example,the landslide inventory and 12 important conditioning factors were acquired.The frequency ratios of each conditioning factor were calculated under five spatial resolutions(15,30,60,90 and 120 m).Landslide and non-landslide samples obtained under each spatial resolution were further divided into five proportions of training and testing datasets(9:1,8:2,7:3,6:4 and 5:5),and four typical machine learning models were applied for LSP modelling.The results demonstrated that different spatial resolution and training and testing dataset proportions induce basically similar influences on the modeling uncertainty.With a decrease in the spatial resolution from 15 m to 120 m and a change in the proportions of the training and testing datasets from 9:1 to 5:5,the modelling accuracy gradually decreased,while the mean values of predicted landslide susceptibility indexes increased and their standard deviations decreased.The sensitivities of the three uncertainty issues to LSP modeling were,in order,the spatial resolution,the choice of machine learning model and the proportions of training/testing datasets.
