Human Reliability Analysis(HRA)is an important part in safety assessment of a large complex system.Human Cognitive Reliability(HCR)model is a method of evaluating the probability that operators fail to complete during...Human Reliability Analysis(HRA)is an important part in safety assessment of a large complex system.Human Cognitive Reliability(HCR)model is a method of evaluating the probability that operators fail to complete during diagnostic decision making within a limited time,which is widely used in HRA.In the application of this method,cognitive patterns of humans are required to be considered and classified,and this process often relies on the evaluation opinions of experts which is highly subjective and uncertain.How to effectively express and process this uncertain and subjective information plays a critical role in improving the accuracy and applicability of HCR.In this paper,a new model was proposed to deal with the uncertain information which exists in the processes of cognitive pattern classification in HCR.First,an evaluation panel was constructed based on expert opinions and processing including setting corresponding anchor points and qualitative indicators of different cognitive patterns,and mapping them to fuzzy numbers and unit intervals.Second,based on the evaluation panel,different analysts judge the cognitive pattern types of actual specific events and provide the level of confidence he or she has in the judgments.Finally,the evaluation opinions of multiple analysts were expressed and fused based on the Dempster-Shafer Evidence Theory(DSET),and the fused results were applied to the HCR model to obtain the Human Error Probability(HEP).A case study was used to demonstrate the procedure and effectiveness of the proposed method.展开更多
Updating an authoritative Land Use and Land Cover(LULC)database requires many resources.Volunteered geographic information(VGI)involves citizens in the collection of data about their spatial environment.There is a gro...Updating an authoritative Land Use and Land Cover(LULC)database requires many resources.Volunteered geographic information(VGI)involves citizens in the collection of data about their spatial environment.There is a growing interest in using existing VGI to update authoritative databases.This paper presents a framework aimed at integrating multi-source VGI based on a data fusion technique,in order to update an authoritative land use database.Each VGI data source is considered to be an independent source of information,which is fused together using Dempster-Shafer Theory(DST).The framework is tested in the updating of the authoritative land use data produced by the French National Mapping Agency.Four data sets were collected from several in-situ and remote campaigns run between 2018 and 2020 by contributors with varying profiles.The data fusion approach achieved an overall accuracy of 85.6%for the 144 features having at least two contributions when the confidence threshold was set to 0.05.Despite the heterogeneity and limited amount of VGI used,the results are promising,with 99%of the LU polygons updated or enriched.These results show the potential of using multi-source VGI to update or enrich authoritative LU data and potentially LULC data more generally。展开更多
基金supported by Shanghai Natural Science Foundation(Grant No.19ZR1420700)sponsored by Shanghai Rising-Star Program(Grant No.21QA1403400)Shanghai Key Laboratory of Power Station Automation Technology(Grant No.13DZ2273800).
文摘Human Reliability Analysis(HRA)is an important part in safety assessment of a large complex system.Human Cognitive Reliability(HCR)model is a method of evaluating the probability that operators fail to complete during diagnostic decision making within a limited time,which is widely used in HRA.In the application of this method,cognitive patterns of humans are required to be considered and classified,and this process often relies on the evaluation opinions of experts which is highly subjective and uncertain.How to effectively express and process this uncertain and subjective information plays a critical role in improving the accuracy and applicability of HCR.In this paper,a new model was proposed to deal with the uncertain information which exists in the processes of cognitive pattern classification in HCR.First,an evaluation panel was constructed based on expert opinions and processing including setting corresponding anchor points and qualitative indicators of different cognitive patterns,and mapping them to fuzzy numbers and unit intervals.Second,based on the evaluation panel,different analysts judge the cognitive pattern types of actual specific events and provide the level of confidence he or she has in the judgments.Finally,the evaluation opinions of multiple analysts were expressed and fused based on the Dempster-Shafer Evidence Theory(DSET),and the fused results were applied to the HCR model to obtain the Human Error Probability(HEP).A case study was used to demonstrate the procedure and effectiveness of the proposed method.
基金supported by Horizon 2020 Framework Programme[grant number 689812].
文摘Updating an authoritative Land Use and Land Cover(LULC)database requires many resources.Volunteered geographic information(VGI)involves citizens in the collection of data about their spatial environment.There is a growing interest in using existing VGI to update authoritative databases.This paper presents a framework aimed at integrating multi-source VGI based on a data fusion technique,in order to update an authoritative land use database.Each VGI data source is considered to be an independent source of information,which is fused together using Dempster-Shafer Theory(DST).The framework is tested in the updating of the authoritative land use data produced by the French National Mapping Agency.Four data sets were collected from several in-situ and remote campaigns run between 2018 and 2020 by contributors with varying profiles.The data fusion approach achieved an overall accuracy of 85.6%for the 144 features having at least two contributions when the confidence threshold was set to 0.05.Despite the heterogeneity and limited amount of VGI used,the results are promising,with 99%of the LU polygons updated or enriched.These results show the potential of using multi-source VGI to update or enrich authoritative LU data and potentially LULC data more generally。
