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公开(公告)号：US10346602B2
公开(公告)日：2019-07-09
申请号：US15765253
申请日：2016-03-29
发明人： Zheng Xiao , Jiachun Zheng , Xiaoping Han , Jinjun He
摘要： Provided are a method and device for authenticating an identity based on fusion of multiple biological characteristics. The method includes: collecting at least two types of biological characteristic identity information of a to-be-identified user; performing characteristic extraction on each type of the collected biological characteristic identify information, to obtain characteristic information corresponding to the type; establishing characteristic matrixes based on the characteristic information; performing normalization processing on each of the characteristic matrixes; performing dynamic weighting fusion on all of the normalized characteristic matrixes, to obtain a fused characteristic matrix; matching the fused characteristic matrix with a preset standard matrix, to obtain a matching score; and obtaining an identity identification result of the to-be-identified user based on a Bayesian decision model and the matching score.
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公开(公告)号：US10394302B2
公开(公告)日：2019-08-27
申请号：US15772061
申请日：2016-07-29
发明人： Xiaoping Han , Jiachun Zheng , Zheng Xiao , Jinjun He
摘要： Provided are an energy-saving control method and device for a self-service device. The method includes: acquiring to-be-learned sample information from historical usage data of users of the self-service device, where the sample information indicates the number of users which use the self-service device in each of different sub-periods of a period of time; learning the to-be-learned sample information by using a preset Bayesian prior probability model, to obtain a learning result; updating the Bayesian prior probability model based on the learning result; predicting the number of users in each of sub-periods of a preset period of time by using the updated Bayesian prior probability model, to obtain the predicted number of users at the self-service device; and modifying a sleep interval of the self-service device in each of the sub-periods based on the predicted number of users.
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