Strabismus significantly impacts human health as a prevalent ophthalmic condition.Early detection of strabismus is crucial for effective treatment and prognosis.Traditional deep learning models for strabismus detectio...Strabismus significantly impacts human health as a prevalent ophthalmic condition.Early detection of strabismus is crucial for effective treatment and prognosis.Traditional deep learning models for strabismus detection often fail to estimate prediction certainty precisely.This paper employed a Bayesian deep learning algorithm with knowledge distillation,improving the model's performance and uncertainty estimation ability.Trained on 6807 images from two tertiary hospitals,the model showed significantly higher diagnostic accuracy than traditional deep-learning models.Experimental results revealed that knowledge distillation enhanced the Bayesian model’s performance and uncertainty estimation ability.These findings underscore the combined benefits of using Bayesian deep learning algorithms and knowledge distillation,which improve the reliability and accuracy of strabismus diagnostic predictions.展开更多
A new knowledge measure with parameter of intuitionistic fuzzy sets (IFSs) is presented based on the membership degree and the non-membership degree of IFSs, which complies with the extended form of Szmidt-Kacprzyk ax...A new knowledge measure with parameter of intuitionistic fuzzy sets (IFSs) is presented based on the membership degree and the non-membership degree of IFSs, which complies with the extended form of Szmidt-Kacprzyk axioms for intuitionistic fuzzy entropy. And a sufficient and necessary condition of order property in the Szmidt-Kacprzyk axioms is discussed. Additionally, some numerical examples are given to illustrate the applications of the proposed knowledge measure and some conventional entropies and knowledge measures of IFSs. The experimental results show that the results of the parametric model proposed in this paper are more accurate than those of most of the classic models.展开更多
In this paper, firstly the current analysis methods of uncertainties in seismic hazard assessment (SHA) are systematically reviewed, and the existing problems are pointed out. At the same time, it is proposed that the...In this paper, firstly the current analysis methods of uncertainties in seismic hazard assessment (SHA) are systematically reviewed, and the existing problems are pointed out. At the same time, it is proposed that the uncertainties can be divided into three kinds, namely, randomness, fuzziness and incompleteness of knowledge, and they can be treated in a unified way. Then, based on marked point process theory, a general equation for SHA is obtained, and a general equation for uncertainty analysis is also given on the basis of total probability theorem. Finally, the relationship between the proposed analysis method with those currently used both at home and abroad is discussed. The analysis shows that they are just the special case of the proposed method.展开更多
Developed from the dynamic causality diagram （DCD） model, a new approach for knowledge representation and reasoning named as dynamic uncertain causality graph （DUCG） is presented, which focuses on the compact repr...Developed from the dynamic causality diagram （DCD） model, a new approach for knowledge representation and reasoning named as dynamic uncertain causality graph （DUCG） is presented, which focuses on the compact representation of complex uncertain causalities and efficient probabilistie inference. It is pointed out that the existing models of compact representation and inference in Bayesian Network （BN） is applicable in single-valued cases, but may not be suitable to be applied in multi-valued cases. DUCG overcomes this problem and beyond. The main features of DUCG are： 1） compactly and graphically representing complex conditional probability distributions （CPDs）, regardless of whether the cases are single-valued or multi-valued; 2） able to perform exact reasoning in the case of the incomplete knowledge representation; 3） simplifying the graphical knowledge base conditional on observations before other calculations, so that the scale and complexity of problem can be reduced exponentially; 4） the efficient two-step inference algorithm consisting of （a） logic operation to find all possible hypotheses in concern for given observations and （b） the probability calculation for these hypotheses; and 5） much less relying on the parameter accuracy. An alarm system example is provided to illustrate the DUCG methodology.展开更多
基金supported in part by the Guangdong Natu-ral Science Foundation(No.2022A1515011396)in part by the National Key R and D Program of China(No.2021ZD0111502)in part by the Science Research Startup Foundation of Shantou University(No.NTF20021)。
文摘Strabismus significantly impacts human health as a prevalent ophthalmic condition.Early detection of strabismus is crucial for effective treatment and prognosis.Traditional deep learning models for strabismus detection often fail to estimate prediction certainty precisely.This paper employed a Bayesian deep learning algorithm with knowledge distillation,improving the model's performance and uncertainty estimation ability.Trained on 6807 images from two tertiary hospitals,the model showed significantly higher diagnostic accuracy than traditional deep-learning models.Experimental results revealed that knowledge distillation enhanced the Bayesian model’s performance and uncertainty estimation ability.These findings underscore the combined benefits of using Bayesian deep learning algorithms and knowledge distillation,which improve the reliability and accuracy of strabismus diagnostic predictions.
文摘A new knowledge measure with parameter of intuitionistic fuzzy sets (IFSs) is presented based on the membership degree and the non-membership degree of IFSs, which complies with the extended form of Szmidt-Kacprzyk axioms for intuitionistic fuzzy entropy. And a sufficient and necessary condition of order property in the Szmidt-Kacprzyk axioms is discussed. Additionally, some numerical examples are given to illustrate the applications of the proposed knowledge measure and some conventional entropies and knowledge measures of IFSs. The experimental results show that the results of the parametric model proposed in this paper are more accurate than those of most of the classic models.
文摘In this paper, firstly the current analysis methods of uncertainties in seismic hazard assessment (SHA) are systematically reviewed, and the existing problems are pointed out. At the same time, it is proposed that the uncertainties can be divided into three kinds, namely, randomness, fuzziness and incompleteness of knowledge, and they can be treated in a unified way. Then, based on marked point process theory, a general equation for SHA is obtained, and a general equation for uncertainty analysis is also given on the basis of total probability theorem. Finally, the relationship between the proposed analysis method with those currently used both at home and abroad is discussed. The analysis shows that they are just the special case of the proposed method.
基金supported by Guangdong Nuclear Power Group of China under Contract No. CNPRI-ST10P005the National Natural Science Foundation of China under Grant No. 60643006
文摘Developed from the dynamic causality diagram （DCD） model, a new approach for knowledge representation and reasoning named as dynamic uncertain causality graph （DUCG） is presented, which focuses on the compact representation of complex uncertain causalities and efficient probabilistie inference. It is pointed out that the existing models of compact representation and inference in Bayesian Network （BN） is applicable in single-valued cases, but may not be suitable to be applied in multi-valued cases. DUCG overcomes this problem and beyond. The main features of DUCG are： 1） compactly and graphically representing complex conditional probability distributions （CPDs）, regardless of whether the cases are single-valued or multi-valued; 2） able to perform exact reasoning in the case of the incomplete knowledge representation; 3） simplifying the graphical knowledge base conditional on observations before other calculations, so that the scale and complexity of problem can be reduced exponentially; 4） the efficient two-step inference algorithm consisting of （a） logic operation to find all possible hypotheses in concern for given observations and （b） the probability calculation for these hypotheses; and 5） much less relying on the parameter accuracy. An alarm system example is provided to illustrate the DUCG methodology.
