In the theory of belief functions,the evidence combination is a kind of decision-level information fusion.Given two or more Basic Belief Assignments(BBAs)originated from different information sources,the combination r...In the theory of belief functions,the evidence combination is a kind of decision-level information fusion.Given two or more Basic Belief Assignments(BBAs)originated from different information sources,the combination rule is used to combine them to expect a better decision result.When only a combined BBA is given and original BBAs are discarded,if one wants to analyze the difference between the information sources,evidence de-combination is needed to determine the original BBAs.Evidence de-combination can be considered as the inverse process of the information fusion.This paper focuses on such a defusion of information in the theory of belief functions.It is an under-determined problem if only the combined BBA is available.In this paper,two optimization-based approaches are proposed to de-combine a given BBA according to the criteria of divergence maximization and information maximization,respectively.The new proposed approaches can be used for two or more information sources.Some numerical examples and an example of application are provided to illustrate and validate our approaches.展开更多
In information fusion,the uncertain information from different sources might be modeled with different theoretical frameworks.When one needs to fuse the uncertain information represented by different uncertainty theor...In information fusion,the uncertain information from different sources might be modeled with different theoretical frameworks.When one needs to fuse the uncertain information represented by different uncertainty theories,constructing the transformation between different frameworks is crucial.Various transformations of a Fuzzy Membership Function(FMF)into a Basic Belief Assignment(BBA)have been proposed,where the transformations based on uncertainty maximization and minimization can determine the BBA without preselecting the focal elements.However,these two transformations that based on uncertainty optimization emphasize the extreme cases of uncertainty.To avoid extreme attitudinal bias,a trade-off or moderate BBA with the uncertainty degree between the minimal and maximal ones is more preferred.In this paper,two moderate transformations of an FMF into a trade-off BBA are proposed.One is the weighted average based transformation and the other is the optimization-based transformation with weighting mechanism,where the weighting factor can be user-specified or determined with some prior information.The rationality and effectiveness of our transformations are verified through numerical examples and classification examples.展开更多
In view of the complexity and uncertainty of system, both the state performances and state probabilities of multi-state components can be expressed by interval numbers. The belief function theory is used to characteri...In view of the complexity and uncertainty of system, both the state performances and state probabilities of multi-state components can be expressed by interval numbers. The belief function theory is used to characterize the uncertainty caused by various factors. A modified Markov model is proposed to obtain the state probabilities of components at any given moment and subsequently the mass function is used to represent the precise belief degree of state probabilities. Based on the primary studies of universal generating function(UGF)method, a belief UGF(BUGF) method is utilized to analyze the reliability and the uncertainty of excavator rectifier feedback system. This paper provides an available method to evaluate the reliability of multi-state systems(MSSs) with interval state performances and state probabilities, and also avoid the interval expansion problem.展开更多
Dempster-Shafer evidence theory, also called the theory of belief function, is widely used for uncertainty modeling and reasoning. However, when the size and number of focal elements are large, the evidence combinatio...Dempster-Shafer evidence theory, also called the theory of belief function, is widely used for uncertainty modeling and reasoning. However, when the size and number of focal elements are large, the evidence combination will bring a high computational complexity. To address this issue,various methods have been proposed including the implementation of more efficient combination rules and the simplifications or approximations of Basic Belief Assignments(BBAs). In this paper,a novel principle for approximating a BBA into a simpler one is proposed, which is based on the degree of non-redundancy for focal elements. More non-redundant focal elements are kept in the approximation while more redundant focal elements in the original BBA are removed first. Three types of degree of non-redundancy are defined based on three different definitions of focal element distance, respectively. Two different implementations of this principle for BBA approximations are proposed including a batch and an iterative type. Examples, experiments, comparisons and related analyses are provided to validate proposed approximation approaches.展开更多
目的:探讨基于健康信念模式的早期功能锻炼对中老年膝关节置换术后患者膝关节功能、并发症及生活质量的影响。方法:根据随机数字表法将2022年2月—2024年1月于绵阳市中心医院就诊的136例需行膝关节置换术的中老年患者分为对照组(n=68)...目的:探讨基于健康信念模式的早期功能锻炼对中老年膝关节置换术后患者膝关节功能、并发症及生活质量的影响。方法:根据随机数字表法将2022年2月—2024年1月于绵阳市中心医院就诊的136例需行膝关节置换术的中老年患者分为对照组(n=68)与信念组(n=68)。对照组采用常规功能锻炼,信念组采用基于健康信念模式的早期功能锻炼。对比两组膝关节功能、并发症发生率及生活质量。结果:信念组干预后美国膝关节协会评分(keen society score,KSS)均高于对照组,关节活动度(range of motion,ROM)大于对照组,差异均有统计学意义(P<0.05);信念组并发症发生率低于对照组,差异有统计学意义(P<0.05);信念组干预1、3个月后的世界卫生组织生活质量-100量表(World Health Organization quality of life-100,WHO-QOL-100)评分均高于对照组,差异均有统计学意义(P<0.05)。结论:基于健康信念模式的早期功能锻炼能够提高中老年膝关节置换术后患者的膝关节功能,减少并发症发生,提高生活质量。展开更多
基金supported by the National Natural Science Foundation of China(No.61671370)Project supported by joint foundation of X Lab–the 2~(nd)Academy of CASIC+1 种基金the Postdoctoral Science Foundation of China(No.2016M592790)the Postdoctoral Science Research Foundation of Shaanxi Province,China(No.2016BSHEDZZ46)。
文摘In the theory of belief functions,the evidence combination is a kind of decision-level information fusion.Given two or more Basic Belief Assignments(BBAs)originated from different information sources,the combination rule is used to combine them to expect a better decision result.When only a combined BBA is given and original BBAs are discarded,if one wants to analyze the difference between the information sources,evidence de-combination is needed to determine the original BBAs.Evidence de-combination can be considered as the inverse process of the information fusion.This paper focuses on such a defusion of information in the theory of belief functions.It is an under-determined problem if only the combined BBA is available.In this paper,two optimization-based approaches are proposed to de-combine a given BBA according to the criteria of divergence maximization and information maximization,respectively.The new proposed approaches can be used for two or more information sources.Some numerical examples and an example of application are provided to illustrate and validate our approaches.
基金supported by the National Natural Science Foundation of China(No.61671370)Postdoctoral Science Foundation of China(No.2016M592790)Postdoctoral Science Research Foundation of Shaanxi Province,China(No.2016BSHEDZZ46)。
文摘In information fusion,the uncertain information from different sources might be modeled with different theoretical frameworks.When one needs to fuse the uncertain information represented by different uncertainty theories,constructing the transformation between different frameworks is crucial.Various transformations of a Fuzzy Membership Function(FMF)into a Basic Belief Assignment(BBA)have been proposed,where the transformations based on uncertainty maximization and minimization can determine the BBA without preselecting the focal elements.However,these two transformations that based on uncertainty optimization emphasize the extreme cases of uncertainty.To avoid extreme attitudinal bias,a trade-off or moderate BBA with the uncertainty degree between the minimal and maximal ones is more preferred.In this paper,two moderate transformations of an FMF into a trade-off BBA are proposed.One is the weighted average based transformation and the other is the optimization-based transformation with weighting mechanism,where the weighting factor can be user-specified or determined with some prior information.The rationality and effectiveness of our transformations are verified through numerical examples and classification examples.
基金the National High Technology Research and Development Program(863)of China(No.2012AA062001)
文摘In view of the complexity and uncertainty of system, both the state performances and state probabilities of multi-state components can be expressed by interval numbers. The belief function theory is used to characterize the uncertainty caused by various factors. A modified Markov model is proposed to obtain the state probabilities of components at any given moment and subsequently the mass function is used to represent the precise belief degree of state probabilities. Based on the primary studies of universal generating function(UGF)method, a belief UGF(BUGF) method is utilized to analyze the reliability and the uncertainty of excavator rectifier feedback system. This paper provides an available method to evaluate the reliability of multi-state systems(MSSs) with interval state performances and state probabilities, and also avoid the interval expansion problem.
基金the National Natural Science Foundation of China (Nos. 61671370, 61573275)Postdoctoral Science Foundation of China (No. 2016M592790)+1 种基金Postdoctoral Science Research Foundation of Shaanxi Province, China (No. 2016BSHEDZZ46)Fundamental Research Funds for the Central Universities, China (No. xjj201066)
文摘Dempster-Shafer evidence theory, also called the theory of belief function, is widely used for uncertainty modeling and reasoning. However, when the size and number of focal elements are large, the evidence combination will bring a high computational complexity. To address this issue,various methods have been proposed including the implementation of more efficient combination rules and the simplifications or approximations of Basic Belief Assignments(BBAs). In this paper,a novel principle for approximating a BBA into a simpler one is proposed, which is based on the degree of non-redundancy for focal elements. More non-redundant focal elements are kept in the approximation while more redundant focal elements in the original BBA are removed first. Three types of degree of non-redundancy are defined based on three different definitions of focal element distance, respectively. Two different implementations of this principle for BBA approximations are proposed including a batch and an iterative type. Examples, experiments, comparisons and related analyses are provided to validate proposed approximation approaches.
文摘目的:探讨基于健康信念模式的早期功能锻炼对中老年膝关节置换术后患者膝关节功能、并发症及生活质量的影响。方法:根据随机数字表法将2022年2月—2024年1月于绵阳市中心医院就诊的136例需行膝关节置换术的中老年患者分为对照组(n=68)与信念组(n=68)。对照组采用常规功能锻炼,信念组采用基于健康信念模式的早期功能锻炼。对比两组膝关节功能、并发症发生率及生活质量。结果:信念组干预后美国膝关节协会评分(keen society score,KSS)均高于对照组,关节活动度(range of motion,ROM)大于对照组,差异均有统计学意义(P<0.05);信念组并发症发生率低于对照组,差异有统计学意义(P<0.05);信念组干预1、3个月后的世界卫生组织生活质量-100量表(World Health Organization quality of life-100,WHO-QOL-100)评分均高于对照组,差异均有统计学意义(P<0.05)。结论:基于健康信念模式的早期功能锻炼能够提高中老年膝关节置换术后患者的膝关节功能,减少并发症发生,提高生活质量。
