Drought conditions at a given location evolve randomly through time and are typically characterized by severity and duration. Researchers interested in modeling the economic effects of drought on agriculture or other ...Drought conditions at a given location evolve randomly through time and are typically characterized by severity and duration. Researchers interested in modeling the economic effects of drought on agriculture or other water users often capture the stochastic nature of drought and its conditions via multiyear, stochastic economic models. Three major sources of uncertainty in application of a multiyear discrete stochastic model to evaluate user preparedness and response to drought are: (1) the assumption of independence of yearly weather conditions, (2) linguistic vagueness in the definition of drought itself, and (3) the duration of drought. One means of addressing these uncertainties is to re-cast drought as a stochastic, multiyear process using a “fuzzy” semi-Markov process. In this paper, we review “crisp” versus “fuzzy” representations of drought and show how fuzzy semi-Markov processes can aid researchers in developing more robust multiyear, discrete stochastic models.展开更多
To overcome the disadvantage of classical recognition model that cannot perform well enough when there are some noises or lost frames in expression image sequences, a novel model called fuzzy buried Markov model （FBM...To overcome the disadvantage of classical recognition model that cannot perform well enough when there are some noises or lost frames in expression image sequences, a novel model called fuzzy buried Markov model （FBMM） is presented in this paper. FBMM relaxes conditional independence assumptions for classical hidden Markov model （HMM） by adding the specific cross-observation dependencies between observation elements. Compared with buried Markov model （BMM）, FBMM utilizes cloud distribution to replace probability distribution to describe state transition and observation symbol generation and adopts maximum mutual information （MMI） method to replace maximum likelihood （ML） method to estimate parameters. Theoretical justifications and experimental results verify higher recognition rate and stronger robustness of facial expression recognition for image sequences based on FBMM than those of HMM and BMM.展开更多
Social network analysis （SNA） is among the hottest topics of current research. Most measurements of SNA methods are certainty oriented, while in reality, the uncertainties in relationships are widely spread to be ov...Social network analysis （SNA） is among the hottest topics of current research. Most measurements of SNA methods are certainty oriented, while in reality, the uncertainties in relationships are widely spread to be overridden. In this paper, fuzzy concept is introduced to model the uncertainty, and a similarity metric is used to build a fuzzy relation model among individuals in the social network. The traditional social network is transformed into a fuzzy network by replacing the traditional relations with fuzzy relation and calculating the global fuzzy measure such as network density and centralization. Finally, the trend of fuzzy network evolution is analyzed and predicted with a fuzzy Markov chain. Experimental results demonstrate that the fuzzy network has more superiority than the traditional network in describing the network evolution process.展开更多
文摘Drought conditions at a given location evolve randomly through time and are typically characterized by severity and duration. Researchers interested in modeling the economic effects of drought on agriculture or other water users often capture the stochastic nature of drought and its conditions via multiyear, stochastic economic models. Three major sources of uncertainty in application of a multiyear discrete stochastic model to evaluate user preparedness and response to drought are: (1) the assumption of independence of yearly weather conditions, (2) linguistic vagueness in the definition of drought itself, and (3) the duration of drought. One means of addressing these uncertainties is to re-cast drought as a stochastic, multiyear process using a “fuzzy” semi-Markov process. In this paper, we review “crisp” versus “fuzzy” representations of drought and show how fuzzy semi-Markov processes can aid researchers in developing more robust multiyear, discrete stochastic models.
基金supported by the National Natural Science Foundation of China under Grant No. 60673190
文摘To overcome the disadvantage of classical recognition model that cannot perform well enough when there are some noises or lost frames in expression image sequences, a novel model called fuzzy buried Markov model （FBMM） is presented in this paper. FBMM relaxes conditional independence assumptions for classical hidden Markov model （HMM） by adding the specific cross-observation dependencies between observation elements. Compared with buried Markov model （BMM）, FBMM utilizes cloud distribution to replace probability distribution to describe state transition and observation symbol generation and adopts maximum mutual information （MMI） method to replace maximum likelihood （ML） method to estimate parameters. Theoretical justifications and experimental results verify higher recognition rate and stronger robustness of facial expression recognition for image sequences based on FBMM than those of HMM and BMM.
基金Project supported by the National Natural Science Foundation of China(Nos.61272422 and 61202353)
文摘Social network analysis （SNA） is among the hottest topics of current research. Most measurements of SNA methods are certainty oriented, while in reality, the uncertainties in relationships are widely spread to be overridden. In this paper, fuzzy concept is introduced to model the uncertainty, and a similarity metric is used to build a fuzzy relation model among individuals in the social network. The traditional social network is transformed into a fuzzy network by replacing the traditional relations with fuzzy relation and calculating the global fuzzy measure such as network density and centralization. Finally, the trend of fuzzy network evolution is analyzed and predicted with a fuzzy Markov chain. Experimental results demonstrate that the fuzzy network has more superiority than the traditional network in describing the network evolution process.
