A fast edge detection method basing on the combination of fuzzy subsets is developed, in which the detection of an edge as a classification problem will be considered, partitioning the image into two portions: the edg...A fast edge detection method basing on the combination of fuzzy subsets is developed, in which the detection of an edge as a classification problem will be considered, partitioning the image into two portions: the edge portion and the non-edge portion. The latter one, as the main constituent of an image, consists of the object and its background. Removing the non-edge portion from an image, the remainder is nothing but the edge of this image. As far as the fuzziness of the edge of an image is concerned, some fuzzy operations can be made. In this paper, the gray level histogram is partitioned into several sub-regions, and some operations are performed with the associated fuzzy subsets corresponding to those sub-edges in the sub-regions on the gray-level-square-difference histogram, and the edge of this image is finally obtained. Practical examples in this paper illustrate that, the described method is simple and effective to achieve an ideal edge image.展开更多
The concept of α-CT2 separation L-fuzzy subsets in L-fuzzy topological spaces is presented by taking the stratiform structure of L-fuzzy subsets as the point of departure,and its basic characterizations and some topo...The concept of α-CT2 separation L-fuzzy subsets in L-fuzzy topological spaces is presented by taking the stratiform structure of L-fuzzy subsets as the point of departure,and its basic characterizations and some topological properties are discussed,and the relation between it and other separateness is exposed,and the action is studied of α-CT2 separateness in N-compact spaces and N-paracompact spaces.展开更多
Background:Altered hydrology is a stressor on aquatic life,but quantitative relations between specific aspects of streamflow alteration and biological responses have not been developed on a statewide scale in Minnesot...Background:Altered hydrology is a stressor on aquatic life,but quantitative relations between specific aspects of streamflow alteration and biological responses have not been developed on a statewide scale in Minnesota.Best sub-sets regression analysis was used to develop linear regression models that quantify relations among five categories of hydrologic metrics(i.e.,duration,frequency,magnitude,rate-of-change,and timing)computed from streamgage records and six categories of biological metrics(i.e.,composition,habitat,life history,reproductive,tolerance,trophic)computed from fish-community samples,as well as fish-based indices of biotic integrity(FIBI)scores and FIBI scores normalized to an impairment threshold of the corresponding stream class(FIBI_BCG4).Relations between hydrology and fish community responses were examined using three hydrologic datasets that represented periods of record,long-term changes,and short-term changes to flow regimes in streams of Minnesota.Results:Regression models demonstrated significant relations between hydrologic explanatory metrics and fish-based biological response metrics,and the five regression models with the strongest linear relations explained over 70%of the variability in the biological metric using three hydrologic metrics as explanatory variables.Tolerance-based biological metrics demonstrated the strongest linear relations to hydrologic metrics.The most commonly used hydrologic metrics were related to bankfull flows and aspects of flow variability.Conclusions:Final regression models represent paired streamgage records and biological samples throughout the State of Minnesota and encompass differences in stream orders,hydrologic landscape units,and watershed sizes.Presented methods can support evaluations of stream fish communities and facilitate targeted efforts to improve the health of fish communities.Methods also can be applied to locations outside of Minnesota with continuous streamgage data and fish-community samples.展开更多
Two new concepts-fuzzy mutuality and average fuzzy entropy are presented. Then based on these concepts, a new algorithm-RSMA （representative subset mining algorithm） is proposed, which can abstract representative su...Two new concepts-fuzzy mutuality and average fuzzy entropy are presented. Then based on these concepts, a new algorithm-RSMA （representative subset mining algorithm） is proposed, which can abstract representative subset from massive data. To accelerate the speed of producing representative subset, an improved algorithm-ARSMA（accelerated representative subset mining algorithm） is advanced, which adopt combining putting forward with backward strategies. In this way, the performance of the algorithm is improved. Finally we make experiments on real datasets and evaluate the representative subset. The experiment shows that ARSMA algorithm is more excellent than RandomPick algorithm either on effectiveness or efficiency.展开更多
In this paper, data from disaster reduction in China have been used to statistically analy ze the disaster by tropical cyclones that occurred from 1979 to 1996 within the area of Guangdong province. By the method of f...In this paper, data from disaster reduction in China have been used to statistically analy ze the disaster by tropical cyclones that occurred from 1979 to 1996 within the area of Guangdong province. By the method of fuzzy subset theory, the conditions of the disaster have been discussed and the evaluation model of the disaster B set up. The index of the disaster of every tropical cyclone have been obtained and divided into five parts. The result shows that the index is almost proportional to the direct economic losses, so this model is reasonable.展开更多
In this paper,the definitions of fuzzy regular subsemigroup and fuzzy left(right,intra-)regular sub-semigroup in semigroups are introduced.Some characterizations of them are given.Proposition 2.1.A fuzzy set A in a se...In this paper,the definitions of fuzzy regular subsemigroup and fuzzy left(right,intra-)regular sub-semigroup in semigroups are introduced.Some characterizations of them are given.Proposition 2.1.A fuzzy set A in a semigroup S is a fuzzy subsemigroup iff for any λ∈[0,1],if A_λ={x∈S|A(x)≥λ}≠ ,then A_λ is a subsemigroup of S.Proposition 2.2.A fuzzy set A in a semigroup S is a fuzzy left(right)ideal iff for any λ∈(0,1],if A={x∈S|A(x)展开更多
Developing and optimizing fuzzy relation equations are of great relevance in system modeling,which involves analysis of numerous fuzzy rules.As each rule varies with respect to its level of influence,it is advocated t...Developing and optimizing fuzzy relation equations are of great relevance in system modeling,which involves analysis of numerous fuzzy rules.As each rule varies with respect to its level of influence,it is advocated that the performance of a fuzzy relation equation is strongly related to a subset of fuzzy rules obtained by removing those without significant relevance.In this study,we establish a novel framework of developing granular fuzzy relation equations that concerns the determination of an optimal subset of fuzzy rules.The subset of rules is selected by maximizing their performance of the obtained solutions.The originality of this study is conducted in the following ways.Starting with developing granular fuzzy relation equations,an interval-valued fuzzy relation is determined based on the selected subset of fuzzy rules(the subset of rules is transformed to interval-valued fuzzy sets and subsequently the interval-valued fuzzy sets are utilized to form interval-valued fuzzy relations),which can be used to represent the fuzzy relation of the entire rule base with high performance and efficiency.Then,the particle swarm optimization(PSO)is implemented to solve a multi-objective optimization problem,in which not only an optimal subset of rules is selected but also a parameterεfor specifying a level of information granularity is determined.A series of experimental studies are performed to verify the feasibility of this framework and quantify its performance.A visible improvement of particle swarm optimization(about 78.56%of the encoding mechanism of particle swarm optimization,or 90.42%of particle swarm optimization with an exploration operator)is gained over the method conducted without using the particle swarm optimization algorithm.展开更多
In handing information regarding various aspects of uncertainty, non-classical-mathematics (fuzzy mathematics or great extension and development of classical mathematics) is considered to be a more powerful technique ...In handing information regarding various aspects of uncertainty, non-classical-mathematics (fuzzy mathematics or great extension and development of classical mathematics) is considered to be a more powerful technique than classical mathematics. The non-classical mathematics, therefore, has now days become a useful tool in applications mathematics and computer science. The purpose of this paper is to apply the concept of the fuzzy sets to some algebraic structures such as an ideal, upper semilattice, lower semilattice, lattice and sub-algebra and gives some properties of these algebraic structures by using the concept of fuzzy sets. Finally, related properties are investigated in fuzzy BCK-algebras.展开更多
In this paper we define N-fuzzy filters,N-fuzzy bi-ideal subsets and N-fuzzy bi-filters of ordered semigroups and characterize ordered semigroups in terms of N-fuzzy filters,N-fuzzy bi-ideal subsets and N-fuzzy bi-fil...In this paper we define N-fuzzy filters,N-fuzzy bi-ideal subsets and N-fuzzy bi-filters of ordered semigroups and characterize ordered semigroups in terms of N-fuzzy filters,N-fuzzy bi-ideal subsets and N-fuzzy bi-filters.We establish relationship of N-fuzzy filters and prime N-fuzzy ideals of ordered semigroups. Also we discuss the relationship of N-fuzzy bi-filters and prime N-fuzzy bi-ideal subsets of ordered semigroups.展开更多
文摘A fast edge detection method basing on the combination of fuzzy subsets is developed, in which the detection of an edge as a classification problem will be considered, partitioning the image into two portions: the edge portion and the non-edge portion. The latter one, as the main constituent of an image, consists of the object and its background. Removing the non-edge portion from an image, the remainder is nothing but the edge of this image. As far as the fuzziness of the edge of an image is concerned, some fuzzy operations can be made. In this paper, the gray level histogram is partitioned into several sub-regions, and some operations are performed with the associated fuzzy subsets corresponding to those sub-edges in the sub-regions on the gray-level-square-difference histogram, and the edge of this image is finally obtained. Practical examples in this paper illustrate that, the described method is simple and effective to achieve an ideal edge image.
文摘The concept of α-CT2 separation L-fuzzy subsets in L-fuzzy topological spaces is presented by taking the stratiform structure of L-fuzzy subsets as the point of departure,and its basic characterizations and some topological properties are discussed,and the relation between it and other separateness is exposed,and the action is studied of α-CT2 separateness in N-compact spaces and N-paracompact spaces.
基金funded through Minnesota Pollution Control Agency Clean Water Legacy Funds(140809)U.S.Geological Survey Cooperative Matching Funds(1632A)。
文摘Background:Altered hydrology is a stressor on aquatic life,but quantitative relations between specific aspects of streamflow alteration and biological responses have not been developed on a statewide scale in Minnesota.Best sub-sets regression analysis was used to develop linear regression models that quantify relations among five categories of hydrologic metrics(i.e.,duration,frequency,magnitude,rate-of-change,and timing)computed from streamgage records and six categories of biological metrics(i.e.,composition,habitat,life history,reproductive,tolerance,trophic)computed from fish-community samples,as well as fish-based indices of biotic integrity(FIBI)scores and FIBI scores normalized to an impairment threshold of the corresponding stream class(FIBI_BCG4).Relations between hydrology and fish community responses were examined using three hydrologic datasets that represented periods of record,long-term changes,and short-term changes to flow regimes in streams of Minnesota.Results:Regression models demonstrated significant relations between hydrologic explanatory metrics and fish-based biological response metrics,and the five regression models with the strongest linear relations explained over 70%of the variability in the biological metric using three hydrologic metrics as explanatory variables.Tolerance-based biological metrics demonstrated the strongest linear relations to hydrologic metrics.The most commonly used hydrologic metrics were related to bankfull flows and aspects of flow variability.Conclusions:Final regression models represent paired streamgage records and biological samples throughout the State of Minnesota and encompass differences in stream orders,hydrologic landscape units,and watershed sizes.Presented methods can support evaluations of stream fish communities and facilitate targeted efforts to improve the health of fish communities.Methods also can be applied to locations outside of Minnesota with continuous streamgage data and fish-community samples.
基金Supported by the National High Technology Research and Development Program of China (2001AA113182)
文摘Two new concepts-fuzzy mutuality and average fuzzy entropy are presented. Then based on these concepts, a new algorithm-RSMA （representative subset mining algorithm） is proposed, which can abstract representative subset from massive data. To accelerate the speed of producing representative subset, an improved algorithm-ARSMA（accelerated representative subset mining algorithm） is advanced, which adopt combining putting forward with backward strategies. In this way, the performance of the algorithm is improved. Finally we make experiments on real datasets and evaluate the representative subset. The experiment shows that ARSMA algorithm is more excellent than RandomPick algorithm either on effectiveness or efficiency.
基金Key scientific and technological project in the 9th five-year economic development plan of China(96-908-03-03)
文摘In this paper, data from disaster reduction in China have been used to statistically analy ze the disaster by tropical cyclones that occurred from 1979 to 1996 within the area of Guangdong province. By the method of fuzzy subset theory, the conditions of the disaster have been discussed and the evaluation model of the disaster B set up. The index of the disaster of every tropical cyclone have been obtained and divided into five parts. The result shows that the index is almost proportional to the direct economic losses, so this model is reasonable.
文摘In this paper,the definitions of fuzzy regular subsemigroup and fuzzy left(right,intra-)regular sub-semigroup in semigroups are introduced.Some characterizations of them are given.Proposition 2.1.A fuzzy set A in a semigroup S is a fuzzy subsemigroup iff for any λ∈[0,1],if A_λ={x∈S|A(x)≥λ}≠ ,then A_λ is a subsemigroup of S.Proposition 2.2.A fuzzy set A in a semigroup S is a fuzzy left(right)ideal iff for any λ∈(0,1],if A={x∈S|A(x)
基金supported by the National Natural Sci-ence Foundation of China(62006184,62076189,61873277).
文摘Developing and optimizing fuzzy relation equations are of great relevance in system modeling,which involves analysis of numerous fuzzy rules.As each rule varies with respect to its level of influence,it is advocated that the performance of a fuzzy relation equation is strongly related to a subset of fuzzy rules obtained by removing those without significant relevance.In this study,we establish a novel framework of developing granular fuzzy relation equations that concerns the determination of an optimal subset of fuzzy rules.The subset of rules is selected by maximizing their performance of the obtained solutions.The originality of this study is conducted in the following ways.Starting with developing granular fuzzy relation equations,an interval-valued fuzzy relation is determined based on the selected subset of fuzzy rules(the subset of rules is transformed to interval-valued fuzzy sets and subsequently the interval-valued fuzzy sets are utilized to form interval-valued fuzzy relations),which can be used to represent the fuzzy relation of the entire rule base with high performance and efficiency.Then,the particle swarm optimization(PSO)is implemented to solve a multi-objective optimization problem,in which not only an optimal subset of rules is selected but also a parameterεfor specifying a level of information granularity is determined.A series of experimental studies are performed to verify the feasibility of this framework and quantify its performance.A visible improvement of particle swarm optimization(about 78.56%of the encoding mechanism of particle swarm optimization,or 90.42%of particle swarm optimization with an exploration operator)is gained over the method conducted without using the particle swarm optimization algorithm.
文摘In handing information regarding various aspects of uncertainty, non-classical-mathematics (fuzzy mathematics or great extension and development of classical mathematics) is considered to be a more powerful technique than classical mathematics. The non-classical mathematics, therefore, has now days become a useful tool in applications mathematics and computer science. The purpose of this paper is to apply the concept of the fuzzy sets to some algebraic structures such as an ideal, upper semilattice, lower semilattice, lattice and sub-algebra and gives some properties of these algebraic structures by using the concept of fuzzy sets. Finally, related properties are investigated in fuzzy BCK-algebras.
基金supported by the Higher Education Commission of Pakistan under Grant No.I-8/HEC/HRD/2007/182
文摘In this paper we define N-fuzzy filters,N-fuzzy bi-ideal subsets and N-fuzzy bi-filters of ordered semigroups and characterize ordered semigroups in terms of N-fuzzy filters,N-fuzzy bi-ideal subsets and N-fuzzy bi-filters.We establish relationship of N-fuzzy filters and prime N-fuzzy ideals of ordered semigroups. Also we discuss the relationship of N-fuzzy bi-filters and prime N-fuzzy bi-ideal subsets of ordered semigroups.
