It is difficult to build accurate model for measurement noise covariance in complex backgrounds. For the scenarios of unknown sensor noise variances, an adaptive multi-target tracking algorithm based on labeled random...It is difficult to build accurate model for measurement noise covariance in complex backgrounds. For the scenarios of unknown sensor noise variances, an adaptive multi-target tracking algorithm based on labeled random finite set and variational Bayesian （VB） approximation is proposed. The variational approximation technique is introduced to the labeled multi-Bernoulli （LMB） filter to jointly estimate the states of targets and sensor noise variances. Simulation results show that the proposed method can give unbiased estimation of cardinality and has better performance than the VB probability hypothesis density （VB-PHD） filter and the VB cardinality balanced multi-target multi-Bernoulli （VB-CBMeMBer） filter in harsh situations. The simulations also confirm the robustness of the proposed method against the time-varying noise variances. The computational complexity of proposed method is higher than the VB-PHD and VB-CBMeMBer in extreme cases, while the mean execution times of the three methods are close when targets are well separated.展开更多
Co-occurrence matrices have been successfully applied in texture classification and segmentation.However,they have poor computation performance in real-time application.In this paper,the efficient co-occurrence matrix...Co-occurrence matrices have been successfully applied in texture classification and segmentation.However,they have poor computation performance in real-time application.In this paper,the efficient co-occurrence matrix solution for defect detection is focused on,and a method of Fuzzy Label Co-occurrence Matrix (FLCM) set is proposed.In this method,all gray levels are supposed to subject to some fuzzy sets called fuzzy tonal sets and three defective features are defined.Features of FLCM set with various parameters are combined for the final judgment.Unlike many methods,image acquired for learning hasn't to be entirely free of defects.It is shown that the method produces high accuracy and can be a competent candidate for plain colour fabric defect detection.展开更多
This paper addresses the open vehicle routing problem with time window(OVRPTW), where each vehicle does not need to return to the depot after completing the delivery task.The optimization objective is to minimize the ...This paper addresses the open vehicle routing problem with time window(OVRPTW), where each vehicle does not need to return to the depot after completing the delivery task.The optimization objective is to minimize the total distance. This problem exists widely in real-life logistics distribution process.We propose a hybrid column generation algorithm(HCGA) for the OVRPTW, embedding both exact algorithm and metaheuristic. In HCGA, a label setting algorithm and an intelligent algorithm are designed to select columns from small and large subproblems, respectively. Moreover, a branch strategy is devised to generate the final feasible solution for the OVRPTW. The computational results show that the proposed algorithm has faster speed and can obtain the approximate optimal solution of the problem with 100 customers in a reasonable time.展开更多
Group decision making problems are investigated with uncertain multiplicative linguistic preference relations.An unbalanced multiplicative linguistic label set is introduced,which can be used by the experts to express...Group decision making problems are investigated with uncertain multiplicative linguistic preference relations.An unbalanced multiplicative linguistic label set is introduced,which can be used by the experts to express their linguistic preference information over alternatives.The uncertain linguistic weighted geometric mean operator is utilized to aggregate all the individual uncertain multiplicative linguistic preference relations into a collective one,and then a simple approach is developed to determine the experts＇ weights by utilizing the consensus degrees among the individual uncertain multiplicative linguistic preference relations and the collective uncertain multiplicative linguistic preference relations.Furthermore,a practical interactive procedure for group decision making is proposed based on uncertain multiplicative linguistic preference relations,in which a possibility degree formula and a complementary matrix are used to rank the given alternatives.Finally,the proposed procedure is applied to solve the group decision making problem of a manufacturing company searching the best global supplier for one of its most critical parts used in assembling process.展开更多
Let G be a graph with vertex set V（G） and edge set E（G）. A labeling f ： V（G） →Z2 induces an edge labeling f＊： E（G） → Z2 defined by f＊（xy） = f（x） ＋ f（y）, for each edge xy ∈ E（G）. For i ∈ Z2, le...Let G be a graph with vertex set V（G） and edge set E（G）. A labeling f ： V（G） →Z2 induces an edge labeling f＊： E（G） → Z2 defined by f＊（xy） = f（x） ＋ f（y）, for each edge xy ∈ E（G）. For i ∈ Z2, let vf（i） = ｜{v ∈ V（G） ： f（v） = i}｜ and ef（i） = ｜{e ∈ E（G） ： f＊（e） =i}｜. A labeling f of a graph G is said to be friendly if ｜vf（0）- vf（1）｜ ≤ 1. The friendly index set of the graph G, denoted FI（G）, is defined as {｜ef（0）- ef（1）｜： the vertex labeling f is friendly}. This is a generalization of graph cordiality. We investigate the friendly index sets of cyclic silicates CS（n, m）.展开更多
In multiple attribute group decision making （MAGDM） problems based on linguistic information, the granularities of linguistic label sets are usually different due to the differences of thinking modes and habits amon...In multiple attribute group decision making （MAGDM） problems based on linguistic information, the granularities of linguistic label sets are usually different due to the differences of thinking modes and habits among decision makers. In order to deal with this inconvenience, the transformation relationships among multigranular linguistic labels （TRMLLs）, which are applied to unify linguistic labels with different granularities into a certain linguistic label set with fixed granularity, are presented in this paper. Furthermore, the reference tables are made according to TRMLLs so that the interrelated calculation will be less complicated, and the method of how to use them is explained in detail. At length, the TRMLLs are illustrated through an application example.展开更多
For k = （k1,...,kn） E Nn, 1 ≤ k1 ≤ ... ≤ kn, let Lkr be the family of labeled r-sets on k given by Lkr：= {{（a1,la1）,...,（ar,lar）} ： {a1,...,ar} [n],lai ∈ [kai],i = 1,...,r}. A family A of labeled r-sets i...For k = （k1,...,kn） E Nn, 1 ≤ k1 ≤ ... ≤ kn, let Lkr be the family of labeled r-sets on k given by Lkr：= {{（a1,la1）,...,（ar,lar）} ： {a1,...,ar} [n],lai ∈ [kai],i = 1,...,r}. A family A of labeled r-sets is intersecting if any two sets in ~4 intersect. In this paper we give the sizes and structures of intersecting families of labeled r-sets.展开更多
基金supported by the National High Technology Research and Development Program of China (No.2014AA7014061)the National Natural Science Foundation of China (No.61501484)
文摘It is difficult to build accurate model for measurement noise covariance in complex backgrounds. For the scenarios of unknown sensor noise variances, an adaptive multi-target tracking algorithm based on labeled random finite set and variational Bayesian （VB） approximation is proposed. The variational approximation technique is introduced to the labeled multi-Bernoulli （LMB） filter to jointly estimate the states of targets and sensor noise variances. Simulation results show that the proposed method can give unbiased estimation of cardinality and has better performance than the VB probability hypothesis density （VB-PHD） filter and the VB cardinality balanced multi-target multi-Bernoulli （VB-CBMeMBer） filter in harsh situations. The simulations also confirm the robustness of the proposed method against the time-varying noise variances. The computational complexity of proposed method is higher than the VB-PHD and VB-CBMeMBer in extreme cases, while the mean execution times of the three methods are close when targets are well separated.
基金Open Fund of the Key Lab of the Ministry of Education for Image Information Processing and Intelligent Control,China(No.200702)
文摘Co-occurrence matrices have been successfully applied in texture classification and segmentation.However,they have poor computation performance in real-time application.In this paper,the efficient co-occurrence matrix solution for defect detection is focused on,and a method of Fuzzy Label Co-occurrence Matrix (FLCM) set is proposed.In this method,all gray levels are supposed to subject to some fuzzy sets called fuzzy tonal sets and three defective features are defined.Features of FLCM set with various parameters are combined for the final judgment.Unlike many methods,image acquired for learning hasn't to be entirely free of defects.It is shown that the method produces high accuracy and can be a competent candidate for plain colour fabric defect detection.
基金supported by the National Natural Science Foundation of China (61963022,51665025,61873328)。
文摘This paper addresses the open vehicle routing problem with time window(OVRPTW), where each vehicle does not need to return to the depot after completing the delivery task.The optimization objective is to minimize the total distance. This problem exists widely in real-life logistics distribution process.We propose a hybrid column generation algorithm(HCGA) for the OVRPTW, embedding both exact algorithm and metaheuristic. In HCGA, a label setting algorithm and an intelligent algorithm are designed to select columns from small and large subproblems, respectively. Moreover, a branch strategy is devised to generate the final feasible solution for the OVRPTW. The computational results show that the proposed algorithm has faster speed and can obtain the approximate optimal solution of the problem with 100 customers in a reasonable time.
基金supported by the National Natural Science Foundation of China (70571087)the National Science Fund for Distinguished Young Scholars of China (70625005)
文摘Group decision making problems are investigated with uncertain multiplicative linguistic preference relations.An unbalanced multiplicative linguistic label set is introduced,which can be used by the experts to express their linguistic preference information over alternatives.The uncertain linguistic weighted geometric mean operator is utilized to aggregate all the individual uncertain multiplicative linguistic preference relations into a collective one,and then a simple approach is developed to determine the experts＇ weights by utilizing the consensus degrees among the individual uncertain multiplicative linguistic preference relations and the collective uncertain multiplicative linguistic preference relations.Furthermore,a practical interactive procedure for group decision making is proposed based on uncertain multiplicative linguistic preference relations,in which a possibility degree formula and a complementary matrix are used to rank the given alternatives.Finally,the proposed procedure is applied to solve the group decision making problem of a manufacturing company searching the best global supplier for one of its most critical parts used in assembling process.
基金Supported by the National Natural Science Foundation of China(Grant No.11371109)
文摘Let G be a graph with vertex set V（G） and edge set E（G）. A labeling f ： V（G） →Z2 induces an edge labeling f＊： E（G） → Z2 defined by f＊（xy） = f（x） ＋ f（y）, for each edge xy ∈ E（G）. For i ∈ Z2, let vf（i） = ｜{v ∈ V（G） ： f（v） = i}｜ and ef（i） = ｜{e ∈ E（G） ： f＊（e） =i}｜. A labeling f of a graph G is said to be friendly if ｜vf（0）- vf（1）｜ ≤ 1. The friendly index set of the graph G, denoted FI（G）, is defined as {｜ef（0）- ef（1）｜： the vertex labeling f is friendly}. This is a generalization of graph cordiality. We investigate the friendly index sets of cyclic silicates CS（n, m）.
基金supported by the National Science Fund for Distinguished Young Scholars of China (No.70625005)
文摘In multiple attribute group decision making （MAGDM） problems based on linguistic information, the granularities of linguistic label sets are usually different due to the differences of thinking modes and habits among decision makers. In order to deal with this inconvenience, the transformation relationships among multigranular linguistic labels （TRMLLs）, which are applied to unify linguistic labels with different granularities into a certain linguistic label set with fixed granularity, are presented in this paper. Furthermore, the reference tables are made according to TRMLLs so that the interrelated calculation will be less complicated, and the method of how to use them is explained in detail. At length, the TRMLLs are illustrated through an application example.
基金Supported by the National Natural Science Foundation of China (No. 11001249)the Mathematical Tianyuan Foundation of China (No. 11026180)
文摘For k = （k1,...,kn） E Nn, 1 ≤ k1 ≤ ... ≤ kn, let Lkr be the family of labeled r-sets on k given by Lkr：= {{（a1,la1）,...,（ar,lar）} ： {a1,...,ar} [n],lai ∈ [kai],i = 1,...,r}. A family A of labeled r-sets is intersecting if any two sets in ~4 intersect. In this paper we give the sizes and structures of intersecting families of labeled r-sets.
