Interval-valued intuitionistic fuzzy aggregation operators

The notion of the interval-valued intuitionistic fuzzy set （IVIFS） is a generalization of that of the Atanassov＇s intuitionistic fuzzy set. The fundamental characteristic of IVIFS is that the values of its membership function and non-membership function are intervals rather than exact numbers. There are various averaging operators defined for IVlFSs. These operators are not monotone with respect to the total order of IVIFS, which is undesirable. This paper shows how such averaging operators can be represented by using additive generators of the product triangular norm, which simplifies and extends the existing constructions. Moreover, two new aggregation operators based on the t.ukasiewicz triangular norm are proposed, which are monotone with respect to the total order of IVIFS. Finally, an application of the interval-valued intuitionistic fuzzy weighted averaging operator is given to multiple criteria decision making.

Linear goal programming approach to obtaining the weights of intuitionistic fuzzy ordered weighted averaging operator

The multiple attribute decision making problems are studied, in which the information about attribute weights is partly known and the attribute values take the form of intuitionistic fuzzy numbers. The operational laws of intuitionistic fuzzy numbers are introduced, and the score function and accuracy function are presented to compare the intuitionistic fuzzy numbers. The intuitionistic fuzzy ordered weighted averaging （IFOWA） operator which is an extension of the well-known ordered weighted averaging （OWA） operator is investigated to aggregate the intuitionistic fuzzy information. In order to determine the weights of intuitionistic fuzzy ordered weighted averaging operator, a linear goal programming procedure is proposed for learning the weights from data. Finally, an example is illustrated to verify the effectiveness and practicability of the developed method.

A Multi-Stage Green Barrier Strategy for the Control of Global SARS-CoV-2 Transmission via Cold Chain Goods

1.The object-to-human transmission route of SARS-CoV-2 At the time of writing,coronavirus disease 2019(COVID-19),which is caused by the severe acute respiratory syndrome coron-avirus 2(SARS-CoV-2),has been a global pandemic for more than a year.As of 22 September 2021,the global cumulative incidence exceeds 229 million cases,and the number of deaths has exceeded 4.75 million[1].The current situation of epidemic prevention and control within the countries around the world can be roughly divided into two stages:the pandemic stage that the United States,the United Kingdom,and India are still experiencing(as of 23 September 2021,newly reported cases in the last 24 h in the United States,United Kingdom,and India are 202840,31095,and 26964,respectively[1]);and the normalized epidemic pre-vention and control stage that countries such as China and New Zealand are currently in.

Some properties of linguistic preference relation and its ranking in group decision making

The group decision making problem with linguistic pref- erence relations is studied. The concept of additive consistent linguistic preference relation is defined, and then some properties of the additive consistent linguistic preference relation are studied. In order to rank the alternatives in the group decision making with the linguistic preference relations, the weighted average is first utilized to combine the group linguistic preference relations to one linguistic preference relation, and then the transformation function is proposed to transform the linguistic preference relation to the multiplicative preference relation, and thus the Saaty＇s eigenvec- tor method （EM） of multiplicative preference relation is utilized to rank the alternatives in group decision making with the linguistic preference relations. Finally, an illustrative numerical example is given to verify the proposed method. A comparative study to the linguistic ordered weighted averaging （LOWA） operator method is also demonstrated.

Multiple Kernel Clustering Based on Self-Weighted Local Kernel Alignment

Multiple kernel clustering based on local kernel alignment has achieved outstanding clustering performance by applying local kernel alignment on each sample.However,we observe that most of existing works usually assume that each local kernel alignment has the equal contribution to clustering performance,while local kernel alignment on different sample actually has different contribution to clustering performance.Therefore this assumption could have a negative effective on clustering performance.To solve this issue,we design a multiple kernel clustering algorithm based on self-weighted local kernel alignment,which can learn a proper weight to clustering performance for each local kernel alignment.Specifically,we introduce a new optimization variable-weight-to denote the contribution of each local kernel alignment to clustering performance,and then,weight,kernel combination coefficients and cluster membership are alternately optimized under kernel alignment frame.In addition,we develop a three-step alternate iterative optimization algorithm to address the resultant optimization problem.Broad experiments on five benchmark data sets have been put into effect to evaluate the clustering performance of the proposed algorithm.The experimental results distinctly demonstrate that the proposed algorithm outperforms the typical multiple kernel clustering algorithms,which illustrates the effectiveness of the proposed algorithm.

基于高阶图融合的多视图聚类算法

基于图的多视图聚类算法通过探索样本点之间的邻近关系,受到了广泛的关注.尽管在实际应用中已经取得了较好的聚类性能,但是观察到大多数算法只是利用一阶邻近关系去构建相似图,这导致信息探索不足和多视图数据表征能力下降.为了解决这个挑战,本文提出了一种新颖的基于高阶图融合的多视图聚类算法(high-order graph fusion for multi-view clustering,HCDMC).具体地,所提出的算法通过一种新颖的隐式权重学习策略,从每个视图对应的一阶和二阶邻近图中学习相应的高阶图.引入希尔伯特-施密特(Hilbert-Schmidt)独立性准则作为一种差异性正则化项,旨在加强一致性高阶图的互补信息.最后,对学习到的一致性高阶图施加连通性约束,直接得到聚类标签矩阵,无需任何后处理步骤.使用交替方向乘子法去解决模型的优化问题.在6个真实的数据集上进行了一系列的实验,相较于最新的算法,本文提出的算法具有更好的聚类性能.

On the room-temperature tensile deformation behavior of a cast dual-phase high-entropy alloy CrFeCoNiAl_(0.7)

The microstructure and room-temperature tensile deformation behavior of the cast CrFeCoNiAl0.7 high-entropy alloy(HEA)were studied in details.The cast HEA consisted of a dual-phase structure of 77.3 vol.%face-centered-cubic(FCC)phase plus 22.7 vol.%B2 phase,and exhibited excellent room-temperature tensile properties with a high yield strength of 876 MPa,ultimate tensile strength of 1198 MPa and a relatively large elongation to fracture of~9%.Dislocations gliding in the FCC phase governed the plastic deformation at the early stage of room-temperature tensile,and disordered dislocations were to form dislocation walls as the deformation proceeded.With further increase in strain to a high level,the stacking faults were generated through the dissociation of the geometrically necessary dislocations,serving as the potential heterogeneous nucleation sites for the deformation twins.