An Intelligent MCGDM Model in Green Suppliers Selection Using Interactional Aggregation Operators for Interval-Valued Pythagorean Fuzzy Soft Sets

Green supplier selection is an important debate in green supply chain management(GSCM),attracting global attention from scholars,especially companies and policymakers.Companies frequently search for new ideas and strategies to assist them in realizing sustainable development.Because of the speculative character of human opinions,supplier selection frequently includes unreliable data,and the interval-valued Pythagorean fuzzy soft set(IVPFSS)provides an exceptional capacity to cope with excessive fuzziness,inconsistency,and inexactness through the decision-making procedure.The main goal of this study is to come up with new operational laws for interval-valued Pythagorean fuzzy soft numbers(IVPFSNs)and create two interaction operators-the intervalvalued Pythagorean fuzzy soft interaction weighted average(IVPFSIWA)and the interval-valued Pythagorean fuzzy soft interaction weighted geometric(IVPFSIWG)operators,and analyze their properties.These operators are highly advantageous in addressing uncertain problems by considering membership and non-membership values within intervals,providing a superior solution to other methods.Moreover,specialist judgments were calculated by the MCGDM technique,supporting the use of interaction AOs to regulate the interdependence and fundamental partiality of green supplier assessment aspects.Lastly,a statistical clarification of the planned method for green supplier selection is presented.

Aggregation Operators for Interval-Valued Pythagorean Fuzzy Hypersoft Set with Their Application to Solve MCDM Problem

Experts use Pythagorean fuzzy hypersoft sets(PFHSS)in their investigations to resolve the indeterminate and imprecise information in the decision-making process.Aggregation operators(AOs)perform a leading role in perceptivity among two circulations of prospect and pull out concerns from that perception.In this paper,we extend the concept of PFHSS to interval-valued PFHSS(IVPFHSS),which is the generalized form of intervalvalued intuitionistic fuzzy soft set.The IVPFHSS competently deals with uncertain and ambagious information compared to the existing interval-valued Pythagorean fuzzy soft set.It is the most potent method for amplifying fuzzy data in the decision-making(DM)practice.Some operational laws for IVPFHSS have been proposed.Based on offered operational laws,two inventive AOs have been established:interval-valued Pythagorean fuzzy hypersoft weighted average(IVPFHSWA)and interval-valued Pythagorean fuzzy hypersoft weighted geometric(IVPFHSWG)operators with their essential properties.Multi-criteria group decision-making(MCGDM)shows an active part in contracts with the difficulties in industrial enterprise for material selection.But,the prevalent MCGDM approaches consistently carry irreconcilable consequences.Based on the anticipated AOs,a robust MCGDMtechnique is deliberate formaterial selection in industrial enterprises to accommodate this shortcoming.A real-world application of the projectedMCGDMmethod for material selection(MS)of cryogenic storing vessels is presented.The impacts show that the intended model is more effective and reliable in handling imprecise data based on IVPFHSS.

Fully implicational methods for approximate reasoning based on interval-valued fuzzy sets

The aim of this paper is to discuss the approximate rea- soning problems with interval-valued fuzzy environments based on the fully implicational idea. First, this paper constructs a class of interval-valued fuzzy implications by means of a type of impli- cations and a parameter on the unit interval, then uses them to establish fully implicational reasoning methods for interval-valued fuzzy modus ponens （IFMP） and interval-valued fuzzy modus tel- lens （IFMT） problems. At the same time the reversibility properties of these methods are analyzed and the reversible conditions are given. It is shown that the existing unified forms of α-triple I （the abbreviation of triple implications） methods for FMP and FMT can be seen as the particular cases of our methods for IFMP and IFMT.

Interval-Valued Intuitionistic Fuzzy-Rough Sets

This paper combines interval-valued intuitionistic fuzzy sets and rough sets.It studies rougheness in interval-valued intuitionistic fuzzy sets and proposes one kind of interval-valued intuitionistic fuzzy-rough sets models under the equivalence relation in crisp sets.That extends the classical rough set defined by Pawlak.

A KIND OF FUZZY LINEAR PROGRAMMING PROBLEMS BASED ON INTERVAL-VALUED FUZZY SETS

The objective of this paper is to deal with a kind of fuzzy linear programming problem based on interval\|valued fuzzy sets (IVFLP) through the medium of procedure that turns IVFLP into parametric linear programming via the mathematical programming.Some useful results for the benefit of solving IVFLP are expounded and proved,developed and discussed.Furthermore,that the proposed techniques in this paper allow the decision\|maker to assign a different degree of importance can provide a useful way to efficiently help the decision\|maker make their decisions.

Aggregation Operators for Interval-Valued Pythagorean Fuzzy Soft Set with Their Application to Solve Multi-Attribute Group Decision Making Problem

Interval-valued Pythagorean fuzzy soft set(IVPFSS)is a generalization of the interval-valued intuitionistic fuzzy soft set(IVIFSS)and interval-valued Pythagorean fuzzy set(IVPFS).The IVPFSS handled more uncertainty comparative to IVIFSS;it is the most significant technique for explaining fuzzy information in the decision-making process.In this work,some novel operational laws for IVPFSS have been proposed.Based on presented operational laws,two innovative aggregation operators(AOs)have been developed such as interval-valued Pythagorean fuzzy soft weighted average(IVPFSWA)and interval-valued Pythagorean fuzzy soft weighted geometric(IVPFSWG)operators with their fundamental properties.A multi-attribute group decision-making(MAGDM)approach has been established utilizing our developed operators.A numerical example has been presented to ensure the validity of the proposed MAGDM technique.Finally,comparative studies have been given between the proposed approach and some existing studies.The obtained results through comparative studies show that the proposed technique is more credible and reliable than existing approaches.

Interval-Valued Neutrosophic Soft Expert Set from Real Space to Complex Space

A fuzzy system is a novel computing technique that accesses uncertain information by fuzzy representation.In the decision-making process,fuzzy system and soft computing are effective tools that are tolerant to imprecision,uncertainty,and partial truths.Evolutionary fuzzy systems have been developed with the appearance of interval fuzzy,dual fuzzy,hesitant fuzzy,neutrosophic,plithogenic representations,etc.Moreover,by capturing compound features and convey multi-dimensional data,complex numbers are utilized to generalize fuzzy and neutrosophic fuzzy sets.In this paper,a representation of neutrosophic soft expert systems based on the real and complex numbers in the interval form is proposed.The interval-valued neutrosophic soft expert set(I-VNSES)is defined,and the interval-valued complex neutrosophic soft expert set(I-VCNSES)is formally generalized from the concept of IVNSES.For both I-VNSES and I-VCNSES,we introduce the relevant basic theoretical operations and study their properties.Based on these new concepts,a generalized algorithm is proposed and applied to handle the imbedded indeterminacy in the two-dimensional interval data.The proposed algorithm is tested on the economic factors that affected the Malaysian economy in 2020 to see which ones are the most influential.Eventually,a comparison of three current approaches is used to back up this study.

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.

Some interval-valued intuitionistic uncertain linguistic hybrid Shapley operators

Two interval-valued intuitionistic uncertain linguistic hybrid operators cal ed the induced interval-valued intuitionistic uncertain linguistic hybrid Shapley averaging （I-IIULHSA） operator and the induced interval-valued intuitionistic uncertain linguistic hybrid Shapley geometric （I-IIULHSG） operator are defined. These operators not only reflect the importance of elements and their ordered positions, but also consider the correlations among elements and their ordered positions. Since the fuzzy measures are defined on the power set, it makes the problem exponentially complex. In order to simplify the complexity of solving a fuzzy measure, we further define the induced interval-valued intuitionistic uncertain linguistic hybrid λ-Shapley averaging （I-IIULHλSA） operator and the induced interval-valued intuitionistic uncertain linguistic hybrid λ-Shapley geometric （I-IIULHλSG） operator. Moreover, an approach for multi-attribute group decision making under the interval-valued intuitionistic uncertain linguistic environment is developed. Finally, a numerical example is provided to verify the developed procedure and demonstrate its practicality and feasibility.

Bipolar Interval-Valued Neutrosophic Optimization Model of Integrated Healthcare System

Bipolar Interval-valued neutrosophic set is another generalization of fuzzy set,neutrosophic set,bipolar fuzzy set and bipolar neutrosophic set and thus when applied to the optimization problem handles uncertain data more efficiently and flexibly.Current work is an effort to design a flexible optimization model in the backdrop of interval-valued bipolar neutrosophic sets.Bipolar interval-valued neutrosophic membership grades are picked so that they indicate the restriction of the plausible infringement of the inequalities given in the problem.To prove the adequacy and effectiveness of the method a unified system of sustainable medical healthcare supply chain model with an uncertain figure of product complaints is used.Time,quality and cost are considered as satisfaction level to choose best supplier for medicine procurement.The proposed model ensures 99%satisfaction for cost reduction,63%satisfaction for the quality of product and 64%satisfaction for total time taken in medicine supply chain.

嵌套集合建模能力和数字表征对儿童贝叶斯推理的影响

为进一步探讨数字表征对贝叶斯推理的影响,特别是自然频数促进效应的作用机制,以285名小学生为被试,考察不同嵌套集合建模能力儿童在解决三种数字表征的贝叶斯推理问题时的表现。结果发现:(1)具有高嵌套集合建模能力的儿童能更好地解决贝叶斯推理问题;(2)相比于概率和几率格式,儿童在自然频数格式下的推理表现更好;(3)概率格式下高低嵌套集合建模能力被试的推理表现无差异,但在自然频数和几率格式下,高嵌套集合建模能力被试的推理成绩好于低嵌套集合能力被试。这表明在适当的数字表征下,嵌套集合建模能力是影响儿童进行贝叶斯推理的关键因素。

新型电力系统多阶段输-储协同分布鲁棒规划

在电力系统中长期规划中,可再生能源发展受到政策变迁、关键技术发展和气候变化等因素的影响,具有不确定性。这一不确定性将对短期规划决策产生不可忽视的影响。针对以上问题,在可再生能源发展场景树之间嵌套距离的基础上引入鲁棒因子,构建概率分布模糊集。随后,综合考察整个规划周期规划方案的经济性与鲁棒性,建立了考虑可再生能源出力不确定性与发展不确定性的电力系统多阶段输-储协同分布鲁棒规划模型,并采用列与约束生成算法对模型进行求解。最后,采用Garver-6节点系统和HRP-38标准测试系统进行算例分析,得到多阶段规划方案。通过调节鲁棒因子可以平衡模型的经济性与鲁棒性,证明了所提方法的有效性。

嵌套加密方案在SET协议中的应用研究

SET协议是Internet电子商务采用的主要安全协议,是电子商务所有安全协议中安全性相对较强的一种协议。对SET协议的加解密原理进行剖析,找出SET协议在安全性和适用性方面存在的一些问题,提出了相应的解决办法。

Target Priority Determination Methods by Interval-Valued Intuitionistic Fuzzy Sets with Unknown Attribute Weights

The paper aims at the problem of multi-targets threat degree being hard to be evaluated accurately in complex air defense battlefield environments. Combined with multi-sensors information fusion and interval-valued intuitionistic fuzzy sets(IVIFS) theories, the target priority determination is studied. The score and accuracy functions of IVIFS are improved with thinking about the hesitating information in order to increase the rationality.Then, the influence factors of target priority and the nonlinear relationship between the influence factors and target priority are analyzed. Next, the algorithms for calculating the factor weights and sensor weights are given. Based on the theory of IVIFS and technique for order preference by similarity to an ideal solution(TOPSIS), two methods of target priority determination based on the IVIFS and TOPSIS are proposed. At last, an application example verifies the effectiveness and flexibility of the proposed algorithms.

基于深度学习的改进型YOLOv4输电线路鸟巢检测与识别

针对输电线路无人机巡视图像经典鸟巢检测算法权重参数范围大、识别效率低、识别精度低的缺点,提出了一种改进型YOLOv4输电线路鸟巢检测与识别方法。首先,选取Mosaic图像增强技术对图片集进行多种变换,变相增加图片集中的小目标数量。其次,在骨干特征提取网络中,通过引入深度可分离卷积来提高检测网络的速度;在YOLO头中,基于K-means++算法改进锚框的大小和比例,基于最小凸集建立回归损失函数。最后,在PANet和YOLO头之间增加2个SPP模块,进一步增强特征融合能力,提高小目标检测能力。利用某供电局无人机巡检图像制作数据集,将提出的算法与其他目标检测算法进行对比实验研究。实验结果表明,改进后的算法有更高的鸟巢检测准确度和更低的运算开销。

基于嵌套型优势关系粗糙集与模糊理论的岩质边坡施工安全性评价

为降低工程建设风险、解决岩质边坡施工安全性评价方法复杂等问题,对基于嵌套型优势关系粗糙集与模糊理论的岩质边坡施工安全性评价方法展开研究。首先,对岩质边坡施工安全性影响因素进行分析,通过文献研究及专家调查将岩质边坡施工安全性影响因素归为勘察设计风险、施工风险、工程地质风险及自然环境风险四大类,包含勘察资料准确性、边坡形式、支护结构及设计参数等19个2级影响因素指标,并针对定量指标构造了三相性隶属函数;其次,介绍了权重确定及指标分级模糊评价的具体方法;再次,阐述了建立评价决策表、数据离散、多级属性约简及偏好规则的生成等操作流程;最后,对15个比较有代表性的边坡进行实例研究,验证了该方法的可靠性。通过实践证明,基于嵌套型优势关系粗糙集与模糊理论的风险评价分析方法可行,可以为岩质边坡施工安全性风险评价与规律分析提供一种新思路。结果表明:嵌套型优势关系粗糙集理论对岩质边坡施工安全性风险的各级指标属性自上而下进行了约简,降低了原有系统的复杂性,同时考虑了指标中含有的偏好信息,考虑综合全面,规则分析可信度高;样本集数量和约简的选取对基于嵌套型优势关系粗糙集与模糊理论进行评价决策时影响较大,样本集越大生成的规则可信度越高,正确选取约简偏好规则生成就越合理。

Knowledge modeling based on interval-valued fuzzy rough set and similarity inference: prediction of welding distortion

Knowledge-based modeling is a trend in complex system modeling technology. To extract the process knowledge from an information system, an approach of knowledge modeling based on interval-valued fuzzy rough set is presented in this paper, in which attribute reduction is a key to obtain the simplified knowledge model. Through defining dependency and inclusion functions, algorithms for attribute reduction and rule extraction are obtained. The approximation inference plays an important role in the development of the fuzzy system. To improve the inference mechanism, we provide a method of similaritybased inference in an interval-valued fuzzy environment. Combining the conventional compositional rule of inference with similarity based approximate reasoning, an inference result is deduced via rule translation, similarity matching, relation modification, and projection operation. This approach is applied to the problem of predicting welding distortion in marine structures, and the experimental results validate the effectiveness of the proposed methods of knowledge modeling and similarity-based inference.

几种新的截集及其应用

本文从“邻域”和“重域”的观点出发引入了三种新的截集：下截集，下重截集和上重截集。我们给出了这些截集的性质及相应的分解定理，表现定理和扩展原理。作为应用，我们讨论了两方面的内容：一是利用落影表现理论，将一个模糊集看作“重云”的落影，然后用下重截集和落影表现理论来定义模糊集的运算，得到了与［２］一致的结果；二是利用下截集给出凹模糊数的定义。

区间值模糊集合的表现定理

针对区间值模糊集合 ,给出了 2类区间值集合套的定义 ,得到了 2个区间值模糊集合的表现定理 .

明确嵌套集合关系对贝叶斯推理的促进效应

以经典的乳癌问题作为实验任务,通过两个实验分别探讨了有助于明确嵌套集合关系的逐步提问、树图表征等外部表征方式以及元认知调控和被试类型等因素对贝叶斯推理的影响。结果发现:(1)逐步提问对改善贝叶斯推理的成绩没有显著作用;(2)完整和不完整的树图表征显著地促进了推理成绩,但简约的树图表征的促进作用不显著;(3)叙述理由引发的元认知监控显著地促进了推理成绩;(4)文科和理科两组被试的推理成绩没有显著差异。