Evaluation of Industrial IoT Service Providers with TOPSIS Based on Circular Intuitionistic Fuzzy Sets

Industrial Internet of Things(IIoT)service providers have become increasingly important in the manufacturing industry due to their ability to gather and process vast amounts of data from connected devices,enabling manufacturers to improve operational efficiency,reduce costs,and enhance product quality.These platforms provide manufacturers with real-time visibility into their production processes and supply chains,allowing them to optimize operations and make informed decisions.In addition,IIoT service providers can help manufacturers create new revenue streams through the development of innovative products and services and enable them to leverage the benefits of emerging technologies such as Artificial Intelligence(AI)and machine learning.Overall,the implementation of IIoT platforms in the manufacturing industry is crucial for companies seeking to remain competitive and meet the ever-increasing demands of customers in the digital age.In this study,the evaluation criteria to be considered in the selection of IIoT service provider in small andmedium-sized(SME)manufacturing enterprises will be determined and IIoT service providers alternatives will be evaluated using the technique for order preference by similarity to an ideal solution(TOPSIS)method based on circular intuitionistic fuzzy sets.Based on the assessments conducted in accordance with the literature review and expert consultations,a set of 8 selection criteria has been established.These criteria encompass industry expertise,customer support,flexibility and scalability,security,cost-effectiveness,reliability,data analytics,as well as compatibility and usability.Upon evaluating these criteria,it was observed that the security criterion holds the highest significance,succeeded by cost-effectiveness,data analytics,flexibility and scalability,reliability,and customer support criteria,in descending order of importance.Following the evaluation of seven distinct alternatives against these criteria,it was deduced that the A6 alternative,a German service provider,emerged as the most favorable option.The identical issue was addressed utilizing sensitivity analysis alongside various multi-criteria decision-making(MCDM)methods,and after comprehensive evaluation,the outcomes were assessed.Spearman’s correlation coefficient was computed to ascertain the association between the rankings derived from solving the problem using diverse MCDM methods.

Complex Decision Modeling Framework with Fairly Operators and Quaternion Numbers under Intuitionistic Fuzzy Rough Context

The main goal of informal computing is to overcome the limitations of hypersensitivity to defects and uncertainty while maintaining a balance between high accuracy,accessibility,and cost-effectiveness.This paper investigates the potential applications of intuitionistic fuzzy sets(IFS)with rough sets in the context of sparse data.When it comes to capture uncertain information emanating fromboth upper and lower approximations,these intuitionistic fuzzy rough numbers(IFRNs)are superior to intuitionistic fuzzy sets and pythagorean fuzzy sets,respectively.We use rough sets in conjunction with IFSs to develop several fairly aggregation operators and analyze their underlying properties.We present numerous impartial laws that incorporate the idea of proportionate dispersion in order to ensure that the membership and non-membership activities of IFRNs are treated equally within these principles.These operations lead to the development of the intuitionistic fuzzy rough weighted fairly aggregation operator(IFRWFA)and intuitionistic fuzzy rough ordered weighted fairly aggregation operator(IFRFOWA).These operators successfully adjust to membership and non-membership categories with fairness and subtlety.We highlight the unique qualities of these suggested aggregation operators and investigate their use in the multiattribute decision-making field.We use the intuitionistic fuzzy rough environment’s architecture to create a novel strategy in situation involving several decision-makers and non-weighted data.Additionally,we developed a novel technique by combining the IFSs with quaternion numbers.We establish a unique connection between alternatives and qualities by using intuitionistic fuzzy quaternion numbers(IFQNs).With the help of this framework,we can simulate uncertainty in real-world situations and address a number of decision-making problems.Using the examples we have released,we offer a sophisticated and systematically constructed illustrative scenario that is intricately woven with the complexity ofmedical evaluation in order to thoroughly assess the relevance and efficacy of the suggested methodology.

Research on θ -Intuitionistic Fuzzy Homomorphism Theory

Fuzzy homomorphism is an important research content of fuzzy group theory, different fuzzy mappings will produce different fuzzy homomorphisms. In this paper, the fuzzy homomorphism of groups is generalized. Firstly, the θ-intuitionistic fuzzy mapping is defined, and the θ-intuitionistic fuzzy homomorphism of groups is obtained. The properties of intuitionistic fuzzy subgroups and intuitionistic fuzzy normal subgroups are studied under the θ-intuitionistic fuzzy homomorphism of groups, and the fundamental theorem of θ-intuitionistic fuzzy homomorphism is proved.

Dynamic Analysis of Customer Demand Based on Intuitionistic Fuzzy Number in Product Planning

To address the fuzziness and variability in determining customer demand importance,a dynamic analysis method based on intuitionistic fuzzy numbers is proposed.First,selected customers use intuitionistic fuzzy numbers to represent the importance of each demand.Then,the preference information is aggregated using customer weights and time period weights through the intuitionistic fuzzy ordered weighted average operator,yielding a dynamic vector of the subjective importance of the demand index.Finally,the feasibility of the proposed method is demonstrated through an application example of a vibrating sorting screen.

A Novel Incremental Attribute Reduction Algorithm Based on Intuitionistic Fuzzy Partition Distance

Attribute reduction,also known as feature selection,for decision information systems is one of the most pivotal issues in machine learning and data mining.Approaches based on the rough set theory and some extensions were proved to be efficient for dealing with the problemof attribute reduction.Unfortunately,the intuitionistic fuzzy sets based methods have not received much interest,while these methods are well-known as a very powerful approach to noisy decision tables,i.e.,data tables with the low initial classification accuracy.Therefore,this paper provides a novel incremental attribute reductionmethod to dealmore effectivelywith noisy decision tables,especially for highdimensional ones.In particular,we define a new reduct and then design an original attribute reduction method based on the distance measure between two intuitionistic fuzzy partitions.It should be noted that the intuitionistic fuzzypartitiondistance iswell-knownas aneffectivemeasure todetermine important attributes.More interestingly,an incremental formula is also developed to quickly compute the intuitionistic fuzzy partition distance in case when the decision table increases in the number of objects.This formula is then applied to construct an incremental attribute reduction algorithm for handling such dynamic tables.Besides,some experiments are conducted on real datasets to show that our method is far superior to the fuzzy rough set based methods in terms of the size of reduct and the classification accuracy.

Application of Intuitionistic Z-Numbers in Supplier Selection

Intuitionistic fuzzy numbers incorporate the membership and non-membership degrees.In contrast,Z-numbers consist of restriction components,with the existence of a reliability component describing the degree of certainty for the restriction.The combination of intuitionistic fuzzy numbers and Z-numbers produce a new type of fuzzy numbers,namely intuitionistic Z-numbers(IZN).The strength of IZN is their capability of better handling the uncertainty compared to Zadeh's Z-numbers since both components of Z-numbers are charac-terized by the membership and non-membership functions,exhibiting the degree of the hesitancy of decision-makers.This paper presents the application of such numbers in fuzzy multi-criteria decision-making problems.A decision-making model is proposed using the trapezoidal intuitionistic fuzzy power ordered weighted average as the aggregation function and the ranking function to rank the alternatives.The proposed model is then implemented in a supplier selection problem.The obtained ranking is compared to the existing models based on Z-numbers.The results show that the ranking order is slightly different from the existing models.Sensitivity analysis is performed to validate the obtained ranking.The sensitivity analysis result shows that the best supplier is obtained using the proposed model with 80%to 100%consistency despite the drastic change of criteria weights.Intuitionistic Z-numbers play a very important role in describing the uncertainty in the decision makers’opinions in solving decision-making problems.

Unique Solution of Integral Equations via Intuitionistic Extended Fuzzy b-Metric-Like Spaces

In this manuscript,our goal is to introduce the notion of intuitionistic extended fuzzy b-metric-like spaces.We establish some fixed point theorems in this setting.Also,we plot some graphs of an example of obtained result for better understanding.We use the concepts of continuous triangular norms and continuous triangular conorms in an intuitionistic fuzzy metric-like space.Triangular norms are used to generalize with the probability distribution of triangle inequality in metric space conditions.Triangular conorms are known as dual operations of triangular norms.The obtained results boost the approaches of existing ones in the literature and are supported by some examples and applications.

Business Blockchain Suitability Determinants: Decision-Making through an Intuitionistic Fuzzy Method

Blockchain is one of the innovative and disruptive technologies that has a wide range of applications in multiple industries beyond cryptocurrency.The widespread adoption of blockchain technology in various industries has shown its potential to solve challenging business problems,as well as the possibility to create new business models which can increase a firm’s competitiveness.Due to the novelty of the technology,whereby many companies are still exploring potential use cases,and considering the complexity of blockchain technology,which may require huge changes to a company’s existing systems and processes,it is important for companies to carefully evaluate suitable use cases and determine if blockchain technology is the best solution for their specific needs.This research aims to provide an evaluation framework that determines the important dimensions of blockchain suitability assessment by identifying the key determinants of suitable use cases in a business context.In this paper,a novel approach that utilizes both qualitative(Delphi method)and quantitative(fuzzy set theory)methods has been proposed to objectively account for the uncertainty associated with data collection and the vagueness of subjective judgments.This work started by scanning available literature to identify major suitability dimensions and collected a range of criteria,indicators,and factors that had been previously identified for related purposes.Expert opinions were then gathered using a questionnaire to rank the importance and relevance of these elements to suitability decisions.Subsequently,the data were analyzed and we proceeded to integrate multi-criteria group decision-making(MCGDM)and intuitionistic fuzzy set(IFS)theory.The findings demonstrated a high level of agreement among experts,with the model being extremely sensitive to variances in expert assessments.Furthermore,the results helped to refine and select the most relevant suitability determinants under three important dimensions:functional suitability of the use case,organizational applicability,and ecosystem readiness.

On similarity measures of interval-valued intuitionistic fuzzy sets and their application to pattern recognitions

The concept of the degree of similarity between interval-valued intuitionistic fuzzy sets （IVIFSs） is introduced, and some distance measures between IVIFSs are defined based on the Hamming distance, the normalized Hamming distance, the weighted Hamming distance, the Euclidean distance, the normalized Euclidean distance, and the weighted Euclidean distance, etc. Then, by combining the Hausdorff metric with the Hamming distance, the Euclidean distance and their weighted versions, two other similarity measures between IVIFSs, i. e., the weighted Hamming distance based on the Hausdorff metric and the weighted Euclidean distance based on the Hausdorff metric, are defined, and then some of their properties are studied. Finally, based on these distance measures, some similarity measures between IVIFSs are defined, and the similarity measures are applied to pattern recognitions with interval-valued intuitionistic fuzzy information.

S型异质结光催化剂ZnFe_(2)O_(4)/WO_(3)的构筑及光催化还原CO_(2)性能

通过在WO_(3)纳米片表面负载ZnFe_(2)O_(4)纳米颗粒,构建了一系列S型异质结光催化剂ZnFe_(2)O_(4)/WO_(3),并研究了其光催化CO_(2)还原性能。在没有助催化剂和牺牲剂的条件下,所制备的ZnFe_(2)O_(4)/WO_(3)复合材料可对CO_(2)与水蒸汽进行光催化反应。优化后的材料光照5 h后CO_(2)还原产物CO和CH_(4)的产量分别为7.87和4.88μmol·g^(-1)。相对于单相组分,CO和CH_(4)的产量明显提高。光催化活性的提高,归因于ZnFe_(2)O_(4)和WO_(3)异质结的形成以及光生载流子的S型电荷传输模式。

2022年四川芦山M_(S)6.1地震前应力状态研究

为研究2022年6月1日四川芦山M_(S)6.1地震的孕育和发生过程,采用CAP方法反演了2013年芦山M_(S)7.0主震及M_(S)≥5.0余震的震源机制解,并基于应力张量方差与b值时空分布特征,探讨了芦山M_(S)6.1地震的力学机制和震源区的应力状态。结果表明:2022年芦山M_(S)6.1地震震源机制表现出与2013年芦山M_(S)7.0主震和5级余震相似的逆冲型破裂特征,压应力轴方位与龙门山断裂带南段区域应力场一致。2013年芦山M_(S)7.0地震后震中及附近的应力张量方差和b值长期处于低值状态,2022年芦山M_(S)6.1地震前震中及附近出现了应力张量方差和b值的低值异常,表明芦山余震区处于较高的应力水平。分析认为:巴颜喀拉块体持续东向运动受到华南块体的阻挡,震中所在区域长期受挤压逆冲作用,从而使芦山余震区长期处于应力积累的状态,芦山M_(S)6.1地震也是在这种动力学背景下发生的。

阴道卷曲乳杆菌S层蛋白多样性研究

目的:分析比较西安市女性阴道卷曲乳杆菌S层蛋白的多样性及NCBI数据库中阴道来源卷曲乳杆菌S层蛋白的多样性。方法:选取100例妇科患者的阴道分泌物标本作为研究对象,从其中10例标本中分离得到480株卷曲乳杆菌,其中两株卷曲乳杆菌S层蛋白有明显差异,对其进行全基因组测序;并从NCBI数据库中下载人阴道源的卷曲乳杆菌全基因组数据,分析并比较西安市来源与数据库来源的卷曲乳杆菌S层蛋白的多样性。结果:S层蛋白注释基因具有菌株内和菌株间的多样性,同一套基因组中有多个S层蛋白注释基因,菌株间的相似性偏差较大,通过系统发育分析发现,中国人阴道卷曲乳杆菌的S层蛋白基因与其他人种的具有系统发育相似性。结论:阴道卷曲乳杆菌S层蛋白具有菌种间和菌株间多样性的特征,不同人种的卷曲乳杆菌具有相似性。

结合主动光源和改进YOLOv5s模型的夜间柑橘检测方法

【目的】解决夜间环境下遮挡和较小柑橘难以准确识别的问题,实现采摘机器人全天候智能化作业。【方法】提出一种结合主动光源的夜间柑橘识别方法。首先,通过分析主动光源下颜色特征不同的夜间柑橘图像,选择最佳的光源色并进行图像采集。然后,提出一种夜间柑橘检测模型BI-YOLOv5s,该模型采用双向特征金字塔网络(Bi-FPN)进行多尺度交叉连接和加权特征融合,提高对遮挡和较小果实的识别能力;引入Coordinate attention(CA)注意力机制模块,进一步加强对目标位置信息的提取;采用融入Transformer结构的C3TR模块,在减少计算量的同时更好地提取全局信息。【结果】本文提出的BI-YOLOv5s模型在测试集上的精准率、召回率、平均准确率分别为93.4%、92.2%和97.1%,相比YOLOv5s模型分别提升了3.2、1.5和2.3个百分点。在所采用的光源色环境下,模型对夜间柑橘识别的正确率为95.3%,相比白光环境下提高了10.4个百分点。【结论】本文提出的方法对夜间环境下遮挡和小目标柑橘的识别具有较高的准确性,可为夜间果蔬智能化采摘的视觉精准识别提供技术支持。

基于改进YOLOv5s的不同成熟度苹果目标检测方法

[目的]本文旨在解决在自然环境下不同成熟度苹果目标检测精度较低的问题。[方法]提出了一种改进的YOLOv5s模型SODSTR-YOLOv5s(YOLOv5s with small detection layer and omni-dimensional dynamic convolution and swin transformer block),用于不同成熟度苹果检测。首先改进YOLOv5s的多尺度目标检测层,在Prediction中构建检测160×160特征图的检测头,提高小尺寸的不同成熟度苹果的检测精度;其次在Backbone结构中融合Swin Transformer Block,加强同级成熟度的苹果纹理特征融合,弱化纹理特征分布差异带来的消极影响,提高模型泛化能力;最后将Neck结构的Conv模块替换为动态卷积模块ODConv,细化局部特征映射,实现局部苹果细粒度特征的充分提取。基于不同成熟度苹果数据集进行试验,验证改进模型的性能。[结果]改进模型SODSTR-YOLOv5s检测的精确率、召回率、平均精度均值分别为89.1%、95.5%、93.6%,高、中、低成熟度苹果平均精度均值分别为94.1%、93.1%、93.7%,平均检测时间为16 ms,参数量为7.34 M。相比于YOLOv5s模型,改进模型SODSTR-YOLOv5s精确率、召回率、平均精度均值分别提高了3.8%、5.0%、2.9%,参数量和平均检测时间分别增加了0.32 M和5 ms。[结论]改进模型SODSTR-YOLOv5s提升了在自然环境下对不同成熟度苹果的检测能力,能较好地满足实际采摘苹果的检测要求。

献血者HBVs基因变异的生物信息学分析

目的分析HBV DNA+/HBsAg-献血者的传染性指标、S区基因序列突变情况及生物信息学特征变化。方法通过PCR方法筛查出1份HBV DNA+/HBsAg-的标本,应用酶联免疫和化学发光方法检测该标本乙肝病毒5项指标,对该标本HBVs区基因片段测序并分析变异情况,应用分析软件对所测序列进行生物信息学分析。结果该标本HBV DNA+、HBsAg-、HBsAb+、HBeAg+、HBeAb-、HBcAb+;HBVs区基因序列内发生氨基酸单位点突变(P151L),空间结构发生改变。结论HBVs区基因序列空间结构改变,可能是导致检测结果出现血清学HBsAg-/HBsAb+/HBV DNA+的原因。

在片S参数计量比对结果浅析

中国电子科技集团公司第十三研究所作为主导实验室开展了在片S参数计量比对工作,对参比实验室提交的在片S参数测量结果进行了汇总分析,并用E_n值对各参比实验室测量结果进行了评价。通过在片S参数计量比对,确保了量值传递的准确、可靠,特别是对在片S参数测量不确定度的主要来源统一了认识。同时也为业内提供了在片S参数测量一致性的比较平台。

基于数值模拟的高速公路S型曲线积水规律分析

S型曲线由于特殊的线型几何特征形成了易积水的路面区域,对于积水规律的掌握有利于在设计和建造阶段提前预防和处理积水路段的危害。该文基于计算流体力学软件Fluent,结合计算流体力学分析中基于欧拉-拉格朗日方法的欧拉液膜模型EWF(Eulerian Wall Film),针对精细化处理的典型S型曲线双向多车道道路模型进行模拟计算。模拟包含了几何特征与降雨量等不同变量因素,得出了积水的水膜厚度以及流速分布规律。模拟结果表明:在降雨阶段,S型曲线横坡坡度0%~1%路段为积水路段,横坡坡度1%~2%路段为易积水路段,横坡坡度大于2%的路段积水情况受到降雨强度影响;在排水阶段,S型曲线横坡坡度0%~1%路段为排水困难路段,横坡坡度1%~2%路段为排水不畅路段,横坡坡度大于2%的路段排水顺畅,在排水时间结束时,路面在不同的几何条件和降雨条件下均能将积水排除至水膜厚度小于2 cm,横坡的大小与渐变影响了积水的横向分布与纵向分布,横向呈现中间高两边低、横断面上标高大的位置水膜厚度小和标高小的位置水膜厚度大的水膜厚度分布规律,总体呈现S型的分布规律;水膜流速在积水阶段呈现纵向上中间低两边高、水膜厚度大则水膜流速大的水膜厚度分布规律,在排水阶段则为纵向上中间高两边低的水膜流速分布规律。道路宽度影响了降雨落到路面的积水总量,进而对积水水膜厚度产生了影响;超高缓和率则主要使得积水路段、易积水路段增长,从而影响了积水分布的范围。

S31603复合板压力容器点蚀行为试验研究

针对S31603复合板压力容器不锈钢层不同点蚀深度的发展进程及穿透至碳钢层后的电偶腐蚀风险,通过预制不同点蚀深度的S31603复合板试片组,分别在实验室和现场S31603复合板压力容器内进行腐蚀试验,并对试片进行宏观分析、低倍观察、高倍观察及腐蚀产物表征分析。结果表明,S31603复合板预制的点蚀坑深度穿透至碳钢层时发生了明显的电偶腐蚀,实验室腐蚀试验和现场试验最高点蚀速率分别达到4.954,1.023 mm/a;用S31603复合板试片预制的点蚀坑深度穿透至碳钢层后碳钢优先发生电偶腐蚀;S31603复合板试片预制的点蚀坑深度处于不锈钢层时腐蚀轻微,也未萌生新的点蚀,相邻点蚀坑之间也未见明显的腐蚀影响。在含Cl-的酸性腐蚀性介质环境下,当S31603复合板压力容器点蚀深度处于不锈钢层时腐蚀轻微,当点蚀深度穿透至碳钢层后,会发生明显的电偶腐蚀,需要缩短S31603复合板压力容器开罐检修周期以保证安全使用。

基于CFS-YOLO算法的复杂工况环境下煤矸图像识别方法

针对煤矿高噪声、低照度、运动模糊与大批量煤矸混杂等复杂工况环境因素导致煤矸识别存在误检、漏检以及检测精度低的问题,提出一种基于CFS-YOLO算法的煤矸智能识别模型。采用ConvNeXt V2(Convolutional Neural Network with NeXt Units Version 2)特征提取模块替换主干网络末端的2个C3(Cross Stage Partial Bottle Neck Mudule)模块,通过将掩码自动编码器(Masked Autoencoders,MAE)和全局响应归一化(Global Response Normalization,GRN)层添加到ConvNeXt架构中,有效缓解特征崩溃问题以及保持特征在网络传递过程中的多样性;采用Focal-EIOU(Focal and Efficient Intersection Over Union)损失函数替换原CIOU(Computer Intersection Over Union)损失函数,通过其Focal-Loss机制和调整样本权重的方式优化边界框回归任务中的样本不平衡问题,提高模型的收敛速度和定位精度;添加无参注意力机制(Simple Attention Mechanism,SimAM)于主干网络每个C3模块的后端,凭借其注意力权重自适应调整策略,提升模型对尺度变化较大或低分辨率煤矸目标关键特征的提取能力。通过消融试验和对比试验验证所提CFS-YOLO模型的有效性与优越性。试验结果表明:CFS-YOLO模型对于煤矸在煤矿高噪声、低照度、运动模糊与大批量煤矸混杂等复杂环境下的检测效果均得到有效提高,模型的平均精度均值达到90.2%,相较于原YOLOv5s模型的平均精度均值提高了3.7%,平均检测速度达到90.09 FPS,可充分满足煤矸实时检测的需求。同时与YOLOv5s、YOLOv7-tiny与YOLOv8n等6种YOLO系列算法相比,CFS-YOLO模型对煤矿复杂环境的适应性最强且综合检测性能最佳,可为煤矸的智能高效分选提供技术支持。

σ相对S32205双相不锈钢力学性能和耐蚀性的影响

通过JMatPro软件模拟计算和试验结合的方式研究了S32205双相不锈钢中σ相的析出行为及对其力学性能和耐蚀性的影响。结果表明:通过JMatPro软件模拟计算得到的S32205双相不锈钢σ相析出鼻尖温度为875℃。当S32205双相不锈钢在875℃保温0~90 min,σ相含量快速增加,当保温时间增加至90~120 min时,σ相含量缓慢上升,而当保温时间为120~240 min时σ相含量达到饱和,最大为6.1%,与模拟计算结果基本符合。随着保温时间逐渐增加,S32205双相不锈钢的屈服强度、抗拉强度及显微硬度先快速增加后逐渐保持稳定,而伸长率呈下降趋势,强度和硬度与σ相体积含量成正比关系,σ相强化机理由Orowan强化机制转变为σ相的高弹性应变能强化机制。不同σ含量的S32205双相不锈钢的电化学试验结果表明随着σ相的含量逐渐增多,基体越容易贫Cr, S32205双相不锈钢越容易产生腐蚀,耐蚀性逐渐降低。