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Dombi-Normalized Weighted Bonferroni Mean Operators with Novel Multiple-Valued Complex Neutrosophic Uncertain Linguistic Sets and Their Application in Decision Making 具有新型多值复中性不确定语言集的dombi归一化加权Bonferroni均值算子及其在决策中的应用
IF 2.4 4区 工程技术 Q2 Mathematics Pub Date : 2022-01-01 DOI: 10.32604/cmes.2022.017998
T. Mahmood, Zeeshan Ali, D. Prangchumpol, T. Panityakul
Although fuzzy set concepts have evolved, neutrosophic sets are attracting more attention due to the greater power of the structure of neutrosophic sets. The ability to account for components that are true, false or neither true nor false is useful in the resolution of real-life problems. However, simultaneous variations render neutrosophic sets unsuitable in specific circumstances. To enable the management of these sorts of issues, we combine the principle of multi-valued neutrosophic uncertain linguistic sets and complex fuzzy sets to develop the principle of multi-valued complex neutrosophic uncertain linguistic sets. Multi-valued complex neutrosophic uncertain linguistic sets can contain grades of truth, abstinence, and falsity, and uncertain linguistic terms, which are expressed as complex numbers whose real and imaginary parts are limited to the unit interval. Some important Dombi laws are elaborated along with Bonferroni mean operators, which offer a flexible general structure with modifiable factors. Bonferroni means aggregation operators perform a significant role in conveying the magnitude level of options and characteristics. To determine relationships among any number of attributes, we develop multi-valued complex neutrosophic uncertain linguistic Dombi-normalized weighted Bonferroni mean operators and discuss their important properties with some special cases. By using these laws, we can deploy the multi-attribute decision-making (MADM) technique using the novel principle of multi-valued complex neutrosophic uncertain linguistic sets. To determine the power and flexibility of the elaborated approach, we resolve some numerical examples based on the proposed operator. Finally, the work is validated with the help of comparative analysis, a discussion of its advantages, and geometric expressions of the elaborated theories.
虽然模糊集的概念已经有了很大的发展,但由于中性集的结构更强大,中性集越来越受到人们的关注。在解决现实生活中的问题时,解释真假或非真假成分的能力很有用。然而,同时的变化使得中性粒细胞不适合特定的环境。为了能够对这类问题进行管理,我们将多值嗜中性不确定语言集原理与复杂模糊集原理相结合,发展了多值复杂嗜中性不确定语言集原理。多值复杂嗜中性不确定语言集可以包含真、断、假等级和不确定语言项,这些不确定语言项表示为复数,其实部和虚部限制在单位区间内。一些重要的Dombi定律与Bonferroni平均算子一起进行了阐述,这些算子提供了具有可修改因子的灵活总体结构。Bonferroni的意思是聚合操作符在传递选项和特征的大小级别方面发挥了重要作用。为了确定任意多个属性之间的关系,我们建立了多值复杂嗜中性不确定语言Dombi-normalized加权Bonferroni mean算子,并结合一些特殊情况讨论了它们的重要性质。利用这些规律,我们可以利用多值复杂嗜中性不确定语言集的新原理来部署多属性决策技术。为了确定该方法的有效性和灵活性,我们基于所提出的算子求解了一些数值算例。最后,通过对比分析,讨论其优点,以及所阐述理论的几何表达式,验证了所做的工作。
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引用次数: 3
Numerical and Experimental Investigations of the Thermal Fatigue Lifetime of CBGA Packages CBGA封装热疲劳寿命的数值与实验研究
IF 2.4 4区 工程技术 Q2 Mathematics Pub Date : 2022-01-01 DOI: 10.32604/cmes.2022.018037
Borui Yang, Jun Luo, B. Wan, Yutai Su, Guicui Fu, Xu Long
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引用次数: 2
Localization of Mobile Robot Aided for Large-Scale Construction Based on Optimized Artificial Landmark Map in Ongoing Scene 基于正在进行场景人工地标地图优化的移动机器人大规模施工辅助定位
IF 2.4 4区 工程技术 Q2 Mathematics Pub Date : 2022-01-01 DOI: 10.32604/cmes.2022.018004
Zhen Xu, Shuai Guo, Tao Song, Yuwen Li, Lingdong Zeng
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引用次数: 2
Seismic Performance of Assembled Shear Wall with Defective Sleeve Connection 缺陷套筒连接组合剪力墙抗震性能研究
IF 2.4 4区 工程技术 Q2 Mathematics Pub Date : 2022-01-01 DOI: 10.32604/cmes.2022.016312
Hua Yan, Bo Song, Dongsheng Xu, Guodong Zhang
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引用次数: 1
Noise Pollution Reduction through a Novel Optimization Procedure in Passive Control Methods 通过被动控制方法中的一种新的优化过程来降低噪声污染
IF 2.4 4区 工程技术 Q2 Mathematics Pub Date : 2022-01-01 DOI: 10.32604/cmes.2022.019705
H. Lian, Leilei Chen, Xiao Lin, Wenchang Zhao, Stephane P. A. Bordas, Mingdong Zhou
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引用次数: 6
New Concepts on Quadripartitioned Bipolar Single Valued Neutrosophic Graph 四分双极单值嗜中性图的新概念
IF 2.4 4区 工程技术 Q2 Mathematics Pub Date : 2022-01-01 DOI: 10.32604/cmes.2022.017032
S. Satham Hussain, G. Muhiuddin, N. Durga, D. Al-Kadi
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引用次数: 0
Predicting the Reflection Coefficient of a Viscoelastic Coating Containing a Cylindrical Cavity Based on an Artificial Neural Network Model 基于人工神经网络模型的圆柱腔粘弹性涂层反射系数预测
IF 2.4 4区 工程技术 Q2 Mathematics Pub Date : 2022-01-01 DOI: 10.32604/cmes.2022.017760
Yiping Sun, Q. Bai, Xuefeng Zhao, Meng Tao
{"title":"Predicting the Reflection Coefficient of a Viscoelastic Coating Containing a Cylindrical Cavity Based on an Artificial Neural Network Model","authors":"Yiping Sun, Q. Bai, Xuefeng Zhao, Meng Tao","doi":"10.32604/cmes.2022.017760","DOIUrl":"https://doi.org/10.32604/cmes.2022.017760","url":null,"abstract":"","PeriodicalId":10451,"journal":{"name":"Cmes-computer Modeling in Engineering & Sciences","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79628671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Numerical Simulation for Bioconvection of Unsteady Stagnation Point Flow of Oldroyd-B Nanofluid with Activation Energy and Temperature-Based Thermal Conductivity Past a Stretching Disk 具有活化能和基于温度的导热系数的old - hydrod - b纳米流体通过拉伸盘非定常驻点流动的生物对流数值模拟
IF 2.4 4区 工程技术 Q2 Mathematics Pub Date : 2022-01-01 DOI: 10.32604/cmes.2022.017277
Muhammad Sami Rashad, Haihu Liu, Shan Ali khan
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引用次数: 0
A Flux Based Approximation to Simulate Coupled Hydromechanical Problems for Mines with Heterogeneous Rock Types Using the Material Point Method 基于通量近似的非均质岩型矿山流体力学耦合问题物质点法模拟
IF 2.4 4区 工程技术 Q2 Mathematics Pub Date : 2022-01-01 DOI: 10.32604/cmes.2022.019112
G. Basson, Andrew P. Bassom, Brianna Salmon
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引用次数: 0
LF-CNN: Deep Learning-Guided Small Sample Target Detection for Remote Sensing Classification LF-CNN:深度学习引导的遥感分类小样本目标检测
IF 2.4 4区 工程技术 Q2 Mathematics Pub Date : 2022-01-01 DOI: 10.32604/cmes.2022.019202
Chengfan Li, Lan Liu, Junjuan Zhao, Xuefeng Liu
{"title":"LF-CNN: Deep Learning-Guided Small Sample Target Detection for Remote Sensing Classification","authors":"Chengfan Li, Lan Liu, Junjuan Zhao, Xuefeng Liu","doi":"10.32604/cmes.2022.019202","DOIUrl":"https://doi.org/10.32604/cmes.2022.019202","url":null,"abstract":"","PeriodicalId":10451,"journal":{"name":"Cmes-computer Modeling in Engineering & Sciences","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73121781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 7
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