Results 121 to 130 of about 173,381 (281)

Optimizing Attribute Reduction in Multi-Granularity Data through a Hybrid Supervised–Unsupervised Model

Mathematics
Attribute reduction is a core technique in the rough set domain and an important step in data preprocessing. Researchers have proposed numerous innovative methods to enhance the capability of attribute reduction, such as the emergence of multi ...
Zeyuan Fan, Jianjun Chen, Hongyang Cui, Jingjing Song, Taihua Xu   +4 more
doaj   +1 more source

Solid‐State Diffusion and Intermetallic Phase Formation in Roll‐Bonded Mg–Zn Composites With Kirigami‐Patterned Inlay

Advanced Engineering Materials, EarlyView.
Mg–Zn composites with a thickness of 0.21 mm were fabricated using roll bonding of a kirigami‐patterned Mg alloy inlay within a Zn matrix. Thermal activation following this process led to the formation of tailored intermetallic structures, which provided the composite with enhanced flexural strength.
Yaroslav Frolov, Oleksandr Bobukh, Christian Klose, Florian Nürnberger, Hans Jürgen Maier   +4 more
wiley   +1 more source

Multi-information fusion welding defect identification combining neighborhood rough set and optimized SVM

Hanjie xuebao
A multi-information fusion welding defect recognition method is proposed by combining NRS with optimized SVM to address the “big data” generated during the multi-sensor information fusion welding process.
Zhiqiang FENG, Xianping ZENG, Naiwen FANG, Daidi ZHAO, Quan LI, Jiutian LUO, Xin LI   +6 more
doaj   +1 more source

Rafting of Ni‐Based Superalloys Under Multiaxial Load as Understood by Phase‐Field Simulations and Critical Experiments

Advanced Engineering Materials, EarlyView.
Phase‐field simulations coupled with dislocation‐density‐based crystal plasticity modeling reproduce γ′ rafting behavior in single‐crystal Ni‐based superalloys under varied loading conditions. The model captures both macroscopic creep and microscopic morphology evolution, with results matching high‐temperature creep experiments.
Micheal Younan, David Bürger, Gunther Eggeler, Oleg Shchyglo, Ingo Steinbach, Muhammad Adil Ali   +5 more
wiley   +1 more source

A Gene selection approach based on the fisher linear discriminant and the neighborhood rough set. [PDF]

Bioengineered, 2018
Sun L, Zhang X, Xu J, Wang W, Liu R.
europepmc   +1 more source

Additive Manufacturing of Continuous Fibre Reinforced Composites: Process, Characterisation, Modelling, and Sustainability

Advanced Engineering Materials, EarlyView.
Additive manufacturing provides precise control over the placement of continuous fibres within polymer matrices, enabling customised mechanical performance in composite components. This article explores processing strategies, mechanical testing, and modelling approaches for additive manufactured continuous fibre‐reinforced composites.
Cherian Thomas, Amir Hosein Sakhaei
wiley   +1 more source

Multi‑objective Feature Selection Algorithm for Neighborhood Rough Set Under Mixed Hierarchical Dependence

Shuju Caiji Yu Chuli
Accuracy and efficiency are the key metrics for evaluating the performance of feature selection algorithms. They correspond to the attribute dependence and reduction scale of neighborhood rough sets respectively. Conventional feature selection algorithms
LUO Gongzhi, ZHANG Shanglei
doaj   +1 more source

An Entropy-Based Neighborhood Rough Set and PSO-SVRM Model for Fatigue Life Prediction of Titanium Alloy Welded Joints. [PDF]

Entropy (Basel), 2019
Zou L, Sun Y, Yang X, Yang X.
europepmc   +1 more source

Phase Field Failure Modeling: Brittle‐Ductile Dual‐Phase Microstructures under Compressive Loading

Advanced Engineering Materials, EarlyView.
The approach by Amor and the approach by Miehe and Zhang for asymmetric damage behavior in the phase field method for fracture are compared regarding their fitness for microcrack‐based failure modeling. The comparison is performed for the case of a dual‐phase microstructure with a brittle and a ductile constituent.
Jakob Huber, Jan Torgersen, Ewald Werner
wiley   +1 more source

Preference Mining Using Neighborhood Rough Set Model on Two Universes. [PDF]

Comput Intell Neurosci, 2016
Zeng K, Zeng K.
europepmc   +1 more source