Molecular models of pure and 10% and 20% swollen nitrile rubber matrices were developed. Different amounts of carbon nanotubes (CNTs) were incorporated into the swollen nitrile rubber matrices. The effects of the swelling behaviors and introduced CNTs on the mechanical properties of the nitrile rubber matrices were investigated using molecular dynamics simulations. The simulation results show that compared to the pure nitrile‐butadiene rubber (NBR) models, the elastic moduli and tensile strengths of the nitrile rubber matrices were reduced by ~31.72% and 20.10% (at a swelling ratio of 10%) and 68.28% and 36.82% (at a swelling ratio of 20%), respectively. Compared to those of the swollen NBR models without CNTs, after the introduction of one CNT, the elastic modulus and tensile strength improved by ~67.02% and 4.17% (at a swelling ratio of 10%) and 215.27% and 7.00% (at a swelling ratio of 20%), respectively. The mechanical properties of the nitrile rubber matrix improved when the content of CNTs increased. The radial distribution functions of the rubber materials, interfacial energies of the reinforced regions, and mean square displacements and diffusion coefficients of the solutions were analyzed. The introduced CNTs blocked the diffusion of the solute in the nitrile rubber matrix. This study elucidates the swelling behaviors of rubber materials from an atomic perspective and mechanical properties of carbon‐nanotube‐reinforced swollen rubber matrices.
Unfavorable driving states can cause a large number of vehicle crashes and are significant factors in leading to traffic accidents. Hence, the aim of this research is to design a robust system to detect unfavorable driving states based on sample entropy feature analysis and multiple classification algorithms. Multi-channel Electroencephalography (EEG) signals are recorded from 16 participants while performing two types of driving tasks. For the purpose of selecting optimal feature sets for classification, principal component analysis (PCA) is adopted for reducing dimensionality of feature sets. Multiple classification algorithms, namely, K nearest neighbor (KNN), decision tree (DT), support vector machine (SVM) and logistic regression (LR) are employed to improve the accuracy of unfavorable driving state detection. We use 10-fold cross-validation to assess the performance of the proposed systems. It is found that the proposed detection system, based on PCA features and the cubic SVM classification algorithm, shows robustness as it obtains the highest accuracy of 97.81%, sensitivity of 96.93%, specificity of 98.73% and precision of 98.75%. Experimental results show that the system we designed can effectively monitor unfavorable driving states.
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