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Established an impact-unbalanced suspended rotor system mechanics models, and the dimensionless motion equation of system is deduced, using the fourth order Runge-Kutta method for the numerical calculation of this model, the integrated use of bifurcation diagram, orbit diagram, Poincare section diagram, the axis displacement time history diagram, maximum moment diagram and duty ratio diagram study the dynamic behavior of the system, identify the impact of the rotor, the maximum torque and impact by the proportion of time, and analyzed the speed ratio and the change of the stiffness coefficient of the influence of the nonlinear dynamic characteristics of the system.The analysis results show that the system presents chaotic motion in a certain parameter domain at the medium velocity stage, while the system tends to stable periodic motion in other parameter domains.With the increase of the stiffness coefficient, the chaotic motion region and the maximum moment of the system increase, and the stability of the system decreases.

Deep learning has made remarkable strides in hyperspectral image (HSI) classification, significantly improving classification performance. However, the challenge of obtaining accurately labeled training samples persists, primarily due to the subjectivity of human annotators and their limited domain knowledge. This often results in erroneous labels, commonly referred to as label noise. Such noisy labels can critically impair the performance of deep learning models, making it essential to address this issue. While previous studies focused on label noise filtering and label correction, these approaches often require estimating noise rates and may inadvertently propagate noisy labels to clean labels, especially in scenarios with high noise levels. In this study, we introduce an ensemble network-based distillation (END) method specifically designed to address the challenges posed by label noise in HSI classification. The core idea is to leverage multiple base neural networks to generate an estimated label distribution from the training data. This estimated distribution is then used alongside the ground-truth labels to train the target network effectively. Moreover, we propose a parameter-adaptive loss function that balances the impact of both the estimated and ground-truth label distributions during the training process. Our approach not only simplifies architectural requirements but also integrates seamlessly into existing deep learning frameworks. Comparative experiments on four hyperspectral datasets demonstrate the effectiveness of our method, highlighting its competitive performance in the presence of label noise. [ABSTRACT FROM AUTHOR]

The high-speed plough tip is the core soil-touching component in southern Xinjiang field cultivation, but the interaction of the plough tip with the soil results in severe wear of the tip. The friction behaviour of sand and soil on plough tips was investigated with a homemade rotary abrasive wear tester in a one-factor multilevel test with three parameters: moisture content, velocity/rotational speed and friction distance. The objective was to study the friction behaviour of the sand soil and plough tip and analyse and characterise the wear amount, wear thickness and compressive stress distribution, three-dimensional wear morphology and microscopic wear morphology of the plough tips. The results show that with increasing speed, the wear amount changes more gently; with increasing soil water content, the soil adhesion force and lubricating water film increase so that the wear amount follows a second-order parabolic law; and with increasing friction distance, the wear amount gradually increases, and the wear rate also shows an upward trend when the plough tip is in the abrasive wear stage. The tip makes contact with the firmer soil with higher surface compressive stresses, causing the most wear. As the friction distance increases, sand particles become embedded in the contact surfaces, creating a groove effect along with spalling pits caused by fatigue wear. During the whole wear period, the groove effect is always accompanied by spalling pits appearing repeatedly. The analysis of the wear micromorphology of the plough tip shows that the number of flaking pits gradually decreases in the direction of soil movement, and the form of damage changes from impact wear to plough groove scratches. Abrasive wear interacts with corrosive wear to exacerbate plough tip wear. [ABSTRACT FROM AUTHOR]

In the "worst-case" selection of hip prosthesis wear, it is necessary to calculate the contact stress of the acetabular liner. However, there are various combinations of acetabular prostheses. If calculated one by one, it will cause a large workload, a repeated and tedious calculation problem. To solve this problem, a machine learning prediction method by combining principal component analysis and support vector regressions (PCA-SVR) was established. First, the finite element method is used to analyze and calculate the contact stress of the acetabular liner in a typical combination to form a basic data set with the key size of the acetabular prosthesis as input and the contact stress as output; then, based on this data set, the PCA reduces the dimension of the input to obtain a new data set. Finally, based on this data set, SVR is used to establish the mapping model, and the optimal value of the model parameter C and is obtained by combining K-fold cross-validation and grid search method. The maximum absolute error of the prediction on the test data set is only 0.1986, the root mean square error RMSE is only 0.09309, and the R² value is 0.9426, which verifies the effectiveness of the prediction model. At the same time, the prediction performance is compared with the Ridge regression and Lasso models, which further verifies the superiority of the proposed method.