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Considering the asymmetric structure of a rolling mill system, a chatter model is established by coupling a rolling process model and structure model to describe the asymmetric vibration characteristics. According to this mathematical model, the critical rolling speed is determined by calculating eigenvalues of the characteristic matrix. Model correctness and validity are confirmed by an experimental observation of a 2030 cold rolling mill. On this basis, the effects of asymmetric structure parameters on the rolling mill stability are investigated with the help of the numerical approach. It is found that the influence of the asymmetric structure parameters on stability and critical conditions is significant. An optimal stiffness ratio and optimal mass ratio is determined, in order to ensure the best stability for the considered system. As the asymmetry in terms of the stiffness and mass becomes considerable, a modal conversion phenomenon occurs. This study serves as a contribution both for the mill vibration suppressing, and for mill optimization design.

As an essential index to evaluate feed quality, feed moisture content which is too high or too low will impose an adverse impact on feed nutritional value. Therefore, the quantitative analysis of feed moisture content is significant. In this paper, the detection of feed moisture content based on terahertz (THz) and near-infrared (NIR) spectroscopy and data fusion technology of THz and NIR (THz-NIR) was investigated. First, feed samples with different water content (29.46%–49.46%) were prepared, and THz (50–3000 μm) and NIR (900–1700 nm) spectral data of samples was collected and preprocessed, and the feed samples were divided into correction set and verification set by 2:1. Second, the spectral data was fused through the head-to-tail splicing, and the feed moisture content prediction model was established combined with partial least squares regression (PLSR). Third, competitive adaptive reweighting sampling (CARS) was applied to extract spectral characteristic variables for feature layer fusion, and the feed moisture content prediction model in feature level was constructed combined with PLSR. Finally, the evaluation parameters validation set correlation coefficient (Rp), the root mean square error of prediction (RMSEP), and the residual predictive deviation (RPD) were employed to evaluate the prediction effect of the model. The results indicated that THz, NIR spectra, and data fusion technology could quickly and effectively predict feed moisture content. Among them, the characteristic layer spectral data fusion model achieved the optimal prediction effect while Rp, RMSEP, and RPD reached 0.9933, 0.0069, and 8.7386 respectively. In conclusion, compared with the prediction model established by single THz and NIR spectrum, THz-NIR spectrum data fusion could more accurately predict feed moisture content and provide certain theoretical and technical support for inspirations and methods for quantitative analysis of feed moisture content of livestock and poultry.

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RGB CF structure having different thicknesses is introduced on the TFE, instead of a polarizer, to reduce the total thickness of the flexible display. Through optical simulation, we choose the appropriate RGB CF thicknesses and demonstrate CF array working with optical cavity could effectively improve the color shift with different view angles.