Aiming at the lack of a precise discrete element model in the simulation analysis of corn straw crushing, this paper established bond models with different structure and particle models with different properties of corn straw based on Hertz-Mindlin with bonding, and verified the discrete element model by combining physical test and simulation optimization design methods. Firstly, this study generates the bonded particle models based on Hertz-Mindlin with bonding for epidermis-epidermis, inner flesh-internal flesh, and epidermis-internal flesh, respectively. The bonding parameters of the model are calibrated with the help of a bending damage test, bending damage simulation test, and actual test with 2 mm/min speed. It shows that the maximum destructive force errors of the epidermis and inner flesh are 2.4% and 1.6%, respectively. On this basis, a discrete element model of corn straw is established by combining the calibrated parameters of the bonding parameters of epidermis—epidermis, inner flesh—inner flesh, and epidermis—inner flesh bond. The bending failure test shows that the mechanical properties of corn straw are similar between the simulated test and the actual test, and the maximum destructive power is 288 N and 292 N, respectively. The relative error is 1.36%. The feasibility of the discrete element model for simulation analysis is verified, which shows that the established discrete element model can be applied to the simulation analysis of corn straw cutting and crushing process.

Transient stability assessment (TSA) based on machine learning techniques is a classical problem of classification. In this paper, a new transient stability assessment model using the weighted random forest (WRF) is proposed. Random forest, which runs fast and can scale to very high dimensional datasets, is one of the most popular methods applied to TSA after faults occurred. However, in the actual operation of modern power system, unstable samples constitute only a very small minority of the cases. Besides, the traditional random forest algorithm does not work very well on imbalanced data. To tackle the problem, weighted random forest is adopted to lean the interest towards to the correct classification of rare class, and improve the prediction performance. Case study on the New England 39-bus test power system exhibits that WRF has better performance than the original random forest.

Moisture content is an important parameter for estimating the quality of pellet feed, which is vital in nutrition, storage, and taste. The ranges of moisture content serve as an index for factors such as safe storage and nutrition stability. A rapid and non-destructive model for the measurement of moisture content in pellet feed was developed. To achieve this, 144 samples of Caragana korshinskii pellet feed from various regions in Inner Mongolia Autonomous Region underwent separate moisture content control, measurement using standard methods, and captured their images using a hyperspectral imaging (HSI) system in the spectral range of 935.5–2539 nm. The Monte Carlo cross validation (MCCV) was used to eliminate abnormal sample data from the spectral data for better model accuracy, and a global model of moisture content was built by using partial least squares regression (PLSR) with seven preprocessing techniques and two spectral feature extraction techniques. The results showed that the regression model developed by PLSR based on second derivative (SD) and competitive adaptive reweighted sampling (CARS) resulted in better performance for moisture content. The model showed predictive abilities for moisture content with a coefficient of determination of 0.9075 and a root mean square error (RMSE) of 0.4828 for the training set; and a coefficient of determination of 0.907 and a root mean square error (RMSE) of 0.5267 for the test set; and a relative prediction error of 3.3 and the standard error of 0.307.

Accurate and efficient object detection in UAV images is a challenging task due to the diversity of target scales and the massive number of small targets. This study investigates the enhancement in the detection head using sparse convolution, demonstrating its effectiveness in achieving an optimal balance between accuracy and efficiency. Nevertheless, the sparse convolution method encounters challenges related to the inadequate incorporation of global contextual information and exhibits network inflexibility attributable to its fixed mask ratios. To address the above issues, the MFFCESSC-SSD, a novel single-shot detector (SSD) with multi-scale feature fusion and context-enhanced spatial sparse convolution, is proposed in this paper. First, a global context-enhanced group normalization (CE-GN) layer is developed to address the issue of information loss resulting from the convolution process applied exclusively to the masked region. Subsequently, a dynamic masking strategy is designed to determine the optimal mask ratios, thereby ensuring compact foreground coverage that enhances both accuracy and efficiency. Experiments on two datasets (i.e., VisDrone and ARH2000; the latter dataset was created by the researchers) demonstrate that the MFFCESSC-SSD remarkably outperforms the performance of the SSD and numerous conventional object detection algorithms in terms of accuracy and efficiency.