Marginal Fisher analysis (MFA) exploits the margin criterion to compact the intraclass data and separate the interclass data, and it is very useful to analyze the high-dimensional data. However, MFA just considers the structure relationships of neighbor points, and it cannot effectively represent the intrinsic structure of hyperspectral imagery (HSI) that possesses many homogenous areas. In this paper, we propose a new dimensionality reduction (DR) method, termed local geometric structure Fisher analysis (LGSFA), for HSI classification. Firstly, LGSFA uses the intraclass neighbor points of each point to compute its reconstruction point. Then, an intrinsic graph and a penalty graph are constructed to reveal the intraclass and interclass properties of hyperspectral data. Finally, the neighbor points and corresponding intraclass reconstruction points are used to enhance the intraclass-manifold compactness and the interclass-manifold separability. LGSFA can effectively reveal the intrinsic manifold structure and obtain the discriminating features of HSI data for classification. Experiments on the Salinas, Indian Pines, and Urban data sets show that the proposed LGSFA algorithm achieves the best classification results than other state-of-the-art methods.

This paper proposes an improved lightweight YOLOv5 model for the real-time detection of strawberry diseases. The ghost convolution (GhostConv) module is incorporated into the YOLOv5 network, reducing the parameter numbers and floating-point operations (FLOPs) for extracting feature information using the backbone network. An involution operator is utilized in the backbone network to expand the receptive field, enhance the spatial information on strawberry disease characteristics, and reduce the number of FLOPs in the model. A convolutional block attention module (CBAM) is incorporated into the backbone network to enhance the network’s ability to extract strawberry disease features and suppress non-critical information. The upsampling module is replaced by a lightweight upsampling operator called Content-Aware ReAssembly of Features (CARAFE), which extracts feature map information and enhances the ability to focus on strawberry disease features. The experimental results on an open-source strawberry disease dataset show that the model achieves mean average precision (mAP)@0.5 of 94.7% with 3.9 M parameters and 3.6 G FLOPs. The improved model has higher detection precision than the original one and lower hardware requirements, providing a new strategy for strawberry disease identification and control.

A dynamic model that includes friction and tooth profile error excitation for herringbone gears is proposed for the dynamic analysis of variable speed processes. In this model, the position of the contact line and relative sliding velocity are determined by the angular displacement of the gear pair. The translational and angular displacements are chosen as generalized coordinates to construct the dynamic model. The friction is calculated using a variable friction coefficient. The tooth profile error excitation is assumed to depend on the position along the contact line and to vary with the angular displacement of the driving gear. Thus, the proposed model can be used in the dynamic analysis of the variable speed process of a herringbone gear transmission system. An example acceleration process is numerically simulated using the model proposed in this paper. The dynamics responses are compared with those from the model utilizing a constant friction coefficient and without friction in cases where the profile error excitations are included and ignored.

The drum shearer is one of the main equipments of the long-wall mining system. A typical condition to adjust the hauling and drum speeds is when the drum load exceeds the allowable value due to the hardness increase of the coal seam. Two schemes are utilized in this condition herein: (1) increasing the drum speed directly and maintaining the hauling speed; (2) decreasing the hauling speed firstly, then increasing the drum speed, finally increasing the hauling speed to the original value. The electromechanical dynamic model is firstly constructed for the Cutting Transmission System, and then the electromechanical dynamic analysis is conducted with both schemes, discovering that: the first scheme is quicker but may bring instability; the second is stable but slower; the resonance in frequencies obtained in different meshing conditions can be excited at the same time. At last, some advices are given for the development of the speed control strategies and mechanical design of the unmanned long-wall shearer.

This study addresses the problem localization deviation caused by cumulative wheel odometry errors in Automated Guided Vehicles (AGVs) operating in complex environments by proposing an adaptive localization method based on multi-sensor fusion. Within an Extended Kalman Filter (EKF) framework, the proposed approach integrates internal sensor predictions with external positioning data corrections, employing an adaptive weighting algorithm to dynamically adjust the contributions of different sensors. This effectively suppresses errors induced by factors such as ground friction and uneven terrain. The experimental results demonstrate that the method achieves a localization accuracy of 13 mm, and the simulation results show a higher accuracy of 10 mm under idealized conditions. The minor discrepancy is attributed to unmodeled noise and systematic errors in the complex real-world environment, thus validating the robustness of the proposed approach while maintaining robustness against challenges such as Non-Line-of-Sight (NLOS) obstructions and low-light conditions. The synergistic combination of LiDAR and odometry not only ensures data accuracy but also enhances system stability, providing a reliable navigation solution for AGVs in industrial settings.