Abstract: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.

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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.

In the sheet metal assembly process, welding operations join two or more sheet metal parts together. Since sheet metals are subject to dimensional variation resulting from manufacturing randomness, a gap may be generated at each weld pair prior to welding. These gaps are forced to close during the welding operation and accordingly undesirable structural deformation results. Optimizing the welding pattern (the number and locations of weld pairs) in the assembly process was proven to improve significantly the quality of the final assembly. This paper presents a genetic-algorithm-based optimization method to search automatically for the optimal weld pattern so that assembly deformation is minimized. The application result for a real industrial part demonstrated that the proposed algorithm effectively achieved the objective.

The drum driving system is one of the weakest parts of the long-wall shearer, and some methods are also needed to monitor and control the long-wall shearer to adapt to the important trend of unmanned operation in future mining systems. Therefore, it is essential to conduct an electromechanical dynamic analysis for the drum driving system of the long-wall shearer. First, a torsional dynamic model of planetary gears is proposed which is convenient to be connected to the electric motor model for electromechanical dynamic analysis. Next, an electromechanical dynamic model for the drum driving system is constructed including the electric motor, the gear transmission system, and the drum. Then, the electromechanical dynamic characteristics are simulated when the shock loads are acted on the drum driving system. Finally, some advices are proposed for improving the reliability, monitoring the operating state, and choosing the control signals of the long-wall shearer based on the simulation.