The relative sensitivity analysis method is an important method in the field of vehicle lightweighting. Combined with optimization algorithms, experiment of design (DOE), etc., it can efficiently explore the impact of unit mass of components on performance and search for components with lightweight space. However, this method does not take into account the size level of each component and the order of magnitude differences in sensitivity under different operating conditions.Therefore, this paper proposed a sensitivity hierarchical comparative analysis method, on the basis of which the thicknesses of 10 groups of components were screened out as design variables by considering the lightweighting effect, cab performance and passive safety.Through the optimal Latin hypercube method, 70 groups of sample points were extracted to carry out the experimental design, the Kriging surrogate model was established and the NSGA-II genetic algorithm was used to obtain the Pareto optimal solution set, and ultimately a weight reduction of 13.13 kg was realized under the premise that all the performance of the cab has been improved.

In this article, we propose a data-driven method for short-circuit fault detection in transmission lines that exploits the capabilities of convolutional neural networks (CNNs). CNNs, a class of deep feedforward neural networks, can autonomously detect different features from data, eliminating the need for manual intervention. To mitigate the effects of noise and increase network robustness, we present a CNN architecture with six convolutional layers. The study uses a single busbar power system model developed with the PSCAD simulation program to evaluate the performance of the proposed method. The proposed CNN method is also compared with machine learning methods such as LSTM, SVM and ELM. Our results show a high success rate of 98.4% across all fault impedances, confirming the effectiveness of the proposed CNN methods in accurately detecting short-circuit faults based on current and voltage measurements.