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Traffic command and scheduling are the core monitoring aspects of railway transportation. Detecting the fatigued state of dispatchers is, therefore, of great significance to ensure the safety of railway operations. In this paper, we present a multi-feature fatigue detection method based on key points of the human face and body posture. Considering...
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... This paper combines the powerful adaptive feature extraction ability of CNN and the advantages of the SVR algorithm in small data sample scenarios, then establishes a CNN-SVR hybrid model for SRN prediction. Among them, CNN, as a representative algorithm of deep learning, has excellent performance in the fields of image recognition [20][21][22], speech recognition [23], and target detection [24][25][26][27]. It establishes the mapping relationship between input and output through multidimensional nonlinear feature extraction, has extremely strong data characterization and mapping ability [28,29], and can obtain higher accuracy and robustness compared with traditional shallow artificial neural networks [30]. ...
In order to enhance the predictive accuracy and control capabilities pertaining to low- and medium-frequency road noise in automotive contexts, this study introduces a methodology for Structural-borne Road Noise (SRN) prediction and optimization. This approach relies on a multi-level target decomposition and a hybrid model combining Convolutional Neural Network (CNN) and Support Vector Regression (SVR). Initially, a multi-level target analysis method is proposed, grounded in the hierarchical decomposition of vehicle road noise along the chassis parts, delineated layer by layer, in accordance with the vibration transmission path. Subsequently, the CNN–SVR hybrid model, predicated on the multi-level target framework, is proposed. Notably, the hybrid model exhibits a superior predictive accuracy exceeding 0.97, surpassing both traditional CNN and SVR models. Finally, the method and model are deployed for sensitivity analysis of chassis parameters in relation to road noise, as well as for the prediction and optimization analysis of SRN in vehicles. The outcomes underscore the high sensitivity of parameters such as the dynamic stiffness of the rear axle bushing and the large front swing arm bushing influencing SRN. The optimization results, facilitated by the CNN–SVR hybrid model, align closely with the measured outcomes, displaying a negligible relative error of 0.82%. Furthermore, the measured results indicate a noteworthy enhancement of 4.07% in the driver’s right-ear Sound Pressure Level (SPL) following the proposed improvements compared to the original state.
