Due to the fact that the traditional fuzzy controller of the suspension system expressively depends on expert experience, and the fact that the control strategy and damper model of the suspension system are verified separately, we put forward a novel control strategy for semi-active suspension systems based on deep neural fuzzy system (DNFS). The dynamic model of semi-active suspension systems is established, and by using adaptive neuro fuzzy inference system (ANFIS), a variable universe Takagi–Sugeno (T–S) fuzzy controller is designed. Then, combining the bench experimental data and ANFIS, a non-parametric model of continuous damping control (CDC) damper is first proposed. Finally, a semi-active suspension controller based on DNFS is constructed by using serial ideas. The effectiveness of the proposed DNFS control strategy is verified by simulation experiments under different working conditions. The results show that the proposed DNFS control strategy has strong adaptability to various working conditions, which can effectively improve the ride comfort and handling stability of vehicles.

The optimal value of the expansion factor and quantization factor parameters is the key to determine the performance of the variable domain fuzzy proportion-integral-derivative (PID) controller. In view of the problem that the relevant parameters are fixed, difficult to determine and cannot be adjusted adaptively in the traditional variable domain fuzzy PID control, a kind of enhanced variable domain fuzzy PID control (EVUFP) is proposed to improve the ride comfort of vehicles. First, based on the traditional variable domain fuzzy PID control, an adaptive expansion factor controller is constructed to determine and adjust adaptively the parameters of the expansion factor in real time. Then the genetic algorithm is used to optimize the quantization factor and scale factor parameters to seek the optimal value of relevant parameters. And the obtained optimal parameters are substituted into the Simulink model to realize the design of the EVUFP controller. The simulation experiment results under different working conditions show that the proposed EVUFP control strategy can reduce the root mean square (RMS) values of the body acceleration, suspension dynamic deflection and tire dynamic load by more than 54%, 50% and 23%, respectively, improve the ride comfort of vehicles, and provide a new idea for the development of active suspensions.

In image classification of tomato leaf diseases based on deep learning, models often focus on features such as edges, stems, backgrounds, and shadows of the experimental samples, while ignoring the features of the disease area, resulting in weak generalization ability. In this study, a self-attention mechanism called GD-Attention is proposed, which considers global pixel value distribution information and guide the deep learning model to give more concern on the leaf disease area. Based on data augmentation, the proposed method inputs both the image and its pixel value distribution information to the model. The GD-Attention mechanism guides the model to extract features related to pixel value distribution information, thereby increasing attention towards the disease area. The model is trained and tested on the Plant Village (PV) dataset, and by analyzing the generated attention heatmaps, it is observed that the disease area obtains greater weight. The results achieve an accuracy of 99.97% and 27 MB parameters only. Compared to classical and state-of-the-art models, our model showcases competitive performance. As a next step, we are committed to further research and application, aiming to address real-world, complex scenarios.

The accuracy of ZPW-2000 frequency shift signal demodulation is related to the safety and efficiency of high-speed trains. This paper proposed a kind of ZPW-2000 frequency-shift signal detection algorithm based on improved Relaxation algorithm (ZFSD-IR) to address the issue of low accuracy in detecting ZPW-2000 frequency shift signal under strong noise interference, as the traditional fast Fourier transform (FFT) spectrum analysis methods are susceptible to fence effects and spectrum leakage effects. Firstly, the principle of ZPW-2000 jointless track circuit was analyzed, and then the ideal and simplified models of ZPW-2000 frequency shift signal were established, respectively. Finally, the simulation experiments were conducted on ZPW-2000 frequency shift signal detection under different sampling durations and signal-to-noise ratios (SNR). The effectiveness of the proposed algorithm was verified through comparative analysis with the traditional algorithms. The research results indicated that the ZFSD-IR algorithm has better accuracy in carrier-frequency and low-frequency estimation than traditional algorithms, and can achieve accurate detection of ZPW-2000 frequency shift signal under the lower SNR conditions. It has high detection accuracy and good anti-interference ability, ensuring the safe operation of high-speed trains.

Aiming at the problems of spectrum leakage, difficulty in guaranteeing demodulation accuracy, and endangering driving safety in signal detection algorithm based on periodogram with limited sampling duration, this paper proposes for the first time a kind of ZPW-20000 frequency-shift signal detection algorithm based on nonlinear least squares, so as to improve the detection performance of ZPW-2000 frequency-shift signal, to ensure the safe operation of trains, and to improve the operational efficiency. The mathematical model of ZPW-2000 frequency-shift signal is established, the Cramér-Rao bound (CRB) for carrier-frequency and low-frequency estimation are derived, and a nonlinear least squares algorithm containing three processes, namely, the coarse estimation, the grid search based on Fast Fourier Transform (FFT), and the exact search, is designed, which is applied to the detection of ZPW-2000 frequency-shift signal. The research results show that the accuracy of carrier-frequency and low-frequency estimation of the proposed algorithm is better than that of the periodogram method for different sampling durations, and the number of decoding errors of the proposed algorithm and the periodogram method is reduced to zero when the sampling durations are greater than or equal to 0.11 s and 0.19 s, respectively, and the proposed algorithm reduces the limitation on the sampling durations. As the signal-to-noise ratio (SNR) increases, the root mean square error (RMSE) of carrier-frequency and low-frequency estimation of the proposed algorithm can approach the CRB. when the SNR of −2 dB, the number of decoding errors of the proposed algorithm is reduced to zero, while the periodogram method requires the SNR of 2 dB to achieve the same effect. The proposed algorithm performs well in detection accuracy, real-time and anti-interference ability, which is of great significance for ensuring driving safety and improving the reliability of the vehicle-ground communication mode based on ZPW-2000 track circuit.