The state of health (SOH) of lithium-ion batteries (LIBs) needs to be accurately estimated to ensure the safety and stability of electric vehicles (EVs) while in operation. In this paper, we proposed a SOH estimation method based on Improved Aquila Optimizer (IAO) and Support Vector Regression (SVR) to achieve an accurate estimation of SOH. During the charging and discharging phases of the battery, we analyzed the trends in current, voltage, and energy, then extracted four features. We used the Kendall coefficient and gray relational grade to prove that features and SOH were highly correlated. On the other hand, IAO was used to optimize the penalty factor and kernel function parameters of the SVR to further improve the generalization and mapping ability. The proposed method was verified under different operating conditions using the CACLE battery data set; the results show that high accuracy can be achieved in SOH estimation via IAO–SVR, and the estimation error of mean MAE is remaining within 2%.

In recent years, extensive research has been conducted on pesticide application technology using unmanned aerial spraying systems (UASS) due to their efficiency and ability to overcome terrain obstacles. However, the coupling effect between the operational parameters of UASS and their influence on droplet deposition has not been sufficiently studied. A thorough and methodical analysis is essential to assess the deposition performance and drift risk of UASS. This study evaluated the spraying performance of an electric six-rotor UASS in wheat fields in Zibo between 2021 and 2022, focusing on three operational modes determined by flight speed and flow rate. Furthermore, the individual effects of these two parameters on droplet deposition quality and drift risk were explored. Based on the deposition quality of in-swath droplets and the drift degree after application, the results demonstrate that the optimal comprehensive characteristics of droplet deposition occur at a flight speed of 4.5 m/s, a flow rate of 2.025 L/min, and a spray amount of 1 L/ha. The increase in spray flow rate (2.475 L/min) results in a 3.92-fold enhancement in the deposition rate within the spray area compared with that of group of the minimum spray flow rate (1.575 L/min). A higher flight speed (5.5 m/s) improves the uniformity of droplet deposition, with the coefficient of variation decreases by 25.2% compared with that of the minimum flight speed (3.5 m/s), and this higher flight speed leads to a drift distance of 28.8 m.

This study addresses the challenges of low accuracy and high computational demands in Tibetan speech recognition by investigating the application of end-to-end networks. We propose a decoding strategy that integrates Connectionist Temporal Classification (CTC) and Attention mechanisms, capitalizing on the benefits of automatic alignment and attention weight extraction. The Conformer architecture is utilized as the encoder, leading to the development of the Conformer-CTC/Attention model. This model first extracts global features from the speech signal using the Conformer, followed by joint decoding of these features through CTC and Attention mechanisms. To mitigate convergence issues during training, particularly with longer input feature sequences, we introduce a Probabilistic Sparse Attention mechanism within the joint CTC/Attention framework. Additionally, we implement a maximum entropy optimization algorithm for CTC, effectively addressing challenges such as increased path counts, spike distributions, and local optima during training. We designate the proposed method as the MaxEnt-Optimized Probabilistic Sparse Attention Conformer-CTC/Attention Model (MPSA-Conformer-CTC/Attention). Experimental results indicate that our improved model achieves a word error rate reduction of 10.68% and 9.57% on self-constructed and open-source Tibetan datasets, respectively, compared to the baseline model. Furthermore, the enhanced model not only reduces memory consumption and training time but also improves generalization capability and accuracy.

The dim lighting and excessive dust in underground mines often result in uneven illumination, blurriness, and loss of detail in surveillance images, which hinders subsequent intelligent image recognition. To address the limitations of the existing image enhancement algorithms in terms of generalization and accuracy, this paper proposes an unsupervised method for enhancing mine images in the hue–saturation–value (HSV) color space. Inspired by the HSV color space, the method first converts RGB images to the HSV space and integrates Retinex theory into the brightness (V channel). Additionally, a random perturbation technique is designed for the brightness. Within the same scene, a U-Net-based reflectance estimation network is constructed by enforcing consistency between the original reflectance and the perturbed reflectance, incorporating ResNeSt blocks and a multi-scale channel pixel attention module to improve accuracy. Finally, an enhanced image is obtained by recombining the original hue (H channel), brightness, and saturation (S channel), and converting back to the RGB space. Importantly, this image enhancement algorithm does not require any normally illuminated images during training. Extensive experiments demonstrated that the proposed method outperformed most existing unsupervised low-light image enhancement methods, qualitatively and quantitatively, achieving a competitive performance comparable to many supervised methods. Specifically, our method achieved the highest PSNR value of 22.18, indicating significant improvements compared to the other methods, and surpassing the second-best WCDM method by 10.3%. In terms of SSIM, our method also performed exceptionally well, achieving a value of 0.807, surpassing all other methods, and improving upon the second-place WCDM method by 19.5%. These results demonstrate that our proposed method significantly enhanced image quality and similarity, far exceeding the performance of the other algorithms.

The abnormal structural state of the pantograph skateboard is a significant and highly concerning issue that has a significant impact on the safety of high-speed railway operation. In order to obtain real-time information on the abnormal state of the skateboard in advance, an intelligent defect identification model suitable to be used as a monitoring device for the pantograph skateboard was designed using a computer vision-based intelligent detection technology for pantograph skateboard defects, combined with an improved YOLO v8 model and traditional image processing algorithms such as edge extraction. The results show that the anomaly detection algorithm for the pantograph sliding plate structure has good robustness, maintaining recognition accuracy of 90% or above in complex scenes, and the average runtime is 12.32 ms. Railway field experiments have proven that the intelligent recognition model meets the actual detection requirements of railway sites and has strong practical application value.

Based on photoacoustic spectroscopy and finite element simulation technology, a simulation model of sound field excitation in a cylindrical resonant photoacoustic cell was established. The finite element simulation method was used to analyze the acoustic mode and sound pressure distribution of the cavity structure of the photoacoustic cell. The effects of the geometric parameters of the resonator and the buffer cavity on the performance of the photoacoustic cell were compared. The frequency response characteristics of the photoacoustic cell and the effects of the air intake and the air outlet were studied. Based on the simulation results, a cylindrical resonant photoacoustic cell was designed, and a photoacoustic sensor for NO₂ detection was built. NO₂ with a volume fraction of 10⁻⁵ was taken as the sample gas through frequency scanning to obtain the frequency response curve of the system. The resonant frequency is 1730 Hz, and the cell constant is about 542.3 (Pa*cm)/W. The quality factor is 10.05. By linear fitting the calibration curve of the sensor, the fitting slope is 0.012 µV/ppb, and R² is 0.998. Atmospheric NO₂ detection was carried out for two consecutive weeks, whose findings are in good agreement with the data released by a Huainan environmental monitoring site. The experimental results show that the system can detect NO₂ in the atmosphere with high sensitivity.