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Although K0.5Na0.5NbO3 (KNN) possesses large maximum polarization and relatively high breakdown strength, the large remnant polarization constrains their practical applications as energy storage materials. In this work, through multi-element doping, (K0.5−0.5xNa0.5−0.5xBix)(Nb1−xSn0.2xZn0.6xTa0.2x)O3 relaxor ferroelectrics were prepared. As the superparaelectric states (SPE) were adjusted to room temperature, orthorhombic, tetragonal, and cubic phases coexisted, accompanied by the highly dynamic polar nanoregions (PNRs). In particular, a high recoverable energy storage density of 4.5 J/cm3 and an energy storage efficiency of 83% were achieved for the x = 0.125 ceramic, with the variations less than 11% and 4%, respectively, in the wide temperature range of 20–180 °C. These results demonstrate that the multiphase PNRs in the SPE state is an effective strategy for improving the energy storage performance of KNN-based ceramics. [ABSTRACT FROM AUTHOR]

The high-order harmonics caused by traditional fixed switching frequency space vector pulse width modulation (SVPWM) technology cause serious electromagnetic interference problems in electric drive systems. Random switching frequency (RSF) SVPWM can be used to solve the problem of severe electromagnetic interference. The principles of SVPWM technology and RSF-SVPWM technology are introduced, and the influence of the increase of the spread spectrum range in RSF-SVPWM strategy on the system is discussed. To achieve good dispersion of high order harmonics in a small spread spectrum range, a novel RSF-SVPWM strategy based on dual frequency division is proposed. Taking the original central switching frequency as the midpoint, the initial complete variation range is divided into two local frequency bands, and their local central frequency points and local spread spectrum range are set. Taking the positive and negative of the ideal β axis voltage component in the two-phase static α-β coordinate system as the standard, after judging the specific frequency division band of each switching cycle, the switching frequency is randomized around the local central frequency point in the frequency division band. The simulation and experimental results show that compared to the traditional RSF-SVPWM strategy with a wider spread spectrum range, the new RSF-SVPWM strategy has equivalent high order harmonic dispersion effect. At the same time, the new RSF-SVPWM reduces the pulsation of the system current loop and outputs a relatively stable electromagnetic torque. The new RSF-SVPWM promotes the application of electric drive systems in high-end industry fields.

The influence line of bridge can reflect the performance of the bridge under the moving vehicle load. It has a wide range of applications in structural damage detection, performance evaluation, model correction and bridge weigh-in-motion. Fast moving vehicles will cause the dynamic effect of the bridge, resulting in the change of the bridge response curve and the difficulty of identifying the influence line. In this paper, an influence line identification method based on variational mode decomposition (VMD) combined with improved wavelet threshold denoising is proposed. Firstly, the mathematical model of influence line identification is established based on vehicle load matrix and bridge response. Then, VMD combined with improved wavelet threshold denoising method is used to eliminate dynamic fluctuation effect and noise. Finally, the elastic net penalty term is introduced in the influence line identification problem, and the B-spline basis function is used to reconstruct the influence line. In order to verify the effectiveness of the proposed method, a vehicle-bridge coupling model is established for numerical calculation, and the identification of bridge displacement influence lines under different conditions such as vehicle speed, noise level, road roughness and vehicle weight is investigated. The results show that the proposed method can effectively eliminate the dynamic fluctuation components and noise of the bridge response. In the case of good road roughness, the influence line can still be accurately identified when the vehicle speed reaches 15 m/s. [ABSTRACT FROM AUTHOR]

This research presents an innovative methodology aimed at monitoring jet trajectory during the jetting process using imagery captured by unmanned aerial vehicles (UAVs). This approach seamlessly integrates UAV imagery with an offline learnable prompt vector module (OPVM) to enhance trajectory monitoring accuracy and stability. By leveraging a high-resolution camera mounted on a UAV, image enhancement is proposed to solve the problem of geometric and photometric distortion in jet trajectory images, and the Faster R-CNN network is deployed to detect objects within the images and precisely identify the jet trajectory within the video stream. Subsequently, the offline learnable prompt vector module is incorporated to further refine trajectory predictions, thereby improving monitoring accuracy and stability. In particular, the offline learnable prompt vector module not only learns the visual characteristics of jet trajectory but also incorporates their textual features, thus adopting a bimodal approach to trajectory analysis. Additionally, OPVM is trained offline, thereby minimizing additional memory and computational resource requirements. Experimental findings underscore the method's remarkable precision of 95.4% and efficiency in monitoring jet trajectory, thereby laying a solid foundation for advancements in trajectory detection and tracking. This methodology holds significant potential for application in firefighting systems and industrial processes, offering a robust framework to address dynamic trajectory monitoring challenges and augment computer vision capabilities in practical scenarios. [ABSTRACT FROM AUTHOR]

A split-type two-axle vehicle for bridge indirect measurement is proposed to address the deficiencies of single-axle and conventional two-axle vehicles. At first, the conceptual design of the split-type two-axle vehicle is put forth, and the analytical solution for the vehicle-bridge interaction (VBI) system response is derived. After that, the regularity of the vehicle frequency response function is studied, and the numerical analysis method is utilized to validate the analytical solution. Based on the theoretical findings, the vehicle frequency and speed are studied to investigate their impacts on extracting bridge frequencies from vehicle responses. The results indicate that a peak will form in the frequency response function (FRF) curves when the input frequency is near to the vehicle frequency. The single peak phenomenon will occur when the zero-point condition of the FRF from the contact point to the vehicle body is remarkably close to the second vibration frequency of the vehicle. It is preferable for the first frequency of the vehicle to be higher than that of the bridge since this helps to extract the bridge frequency. On the other hand, at low speeds (≤ 43.2km/h), higher vehicle speeds will result in a greater distance between the peaks of the left- and right-shifted frequencies of the bridge, wider frequency bands, and lower spectral resolution, which is not conducive to bridge frequency identification. [ABSTRACT FROM AUTHOR]