With the large-scale direction development of wind turbine, the vibrations of wind turbines, which are rigid-flexible coupled multi-body systems, can be easily excited. Control system can be improved to reduce the vibration amplitude and load levels. Fluctuation-suppression pitch control strategy has been improved in this paper. The reference input rotation speed, which is normally constant, is required to change with rotation acceleration in pitch control in this new control strategy. Theory and simulation show that Fluctuation-suppression pitch control (FSPC) strategy can improve the vibration characters of wind turbine and suppress rotation speed and power fluctuation, and it is also quite effective for gust situation
A nonuniform sampling digital signal processing method based on wavelet transform and its application in the frequency detection studied in this paper. The fundamental principle of nonuniform sampling wavelet transform is explored, and the processing procedure is illustrated from the uniform wavelet to nonuniform wavelet. The experimental results demonstrated that the wavelet transform based nonuniform sampling was effective in signal detecting digital signal processes, and accurate in estimating the frequencies of sinusoidal signals.
Unknown motions will make the synthetic aperture radar (SAR) images of ground moving targets defocused. The target signal easily exhibits Doppler ambiguity due to the limitation of pulse repetition frequency, which leads to the focusing difficulty of moving targets. To address these issues, a fast approach for SAR imaging of ground moving target with Doppler ambiguity is proposed. In this method, the first-order and quadratic phase are initially estimated by using proposed operations based on 2-D scaled Fourier transform and improved range frequency cross correlation function, respectively. With the estimated parameters, the moving target is then focused in the range–azimuth time domain by the matched filtering. The presented approach is fast, because its realization procedure does not have any parameter-searching step and can be sped up by nonuniform fast Fourier transform. Moreover, the proposed approach can handle Doppler ambiguity (including Doppler center blur and spectrum ambiguity), blind speed sidelobe, and scaled frequency spectrum aliasing. Both spaceborne and airborne real data-processing results are presented to confirm the effectiveness of the proposed method.
Conventional theoretical and numerical studies on photonic crystal, which often does not consider the film thickness error during the experimental preparation process, will meet a large deviation between the experiment and the simulation. The filtering characteristics of one-dimensional (1D) photonic crystals with random film thickness errors (modeled as the Gaussian distribution) are systematically investigated by statistical method and numerical simulations. By studying the influence of the deviation of film thickness and the period number on the filter characteristics, it shows that the forbidden bandwidth is reduced to 80.27% of the intrinsic energy band when the film thickness deviation is σ=0.25a. Furthermore, we found that introduction of a slight disturbance of the film thickness (σ=0.01a) to photonic crystal will broaden the forbidden bandwidth to 100.49%. The proposed photonic crystal model with film thickness deviation can reduce the error between experiment and theory, which can be used for designing broadband photonic bandgaps. These structures have potential applications such as light-matter interactions, ultra-small filters, and photonic chips.
Despite the fact that transformer-based models have yielded great success in computer vision tasks, they suffer from the challenge of high computational costs that limits their use on resource-constrained devices. One major reason is that vision transformers have redundant calculations since the self-attention operation generates patches with high similarity at a later stage in the network. Hierarchical architectures have been proposed for vision transformers to alleviate this challenge. However, by shrinking the spatial dimensions to half of the originals with downsampling layers, the challenge is actually overcompensated, as too much information is lost. In this paper, we propose FDViT to improve the hierarchical architecture of the vision transformer by using a flexible downsampling layer that is not limited to integer stride to smoothly reduce the sizes of the middle feature maps. Furthermore, a masked auto-encoder architecture is used to facilitate the training of the proposed flexible downsampling layer and produces informative outputs. Experimental results on benchmark datasets demonstrate that the proposed method can reduce computational costs while increasing classification performance and achieving state-of-the-art results. For example, the proposed FDViT-S model achieves a top-1 accuracy of 81.5%, which is 1.7 percent points higher than the ViT-S model and reduces 39% FLOPs.
Human actions are typically of combinatorial structures or patterns, i.e., subjects, objects, plus spatio-temporal interactions in between. Discovering such structures is therefore a rewarding way to reason about the dynamics of interactions and recognize the actions. In this paper, we introduce a new design of sub-graphs to represent and encode the discriminative patterns of each action in the videos. Specifically, we present MUlti-scale Sub-graph LEarning (MUSLE) framework that novelly builds space-time graphs and clusters the graphs into compact sub-graphs on each scale with respect to the number of nodes. Technically, MUSLE produces 3D bounding boxes, i.e., tubelets, in each video clip, as graph nodes and takes dense connectivity as graph edges between tubelets. For each action category, we execute online clustering to decompose the graph into sub-graphs on each scale through learning Gaussian Mixture Layer and select the discriminative sub-graphs as action prototypes for recognition. Extensive experiments are conducted on both Something-Something V1 & V2 and Kinetics-400 datasets, and superior results are reported when comparing to state-of-the-art methods. More remarkably, our MUSLE achieves to-date the best reported accuracy of 65.0% on Something-Something V2 validation set.
Clutter removal in ground-penetrating radar (GPR) based on deep learning has been studied in recent years. However, existing methods are primarily designed for homogeneous background conditions and utilize only local spatial information via the convolution operation. In order to solve these issues, a subspace projection attention network is proposed for GPR heterogeneous clutter removal in this paper. Firstly, a heterogeneous concrete dataset based on a numerical model with randomly placed aggregates is constructed, which incorporates the complex electromagnetic propagation process accurately to improve the effectiveness for heterogeneous clutter removal. In addition, the Clutter Basis learning neural Network (CBNet) is designed by integrating the subspace projection attention (SPA) module into the skip connection paths of U-Net architecture. By learning the subspace basis vectors adaptively, the SPA exploits both local and global spatial information to extract target features precisely. At the same time, the feature maps are projected to the target subspace to remove heterogeneous clutter features. Finally, the performance and effectiveness of proposed method is validated by simulations and experiments.
Although space-time adaptive processing (STAP) is recognized as the optimal clutter suppression way for synthetic aperture radar (SAR) in theory, the deficient of independent and identically distributed range samples in real scenario limits its application. The reduce-dimension STAP methods can decrease the demand for range samples, but the assumption of moving target-free is always unsatisfied. The direct data domain methods only use the data of the range cell under test (RCUT) to avoid the assumption, but they are conducive to interference suppression than clutter suppression and have huge computational burden. Thus, in this letter, a single range data-based STAP method is proposed not only exploring the space-time statistical properties of clutter to suppress it, but also operating solely on the RCUT without recourse to range samples. Theoretical analyses and simulation results verify the effectiveness of the proposed method.
The strong vibration record contains a lot of information on the site during the earthquake, and the dynamic characteristics of the soil layer in the site can be expressed through this information. Currently, the H/V spectral ratio recorded by acceleration is often used to study the seismic effect of the site. Inspired by this, the thesis puts forward the idea of using the instantaneous H/V spectral ratio and its corresponding instantaneous frequency to judge the site liquefaction. The time-varying VARMA model is used to represent the horizontal ground motion component as the output of a time-varying system with vertical ground motion component as input. According to the time-varying VARMA parameters, the instantaneous spectral ratio, instantaneous frequency of the system and instantaneous damping ratio are used to judge the site liquefaction.
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