The construction of one-dimensional Daubechies wavelet-based finite elements for structural response analysis

The objective of this paper is to develop a family of wavelet-based finite elements for structural response analysis. First, independent wavelet bases are used to approximate displacement functions, unknown coefficients are determined through imposing the continuity, linear independence, completeness, and essential boundary conditions. A family of Daubechies wavelet-based shape functions are then developed, which are hierarchical due to multiresolution property of wavelet. Secondly, to construct wavelet-based finite elements, derivation of the shape functions for a subdomain is employed. Thus, the wavelet-based finite elements being presented are embodied with properties in adaptivity as well as locality. By wavelet preconditioning technology, the two difficulties involving imposition of boundary conditions and compatibility with the traditional finite element methods, which are gathered in the experiments of wavelet-Galerkin context, are well overcome. Numerical examples are used to illustrate the characteristics of the current elements and to assess their accuracy and efficiency

A joint stiffness identification method based on finite element modeling and frequency response functions

Accurate finite element (FE) modeling of mechanical structures is extremely difficult with unknown joints or boundary conditions. An alternative joint stiffness identification method that involves a hybrid of FE model and frequency response functions (FRFs) is presented. Firstly, the joint stiffness is assumed by experience and the mechanical structure is modeled with the FE method. Secondly, the FRFs at the concerned nodes of the structure are simulated and measured, respectively. Then the norm of residual FRFs between the simulations and measurements is calculated. Finally, a sensitivity-based iterative algorithm is derived for minimizing the norm of residual FRFs and the least square method is used to solve over-determined iterative equation. The joints stiffness parameters are identified through the iteration process, while the FE model is updated simultaneously. The proposed joint stiffness identification method is applied on a clamped beam assembly. The first three natural frequencies calculated by the FE model are compared with the measured values. The largest relative error of the simulation deceases from 16.7 % to 2.5 % after the joint stiffness parameters are identified, which demonstrates the effectiveness of the presented method

Unsupervised Semantic Representation Learning of Scientific Literature Based on Graph Attention Mechanism and Maximum Mutual Information

Since most scientific literature data are unlabeled, this makes unsupervised graph-based semantic representation learning crucial. Therefore, an unsupervised semantic representation learning method of scientific literature based on graph attention mechanism and maximum mutual information (GAMMI) is proposed. By introducing a graph attention mechanism, the weighted summation of nearby node features make the weights of adjacent node features entirely depend on the node features. Depending on the features of the nearby nodes, different weights can be applied to each node in the graph. Therefore, the correlations between vertex features can be better integrated into the model. In addition, an unsupervised graph contrastive learning strategy is proposed to solve the problem of being unlabeled and scalable on large-scale graphs. By comparing the mutual information between the positive and negative local node representations on the latent space and the global graph representation, the graph neural network can capture both local and global information. Experimental results demonstrate competitive performance on various node classification benchmarks, achieving good results and sometimes even surpassing the performance of supervised learning

A spring dashpot model for dynamic analysis of beam-like structure with clearance

In a large number of engineering structures, clearance always exists due to assemblage, manufacturing errors and wear. The presence of clearance may lead to intermittent contact or impacts. For such structures accurate assessment of dynamic response is necessary for design against excessive vibration and wear as well as noise. In this paper we are interested in the study of the dynamic behavior of a cantilever beam structure with clearance. Simulation and experimental tests were carried out for this goal. For simulation tests, clearance was equivalent to a spring-dashpot model with consideration of vertical and angular motions, the impact of beam and boundary face was also taken into consideration. A cantilever beam set-up was designed and built for experimental validations. The presented results showed that the system responses were greatly influenced in the presence of clearance. The peak value of beam’s time-domain signal is larger with the clearance enlargement. The high-order harmonics are more possible to exist in frequency-domain signals when clearance size increases. The effects of clearance should not be ignored when analyzing the dynamic performance and vibration characteristic of engineering structures

Self-Supervised Dynamic Hypergraph Recommendation based on Hyper-Relational Knowledge Graph

Knowledge graphs (KGs) are commonly used as side information to enhance collaborative signals and improve recommendation quality. In the context of knowledge-aware recommendation (KGR), graph neural networks (GNNs) have emerged as promising solutions for modeling factual and semantic information in KGs. However, the long-tail distribution of entities leads to sparsity in supervision signals, which weakens the quality of item representation when utilizing KG enhancement. Additionally, the binary relation representation of KGs simplifies hyper-relational facts, making it challenging to model complex real-world information. Furthermore, the over-smoothing phenomenon results in indistinguishable representations and information loss. To address these challenges, we propose the SDK (Self-Supervised Dynamic Hypergraph Recommendation based on Hyper-Relational Knowledge Graph) framework. This framework establishes a cross-view hypergraph self-supervised learning mechanism for KG enhancement. Specifically, we model hyper-relational facts in KGs to capture interdependencies between entities under complete semantic conditions. With the refined representation, a hypergraph is dynamically constructed to preserve features in the deep vector space, thereby alleviating the over-smoothing problem. Furthermore, we mine external supervision signals from both the global perspective of the hypergraph and the local perspective of collaborative filtering (CF) to guide the model prediction process. Extensive experiments conducted on different datasets demonstrate the superiority of the SDK framework over state-of-the-art models. The results showcase its ability to alleviate the effects of over-smoothing and supervision signal sparsity

Single-pixel imaging based on deep learning

Single-pixel imaging can collect images at the wavelengths outside the reach of conventional focal plane array detectors. However, the limited image quality and lengthy computational times for iterative reconstruction still impede the practical application of single-pixel imaging. Recently, deep learning has been introduced into single-pixel imaging, which has attracted a lot of attention due to its exceptional reconstruction quality, fast reconstruction speed, and the potential to complete advanced sensing tasks without reconstructing images. Here, this advance is discussed and some opinions are offered. Firstly, based on the fundamental principles of single-pixel imaging and deep learning, the principles and algorithms of single-pixel imaging based on deep learning are described and analyzed. Subsequently, the implementation technologies of single-pixel imaging based on deep learning are reviewed. They are divided into super-resolution single-pixel imaging, single-pixel imaging through scattering media, photon-level single-pixel imaging, optical encryption based on single-pixel imaging, color single-pixel imaging, and image-free sensing according to diverse application fields. Finally, major challenges and corresponding feasible approaches are discussed, as well as more possible applications in the future

Research on quantitative inversion of ion adsorption type rare earth ore based on convolutional neural network

Rare earth resource is a national strategic resource, which plays an essential role in the field of high technology research and development. In this paper, we aim to use remote sensing quantitative inversion prospecting technology, use surface-to-surface mode, and model inversion and evaluation through convolutional neural network model to achieve a new research method for large-scale, low-cost, rapid and efficient exploration of ion-adsorbed rare earth ore. The results show that the RE2O3 content of samples has significant negative correlation with the second, third and fourth band of GF-2 image, but has no significant correlation with the first band of GF-2 image; the convolution neural network model can be used to reconstruct the RE2O3 content. The content distribution map of RE2O3 obtained by inversion is similar to that of geochemical map, which indicates that the convolution neural network model can be used to invert the RE2O3 content in the sampling area. The quantitative inversion results show that the content distribution characteristics of ion adsorption rare earth ore in the study area are basically consistent with the actual situation; there are two main high anomaly areas in the study area. The high anomaly area I is a known mining area, and the high anomaly area II can be a prospective area of ion adsorption type rare earth deposit. It shows that the remote sensing quantitative inversion prospecting method of ion adsorption type rare earth deposit based on Convolutional Neural Networks (CNN) model is feasible