Search Results (85)

To address the challenge of incomplete fault feature extraction in rolling bearing fault diagnosis under small-sample conditions, this paper proposes a Temporal-Graph Convolutional Fusion Network (T-GCFN). The method enhances diagnostic robustness through collaborative extraction and dynamic fusion of features from time-domain and frequency-domain branches. First, Variational Mode Decomposition (VMD) was employed to extract time-domain Intrinsic Mode Functions (IMFs). These were then input into a Temporal Convolutional Network (TCN) to capture multi-scale temporal dependencies. Simultaneously, frequency-domain features obtained via Fast Fourier Transform (FFT) were used to construct a K-Nearest Neighbors (KNN) graph, which was processed by a Graph Convolutional Network (GCN) to identify spatial correlations. Subsequently, a channel attention fusion layer was designed. This layer utilized global max pooling and average pooling to compress spatio-temporal features. A shared Multi-Layer Perceptron (MLP) then established inter-channel dependencies to generate attention weights, enhancing critical features for more complete fault information extraction. Finally, a SoftMax classifier performed end-to-end fault recognition. Experiments demonstrated that the proposed method significantly improved fault recognition accuracy under small-sample scenarios. These results validate the strong adaptability of the T-GCFN mechanism. Full article

This paper introduces a novel approach to hand motion gesture recognition by integrating the Fourier transform with hypergraph convolutional networks (HGCNs). Traditional recognition methods often struggle to capture the complex spatiotemporal dynamics of hand gestures. HGCNs, which are capable of modeling intricate relationships among joints, are enhanced by Fourier transform to analyze gesture features in the frequency domain. A hypergraph is constructed to represent the interdependencies among hand joints, allowing for dynamic adjustments based on joint movements. Hypergraph convolution is applied to update node features, while the Fourier transform facilitates frequency-domain analysis. The T-Module, a multiscale temporal convolution module, aggregates features from multiple frames to capture gesture dynamics across different time scales. Experiments on the dynamic hypergraph (DHG14/28) and shape retrieval contest (SHREC’17) datasets demonstrate the effectiveness of the proposed method, achieving accuracies of 96.4% and 97.6%, respectively, and outperforming traditional gesture recognition algorithms. Ablation studies further validate the contributions of each component in enhancing recognition performance. Full article

The application of ceramic particle-reinforced metal matrix composites (CPRMMCs) in the nuclear power sector is primarily dependent on their mechanical and thermal properties. A comprehensive understanding of the structure–property (SP) linkages between microstructures and macroscopic properties is critical for optimizing material properties. However, traditional studies on SP linkages generally rely on experimental methods, theoretical analysis, and numerical simulations, which are often associated with high time and economic costs. To address this challenge, this study proposes a novel method based on Materials Informatics (MI), combining the finite element method (FEM), graph Fourier transform, principal component analysis (PCA), and machine learning models to establish the SP linkages between the microstructure and thermodynamic properties of CPRMMCs. Specifically, FEM is used to model the microstructures of CPRMMCs with varying particle volume fractions and sizes, and their elastic modulus, thermal conductivity, and coefficient of thermal expansion are computed. Next, the statistical features of the microstructure are captured using graph Fourier transform based on two-point spatial correlations, and PCA is applied to reduce dimensionality and extract key features. Finally, a polynomial kernel support vector regression (Poly-SVR) model optimized by Bayesian methods is employed to establish the nonlinear relationship between the microstructure and thermodynamic properties. The results show that this method can effectively predict FEM results using only 5–6 microstructure features, with the R² values exceeding 0.91 for the prediction of thermodynamic properties. This study provides a promising approach for accelerating the innovation and design optimization of CPRMMCs. Full article

Coal-fired boilers significantly contribute to nitrogen oxides (NOx) emissions, posing critical environmental and health risks. Effective prediction of NOx emissions is essential for optimizing control measures and achieving stringent emission standards. This study applies a Multiscale Graph Convolutional Network (MSGNet) designed to capture multiscale dynamic relationships among operational parameters of a 660 MW coal-fired boiler. MSGNet employs Fast Fourier Transform (FFT) for automatic periodic pattern recognition, adaptive graph convolution for dynamic inter-variable relationships, and a multihead attention mechanism to assess temporal dependencies comprehensively. Compared with the existing state of the art, the proposed structure achieves a good performance of 2.176 mg/m³, 1.652 mg/m³, and 0.988 of RMSE, MAE, and $R^2$. Experimental evaluations demonstrate that MSGNet achieves superior predictive performance compared with traditional methods such as LSTM, BiLSTM, and GRU. Results underscore MSGNet’s robust accuracy, stability, and generalization capability, highlighting its potential for advanced emission control and environmental management applications in thermal power generation. Full article

In this paper, a numerical study of two-phase flow instability in parallel rectangular channels is presented. Using the homogeneous flow model, marginal stability boundaries (MSBs) are derived in the parameter space defined by the phase change number (N_pch) and subcooling number (N_sub) under various operating conditions. Comparison with experimental data shows that the model predicts stability trends with a deviation of ±12.5%. The study reveals that, under constant mass flux conditions, stability decreases as the equivalent diameter (De) of the channels increases. Additionally, the exit area ratio of the two parallel tubes has minimal effect on the MSB, indicating that exit geometry does not significantly influence system stability. However, an increase in the inlet area ratio, from 0.1 to 1, reduces system stability, suggesting that larger inlet areas relative to tube cross-sectional areas may lead to greater flow disturbances, thereby decreasing stability. Moreover, increasing the length of the tubes enhances system stability, which may be attributed to the extended development length allowing for dissipation of flow disturbances. The study further demonstrates that higher flow rates, between 0.15 kg/s and 0.25 kg/s, enhance stability, while increasing the outlet flow resistance coefficient reduces stability. Conversely, increasing the inlet flow resistance coefficient improves stability. At system pressures of 3 MPa, 6 MPa, and 9 MPa, it is observed that higher pressures shift the boundary of complete vaporization (X_e = 1) to the left on the N_pch and N_sub graph, reducing the region susceptible to instability. The study also employs Fast Fourier Transform (FFT) analysis to identify peak frequencies across different parameter ranges. By examining the stability map and frequency spectra, the study provides deeper insights into two-phase flow instabilities in parallel channels. Full article

The wet chemical auto-combustion technique was used to synthesize barium zirconate ceramic (BaZrO₃). Many strategies were applied to regulate the functional properties of the perovskite-structured sample which was calcinated at 800 °C for 9 h. A Fourier-transform IR spectrometer, an X-ray diffractometer, a scanning electron microscope (SEM)-EDAX, an LCR meter, and a UV–visible spectrometer were employed to study the structural, morphological, optical, and electrical properties of the prepared barium zirconate sample. Using data derived from XRD, the perovskite phase was confirmed, and the average value of the crystallite size was found to be 17.68 nm. The lattice constant, crystallinity, unit cell volume, tolerance factor, and X-ray density were also calculated. SEM-EDAX confirmed the elemental composition of the product and verified that it contained only the major constituents (Ba, Zr, and O). The vibrational modes of the prepared sample were investigated using FTIR in wavelengths ranging from 400 to 4000 cm⁻¹. Energy bandgap was observed using Tauc’s plot, where a graph was prepared for photon energy (hυ) and (αhυ)². The powder sample was blended with PVA and made into pellets of 13 mm diameter using a pelletizer to explore dielectric parameters like the dielectric constant, while the loss factor was recorded at a frequency ranging from 100 Hz to 4 MHz at room temperature. With its high dielectric constant and low dielectric loss factor, barium zirconate ceramic stands as an excellent material for several microwave applications. Full article

The core idea of prediction-based anomaly detection is to identify anomalies by constructing a prediction model and comparing predicted and observed values. However, most existing traffic flow prediction models primarily focus on spatio-temporal features, neglecting comprehensive frequency-domain feature learning. Additionally, anomaly detection accuracy is often limited by insufficient prediction error analysis. To address this limitation, this paper proposes a prediction-based anomaly detection method for traffic flow data with multi-domain feature extraction. The prediction model is built as follows: first, Bidirectional Long Short-Term Memory network (Bi-LSTM) and a Graph Attention Network (GAT) extract temporal and spatial features, respectively. Then, Fast Fourier Transform (FFT) converts time-domain signals into the frequency domain, where Transformer learns magnitude and phase features. Finally, a prediction model is constructed using the extracted time-domain and frequency-domain features. For error analysis, this paper innovatively applies Chebyshev’s inequality to determine the error threshold, identifying anomalies based on whether errors exceed this threshold. Experimental results show that integrating multi-domain features can more comprehensively capture data characteristics and improve model prediction accuracy. In the anomaly detection experiment, it was verified that constructing a high-accuracy prediction model and conducting reasonable error analysis can effectively enable anomaly detection in the data. Full article

In order to investigate the changes in winter wheat yield and the factors influencing it, five meteorological factors—such as rainfall and soil moisture—collected from the experimental area between 2010 and 2022 were used as characteristic features. A combined model of GNN (Graph Neural Network), based on the Fourier transform and the Random Forest algorithm was developed to predict winter wheat yield. Matrix multiplication in Fourier space was performed to predict yield, while the Random Forest algorithm was employed to quantify the contribution of various yield factors to winter wheat yield. The combined model effectively captured the dynamic dependencies between yield factors and time series, improving predictive accuracy by 5.00%, 10.00%, and 27.00%, and reducing the root mean square error by 26.26%, 29.31%, and 88.20%, respectively, compared to the StemGNN, Informer, and Random Forest models. The predicted outputs ranged from 520 to 720 g/m², with an average error of 2.69% compared to the actual measure outputs. Under the insufficient real-time irrigation mode, winter wheat yield was highest at 90% irrigation upper limit and 70% irrigation lower limit, with a medium fertilization level (850 mg/kg). The yield showed an overall decreasing trend as both irrigation limits and fertilizer application decreased. Rainfall and soil moisture were the most significant factors influencing winter wheat yield, followed by air temperature and evapotranspiration. Solar radiation and sunshine duration had the least impact. The results of this study provide a valuable reference for accurately predicting winter wheat yield. Full article

In this paper, a novel Doppler shift estimation method for frequency diverse array (FDA) radar based on graph signal processing (GSP) theory is proposed and investigated. First, a well-designed graph signal model for a monostatic linear FDA is formulated. Subsequently, spectral decomposition is conducted on the constructed signal model utilizing graph Fourier transform (GFT) techniques, enabling the extraction of the target’s Doppler shift parameter through spectral peak search. A comprehensive series of simulation experiments demonstrates that the proposed method can achieve the accurate estimation of target parameters even under low signal-to-noise ratio (SNR) conditions. Furthermore, the proposed method exhibits superior performance compared to the MUSIC algorithm, offering enhanced resolution and estimation accuracy. Additionally, the method is highly amenable to parallel processing, significantly reducing the computational burden associated with traditional procedures. Full article

This research explores the synthesis of carboxymethyl cellulose (CMC) for the development of a cost-effective bioplastic film that can serve as a sustainable alternative to synthetic plastic. Replacing plastic packaging with CMC-based films offers a solution for mitigating environmental pollution, although the inherent hydrophilicity and low mechanical strength of CMC present significant challenges. To address these limitations, zinc oxide nanoparticles (ZnO NPs) were employed as a biocompatible and non-toxic reinforcement filler to improve CMC’s properties. A solution casting method which incorporated varying concentrations of ZnO NPs (0%, 5%, 10%, 15%, 20%, and 25%) into the CMC matrix allowed for the preparation of composite bioplastic films, the physicochemical properties of which were analyzed using scanning electron microscopy, Fourier transform infrared spectroscopy, and X-ray diffraction. The results revealed that the ZnO NPs were well-integrated into the CMC matrix, thereby improving the film’s crystallinity, with a significant shift from amorphousness to the crystalline phase. The uniform dispersion of ZnO NPs and the development of hydrogen bonding between ZnO and the CMC matrix resulted in enhanced mechanical properties, with the film CZ₂₀ exhibiting the greatest tensile strength—15.12 ± 1.28 MPa. This film (CZ₂₀) was primarily discussed and compared with the control film in additional comparison graphs. Thermal stability, assessed via thermogravimetric analysis, improved with an increasing percentage of ZnO Nps, while a substantial decrease in water vapor permeability and oil permeability coefficients was observed. In addition, such water-related properties as water contact angle, moisture content, and moisture absorption were also markedly improved. Furthermore, biodegradability studies demonstrated that the films decomposed by 71.43% to 100% within 7 days under ambient conditions when buried in soil. Thus, CMC-based eco-friendly composite films have the clear potential to become viable replacements for conventional plastics in the packaging industry. Full article

Fault diagnosis in modern industrial and information systems is critical for ensuring equipment reliability and operational safety, but traditional methods have difficulty in effectively capturing spatiotemporal dependencies and fault-sensitive features in multi-sensor data, especially rarely considering dynamic features between multi-sensor data. To address these challenges, this study proposes DyGAT-FTNet, a novel graph neural network model tailored to multi-sensor fault detection. The model dynamically constructs association graphs through a learnable dynamic graph construction mechanism, enabling automatic adjacency matrix generation based on time–frequency features derived from the short-time Fourier transform (STFT). Additionally, the dynamic graph attention network (DyGAT) enhances the extraction of spatiotemporal dependencies by dynamically assigning node weights. The time–frequency graph pooling layer further aggregates time–frequency information and optimizes feature representation.Experimental evaluations on two benchmark multi-sensor fault detection datasets, the XJTUSuprgear dataset and SEU dataset, show that DyGAT-FTNet significantly outperformed existing methods in classification accuracy, with accuracies of 1.0000 and 0.9995, respectively, highlighting its potential for practical applications. Full article

Urban transportation efficiency is critical in densely populated cities, such as Seoul, South Korea, where subway transfer stations are vital. This study investigates the spatial efficiency and passenger flow dynamics of multilayered transfer stations, using triangular Fourier transform as the primary analytical method. The research incorporates principal component analysis (PCA) and K-means clustering to classify stations based on structural characteristics and congestion patterns. Data derived from transportation card usage during peak hours and architectural layouts were analysed to identify critical bottlenecks. The results highlighted notable inefficiencies in transfer times and congestion. For example, the analysis revealed that optimising transfer corridors at Seoul Station could reduce average transfer times by over 10 min. Dongdaemun History & Culture Park Station would benefit from ground-level pathways to address inefficiencies caused by its extensive underground network. Sindorim Station’s reorganisation of above-ground and underground connectivity was found to enhance passenger flow. By introducing the concept of the ‘entry baseline for passenger flow in public buildings’, this study offers a novel framework for evaluating and improving urban transit infrastructure. The findings provide actionable insights into transfer station design, supporting strategies for addressing the challenges of urban mobility in megacities while contributing to transit-oriented development. Full article

The strong development of distributed energy sources has become one of the most important measures for low-carbon development worldwide. With a significant quantity of photovoltaic (PV) power generation being integrated to the grid, accurate and efficient prediction of PV power generation is an essential guarantee for the security and stability of the electricity grid. Due to the shortage of data from PV stations and the influence of weather, it is difficult to obtain satisfactory performance for accurate PV power prediction. In this regard, we present a PV power forecasting model based on a Fourier graph neural network (FourierGNN). Firstly, the hypervariable graph is constructed by considering the electricity and weather data of neighbouring PV plants as nodes, respectively. The hypervariance graph is then transformed in Fourier space to capture the spatio-temporal dependence among the nodes via the discrete Fourier transform. The multilayer Fourier graph operator (FGO) can be further exploited for spatio-temporal dependence information. Experiments carried out at six photovoltaic plants show that the presented approach enables the optimal performance to be obtained by adequately exploiting the spatio-temporal information. Full article

Understanding the relationship between elastic, chemical, and thermal properties is essential for the prevention of the behavior of SiO₂ flint aggregates during their application. In fact, the elastic properties of silica depend on chemical and heat treatment. In order to identify the crystallite sizes for natural SiO₂ before and after chemical treatment samples, Williamson–Hall plots and Scherer’s formulas are used. The silica nanofibers obtained and their microstructure changes under thermal and chemical treatment are characterized using different techniques (XRD, VP-SEM, TEM, FTIR, TDA, and TGA). Both the strains (ε) and the crystallite sizes (DW–H) are obtained from the slope and from the βcosθ-intercept of a graph, respectively. The crystalline quality is improved upon heating, as shown by the decrease in the FWHM of the SiO₂(101) peaks, which is confirmed by Fourier transform infrared spectroscopy (FTIR). The microstrain estimated at 1.50 × 10⁻⁴ units for natural SiO₂ is smaller than that for SiO₂ after chemical attack which is estimated at 2.01 × 10⁻⁴ units. Based on the obtained results, SiO₂ characterized with controlled micromechanical, thermal, and chemical properties may be used as a filler to improve the performance properties of the strength, microstructure, and durability of some composites. Full article

In engineering applications, the accuracy of on-load tap changer (OLTC) mechanical fault identification methods based on vibration signals is constrained by the quantity and quality of the samples. Therefore, a novel small-sample-size OLTC mechanical fault identification method incorporating short-time Fourier transform (STFT), synchrosqueezed wavelet transform (SWT), a dual-stream convolutional neural network (DSCNN), and support vector machine (SVM) is proposed. Firstly, the one-dimensional time-series vibration signals are transformed using STFT and SWT to obtain time–frequency graphs. STFT time–frequency graphs capture the global features of the OLTC vibration signals, while SWT time–frequency graphs capture the local features of the OLTC vibration signals. Secondly, these time–frequency graphs are input into the CNN to extract key features. In the fusion layer, the feature vectors from the STFT and SWT graphs are combined to form a fusion vector that encompasses both global and local time–frequency features. Finally, the softmax classifier of the traditional CNN is replaced with an SVM classifier, and the fusion vector is input into this classifier. Compared to the traditional fault identification methods, the proposed method demonstrates higher identification accuracy and stronger generalization ability under the conditions of small sample sizes and noise interference. Full article