Frequency Domain Decomposition Research Articles

Medical image segmentation based on frequency domain decomposition SVD linear attention

Convolutional Neural Networks (CNNs) have achieved remarkable segmentation accuracy in medical image segmentation tasks. However, the Vision Transformer (ViT) model, with its capability of extracting global information, offers a significant advantage in contextual information compared to the limited receptive field of convolutional kernels in CNNs. Despite this, ViT models struggle to fully detect and extract high-frequency signals, such as textures and boundaries, in medical images. These high-frequency features are essential in medical imaging, as targets like tumors and pathological organs exhibit significant differences in texture and boundaries across different stages. Additionally, the high resolution of medical images leads to computational complexity in the self-attention mechanism of ViTs. To address these limitations, we propose a medical image segmentation network framework based on frequency domain decomposition using a Laplacian pyramid. This approach selectively computes attention features for high-frequency signals in the original image to enhance spatial structural information effectively. During attention feature computation, we introduce Singular Value Decomposition (SVD) to extract an effective representation matrix from the original image, which is then applied in the attention computation process for linear projection. This method reduces computational complexity while preserving essential features. We demonstrated the segmentation validity and superiority of our model on the Abdominal Multi-Organ Segmentation dataset and the Dermatological Disease dataset, and on the Synapse dataset our model achieved a score of 82.68 on the Dice metrics and 17.23 mm on the HD metrics. Experimental results indicate that our model consistently exhibits segmentation effectiveness and improved accuracy across various datasets.

A Review of Health Monitoring and Model Updating of Vibration Dissipation Systems in Structures

Given that numerous countries are located near active fault zones, this review paper assesses the seismic structural functionality of buildings subjected to dynamic loads. Earthquake-prone countries have implemented structural health monitoring (SHM) systems on base-isolated structures, focusing on modal parameters such as frequencies, mode shapes, and damping ratios related to isolation systems. However, many studies have investigated the dissipating energy capacity of isolation systems, particularly rubber bearings with different damping ratios, and demonstrated that changes in these parameters affect the seismic performance of structures. The main objective of this review is to evaluate the performance of damage detection computational tools and examine the impact of damage on structural functionality. This literature review’s strength lies in its comprehensive coverage of prominent studies on SHM and model updating for structures equipped with dampers. This is crucial for enhancing the safety and resilience of structures, particularly in mitigating dynamic loads like seismic forces. By consolidating key research findings, this review identifies technological advancements, best practices, and gaps in knowledge, enabling future innovation in structural health monitoring and design optimization. Various identification techniques, including modal analysis, model updating, non-destructive testing (NDT), and SHM, have been employed to extract modal parameters. The review highlights the most operational methods, such as Frequency Domain Decomposition (FDD) and Stochastic Subspace Identification (SSI). The review also summarizes damage identification methodologies for base-isolated systems, providing useful insights into the development of robust, trustworthy, and effective techniques for both researchers and engineers. Additionally, the review highlights the evolution of SHM and model updating techniques, distinguishing groundbreaking advancements from established methods. This distinction clarifies the trajectory of innovation while addressing the limitations of traditional techniques. Ultimately, the review promotes innovative solutions that enhance accuracy, reliability, and adaptability in modern engineering practices.

Hybrid Significant Stroke Feature: A Novel Stroke Feature Analysis Approach for Stroke Severity Classification of EEG Signals Based on Time Domain, Frequency Domain, and Signal Decomposition Domain

Hybrid Significant Stroke Feature: A Novel Stroke Feature Analysis Approach for Stroke Severity Classification of EEG Signals Based on Time Domain, Frequency Domain, and Signal Decomposition Domain

A Grounding Current Prediction Method Based on Frequency-Enhanced Transformer

Concerning the problem that the coupling relationship in substation scenarios is complex and the Transformer model makes it difficult to capture the correlation between multiple variables of grounding current, resulting in low accuracy of grounding current prediction, a ground current prediction method based on frequency-enhanced Transformer is proposed. Firstly, in the data preprocessing stage, the best frequency domain decomposition algorithm is designed to obtain the high-frequency and low-frequency component data containing different component features so as to enhance the initial features that the model focuses on. Secondly, the data slicing and embedding module is designed to replace the original embedding module of the Transformer to realize the enhanced extraction of local features of the data. Finally, in the feature extraction stage, an enhanced attention mechanism is introduced to replace the standard attention mechanism to capture the intrinsic features of the sequence time dimension and the variable dimension in parallel so as to improve the extraction ability of Transformer multivariate features. Experimental results on the self-built grounding current dataset and the public dataset show that the proposed method outperforms existing advanced methods, verifying the effectiveness of the proposed method.

Correlation Analysis Approach Between Features and Motor Movement Stimulus for Stroke Severity Classification of EEG Signal Based on Time Domain, Frequency Domain, and Signal Decomposition Domain

The healing process of a stroke necessitates tools for measuring relevant parameters to facilitate monitoring, evaluation, and medical rehabilitation. Accurate parameter measures can be observed in stroke patients' severity to ascertain suitable interventions by identifying components pertinent to monitoring, evaluation, and medical rehabilitation. The components are derived from the observation collection process utilizing an EEG device, accompanied by a motor stimulus, to ensure the acquisition of EEG signals for monitoring, evaluation, and medical rehabilitation while preventing any loss of information during data collection. The acquired information encounters challenges due to the signal's unstable, nonlinear, and non-stationary characteristics, necessitating efforts to stabilize, render stationary, and linearize it through suitable signal processing and feature extraction techniques to achieve a pertinent feature composition. The subsequent difficulty is achieving the objectives of medical monitoring, evaluation, and rehabilitation, necessitating the correlation between EEG signal characteristics and motor movement stimuli, ensuring that the process adheres to appropriate parameter identification and scheduling per the established plan. In response to this difficulty, a correlation analysis methodology is established, incorporating normalcy tests, significance tests, and correlation analysis to ensure that the relevant factors for identifying stroke severity categorization patterns are precisely identified beforehand. The correlation analysis strategy employs raw data situations, preprocessing, feature extraction, feature selection, and correlation analysis for classification purposes. Our experimental findings indicate that the correlation analysis approach for assessing stroke severity classification patterns is evident in the Hajorth Complexity feature, utilizing the Shoulder motor movement stimulus and the SVM classification type, achieving an accuracy significant value of 98%. These findings confirm the efficacy of correlation analysis between EEG signal features and motor movement stimuli in identifying the optimal parameters within a reduced dimensional space to assess stroke severity effectively.

On the variability of the site-response parameters of the active rock slope in Brienz/Brinzauls (Switzerland)

Summary Unstable rock slopes, prone to collapse, pose an increasingly severe threat to both people and infrastructures, necessitating effective monitoring for risk mitigation. While many techniques rely on surface displacements to assess slope stability, seismic indicators such as resonant frequency, variations in seismic shear wave velocity, and site amplification offer valuable insights into the structural integrity of the slope, aspects not captured by surface deformation alone. Research has demonstrated that these site-response parameters can serve as monitoring tools to detect precursory signs of failure, such as a drop in resonant frequency and relative seismic velocity prior to collapse. Still, environmental factors like temperature, precipitation, snow melt, earthquakes, and freeze-thaw cycles transiently influence the seismic response. Our main objective is to understand the correlations and drivers between environmental parameters and seismic response, distinguishing between reversible and irreversible changes in dynamic behaviour. Over a five-year monitoring period, we continuously recorded ambient vibration data at the Brienz/Brinzauls landslide and monitored three different site-response parameters (resonant frequency, site amplification, relative seismic wave velocity variation) using enhanced frequency domain decomposition, site-to-reference spectral ratio, and single station ambient vibrations correlation techniques. Our results highlight a long-term increase in site amplification and a long-term decrease in first and second resonant frequencies, indicating ongoing structural weakening. Temperature was found to correlate with seasonal variations of seismic wave velocity with a few day’s time lag. Snow melting and rainfalls exerted a secondary influence, temporarily reducing relative seismic wave velocity during snowmelt and rainfall. Our findings suggest that single-station relative seismic velocity variations are mainly influenced by the shallow sub-surface (depth of about 30 m), limiting its application to study the stability of deep structures.

An integrated frequency domain decomposition and deep neural network approach for short-term PV power forecast

An integrated frequency domain decomposition and deep neural network approach for short-term PV power forecast

A refined output-only modal identification technique for structural health monitoring of civil infrastructures

The increasing installation of structural health monitoring systems has raised the need for efficient and rapid data interpretation algorithms. Operational modal analysis is currently one of the most used techniques for extracting modal properties, and some attempts have been made to automate this procedure. However, automated techniques often require manual calibration of hyperparameters and show inconsistencies across different case studies. This paper proposes a refined version of the automated frequency domain decomposition (AFDD) using the modal assurance criterion (MAC) to obtain natural frequencies and mode shapes. Sensitivity analyses were conducted on the ambient vibration response of the Yonghe cable-stayed bridge in China, accounting for influential factors including noise levels, acceleration record length, sensor layouts. A novel approach is then introduced to interpret the results of the sensitivity analyses. The approach consists in plotting the result of each analysis in a stabilization diagram and then using a Gaussian mixture model that clusters the poles into core and outliers. This allows to identify regions where the MAC thresholds are optimal. By comparing the results of all the sensitivity analyses it was possible to define a single optimal MAC threshold, avoiding the need for fixing a value based on the user’s experience. Three substantially different case studies were analyzed to extensively test the methodology: the Yonghe cable-stayed bridge, the PolyU footbridge in Hong Kong, and Moletta Tower in the Circus Maximus archeological site in Italy. The analysis compared the proposed AFDD algorithm to the traditional frequency domain decomposition and covariance-driven stochastic subspace identification. Specifically, the efficiency in identifying close frequencies, weakly excited modes, spurious peaks, and complex modes was evaluated for each method, which highlighted the robustness of the proposed optimized AFDD. The analysis showcases peculiar characteristics and drawbacks of each method when trying to identify complex vibrational modes of the specific case studies. It was found that the proposed AFDD procedure performs better than traditional methods despite it may misidentify complex modes due to the constraints of a narrower modal domain and similar geometries.

An Experimental Study on the Effect of GFRP and CFRP Strengthening on the Static and Dynamic Behavior of R/C Beams Under Progressive Damage

This paper aims to investigate the effect of glass fiber-reinforced polymer (GFRP) and carbon fiber-reinforced polymer (CFRP) strengthening materials on the static and dynamic behavior of reinforced concrete (R/C) beams subjected to progressive damage. Four identical beams, each strengthened with either GFRP or CFRP, are tested under a cyclic quasi-static loading pattern. Impact hammer tests are performed for undamaged states and various damage levels of the beams. The dynamic test data are analyzed using the Enhanced Frequency Domain Decomposition (EFDD) method to estimate the dynamic characteristics of the beams. In this context, the first three vibration modes in both vertical and horizontal directions are considered. Strengthening is applied to both pre-damaged and undamaged beams, enabling a comparison of their performance before and after the strengthening procedure. Beams strengthened with CFRP exhibit a higher load-bearing capacity and stiffness but also fail at lower displacement levels compared to those strengthened with GFRP, which demonstrate more ductile behavior. Furthermore, the modal frequency ratios indicate that the first vibration mode is more sensitive to damage than the second and third modes. This study highlights the effectiveness of both strengthening materials in enhancing the structural performance of both undamaged and damaged beams.

Bridge monitoring using mobile sensing data with traditional system identification techniques

AbstractMobile sensing has emerged as an economically viable alternative to spatially dense stationary sensor networks, leveraging crowdsourced data from today's widespread population of smartphones. Recently, field experiments have demonstrated that using asynchronous crowdsourced mobile sensing data, bridge modal frequencies, and absolute mode shapes (the absolute value of mode shapes, i.e., mode shapes without phase information) can be estimated. However, time‐synchronized data and improved system identification techniques are necessary to estimate frequencies, full mode shapes, and damping ratios within the same context. This paper presents a framework that uses only two time‐synchronous mobile sensors to estimate a spatially dense frequency response matrix. Subsequently, this matrix can be integrated into existing system identification methods and structural health monitoring platforms, including the natural excitation technique eigensystem realization algorithm and frequency domain decomposition. The methodology was tested numerically and using a lab‐scale experiment for long‐span bridges. In the lab‐scale experiment, synchronized smartphones atop carts traverse a model bridge. The resulting cross‐spectrum was analyzed with two system identification methods, and the efficacy of the proposed framework was demonstrated, yielding high accuracy (modal assurance criterion values above 0.94) for the first six modes, including both vertical and torsional. This novel framework combines the monitoring scalability of mobile sensing with user familiarity with traditional system identification techniques.

Automatic High-Resolution Operational Modal Identification of Thin-Walled Structures Supported by High-Frequency Optical Dynamic Measurements.

High-frequency optical dynamic measurement can realize multiple measurement points covering the whole surface of the thin-walled structure, which is very useful for obtaining high-resolution spatial information for damage localization. However, the noise and low calculation efficiency seriously hinder its application to real-time, online structural health monitoring. To this end, this paper proposes a novel high-resolution frequency domain decomposition (HRFDD) modal identification method, combining an optical system with an accelerometer for measuring high-accuracy vibration response and introducing a clustering algorithm for automated identification to improve efficiency. The experiments on the cantilever aluminum plate were carried out to evaluate the effectiveness of the proposed approach. Natural frequency and damping ratios were obtained by the least-squares complex frequency domain (LSCF) method to process the acceleration responses; the high-resolution mode shapes were acquired by the singular value decomposition (SVD) processing of global displacement data collected by high-speed cameras. Finally, the complete set of the first nine order modal parameters for the plate within the frequency range of 0 to 500 Hz has been determined, which is closely consistent with the results obtained from both experimental modal analysis and finite element analysis; the modal parameters could be automatically picked up by the DBSCAN algorithm. It provides an effective method for applying optical dynamic technology to real-time, online structural health monitoring, especially for obtaining high-resolution mode shapes.

Hybrid operational modal analysis of an operative two-bladed offshore wind turbine

As with any strategic structure, vibration-based structural health monitoring techniques are often used to ensure the structurally safe operation of offshore wind turbines. Among such techniques, Operational Modal Analysis (OMA) methods allow the identification of modal properties, such as natural frequencies, mode shapes and damping, which variation might be caused by damage or operational/environmental factors. This paper investigates the application of OMA techniques on a two-bladed offshore wind turbine, which poses multiple challenges: fundamental OMA assumptions about the applied loads are violated by environmental and operational loads; the closely spaced modes of an offshore wind turbine are hard to identify; and an operative two-bladed offshore wind turbine is a time-variant system. Within this study, three OMA procedures to overcome some of the preceding challenges are discussed: (1) a standard frequency domain decomposition method; (2) a proposed enhanced transmissibility-based approach with a post-processing technique based on the Kurtosis index; and (3) a proposed refined hybrid OMA procedure that combines a transmissibility-based approach, the dedicated post-processing technique based on the Kurtosis index, and the frequency domain decomposition method. A numerical model representative of an operative two-bladed offshore wind turbine is used to compare the three procedures. Based on the comparison, the hybrid method is proven to be a promising new OMA-based procedure that outperforms the stand-alone transmissibility-based approach and the frequency domain decomposition method in identifying the modal properties of a two-bladed offshore wind turbine.

SEAformer: frequency domain decomposition transformer with signal enhanced for long-term wind power forecasting

SEAformer: frequency domain decomposition transformer with signal enhanced for long-term wind power forecasting

Comparative study of earthquake effects on the Canton Tower based on full-scale measurements

From 2011 to 2023, the monitoring system of a super high-rise structure, the Canton Tower, recorded many earthquakes. This study categorizes 11 earthquakes over these 13 years based on the distances between the epicenters and the tower for investigation. Firstly, the acceleration time histories and frequency spectra of these earthquakes are compared. Secondly, different system identification techniques, i.e., automated frequency domain decomposition (AFDD) and covariance-driven stochastic subspace identification (SSI-COV), are applied to the acceleration records to study the dynamic characteristics of the tower under earthquakes. Thirdly, three time-domain indexes based on the singular value decomposition (SVD) are proposed to measure and compare the impact of these earthquakes on the tower. The proposed indexes show good performance in assessing the impact of earthquakes and the structural condition. This study on the dynamic characteristics of Canton Tower under earthquakes at different distances can serve as a benchmark for further structural health monitoring and structural dynamic analysis.

Vibration-based damage detection of beams using Hybrid Genetic Algorithm with combined l0 and l1 regularization

Vibration-based damage detection is an effective way of identifying beam damages before they become severe and potentially catastrophic. This paper introduces a novel and robust method for detecting multiple cracks in beams using a Hybrid Genetic Algorithm (HGA). The objective function of the proposed method, based on dynamic properties, can be employed with partial modal information and is enhanced by including the initial residuals between numerical and experimental models along with combined l0 and l1 regularization. This enhancement mitigates the impact of limited modal data, experimental noise, and modeling inaccuracies. Additionally, a method of selecting an appropriate regularization parameter by analyzing residual and solution norm curves is presented. The performance of the proposed method is evaluated and verified using both numerical simulations and experimental studies on a steel cantilever beam under four damage scenarios. In the experimental study, natural frequencies and mode shapes were determined under ambient vibration conditions using Enhanced Frequency Domain Decomposition (EFDD) and the Stochastic Subspace Identification (SSI) method. Finite element models of the beam were developed in ABAQUS software for numerical analysis, and a comparative study of numerical and experimental data was conducted. The proposed approach successfully identifies, locates and quantifies single and multiple damages in the beam, demonstrating its reliability and effectiveness, particularly in scenarios with limited modal data and significant experimental noise.

Modal identification from turbulence response based on improved frequency domain decomposition

Turbulence excitation is an unavoidable form of excitation in flutter flight tests, and it is also a necessary and effective excitation method in high-speed flights, dives, other high-risk test, and high-frequency modal information mining. However, because the turbulence excitation signals are unmeasurable in the time domain, although the turbulence response contains rich and valuable flutter test information, the randomness and low quality of the data often cause difficulties in modal analysis. Therefore, an improved frequency domain decomposition algorithm for turbulence response processing is proposed in this paper. First, due to the irrelevancy of the atmospheric excitation, the power spectral density function matrix of the multi-channel turbulence response is subjected to singular value decomposition. There is a mathematical relationship between the maximum singular value curve and the system frequency response function. Second, the modal assurance criteria are used to calculate the maximum singular value of a single-degree-of-freedom system. Finally, an orthogonal polynomial method is applied to fit the maximum singular value curve, and the system identification is performed directly in the frequency domain. The simulated data and a certain type of aircraft flutter flight test data are used to verify the proposed method, and the results confirm the effectiveness and engineering applicability of the method developed in this work.

Lightweight Infrared and Visible Image Fusion Based on Nested Connections and Res2Net

Image fusion is a pivotal image-processing technology designed to merge multiple images from various sensors or imaging modalities into a single composite image. This process enhances and extracts the information contained across the images, resulting in a final image that is more informative and of superior quality. This paper introduces a novel method for infrared and visible image fusion, utilizing nested connections and frequency-domain decomposition techniques to effectively solve the problem of lost image detail features. By incorporating depthwise separable convolution technology, the method reduces the computational complexity and model size, thereby increasing computational efficiency. A multi-scale residual fusion network, R2FN (Res2Net Fusion Network), has been designed to replace traditional manually designed fusion strategies, enabling the network to better preserve detail information in the image while improving the quality of the fused image. Moreover, a new loss function is proposed, which is aimed at enhancing important feature information while preserving more significant features. Experimental results on public datasets indicate that the method not only retains the detail information of visible-light images but also highlights the significant features of infrared images while maintaining a minimal number of parameters.

High-resolution frequency domain decomposition for modal analysis of bridges using train-induced free-vibrations

Modal parameters are structural inherent characteristics that can be applied for revealing performance of railway bridges. Free vibration signals generated by a passage of train are commonly utilized to estimate the modal parameters of railway bridges due to their higher signal-to-noise ratios compared to random vibrations caused by ambient loads. However, since free vibration signals rapidly decay over time, the available free-vibration data is typically short-time. When using the fast Fourier transform-based spectral estimation method for modal identification from short-time vibration data, a phenomenon known as spectral leakage occurs, leading to miss-identification of some structural modes. In this study, the classical frequency domain decomposition (FDD) is improved for modal identification of railway bridges, in which the higher resolution auto-power spectral density (PSD) and cross-PSD functions are calculated through the autoregressive (AR) model-based method. The AR model-based method improves both the smoothness and resolution of the PSD functions compared to the fast Fourier transform technique. These AR model-based PSD functions are then employed in the FDD process to facilitate frequency and mode shape identification while avoiding spurious noise modes. The proposed eigenvalue fitting technique is subsequently utilized to estimate damping ratios. Numerical simulation data as well as vibration data from an actual bridge are analyzed to validate the proposed method, with a comparison made to the Welch’s PSD-based method. The results demonstrate that the modified FDD approach enables more effective identification of structural modes, even in the presence of closely-spaced modes.

Development of a low-cost flutter test bed with an EPS foam model for preliminary wing design

This paper discusses a novel, low-cost approach for the design and testing of a flutter test article made out of expanded polystyrene (EPS) foam. The low mass of this test article makes it especially suitable for serving as a test bed for similar low structure-to-fluid mass ratio wing configurations, though it could just as easily be used as the first step in the flutter testing of any structure with complex shape and mechanical properties. The material properties of EPS foam were tested using two different approaches: a 3-point bending test based on ASTM Standards for cellular materials and a new finite element model updating approach that used experimental data collected from simple ground vibration tests (GVT). It was found that the second approach provided material properties that were the most representative of the behavior of the specimen under flutter loads. That information was then used in a computational aeroelastic flutter model of the EPS foam wing. Wind tunnel flutter tests were performed for the EPS foam model. The computational frequency domain decomposition (CFDD) method was used to identify modal parameters and the damping trend extrapolating method was used to predict the critical flutter speed from pre-flutter experimental data. The flutter results from the aeroelastic model were in good agreement with the test data.

Enhancing Wireless Sensor Network in Structural Health Monitoring through TCP/IP Socket Programming-Based Mimic Broadcasting: Experimental Validation

This paper presents the implementation of a synchronous Structural Health Monitoring (SHM) framework utilizing wireless, low-cost, and off-the-shelf components. Vibration-based condition monitoring plays a crucial role in assessing the reliability of structural systems by detecting damage through changes in vibration parameters. The adoption of low-cost Micro-Electro-Mechanical Systems (MEMS) sensors in Wireless Sensor Networks (WSNs) has gained traction, emphasizing the need for precise time synchronization to schedule wake-up times of multiple sensor nodes for data collection. To address this challenge, our proposed method introduces a TCP/IP socket programming-based mimic broadcasting mechanism and a scalable sensing network controlled by a central gateway, leveraging the Raspberry Pi Python platform. The system operates using Internet of Things (IoT) concepts and adopts a star topology, where a packet is transmitted from the gateway to initiate measurements simultaneously on multiple sensor nodes. The sensor node comprises a MEMS accelerometer, a real time clock DS3231 module and Raspberry Pi Zero 2W (RPi0-2W), while the gateway employs a Raspberry Pi 4 (RPi4). To ensure accurate time synchronization, all Pi0-2W nodes were configured as Network Time Protocol (NTP) clients, synchronizing with an RPi4 server using chrony, the reliable implementation of the NTP. Through experimental evaluations, the system demonstrates its effectiveness and reliability in achieving initial time synchronization. This study addresses the challenge of achieving precise time alignment between sensor nodes through the utilization of the Dynamic Time Wrapping (DTW) method for Frequency Domain Decomposition (FDD) applications. The contribution of this research significantly enhances the field by improving the accuracy and reliability of time-aligned measurements, with a specific focus on utilizing low-cost sensors. By developing a practical and cost-effective SHM framework, this work advances the accessibility and scalability of structural health monitoring solutions, facilitating more widespread adoption and implementation in various engineering applications