Integration In Frequency Domain Research Articles

PSTNet: Enhanced Polyp Segmentation With Multi-Scale Alignment and Frequency Domain Integration.

Accurate segmentation of colorectal polyps in colonoscopy images is crucial for effective diagnosis and management of colorectal cancer (CRC). However, current deep learning-based methods primarily rely on fusing RGB information across multiple scales, leading to limitations in accurately identifying polyps due to restricted RGB domain information and challenges in feature misalignment during multi-scale aggregation. To address these limitations, we propose the Polyp Segmentation Network with Shunted Transformer (PSTNet), a novel approach that integrates both RGB and frequency domain cues present in the images. PSTNet comprises three key modules: the Frequency Characterization Attention Module (FCAM) for extracting frequency cues and capturing polyp characteristics, the Feature Supplementary Alignment Module (FSAM) for aligning semantic information and reducing misalignment noise, and the Cross Perception localization Module (CPM) for synergizing frequency cues with high-level semantics to achieve efficient polyp segmentation. Extensive experiments on challenging datasets demonstrate PSTNet's significant improvement in polyp segmentation accuracy across various metrics, consistently outperforming state-of-the-art methods. The integration of frequency domain cues and the novel architectural design of PSTNet contribute to advancing computer-assisted polyp segmentation, facilitating more accurate diagnosis and management of CRC.

Vortex-Induced Force Identification of a Long-Span Bridge Based on Field Measurement Data

Vortex-induced force (VIF) identification and modelling of a long-span bridge are often conducted in terms of aeroelastic sectional model tests in wind tunnels. However, there are uncertainties inherent in wind tunnel model tests so that vortex-induced vibration (VIV) still occurs in real long-span bridges designed according to wind tunnel test results. This paper presents a framework for VIF identification of a long-span bridge based on field-measured wind and acceleration data. The framework is composed of the four steps: (1) decompose field-measured acceleration response time histories using variational mode decomposition (VMD) method; (2) obtain velocity and displacement response time histories using frequency domain integration (FDI) method; (3) establish and update the finite element model and identify the generalized VIF time histories of the bridge; and (4) identify the parameters in the polynomial VIF models and decide the most suitable VIF model. The proposed framework is finally applied to a real suspension bridge with a recent VIV event. The results show that the proposed framework can accurately identify the generalized VIF acting on the bridge from the field-measured acceleration and wind data, and the derived most suitable VIF model can produce almost the same vortex-induced response (VIR) as the measured ones.

An approach for the estimation of lateral track irregularity using axle-box acceleration

PurposeThis article aims to predict the rapid track geometry change in the short term with a higher detection frequency, and realize the monitoring and maintenance of the railway state.Design/methodology/approachFirstly, the ABA data needs to be filtered to remove the DC component to reduce the drift due to integration. Secondly, the quadratic integration in frequency domain for concern components of the vertical and lateral ABA needs to be done. Thirdly, the displacement in lateral of the wheelset to rail needs to be calculated. Then the track alignment irregularity needs to be calculated by the integration of lateral ABA and the lateral displacement of the wheelset to rail.FindingsBy comparing with a commercial track geometry measurement system, the high-speed railway application results in different conditions, after removal of the influence of LDWR, identified that the proposed method can produce a satisfactory result.Originality/valueThis article helps realize detection of track irregularity on operating vehicle, reduce equipment production, installation and maintenance costs and improve detection density.

Acceleration response of part-screw pile composite foundation under long-term train load excitation

This paper investigates the vibration response of a new part-screw pile composite foundation under moving train loads via a model test method. Based on the acceleration signals of the pile and the surrounding soil, a non-linear fitting method is used to establish the relationship between the dynamic attenuation coefficient and the depth of the foundation. Next, the effect of spatial location and train speed on the pile-soil dynamic interaction is investigated by introducing the pile-soil spectral amplitude ratio δ p − s . In addition, the dynamic susceptibility of the part-screw pile to damage under dynamic train loads is revealed with a wavelet packet energy distribution index E DIS . Afterwards, the cumulative deformation evolution characteristics of screw pile composite foundations under long-term train loading were revealed using the frequency domain integration method. The results of this work reveal that the faster the train runs, the larger the pile-soil spectrum amplitude ratio and the more prominent the ‘dynamic concentration effect’ of the part-screw pile. The change in cross-section of the part-screw pile results in a sudden change in the vibration energy transmitted from the top of the pile, which makes the variable section of the part-screw pile more likely to cause dynamic damage or even deformation. The cumulative deformation evolution process of soil between part-screw piles under long-term cyclic train loading can be divided into three stages: slow change period, fluctuation rising period and stable deformation period. The research results have important reference significance for the optimization design of high-speed railway part-screw pile composite foundation and the planning of railway running speed.

High-speed railway wheel polygon detection framework using improved frequency domain integration

Timely monitoring of wheel polygon is of great importance for the formulation of railway wheel maintenance strategies. In this study, a novel data-driven method for onboard and quantitative detection of wheel polygon is presented. First, the axle box acceleration (ABA) signal preprocessing method and stationarity test are introduced to select the relatively stationary signal from the measured data of ABA. Next, an iterative algorithm is developed to accurately extract the quasi-stationary ABA signals, representing each wheel rotation period. Then, an improved frequency domain integration method is developed to quantitatively capture the orders and roughness levels of the wheel polygon. Finally, the effectiveness and superiority of the proposed method is verified using the field-measured data of ABA and the wheel polygon in one cycle of wheel re-profiling. The results show that the proposed method can quantitatively capture the dominant characteristics of single- and multi-orders wheel polygons at different operating mileages with minimum and maximum absolute errors of 0.04 dB re 1 µm and −2.33 dB re 1 µm, respectively. The comparative analysis demonstrates that the proposed method outperforms the traditional time and frequency domain integration algorithms in the detailed characterisation of wheel polygon roughness levels.

An Absolute Displacement Measurement Method and Its Application in Ship Motion Measurement

Due to the fact that there is no static reference point in ocean space, relative measurement sensors such as laser sensors cannot be applied to the measurement of ship motion. In order to collect the motion signal of a ship in the ocean more accurately, an absolute measurement method for the motion of a ship is proposed. Firstly, the acceleration signal in the heave direction of the ship is obtained by using an acceleration sensor; secondly, the time–frequency domain integration algorithm is used to avoid the problem of the quadratic trend item and the influence of the low-frequency signal, and the ship’s heave displacement value is obtained by integration; finally, the displacement signal measured by the laser sensor and the integrated displacement signal of the acceleration sensor are compared with each other to verify the effectiveness of the absolute measurement of the heave displacement of the ship based on the acceleration sensor. At the same time, an angle sensor is used to measure the roll angle and pitch angle in the motion of the ship, form an absolute measurement system for the motion of the ship, and verify that the measurement points are arranged on the ship, which proves that the system can collect data from the ship in sea areas far from the coast. Motion signals are more advantageous.

Research on system identification method of buffer system based on experimental signal pre-processing

In order to obtain the theoretical model of a gun buffer system, the dynamic vibration characteristics of the buffer system were studied by force hammer experiments, and the vibration acceleration response curves of the buffer system after being excited by the impact load were obtained. The acquired signals were processed by noise reduction, detrending, data smoothing and frequency domain integration, and the second-order free vibration model was used to identify the stiffness, damping and other parameters of the system, and compared with the experimental results. The deviation between the calculated results of the identification model and the experimental results is less than 4%, which compared with the general direct identification method, the error is reduced by about 10%. This method can provide a simple and fast technical way for the accurate identification of vibration system models.

Dynamic Deformation Monitoring of Offshore Oil Platforms with Integrated GNSS and Accelerometer

Dynamic deformation monitoring is a crucial component of the structural health monitoring of an offshore oil platform. Given the insensitivity of the Global Navigation Satellite System (GNSS) to high-frequency vibration information, a combination of GNSS and accelerometer is used for vibration monitoring of platform structures. A hybrid filter based on complementary ensemble empirical mode decomposition (CEEMD) combined with a Chebyshev filter is proposed to process the monitoring data. The extracted GNSS low-frequency displacement is fused with the high-frequency displacement obtained from acceleration integration to obtain the overall dynamic displacement of the platform. The experimental analysis shows that the combination of t-test and correlation coefficient can select the required intrinsic mode function (IMF), the proposed hybrid filter can reduce the noise error to a certain extent, and the quadratic frequency domain integration of the acceleration data can avoid the influence of the integration trend term. The correlation coefficient between the overall reconstructed displacement and the original GNSS monitoring data was 0.8576, and the signal correlation after denoising and refactoring was more than 85%, thereby preserving the essential information components. Integrated GNSS and accelerometer monitoring systems complement each other’s advantages, compensating for their shortcomings in monitoring the dynamic deformation of a structure.

FISI: frequency domain integration sequential imaging at 1.26×1013 frames per second and 108 lines per millimeter.

High spatial resolution on the image plane (intrinsic spatial resolution) has always been a problem for ultrafast imaging. Single-shot ultrafast imaging methods can achieve high spatial resolution on the object plane through amplification systems but with low intrinsic spatial resolutions. We present frequency domain integration sequential imaging (FISI), which encodes a transient dynamic by an inversed 4f (IFF) system and decodes it using optical spatial frequencies recognition (OFR), which overcomes the limitation of the spatial frequencies recognition algorithm. In an experiment on the process of an air plasma channel, FISI achieved shadow imaging of the channel with a framing rate of 1.26×1013 fps and an intrinsic spatial resolution of 108 lp/mm (the spatial resolution on the image plane). Owing to its excellent framing time and high intrinsic spatial resolution, FISI can probe both repeatable and unrepeatable ultrafast phenomena, such as laser-induced damage, plasma physics, and shockwave interactions in living cells with high quality.

Microseismic Time Delay Estimation Method Based on Continuous Wavelet.

The microseismic signal is easily affected by observation noise and the inaccurate estimation of traditional methods will seriously reduce the location accuracy of the microseismic event. Therefore, based on the continuous wavelet spectrum and the similarity coefficient, a fast and efficient microseismic time delay estimation method is proposed. Firstly, the original signals are denoised by continuous wavelet transform. Subsequently, the time-frequency transform of the original signal by continuous wavelet transform, time-frequency signal extraction is the process of band-pass filtering, which can further reduce the influence of noise interference on the time delay estimation. Finally, we calculated the similarity between the time-frequency signals via the time domain and frequency domain integration. The similarity function is based on correlation and proposed according to the time-frequency transformation provided by the phase spectrum to evaluate the similarity between two noisy signals. The time delay estimation is determined by searching for the similarity function peak. The experimental results show the precision and accuracy of the method over the cross-correlation method and generalized cross-correlation phase transformation method, especially when the signal-to-noise ratio is low. Therefore, a new time delay estimation method for non-stationary random signals is presented in this paper.

Body Leveling Control Model Establishment and Experiment Analysis

Highlights The vehicle body leveling control model based on tire stiffness and wheel centroid displacement is established in this article.The average pitch angle error is 7.94%, and the tilt angle average error is 4.37%.The body leveling control algorithm developed based on this model can be applied to the automatic leveling control of hilly tractors and cars.Abstract. The body leveling control system is the core part of the attitude control of the hilly mountain tractor. It calculates the body posture in real-time according to the body leveling control model and makes corresponding control strategies. The accuracy of the control model directly affects the vehicle leveling accuracy. This article proposes the leveling control model based on tire stiffness and tire mass displacement. The slope road surface excitation information is collected in the field, and the road surface excitation data is processed appropriately. The accurate road surface excitation information is obtained. The input of the simulation model is displacement data. However, the collected road excitation information is the acceleration signal. Thus, the acceleration signal is converted to the displacement signal through the frequency domain integration method. The comparison between the calculated calculation results and the experimentally acquired attitude data indicates that the average pitch angle error is 7.94%, and the average tilt angle error is 4.37%. Accordingly, the vehicle body leveling control model established in this article is compatible with the experimental results and can accurately output the body posture and develop the body tone. Keywords: Control, Hydraulic, Leveling, Pavement, Road

Research on Seismic Acceleration Waveform Reproduction Based on Time-Frequency Hybrid Integration Algorithm

For the quadratic integration of seismic acceleration signal with noise and unknown initial velocity and displacement, the obtained displacement signal has serious trend error, which leads to the problem that the electrodynamic seismic simulation shaker under displacement control mode cannot accurately reproduce the seismic wave. This paper proposes the time-frequency hybrid integration algorithm based on time domain and frequency domain integration characteristics. The algorithm mainly includes two steps. The first step is to use the improved low-frequency attenuation algorithm to integrate the seismic acceleration for the first time in the frequency domain to obtain the corresponding velocity signal. The second step is to integrate the velocity signal directly in the time domain and combine it with the removal of the constant and linear terms algorithm to obtain the corresponding displacement signal. The accuracy and effectiveness of the algorithm are verified by numerical analysis and shaking table test. The results show that compared with the traditional hybrid integral algorithm, the performance improvement rates of displacement peak error and absolute error of the proposed time-frequency hybrid integral algorithm are 31.75% and 26.01%, respectively, and the accuracy of acceleration waveform reproduction is improved by 27.29%. Furthermore, when using this algorithm for the shaking table test of the seismic acceleration signal, the maximum peak error rate is only 4.92%. Therefore, this algorithm can effectively suppress the baseline drift and error accumulation caused by low-frequency noise and unknown initial state to realize the accurate reproduction of the acceleration waveform of the seismic simulation shaking table.

Modal Parameter Identification of Structures Using Reconstructed Displacements and Stochastic Subspace Identification

A method of modal parameter identification of structures using reconstructed displacements was proposed in the present research. The proposed method was developed based on the stochastic subspace identification (SSI) approach and used reconstructed displacements of measured accelerations as inputs. These reconstructed displacements suppressed the high-frequency component of measured acceleration data. Therefore, in comparison to the acceleration-based modal analysis, the operational modal analysis obtained more reliable and stable identification parameters from displacements regardless of the model order. However, due to the difficulty of displacement measurement, different types of noise interferences occurred when an acceleration sensor was used, causing a trend term drift error in the integral displacement. A moving average low-frequency attenuation frequency-domain integral was used to reconstruct displacements, and the moving time window was used in combination with the SSI method to identify the structural modal parameters. First, measured accelerations were used to estimate displacements. Due to the interference of noise and the influence of initial conditions, the integral displacement inevitably had a drift term. The moving average method was then used in combination with a filter to effectively eliminate the random fluctuation interference in measurement data and reduce the influence of random errors. Real displacement results of a structure were obtained through multiple smoothing, filtering, and integration. Finally, using reconstructed displacements as inputs, the improved SSI method was employed to identify the modal parameters of the structure.

Online gear hobbing error estimation based on shaft vibration signal analysis

This paper presents a method for online estimation of hobbing error based on the workpiece shaft vibration analysis. The hobbing vibration originates from the intermittent cutting process and directly influences the machining accuracy of gears. By analyzing the vibration data of workpiece shaft, the gear error estimation is completed. Firstly, mathematical models, combining tooth geometric characteristics with vibration displacements, for evaluating helical gear profile deviation, pitch deviation, and helix deviation are established. Secondly, an orthogonal experiment of gear machining is carried out and the vibration acceleration data of workpiece shaft are collected during processing. Finally, based on the vibration displacements obtained by frequency domain integration of acceleration data, the gear errors are estimated. The comparison between the estimation errors and the actual errors proves that the estimation model is effective. In addition, by analyzing the orthogonal experiment results and factors, and the spectrum analysis of acceleration signals, the order of the influence degree of machining parameters and the vibration characteristics under different parameters were obtained. The research results can provide theoretical and practical references for error compensation and parameter optimization of gear machining.

GNSS-aided accelerometer frequency domain integration approach to monitor structural dynamic displacements

ABSTRACT The accelerometer frequency domain integration approach (FDIA) is being actively applied to calculate dynamic displacement responses of large engineering structures. However, it is a relative acceleration measurement as the initial position is unavailable. GNSS offers direct displacement measurements, but has the limitation of relatively low frequency of data compared with alternative measurement techniques. Therefore, this paper proposes an improved FDIA utilising the advantages of GNSS to gain accurate information about the initial position. The performance of the proposed approach is first validated through software simulation. Following the validation, a series of shaking table tests using various vibration frequencies (0.5 HZ, 1 HZ, 1.5 HZ, 2 HZ and 2.5 HZ) are performed at the south square of Beijing University of Civil Engineering and Architecture (BUCEA) using one GNSS receiver and one accelerometer. The results show that the proposed approach can effectively avoid the uncertainty of the initial value and thus enhance the direct measurement accuracy of the dynamic displacements of structures, with root mean square error (RMSE) decreasing from 11.4 mm to 6.8 mm.

Time–Frequency Extraction Model Based on Variational Mode Decomposition and Hilbert–Huang Transform for Offshore Oil Platforms Using MIMU Data

Time–frequency extraction is a key issue to understand structural symmetry of dynamic responses of offshore oil platforms for early warning during drilling operations. Current popular methods for signal characteristics extraction can only obtain the attributes with a single dimension or poor precision. To solve this, a combined Hilbert–Huang transform (HHT) and variational mode decomposition (VMD) method is proposed to extract multidimensional dynamic response characteristics of time, frequency, and energy of offshore oil platforms. Based on the extracted time–frequency–energy information, the frequency-domain integration approach (FDIA) can be applied to calculate the displacement using accelerometer in the micro inertial measurement unit (MIMU). A complementary filtering algorithm was designed to measure the torsion angle of platforms using six degrees of freedom data from the MIMU to obtain the torsion angle information. The performance of the proposed method was validated using a series of simulation shaking-table tests and a field test conducted on an offshore oil platform at Dongying City, Shandong Province, China. During the field test, seven out of eight collisions were detected in the frequency range 5 Hz to 12 Hz. The intensity of the fifth collision was the highest, and the maximum displacement obtained by the accelerometer was 6 mm. In addition, the results show a correlation between the axes of the accelerometer and gyroscope, and their combination can measure a torsion angle up to 1.1°.

Spatial and Frequency Approaches for Audio File Protection

In order to protect audio files, we propose in this paper a new integration scheme for blind audio file watermarking. The goal is to find a compromise between capacity and imperceptibility in order to hide as much data as possible while minimizing file degradation. This integration scheme is implemented in the three insertion domains: spatial, frequency and multi-resolution domains. For the spatial-domain integration, the mark is inserted directly into the data samples. For the frequency-domain integration, a Discrete Cosine Transform is applied to the audio frames; after the thresholding and quantification step, the watermark is inserted into the Discrete Cosine Transform coefficients to obtain the watermarked file. For the multi-resolution-domain insertion, a single-level Discrete Wavelet Transform is applied using the scaling low-pass filter and wavelet high-pass filter. The watermark integration is then performed using the obtained AC coefficients. The proposed concealment process combines three values to integrate two bits and only one may be modified, which reduces the probability of change unlike other approaches. This implies less modification and therefore less distortion of the host file; this explains the good Signal-to-Noise Ratio obtained of more than 59[Formula: see text]dB for the spatial-domain integration and therefore a reasonable imperceptibility. An evaluation of the watermark’s robustness demonstrates that the proposed schemes generate reasonably robust watermarked samples against various attacks with a high-quality watermark with normalized cross-correlation greater than 0.9 for the three insertion domains.

Monitoring of Induced Groundborne Vibrations in Cultural Heritage Buildings: Miscellaneous Errors and Aliasing through Integration and Filtering

ABSTRACT Cultural Heritage Buildings are considered highly complex, having evolved over time, and contain various heritage components. These assets are becoming increasingly at risk due to unprecedented transformation processes in cities, from underground railways. Experimental and monitoring strategies can serve as diagnostic tools on the effects of long-term exposure to induced vibrations, from this new source. The current work presents the Structural Health Monitoring (SHM) outcomes in the Church of the Angels (Igreja dos Anjos), a monument of the early 20th century, in Lisbon, Portugal, directly over a subway station, in a shallow railway line, subjected to groundborne vibration. Criteria from international standards provide threshold values, mostly in terms of velocity, which are linearly related to stress and strain fields. Specific criteria for non-structural components are usually set, accounting among others, frequency bandwidths and levels of intermittency. Even if recommendations specify the velocity as a primary measurement, it is often needed to obtain velocity–time histories from acceleration data, as accelerometers are widely used in practice. Weak signal processing, low sampling frequencies, the type of integration in time or frequency domain and digital filtering, with incorrect cut-off frequency bandwidths, may lead to processed signals, distorted in terms of shape, phase and amplitude.

Research on Shaking Table Test of Earthquake Simulation Based on Hybrid Integration Algorithm

Aiming at the problem of poor accuracy of acceleration waveform reproduction during the shaking table test of earthquake simulation, a hybrid integration algorithm based on seismic acceleration signal is proposed to control the trend term error. The low-frequency attenuation integral algorithm in the frequency domain is used to integrate the original seismic acceleration signal once to obtain the vibration velocity signal, then, the polynomial fitting integration algorithm is introduced to integrate of the vibration velocity signal once in the time domain to obtain the vibration displacement signal. Through these two integrals, the sensitivity of low frequency band during frequency domain integration and the accumulation of small errors in time domain are reduced. Finally, the hybrid integration algorithm is used to perform seismic wave reproduction experiments on the seismic simulation shaker and good results are obtained, which proves that the hybrid integration calculation has high practical value and practical significance in improving the accuracy of the vibration table waveform reproduction.

Real-Time Estimation of Wind-Induced Pointing Error via Accelerations Measurement for Large Reflector Antenna

The online monitoring of pointing error (PE) caused by the main reflector (MR) deformation under wind disturbance has become a knotty problem for large reflector antennas. This paper presents a real-time estimation algorithm for monitoring wind-induced PE by optimized acceleration measurement points. First, based on an unbiased minimum-variance input-state estimation theory via accelerations measurement, we set up the PE observation model which illustrates the relation between the structural vibration state and PE. Moreover, compared with the frequency domain integration approach, the non-integral observation model without introducing a low-frequency-cut filter can sense pseudo-static and low-frequency PE. Second, a sensor placement method considering sensor constraint spacing (SPMCSCS) is proposed to achieve a high-quality monitoring effect with low information redundancy. In the method, the constraint spacing between sensors is determined by a novel scan recognition algorithm in which regional division, and regional correlation analysis of the modal shape are carried out successively. Finally, the simulation implementation of a 7.3-m antenna under a wind disturbance is used to illustrate the algorithm.