Fusion of GNSS and Speedometer Based on VMD and Its Application in Bridge Deformation Monitoring

Real-time dynamic displacement and spectral response on the midspan of Jiangyin Bridge were calculated using Global Navigation Satellite System (GNSS) and a speedometer for the purpose of understanding the dynamic behavior and the temporal evolution of the bridge structure. Considering that the GNSS measurement noise is large and the velocity/acceleration sensors cannot measure the low-frequency displacement, the Variational Mode Decomposition (VMD) algorithm was used to extract the low-frequency displacement of GNSS. Then, the low-frequency displacement extracted from the GNSS time series and the high-frequency vibration calculated by speedometer were combined in this paper in order to obtain the high precision three-dimensional dynamic displacement of the bridge in real time. Simulation experiment and measured data show that the VMD algorithm could effectively resist the modal aliasing caused by noise and discontinuous signals compared with the commonly used Empirical Mode Decomposition (EMD) algorithm, which is guaranteed to get high-precision fusion data. Finally, the fused displacement results can identify high-frequency vibrations and low-frequency displacements of a mm level, which can be used to calculate the spectral characteristics of the bridge and provide reference to evaluate the dynamic and static loads, and the health status of the bridge in the full frequency domain and the full time domain.

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An Improved Multipath Mitigation Method and Its Application in Real-Time Bridge Deformation Monitoring

Remote Sensing ◽

10.3390/rs13122259 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2259

Author(s):

Ruicheng Zhang ◽

Chengfa Gao ◽

Qing Zhao ◽

Zihan Peng ◽

Rui Shang

Keyword(s):

Real Time ◽

Sampling Rate ◽

Threshold Value ◽

Deformation Monitoring ◽

Success Rates ◽

Multipath Mitigation ◽

Mode Decomposition ◽

Observation Noise ◽

Original Observation ◽

Better Than

A multipath is a major error source in bridge deformation monitoring and the key to achieving millimeter-level monitoring. Although the traditional MHM (multipath hemispherical map) algorithm can be applied to multipath mitigation in real-time scenarios, accuracy needs to be further improved due to the influence of observation noise and the multipath differences between different satellites. Aiming at the insufficiency of MHM in dealing with the adverse impact of observation noise, we proposed the MHM_V model, based on Variational Mode Decomposition (VMD) and the MHM algorithm. Utilizing the VMD algorithm to extract the multipath from single-difference (SD) residuals, and according to the principle of the closest elevation and azimuth, the original observation of carrier phase in the few days following the implementation are corrected to mitigate the influence of the multipath. The MHM_V model proposed in this paper is verified and compared with the traditional MHM algorithm by using the observed data of the Forth Road Bridge with a seven day and 10 s sampling rate. The results show that the correlation coefficient of the multipath on two adjacent days was increased by about 10% after residual denoising with the VMD algorithm; the standard deviations of residual error in the L1/L2 frequencies were improved by 37.8% and 40.7%, respectively, which were better than the scores of 26.1% and 31.0% for the MHM algorithm. Taking a ratio equal to three as the threshold value, the fixed success rates of ambiguity were 88.0% without multipath mitigation and 99.4% after mitigating the multipath with MHM_V. The MHM_V algorithm can effectively improve the success rate, reliability, and convergence rate of ambiguity resolution in a bridge multipath environment and perform better than the MHM algorithm.

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Offline Algorithms in Low-Frequency Trading

Queue ◽

10.1145/3442632.3448307 ◽

2020 ◽

Vol 18 (6) ◽

pp. 37-51

Author(s):

Terence Kelly

Keyword(s):

Real Time ◽

Real World ◽

High Frequency ◽

Low Frequency ◽

Combinatorial Auctions ◽

Hard Problem ◽

Np Hard ◽

High Stakes ◽

Drill Bits ◽

Np Hard Problem

Expectations run high for software that makes real-world decisions, particularly when money hangs in the balance. This third episode of the Drill Bits column shows how well-designed software can effectively create wealth by optimizing gains from trade in combinatorial auctions. We'll unveil a deep connection between auctions and a classic textbook problem, we'll see that clearing an auction resembles a high-stakes mutant Tetris, we'll learn to stop worrying and love an NP-hard problem that's far from intractable in practice, and we'll contrast the deliberative business of combinatorial auctions with the near-real-time hustle of high-frequency trading. The example software that accompanies this installment of Drill Bits implements two algorithms that clear combinatorial auctions.

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Refactoring and Optimization of Bridge Dynamic Displacement Based on Ensemble Empirical Mode Decomposition

Sensors ◽

10.3390/s19143125 ◽

2019 ◽

Vol 19 (14) ◽

pp. 3125

Author(s):

Zou ◽

Chen ◽

Liu

Keyword(s):

Empirical Mode Decomposition ◽

Field Experiments ◽

Low Frequency ◽

Vibration Signal ◽

Micro Electro Mechanical System ◽

Ensemble Empirical Mode Decomposition ◽

Reconstruction Method ◽

Monitoring Methods ◽

Dynamic Displacement ◽

Mode Decomposition

Considering the lack of precision in transforming measured micro-electro-mechanical system (MEMS) accelerometer output signals into elevation signals, this paper proposes a bridge dynamic displacement reconstruction method based on the combination of ensemble empirical mode decomposition (EEMD) and time domain integration, according to the vibration signal traits of a bridge. Through simulating bridge analog signals and verifying a vibration test bench, four bridge dynamic displacement monitoring methods were analyzed and compared. The proposed method can effectively eliminate the influence of low-frequency integral drift and high-frequency ambient noise on the integration process. Furthermore, this algorithm has better adaptability and robustness. The effectiveness of the method was verified by field experiments on highway elevated bridges.

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Load Prediction Based on Hybrid Model of VMD-mRMR-BPNN-LSSVM

Complexity ◽

10.1155/2020/6940786 ◽

2020 ◽

Vol 2020 ◽

pp. 1-20

Author(s):

Gang Zhang ◽

Hongchi Liu ◽

Pingli Li ◽

Meng Li ◽

Qiang He ◽

...

Keyword(s):

Power System ◽

High Frequency ◽

Prediction Accuracy ◽

Load Forecasting ◽

Low Frequency ◽

Load Prediction ◽

System Load ◽

Mode Decomposition ◽

Frequency Components ◽

Feature Information

Power system load forecasting is an important part of power system scheduling. Since the power system load is easily affected by environmental factors such as weather and time, it has high volatility and multi-frequency. In order to improve the prediction accuracy, this paper proposes a load forecasting method based on variational mode decomposition (VMD) and feature correlation analysis. Firstly, the original load sequence is decomposed using VMD to obtain a series of intrinsic mode function (IMF), it is referred to below as a modal component, and they are divided into high frequency, intermediate frequency, and low frequency signals according to their fluctuation characteristics. Then, the feature information related to the power system load change is collected, and the correlation between each IMF and each feature information is analyzed using the maximum relevance minimum redundancy (mRMR) based on the mutual information to obtain the best feature set of each IMF. Finally, each component is input into the prediction model together with its feature set, in which back propagation neural network (BPNN) is used to predict high-frequency components, least square-support vector machine (LS-SVM) is used to predict intermediate and low frequency components, and BPNN is also used to integrate the prediction results to obtain the final load prediction value, and compare the prediction results of method in this paper with that of the prediction models such as autoregressive moving average model (ARMA), LS-SVM, BPNN, empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and VMD. This paper carries out an example analysis based on the data of Xi’an Power Grid Corporation, and the results show that the prediction accuracy of method in this paper is higher.

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High Precision Timing of the Copying Control System Based on Windows 2000

Materials Science Forum ◽

10.4028/www.scientific.net/msf.626-627.87 ◽

2009 ◽

Vol 626-627 ◽

pp. 87-92

Author(s):

X.H. Lu ◽

Zhen Yuan Jia ◽

Y.Z. Lv ◽

J.Y. Yang

Keyword(s):

Operating System ◽

Control System ◽

Real Time ◽

High Precision ◽

High Frequency ◽

Timing Error ◽

The Real ◽

Timing Function ◽

Design Proposal ◽

Windows 2000

A design proposal for developing a high-precision timer of the real-time digital copying control system is put forward by analyzing the real-time characteristics of Windows 2000 operating system. Proposals of using information systems timer—WM_TIMER, multimedia timer, VxD and hardware to achieve high-precision timing in Windows operating system are analyzed and compared. Based on computer high-frequency timer and multi-thread technology, timing function provided by Microsoft Visual C++ is used to achieve a high-precision software timer of the system. Finally, the researched timing method is tested in Advantech IPC-610 host with Intel Pentium 4 processor, and the maximum timing error is less than 5μs which meets the system timing requirements.

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Application of Mathematical Morphological Filtering to Improve the Resolution of Chang'e-3 Lunar Penetrating Radar Data

Remote Sensing ◽

10.3390/rs11050524 ◽

2019 ◽

Vol 11 (5) ◽

pp. 524 ◽

Cited By ~ 5

Author(s):

Jianmin Zhang ◽

Zhaofa Zeng ◽

Ling Zhang ◽

Qi Lu ◽

Kun Wang

Keyword(s):

High Frequency ◽

Noise Suppression ◽

Low Frequency ◽

Radar Data ◽

Structural Elements ◽

Geological Structures ◽

Mode Decomposition ◽

Morphological Filtering ◽

Scale Range ◽

Band Pass

As one of the important scientific instruments of lunar exploration, the Lunar Penetrating Radar (LPR) onboard China’s Chang'E-3 (CE-3) provides a unique opportunity to image the lunar subsurface structure. Due to the low-frequency and high-frequency noises of the data, only a few geological structures are visible. In order to better improve the resolution of the data, band-pass filtering and empirical mode decomposition filtering (EMD) methods are usually used, but in this paper, we present a mathematical morphological filtering (MMF) method to reduce the noise. The MMF method uses two structural elements with different scales to extract certain scale-range information from the original signal, at the same time, the noise beyond the scale range of the two different structural elements is suppressed. The application on synthetic signals demonstrates that the morphological filtering method has a better performance in noise suppression compared with band-pass filtering and EMD methods. Then, we apply band-pass filtering, EMD, and MMF methods to the LPR data, and the MMF method also achieves a better result. Furthermore, according to the result by MMF method, three stratigraphic zones are revealed along the rover's route.

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Low-Noise Chopper-Stabilized Multi-Path Operational Amplifier with Nested Miller Compensation for High-Precision Sensors

Applied Sciences ◽

10.3390/app10010281 ◽

2019 ◽

Vol 10 (1) ◽

pp. 281 ◽

Cited By ~ 3

Author(s):

Jaesung Kim ◽

Hyungseup Kim ◽

Kwonsang Han ◽

Donggeun You ◽

Hyunwoo Heo ◽

...

Keyword(s):

High Precision ◽

Operational Amplifier ◽

High Frequency ◽

Flicker Noise ◽

Low Frequency ◽

Low Noise ◽

Cmos Process ◽

Miller Compensation ◽

Chopper Stabilization ◽

Nested Miller Compensation

This paper presents a low-noise multi-path operational amplifier for high-precision sensors. A chopper stabilization technique is applied to the amplifier to remove offset and flicker noise. A ripple reduction loop (RRL) is designed to remove the ripple generated in the process of up-modulating the flicker noise and offset. To cancel the notch in the overall transfer function due to the RRL operation, a multi-path architecture using both a low-frequency path (LFP) and high-frequency path (HFP) is implemented. The low frequency path amplifier is implemented using the chopper technique and the RRL. In the high-frequency path amplifier, a class-AB output stage is implemented to improve the power efficiency. The transfer functions of the LFP and HFP induce a first-order frequency response in the system through nested Miller compensation. The low-noise multi-path amplifier was fabricated using a 0.18 µm 1P6M complementary metal-oxide-semiconductor (CMOS) process. The power consumption of the proposed low-noise operational amplifier is 0.174 mW with a 1.8 V supply and an active area of 1.18 mm2. The proposed low-noise amplifier has a unit gain bandwidth (UGBW) of 3.16 MHz, an input referred noise of 11.8 nV/√Hz, and a noise efficiency factor (NEF) of 4.46.

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Random Noise Reduction Based on Ensemble Empirical Mode Decomposition and Wavelet Threshold Filtering

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.518-523.3887 ◽

2012 ◽

Vol 518-523 ◽

pp. 3887-3890 ◽

Cited By ~ 1

Author(s):

Wei Chen ◽

Shang Xu Wang ◽

Xiao Yu Chuai ◽

Zhen Zhang

Keyword(s):

Noise Reduction ◽

Empirical Mode Decomposition ◽

High Frequency ◽

Reduction Method ◽

Random Noise ◽

Low Frequency ◽

Ensemble Empirical Mode Decomposition ◽

Intrinsic Mode Functions ◽

Mode Decomposition ◽

Eemd Method

This paper presents a random noise reduction method based on ensemble empirical mode decomposition (EEMD) and wavelet threshold filtering. Firstly, we have conducted spectrum analysis and analyzed the frequency band range of effective signals and noise. Secondly, we make use of EEMD method on seismic signals to obtain intrinsic mode functions (IMFs) of each trace. Then, wavelet threshold noise reduction method is used on the high frequency IMFs of each trace to obtain new high frequency IMFs. Finally, reconstruct the desired signal by adding the new high frequency IMFs on the low frequency IMFs and the trend item together. When applying our method on synthetic seismic record and field data we can get good results.

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High-G Calibration Denoising Method for High-G MEMS Accelerometer Based on EMD and Wavelet Threshold

Micromachines ◽

10.3390/mi10020134 ◽

2019 ◽

Vol 10 (2) ◽

pp. 134 ◽

Cited By ~ 11

Author(s):

Qing Lu ◽

Lixin Pang ◽

Haoqian Huang ◽

Chong Shen ◽

Huiliang Cao ◽

...

Keyword(s):

High Frequency ◽

Low Frequency ◽

Original Signal ◽

Denoising Method ◽

Mems Accelerometer ◽

Mode Decomposition ◽

Impact Peak ◽

Reconstructed Signal ◽

Mems Accelerometers ◽

The Impact

High-G MEMS accelerometers have been widely used in monitoring natural disasters and other fields. In order to improve the performance of High-G MEMS accelerometers, a denoising method based on the combination of empirical mode decomposition (EMD) and wavelet threshold is proposed. Firstly, EMD decomposition is performed on the output of the main accelerometer to obtain the intrinsic mode function (IMF). Then, the continuous mean square error rule is used to find energy cut-off point, and then the corresponding high frequency IMF component is denoised by wavelet threshold. Finally, the processed high-frequency IMF component is superposed with the low-frequency IMF component, and the reconstructed signal is denoised signal. Experimental results show that this method integrates the advantages of EMD and wavelet threshold and can retain useful signals to the maximum extent. The impact peak and vibration characteristics are 0.003% and 0.135% of the original signal, respectively, and it reduces the noise of the original signal by 96%.

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A nonlinear ultrasonic method for real-time bolt looseness monitoring using PZT transducer–enabled vibro-acoustic modulation

Journal of Intelligent Material Systems and Structures ◽

10.1177/1045389x19891534 ◽

2019 ◽

Vol 31 (3) ◽

pp. 364-376 ◽

Cited By ~ 6

Author(s):

Nan Zhao ◽

Linsheng Huo ◽

Gangbing Song

Keyword(s):

Real Time ◽

Lead Zirconate Titanate ◽

High Frequency ◽

Ultrasonic Method ◽

Low Frequency ◽

Lead Zirconate ◽

Modulation Method ◽

Nonlinear Ultrasonic ◽

Acoustic Modulation ◽

Nonlinear Ultrasonic Method

A real-time nonlinear ultrasonic method based on vibro-acoustic modulation is applied to monitor early bolt looseness quantitatively by using piezoceramic transducers. In addition to the ability to detect the early bolt looseness, a major contribution is that we replaced the shaker, which is commonly used in a vibro-acoustic modulation method, by a permanently installed and low-cost lead zirconate titanate patch. In vibro-acoustic modulation, when stimulating two input waves with distinctive frequencies, namely the high-frequency probing wave and the low-frequency pumping wave, the high-frequency probing wave will be modulated by the low-frequency pumping wave to generate sidebands in terms of bolt looseness. Thus, the influence of low-frequency voltage amplitudes on the modulation results, which is ambiguous in previous research, is also analyzed in this article. The results of experiment demonstrated that the lead zirconate titanate–enabled vibro-acoustic modulation method is reliable and easy to implement to identify the bolt looseness continuously and quantitatively. In addition, low-frequency amplitudes of actuating voltage should be selected in a reasonable range. Finally, we compared the vibro-acoustic modulation method with the time-reversal method based on the linear ultrasonic theory, and the result illustrates that vibro-acoustic modulation method has better performance in monitoring the early bolt looseness.

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