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1 – 10 of 127
Article
Publication date: 4 July 2018

Yining Li and Peilin Zhang

In real working condition, signal is highly disturbed and even drowned by noise, which extremely interferes in detecting results. Therefore, this paper aims to provide an…

Abstract

Purpose

In real working condition, signal is highly disturbed and even drowned by noise, which extremely interferes in detecting results. Therefore, this paper aims to provide an effective de-noising method for the debris particle in lubricant so that the ultrasonic technique can be applied to the online debris particle detection.

Design/methodology/approach

For completing the online ultrasonic monitoring of oil wear debris, the research is made on some selected wear debris signals. It applies morphology component analysis (MCA) theory to de-noise signals. To overcome the potential weakness of MCA threshold process, it proposes fuzzy morphology component analysis (FMCA) by fuzzy threshold function.

Findings

According to simulated and experimental results, it eliminates most of the wear debris signal noises by using FMCA through the signal comparison. According to the comparison of simulation evaluation index, it has highest signal noise ratio, smallest root mean square error and largest similarity factor.

Research limitations/implications

The rapid movement of the debris particles, as well as the lubricant temperature, may influence the measuring signals. Researchers are encouraged to solve these problems further.

Practical implications

This paper includes implications for the improvement in the online debris detection and the development of the ultrasonic technique applied in online debris detection.

Originality value

This paper provides a promising way of applying the MCA theory to de-noise signals. To avoid the potential weakness of the MCA threshold process, it proposes FMCA through fuzzy threshold function. The FMCA method has great obvious advantage in de-noising wear debris signals. It lays the foundation for online ultrasonic monitoring of lubrication wear debris.

Details

Industrial Lubrication and Tribology, vol. 70 no. 6
Type: Research Article
ISSN: 0036-8792

Keywords

Article
Publication date: 25 September 2018

Jianhua Cai

This paper aims to explore a new wavelet adaptive threshold de-noising method to resolve the shortcomings of wavelet hard-threshold method and wavelet soft-threshold method, which…

Abstract

Purpose

This paper aims to explore a new wavelet adaptive threshold de-noising method to resolve the shortcomings of wavelet hard-threshold method and wavelet soft-threshold method, which are usually used in gear fault diagnosis.

Design/methodology/approach

A new threshold function and a new determined method of threshold for each layer are proposed. The principle and the implementation of the algorithm are given. The simulated signal and the measured gear fault signal are analyzed, and the obtained results are compared with those from wavelet soft-threshold method, wavelet hard-threshold method and wavelet modulus maximum method.

Findings

The presented wavelet adaptive threshold method overcomes the defects of the traditional wavelet threshold method, and it can effectively eliminate the noise hidden in the gear fault signal at different decomposition scales. It provides more accurate information for the further fault diagnosis.

Originality/value

A new threshold function is adopted and the multi-resolution unbiased risk estimation is used to determine the adaptive threshold, which overcomes the defect of the traditional wavelet method.

Details

Industrial Lubrication and Tribology, vol. 71 no. 1
Type: Research Article
ISSN: 0036-8792

Keywords

Article
Publication date: 12 September 2023

Zengli Mao and Chong Wu

Because the dynamic characteristics of the stock market are nonlinear, it is unclear whether stock prices can be predicted. This paper aims to explore the predictability of the…

Abstract

Purpose

Because the dynamic characteristics of the stock market are nonlinear, it is unclear whether stock prices can be predicted. This paper aims to explore the predictability of the stock price index from a long-memory perspective. The authors propose hybrid models to predict the next-day closing price index and explore the policy effects behind stock prices. The paper aims to discuss the aforementioned ideas.

Design/methodology/approach

The authors found a long memory in the stock price index series using modified R/S and GPH tests, and propose an improved bi-directional gated recurrent units (BiGRU) hybrid network framework to predict the next-day stock price index. The proposed framework integrates (1) A de-noising module—Singular Spectrum Analysis (SSA) algorithm, (2) a predictive module—BiGRU model, and (3) an optimization module—Grid Search Cross-validation (GSCV) algorithm.

Findings

Three critical findings are long memory, fit effectiveness and model optimization. There is long memory (predictability) in the stock price index series. The proposed framework yields predictions of optimum fit. Data de-noising and parameter optimization can improve the model fit.

Practical implications

The empirical data are obtained from the financial data of listed companies in the Wind Financial Terminal. The model can accurately predict stock price index series, guide investors to make reasonable investment decisions, and provide a basis for establishing individual industry stock investment strategies.

Social implications

If the index series in the stock market exhibits long-memory characteristics, the policy implication is that fractal markets, even in the nonlinear case, allow for a corresponding distribution pattern in the value of portfolio assets. The risk of stock price volatility in various sectors has expanded due to the effects of the COVID-19 pandemic and the R-U conflict on the stock market. Predicting future trends by forecasting stock prices is critical for minimizing financial risk. The ability to mitigate the epidemic’s impact and stop losses promptly is relevant to market regulators, companies and other relevant stakeholders.

Originality/value

Although long memory exists, the stock price index series can be predicted. However, price fluctuations are unstable and chaotic, and traditional mathematical and statistical methods cannot provide precise predictions. The network framework proposed in this paper has robust horizontal connections between units, strong memory capability and stronger generalization ability than traditional network structures. The authors demonstrate significant performance improvements of SSA-BiGRU-GSCV over comparison models on Chinese stocks.

Details

Kybernetes, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0368-492X

Keywords

Article
Publication date: 2 January 2018

Anan Zhang, Cong He, Maoyi Sun, Qian Li, Hong Wei Li and Lin Yang

Noise abatement is one of the key techniques for Partial Discharge (PD) on-line measurement and monitoring. However, how to enhance the efficiency of PD signal noise suppression…

Abstract

Purpose

Noise abatement is one of the key techniques for Partial Discharge (PD) on-line measurement and monitoring. However, how to enhance the efficiency of PD signal noise suppression is a challenging work. Hence, this study aims to improve the efficiency of PD signal noise abatement.

Design/methodology/approach

In this approach, the time–frequency characteristics of PD signal had been obtained based on fast kurtogram and S-transform time–frequency spectrum, and these characteristics were used to optimize the parameters for the signal matching over-complete dictionary. Subsequently, a self-adaptive selection of matching atoms was realized when using Matching Pursuit (MP) to analyze PD signals, which leading to seldom noise signal element was represented in sparse decomposition.

Findings

The de-noising of PD signals was achieved efficiently. Simulation and experimental results show that the proposed method has good adaptability and significant noise abatement effect compared with Empirical Mode Decomposition, Wavelet Threshold and global signal sparse decomposition of MP.

Originality/value

A self-adaptive noise abatement method was proposed to improve the efficiency of PD signal noise suppression based on the signal sparse representation and its MP algorithm, which is significant to on-line PD measurement.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, vol. 37 no. 1
Type: Research Article
ISSN: 0332-1649

Keywords

Article
Publication date: 14 August 2017

Julius Owowo and S. Olutunde Oyadiji

The purpose of this paper is to employ the acoustic wave propagation method for leakage detection in pipes. The first objective is to use acoustic finite element analysis (AFEA…

Abstract

Purpose

The purpose of this paper is to employ the acoustic wave propagation method for leakage detection in pipes. The first objective is to use acoustic finite element analysis (AFEA) method to simulate acoustic wave propagation and acoustic wave reflectometry in an intact pipe and in pipes with leaks of various sizes. This is followed by the second objective which is to validate the effectiveness and the practicability of the acoustic wave method via experimental testing. The third objective involves the decomposition and de-noising of the measured acoustic waves using stationary wavelet transform (SWT). It is shown that this approach, which is used for the first time on leakage detection in pipes, can be used to identify, locate and estimate the size of a leakage defect in a pipe.

Design/methodology/approach

The research work was designed inline with best practices and acceptable standards. The research methodology focusses on five basic areas: literature review; experimental measurements; simulations; data analysis and writing-up of the study with clear-cut communication of the findings. The approach used was acoustic wave propagation-based method in conjunction with SWT for leakage detection in fluid-filled pipe.

Findings

First, the simulation of acoustic wave propagation and acoustic wave reflectometry in fluid-filled pipes with and without leakage have great potential in leakage detection in pipeline systems and can detect very small leaks of 1 mm diameter. Second, the measured noise-contaminated acoustic wave propagation in a fluid-filled pipe can be successfully de-noised using the SWT method in order to clearly identify and locate leakage as little as 5 mm diameter in a pipe. Third, AFEA of a fluid-filled pipe can be achieved with the simulation of only the fluid content of the pipe and without the inclusion of the pipe in the model. This eliminates contact interaction of the solid pipe walls and the fluid, and as a consequence reduces computational time and resources. Fourth, the relationship of the ratio of the leakage diameter to the ratio of the first and second secondary wave amplitudes caused by the leakage can be represented by a second-order polynomial function. Fifth, the identification of leakage in a pipe is intuitive from mere comparison of the acoustic waveforms of an intact pipe with that of a pipe with a leakage.

Originality/value

The research work is a novelty and was developed from the scratch. The AFEA of acoustic wave propagation and acoustic wave reflectometry in a static fluid-filled pipe, and the SWT method have been used for the first time to detect, locate and estimate the size of a leakage in a fluid-filled pipe.

Details

International Journal of Structural Integrity, vol. 8 no. 4
Type: Research Article
ISSN: 1757-9864

Keywords

Article
Publication date: 1 February 2018

Chunhua Ren, Xiaoming Hu, Poyun Qin, Leilei Li and Tong He

Measurement-while-drilling (MWD) system has been used to provide trajectory and inclination parameters of the oil and gas well. Fluxgate magnetometer is a traditional choice for…

Abstract

Purpose

Measurement-while-drilling (MWD) system has been used to provide trajectory and inclination parameters of the oil and gas well. Fluxgate magnetometer is a traditional choice for one MWD system; however, it cannot obtain effective trajectory parameters in nonmagnetic environments. Fiber-optic-gyroscope (FOG) inclinometer system is a favorable substitute of fluxgate magnetometer, which can avoid the flaws associated with magnetic monitoring devices. However, there are some limitations and increasing surveying errors in this system under high impact conditions. This paper aims to overcome these imperfections of the FOG inclinometer system.

Design/methodology/approach

To overcome the imperfections, filtering algorithms are used to improve the precision of the equipment. The authors compare the low-pass filtering algorithm with the wavelet de-noising algorithm applied to real experimental data. Quantitative comparison of the error between the true and processed signal revealed that the wavelet de-noising method outperformed the low-pass filtering method. To achieve optimal positioning effects, the wavelet de-noising algorithm is finally used to inhibit the interference caused by the impact.

Findings

The experimental results show that the method proposed can ensure the azimuth accuracy lower than ±2 degrees and the inclination accuracy lower than ± 0.15 degrees under the condition of interval impact. The method proposed can overcome the interference generated by the impact in the well, which makes the instrument suitable for the measurement of small-diameter casing well.

Originality/value

After conducting the wavelet threshold filtering on the raw data of accelerometers, the noise generated by the impact is successfully suppressed, which is expected to meet the special requirement of the down-hole survey environment.

Details

Sensor Review, vol. 38 no. 3
Type: Research Article
ISSN: 0260-2288

Keywords

Article
Publication date: 23 December 2022

Jinchao Huang

Recently, the convolutional neural network (ConvNet) has a wide application in the classification of motor imagery EEG signals. However, the low signal-to-noise…

78

Abstract

Purpose

Recently, the convolutional neural network (ConvNet) has a wide application in the classification of motor imagery EEG signals. However, the low signal-to-noise electroencephalogram (EEG) signals are collected under the interference of noises. However, the conventional ConvNet model cannot directly solve this problem. This study aims to discuss the aforementioned issues.

Design/methodology/approach

To solve this problem, this paper adopted a novel residual shrinkage block (RSB) to construct the ConvNet model (RSBConvNet). During the feature extraction from EEG signals, the proposed RSBConvNet prevented the noise component in EEG signals, and improved the classification accuracy of motor imagery. In the construction of RSBConvNet, the author applied the soft thresholding strategy to prevent the non-related motor imagery features in EEG signals. The soft thresholding was inserted into the residual block (RB), and the suitable threshold for the current EEG signals distribution can be learned by minimizing the loss function. Therefore, during the feature extraction of motor imagery, the proposed RSBConvNet de-noised the EEG signals and improved the discriminative of classification features.

Findings

Comparative experiments and ablation studies were done on two public benchmark datasets. Compared with conventional ConvNet models, the proposed RSBConvNet model has obvious improvements in motor imagery classification accuracy and Kappa coefficient. Ablation studies have also shown the de-noised abilities of the RSBConvNet model. Moreover, different parameters and computational methods of the RSBConvNet model have been tested on the classification of motor imagery.

Originality/value

Based on the experimental results, the RSBConvNet constructed in this paper has an excellent recognition accuracy of MI-BCI, which can be used for further applications for the online MI-BCI.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 16 no. 3
Type: Research Article
ISSN: 1756-378X

Keywords

Article
Publication date: 13 September 2021

Naresh Kattekola, Amol Jawale, Pallab Kumar Nath and Shubhankar Majumdar

This paper aims to improve the performance of approximate multiplier in terms of peak signal to noise ratio (PSNR) and quality of the image.

Abstract

Purpose

This paper aims to improve the performance of approximate multiplier in terms of peak signal to noise ratio (PSNR) and quality of the image.

Design/methodology/approach

The paper proposes an approximate circuit for 4:2 compressor, which shows a significant amount of improvement in performance metrics than that of the existing designs. This paper also reports a hybrid architecture for the Dadda multiplier, which incorporates proposed 4:2 compressor circuit as a basic building block.

Findings

Hybrid Dadda multiplier architecture is used in a median filter for image de-noising application and achieved 20% more PSNR than that of the best available designs.

Originality/value

The proposed 4:2 compressor improves the error metrics of a Hybrid Dadda multiplier.

Details

Circuit World, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0305-6120

Keywords

Article
Publication date: 1 June 2002

Hojjat Adeli

This paper reviews innovative research done during the past few years on automatic detection of traffic incidents by the author and his associates using data obtained from sensors…

1457

Abstract

This paper reviews innovative research done during the past few years on automatic detection of traffic incidents by the author and his associates using data obtained from sensors embedded in intelligent freeways. A multi‐paradigm intelligent system approach is employed to solve the complicated and chaotic pattern recognition problem using neural networks, fuzzy logic, and wavelets. Wavelet‐based de‐noising and feature extraction techniques are employed to eliminate undesirable fluctuations in observed data from traffic sensors. The result is reliable algorithms with high incident detection and very low false alarm rates.

Details

Sensor Review, vol. 22 no. 2
Type: Research Article
ISSN: 0260-2288

Keywords

Article
Publication date: 8 April 2016

Tarek Bentahar, Djamel Benatia and Mohamed Boulila

In this paper, a new efficient method to de-noise the interferometric Synthetic Aperture Radar interferogram, also called wrapped phase image, is proposed with the aim to reduce…

66

Abstract

Purpose

In this paper, a new efficient method to de-noise the interferometric Synthetic Aperture Radar interferogram, also called wrapped phase image, is proposed with the aim to reduce the residue number and make the phase unwrapping process easy.

Design/methodology/approach

This method is based on two statistics functions, the former is the phase derivative variance (PDV) defined as a quality map to select the badness areas, the second one is the phase derivative variance (PAD) for a local 3 × 3 pixels filtering which allows to assign an estimated phase for each bad area selected by PDV function. Our filter was tested with a simulated interferograms and compared to other most used filters.

Findings

With this proposed method, the residues in the interferogram are minimized better than using a conventional filters, and the phase unwrapping process gives a better estimation.

Originality/value

Combining two statistical functions (PDV and PAD) is efficient in terms of minimizing the noise in the interferogram; this is very helpful to minimize the processing time of the InSAR image particularly the phase unwrapping treatment and have a good quality of the image.

Details

World Journal of Engineering, vol. 13 no. 2
Type: Research Article
ISSN: 1708-5284

Keywords

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