Research on blind source separation of operation sounds of metro power transformer through an Adaptive Threshold REPET algorithm

Purpose–The safe operation of the metro power transformer directly relates to the safety and efficiency of the entire metro system.Through voiceprint technology,the sounds emitted by the transformer can be monitored in real-time,thereby achieving real-time monitoring of the transformer’s operational status.However,the environment surrounding power transformers is filled with various interfering sounds that intertwine with both the normal operational voiceprints and faulty voiceprints of the transformer,severely impacting the accuracy and reliability of voiceprint identification.Therefore,effective preprocessing steps are required to identify and separate the sound signals of transformer operation,which is a prerequisite for subsequent analysis.Design/methodology/approach–This paper proposes an Adaptive Threshold Repeating Pattern Extraction Technique(REPET)algorithm to separate and denoise the transformer operation sound signals.By analyzing the Short-Time Fourier Transform(STFT)amplitude spectrum,the algorithm identifies and utilizes the repeating periodic structures within the signal to automatically adjust the threshold,effectively distinguishing and extracting stable background signals from transient foreground events.The REPET algorithm first calculates the autocorrelation matrix of the signal to determine the repeating period,then constructs a repeating segment model.Through comparison with the amplitude spectrum of the original signal,repeating patterns are extracted and a soft time-frequency mask is generated.Findings–After adaptive thresholding processing,the target signal is separated.Experiments conducted on mixed sounds to separate background sounds from foreground sounds using this algorithm and comparing the results with those obtained using the FastICA algorithm demonstrate that the Adaptive Threshold REPET method achieves good separation effects.Originality/value–A REPET method with adaptive threshold is proposed,which adopts the dynamic threshold adjustment mechanism,adaptively calculates the threshold for blind source separation and improves the adaptability and robustness of the algorithm to the statistical characteristics of the signal.It also lays the foundation for transformer fault detection based on acoustic fingerprinting.

TRANSLATION-INVARIANT BASED ADAPTIVE THRESHOLD DENOISING FOR IMPACT SIGNAL

A translation-invariant based adaptive threshold denoising method formechanical impact signal is proposed. Compared with traditional wavelet denoising methods, itsuppresses pseudo-Gibbs phenomena in the neighborhood of signal discontinuities. To remedy thedrawbacks of conventional threshold functions, a new improved threshold function is introduced. Itpossesses more advantages than others. Moreover, based on utilizing characteristics of signal, aadaptive threshold selection procedure for impact signal is proposed. It is data-driven andlevel-dependent, therefore, it is more rational than other threshold estimation methods. Theproposed method is compared to alternative existing methods, and its superiority is revealed bysimulation and real data examples.

An automatic detection of green tide using multi-windows with their adaptive threshold from Landsat TM/ETM plus image

Since the atmospheric correction is a necessary preprocessing step of remote sensing image before detecting green tide, the introduced error directly affects the detection precision. Therefore, the detection method of green tide is presented from Landsat TM/ETM plus image which needs not the atmospheric correction. In order to achieve an automatic detection of green tide, a linear relationship（y =0.723 x＋0.504） between detection threshold y and subtraction x（x=λnir–λred） is found from the comparing Landsat TM/ETM plus image with the field surveys.Using this relationship, green tide patches can be detected automatically from Landsat TM/ETM plus image.Considering there is brightness difference between different regions in an image, the image will be divided into a plurality of windows（sub-images） with a same size firstly, and then each window will be detected using an adaptive detection threshold determined according to the discovered linear relationship. It is found that big errors will appear in some windows, such as those covered by clouds seriously. To solve this problem, the moving step k of windows is proposed to be less than the window width n. Using this mechanism, most pixels will be detected[n/k]×[n/k] times except the boundary pixels, then every pixel will be assigned the final class（green tide or sea water） according to majority rule voting strategy. It can be seen from the experiments, the proposed detection method using multi-windows and their adaptive thresholds can detect green tide from Landsat TM/ETM plus image automatically. Meanwhile, it avoids the reliance on the accurate atmospheric correction.

Reconstruction method of irregular seismic data with adaptive thresholds based on different sparse transform bases

Oil and gas seismic exploration have to adopt irregular seismic acquisition due to the increasingly complex exploration conditions to adapt to complex geological conditions and environments.However,the irregular seismic acquisition is accompanied by the lack of acquisition data,which requires high-precision regularization.The sparse signal feature in the transform domain in compressed sensing theory is used in this paper to recover the missing signal,involving sparse transform base optimization and threshold modeling.First,this paper analyzes and compares the effects of six sparse transformation bases on the reconstruction accuracy and efficiency of irregular seismic data and establishes the quantitative relationship between sparse transformation and reconstruction accuracy and efficiency.Second,an adaptive threshold modeling method based on sparse coefficient is provided to improve the reconstruction accuracy.Test results show that the method has good adaptability to different seismic data and sparse transform bases.The f-x domain reconstruction method of effective frequency samples is studied to address the problem of low computational efficiency.The parallel computing strategy of curvelet transform combined with OpenMP is further proposed,which substantially improves the computational efficiency under the premise of ensuring the reconstruction accuracy.Finally,the actual acquisition data are used to verify the proposed method.The results indicate that the proposed method strategy can solve the regularization problem of irregular seismic data in production and improve the imaging quality of the target layer economically and efficiently.

Edge Detection of COVID-19 CT Image Based on GF_SSR, Improved Multiscale Morphology, and Adaptive Threshold

Edge detection is an effective method for image segmentation and feature extraction.Therefore,extracting weak edges with the inhomogeneous gray of Corona Virus Disease 2019(COVID-19)CT images is extremely important.Multiscale morphology has been widely used in the edge detection of medical images due to its excellent boundary detection accuracy.In this paper,we propose a weak edge detection method based on Gaussian filtering and singlescale Retinex(GF_SSR),and improved multiscale morphology and adaptive threshold binarization(IMSM_ATB).As all the CT images have noise,we propose to remove image noise by Gaussian filtering.The edge of CT images is enhanced using the SSR algorithm.In addition,based on the extracted edge of CT images using improved Multiscale morphology,a particle swarm optimization(PSO)algorithm is introduced to binarize the image by automatically getting the optimal threshold.To evaluate our method,we use images from three datasets,namely COVID-19,Kaggle-COVID-19,and COVID-Chestxray,respectively.The average values of results are worthy of reference,with the Shannon information entropy of 1.8539,the Precision of 0.9992,the Recall of 0.8224,the F-Score of 1.9158,running time of 11.3000.Finally,three types of lesion images in the COVID-19 dataset are selected to evaluate the visual effects of the proposed algorithm.Compared with the other four algorithms,the proposed algorithm effectively detects the weak edge of the lesion and provides help for image segmentation and feature extraction.

Defocus Blur Segmentation Using Local Binary Patterns with Adaptive Threshold

Enormousmethods have been proposed for the detection and segmentation of blur and non-blur regions of the images.Due to the limited available information about blur type,scenario and the level of blurriness,detection and segmentation is a challenging task.Hence,the performance of the blur measure operator is an essential factor and needs improvement to attain perfection.In this paper,we propose an effective blur measure based on local binary pattern(LBP)with adaptive threshold for blur detection.The sharpness metric developed based on LBP used a fixed threshold irrespective of the type and level of blur,that may not be suitable for images with variations in imaging conditions,blur amount and type.Contrarily,the proposed measure uses an adaptive threshold for each input image based on the image and blur properties to generate improved sharpness metric.The adaptive threshold is computed based on the model learned through support vector machine(SVM).The performance of the proposed method is evaluated using two different datasets and is compared with five state-of-the-art methods.Comparative analysis reveals that the proposed method performs significantly better qualitatively and quantitatively against all of the compared methods.

Research on Adaptive Threshold of Received Signal in Communication System

When the light beam propagates in the atmosphere, the signal will be absorbed and scattered by the gas molecules and water mist in the atmosphere, which will cause the loss of power rate. The complex atmospheric environment will produce a variety of adverse effects on the signal. The interference produced by these effects overlaps with each other, which will seriously affect the strength of the received signal. Therefore, how to effectively suppress the atmospheric turbulence effect in the random atmospheric turbulence channel, ensure the normal transmission of the signal in the atmospheric channel, and reduce the bit error rate of the communication system, is very necessary to improve the communication system. When processing the received signal, it is an important step to detect the transmitted signal by comparing the received signal with the threshold. In this paper, based on the atmospheric turbulence distribution model, the adaptive signal decision threshold is obtained through the estimation of high-order cumulant. Monte Carlo method is used to verify the performance of adaptive threshold detection. The simulation results show that the high-order cumulant estimation of atmospheric turbulence parameters can realize the adaptive change of the decision threshold with the channel condition. It is shown that the adaptive threshold detection can effectively restrain atmospheric turbulence, improve the performance of free space optical and improve the communication quality.

Adaptive Threshold Estimation of Open Set Voiceprint Recognition Based on OTSU and Deep Learning

Aiming at the problem of open set voiceprint recognition, this paper proposes an adaptive threshold algorithm based on OTSU and deep learning. The bottleneck technology of open set voiceprint recognition lies in the calculation of similarity values and thresholds of speakers inside and outside the set. This paper combines deep learning and machine learning methods, and uses a Deep Belief Network stacked with three layers of Restricted Boltzmann Machines to extract deep voice features from basic acoustic features. And by training the Gaussian Mixture Model, this paper calculates the similarity value of the feature, and further determines the threshold of the similarity value of the feature through OTSU. After experimental testing, the algorithm in this paper has a false rejection rate of 3.00% for specific speakers, a false acceptance rate of 0.35% for internal speakers, and a false acceptance rate of 0 for external speakers. This improves the accuracy of traditional methods in open set voiceprint recognition. This proves that the method is feasible and good recognition effect.

Adaptive Threshold Median Filter for Multiple-Impulse Noise

Attenuating the noises plays an essential role in the image processing. Almost all the traditional median filters concern the removal of impulse noise having a single layer, whose noise gray level value is constant. In this paper, a new adaptive median filter is proposed to handle those images corrupted not only by single layer noise. The adaptive threshold median filter （ATMF） has been developed by combining the adaptive median filter （AMF） and two dynamic thresholds. Because of the dynamic threshold being used, the ATMF is able to balance the removal of the multiple-impulse noise and the quality of image. Comparison of the proposed method with traditional median filters is provided. Some visual examples are given to demonstrate the performance of the proposed filter.

QRS waves detection algorithm based on positive-negative adaptive threshold method

In order to accurately detect the occasional negative R waves in electrocardiography （ECG） signals, the positive-negative adaptive threshold method is adopted to determine the positive R waves and the negative R waves, according to difference characteristics of ECG signals. The Q and S waves can then be accurately positioned based on the basic characteristics of QRS waves. Finally, the algorithm simulation is made based on the signals from MIT-BIH database with MATLAB. The ex- perimental results show that the algorithm can improve the detection accuracy rate to 99. 91% and o- vercome the problem of larger computation load for wavelet transform and other methods, so the al- gorithm is suitable for real-time detection.

Detection and threshold-adaptive segmentation of farmland residual plastic film images based on CBAM-DBNet

Robust, accurate, and fast monitoring of residual plastic film (RPF) pollution in farmlands has great significance. Based on CBAM-DBNet, this study proposed a threshold-adaptive joint framework for identifying the RPF on farmland surfaces and estimating its coverage rate. UAV imaging was used to gather images of the RPF from several locations with various soil backgrounds. RPFs were manually labeled, and the degree of RPF pollution was defined based on the RPF coverage rate. Combining differentiable binarization network (DBNet) with the convolutional block attention module (CBAM), whose feature extraction module was improved. A dynamic adaptive binarization threshold formula was defined for segmenting the RPF’s approximate binary map. Regarding the RPF image detection branch, the CBAM-DBNet exhibited a precision (P) value of 85.81%, a recall (R) value of 82.69%, and an F1-score (F1) value of 84.22%, which was 1.09 percentage points higher than the DBNet in the comprehensive index F1 value. For the RPF image segmentation branch, using CBAM-DBNet to segment the RPF image combined with an adaptive binarization threshold formula. Subsequently, the mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute error (MAE) of the prediction of RPF’s coverage rate were 0.276, 0.366, and 0.605, respectively, outperforming the DBNet and the Iterative Threshold method. This study provides a theoretical reference for the further development of evaluation technology for RPF pollution based on UAV imaging.

Improving Video Watermarking through Galois Field GF(2^(4)) Multiplication Tables with Diverse Irreducible Polynomials and Adaptive Techniques

Video watermarking plays a crucial role in protecting intellectual property rights and ensuring content authenticity.This study delves into the integration of Galois Field(GF)multiplication tables,especially GF(2^(4)),and their interaction with distinct irreducible polynomials.The primary aim is to enhance watermarking techniques for achieving imperceptibility,robustness,and efficient execution time.The research employs scene selection and adaptive thresholding techniques to streamline the watermarking process.Scene selection is used strategically to embed watermarks in the most vital frames of the video,while adaptive thresholding methods ensure that the watermarking process adheres to imperceptibility criteria,maintaining the video's visual quality.Concurrently,careful consideration is given to execution time,crucial in real-world scenarios,to balance efficiency and efficacy.The Peak Signal-to-Noise Ratio(PSNR)serves as a pivotal metric to gauge the watermark's imperceptibility and video quality.The study explores various irreducible polynomials,navigating the trade-offs between computational efficiency and watermark imperceptibility.In parallel,the study pays careful attention to the execution time,a paramount consideration in real-world scenarios,to strike a balance between efficiency and efficacy.This comprehensive analysis provides valuable insights into the interplay of GF multiplication tables,diverse irreducible polynomials,scene selection,adaptive thresholding,imperceptibility,and execution time.The evaluation of the proposed algorithm's robustness was conducted using PSNR and NC metrics,and it was subjected to assessment under the impact of five distinct attack scenarios.These findings contribute to the development of watermarking strategies that balance imperceptibility,robustness,and processing efficiency,enhancing the field's practicality and effectiveness.

Implementation of Adaptive Wavelet Thresholding Denoising Algorithm Based on DSP

By utilizing the capability of high-speed computing,powerful real-time processing of TMS320F2812 DSP,wavelet thresholding denoising algorithm is realized based on Digital Signal Processors.Based on the multi-resolution analysis of wavelet transformation,this paper proposes a new thresholding function,to some extent,to overcome the shortcomings of discontinuity in hard-thresholding function and bias in soft-thresholding function.The threshold value can be abtained adaptively according to the characteristics of wavelet coefficients of each layer by adopting adaptive threshold algorithm and then the noise is removed.The simulation results show that the improved thresholding function and the adaptive threshold algorithm have a good effect on denoising and meet the criteria of smoothness and similarity between the original signal and denoising signal.

Adaptive feature extraction method for capsule endoscopy images

The traditional feature-extraction method of oriented FAST and rotated BRIEF(ORB)detects image features based on a fixed threshold;however,ORB descriptors do not distinguish features well in capsule endoscopy images.Therefore,a new feature detector that uses a new method for setting thresholds,called the adaptive threshold FAST and FREAK in capsule endoscopy images(AFFCEI),is proposed.This method,first constructs an image pyramid and then calculates the thresholds of pixels based on the gray value contrast of all pixels in the local neighborhood of the image,to achieve adaptive image feature extraction in each layer of the pyramid.Subsequently,the features are expressed by the FREAK descriptor,which can enhance the discrimination of the features extracted from the stomach image.Finally,a refined matching is obtained by applying the grid-based motion statistics algorithm to the result of Hamming distance,whereby mismatches are rejected using the RANSAC algorithm.Compared with the ASIFT method,which previously had the best performance,the average running time of AFFCEI was 4/5 that of ASIFT,and the average matching score improved by 5%when tracking features in a moving capsule endoscope.

Adaptive median threshold algorithm used in FDIS of DSSS receivers

For direct sequence spread spectrum （DSSS） receivers, the capability of rejecting narrow-band interference can be significantly improved by a process of frequency-domain interference suppression （FDIS）. The key issue of this process is how to determine a threshold to eliminate interference in the frequency domain, which has been extensively studied. However, these previous methods are tedious or very complex. A simple and ef- ficient algorithm based on medians is proposed. The elimination threshold is only related to the median by a scale factor, which can be obtained by the numerical analysis. Simulation results show that the algorithm provides excellent narrow-band interfer- ence suppression while only slightly degrading the signal-to-noise ratio （SNR）. A one-pass algorithm using logarithmic segmentation is further derived to estimate medians with low computational complexity. Finally, the FDIS is implemented in a field programmable gate array （FPGA） of Xilinx. Experiments are carried out by connecting the FDIS FPGA to a DSSS receiver, and the results show that the receiver has an effective countermeasure for a 60 dB interference-to-signal ratio （ISR）.

Water Gauge Image Denoising Model Based on Improved Adaptive Total Variation

As an important part of water level warning in water conservancy projects,often due to the influence of environmental factors such as light and stains,the acquired water gauge images have sticky,broken and bright spot conditions,which affect the identification of water gauges.To solve this problem,a water gauge image denoising model based on improved adaptive total variation is proposed.Firstly,the regular term exponent in the adaptive total variational equation is changed to an inverse cosine function;secondly,the differential curvature is used to distinguish the image noise points and increase the smoothing strength at the noise points;finally,according to the characteristics of the gradient mode and adaptive gradient threshold after Gaussian filtering,the New model can adaptively denoise in the smooth area and protect the edge area,so as to have the characteristics of both edge-preserving denoising.The experimental results show that the new model has a great improvement in image vision,higher iteration efficiency and an average increase of 1.6 dB in peak signal-to-noise ratio,and an average increase of 9%in structural similarity,which is more beneficial to practical applications.

Research on Defect Detection of Wind Turbine Blades Based on Morphology and Improved Otsu Algorithm Using Infrared Images

To address the issues of low accuracy and high false positive rate in traditional Otsu algorithm for defect detection on infrared images of wind turbine blades(WTB),this paper proposes a technique that combines morphological image enhancement with an improved Otsu algorithm.First,mathematical morphology’s differential multi-scale white and black top-hat operations are applied to enhance the image.The algorithm employs entropy as the objective function to guide the iteration process of image enhancement,selecting appropriate structural element scales to execute differential multi-scale white and black top-hat transformations,effectively enhancing the detail features of defect regions and improving the contrast between defects and background.Afterwards,grayscale inversion is performed on the enhanced infrared defect image to better adapt to the improved Otsu algorithm.Finally,by introducing a parameter K to adjust the calculation of inter-class variance in the Otsu method,the weight of the target pixels is increased.Combined with the adaptive iterative threshold algorithm,the threshold selection process is further fine-tuned.Experimental results show that compared to traditional Otsu algorithms and other improvements,the proposed method has significant advantages in terms of defect detection accuracy and reducing false positive rates.The average defect detection rate approaches 1,and the average Hausdorff distance decreases to 0.825,indicating strong robustness and accuracy of the method.

Adaptive Change Detection for Long-Term Machinery Monitoring Using Incremental Sliding-Window

Detection of structural changes from an opera- tional process is a major goal in machine condition moni- toring. Existing methods for this purpose are mainly based on retrospective analysis, resulting in a large detection delay that limits their usages in real applications. This paper presents a new adaptive real-time change detection algorithm, an extension of the recent research by combin- ing with an incremental sliding-window strategy, to handle the multi-change detection in long-term monitoring of machine operations. In particular, in the framework, Hil- bert space embedding of distribution is used to map the original data into the Re-producing Kernel Hilbert Space （RK_HS） for change detection; then, a new adaptive threshold strategy can be developed when making change decision, in which a global factor （used to control the coarse-to-fine level of detection） is introduced to replace the fixed value of threshold. Through experiments on a range of real testing data which was collected from an experimental rotating machinery system, the excellent detection performances of the algorithm for engineering applications were demonstrated. Compared with state-of- the-art methods, the proposed algorithm can be more suitable for long-term machinery condition monitoring without any manual re-calibration, thus is promising in modern industries.

Adaptive Segmentation Based on Local Image Attribute

The problem of automatic threshold selection is considered.After a brief discussion of several available techniques,an adaptive thresholding technique is proposed.It is based on the local edge information which can be computed without histogramming the gray level value of the image.This method is specifically designed to deal with gradual shading situation.The effectiveness of the method is shown on a practical example.

An improved ViBe algorithm based on adaptive detection of moving targets

There exists a Ghost region in the detection result of the traditional visual background extraction(ViBe)algorithm,and the foreground extraction is prone to false detection or missed detection due to environmental changes.Therefore,an improved ViBe algorithm based on adaptive detection of moving targets was proposed.Firstly,in the background model initialization process,the real background could be obtained by setting adjusting parameters in mean background modeling,and the ViBe background model was initialized by using the background.Secondly,in the foreground detection process,an adaptive radius threshold was introduced according to the scene change to adaptively detect the foreground.Finally,mathematical morphological close operation was used to fill the holes in the detection results.The experimental results show that the improved method can effectively suppress the Ghost region and detect the foreground target more completely under the condition of environmental changes.Compared with the traditional ViBe algorithm,the detection accuracy is improved by more than 10%,the false detection rate and the missed detection rate are reduced by 20% and 7% respectively.In addition,the improved method satisfies the real-time requirements.