Wavelet Modulus Research Articles

Evolutionary intelligence for breast lesion detection in ultrasound images: A wavelet modulus maxima and SVM based approach

Intelligent lesion detection system for medical ultrasound images are aimed at reducing physicians’ effort during cancer diagnosis process. Automatic separation and classification of tumours in ultrasound images is challenging owing to the low contra

Energy Storage State Identification Of Circuit Breaker Based On Fast Extraction Of Interval Feature Of Vibration Signal

Aiming at the problems of slow feature extraction and poor real-time diagnosis of some mainstream state identification methods in actual operation, a fast feature extraction method of vibration signal interval is proposed to identify the energy storage state of circuit breaker. Firstly, according to the kurtosis wavelet modulus maxima, the starting point of the energy storage state of the circuit breaker is detected. Then, the signal envelope sum is extracted as the feature vector by marking the obvious range of envelope amplitude difference through KS test, and the feature is filtered and dimensionally reduced by the releiff-sfs method to get the optimal feature subset. At last, the fuzzy c-means clustering (KFCM) is used to pre classify the features to obtain the optimal hyperplane with the least risk, and support vector machine (SVM) is used to establish the training model for state identification. The experimental results show that the energy storage state identification algorithm proposed in this paper only needs 0.2S to extract features on the premise of ensuring the accuracy, which is of great research value for on-line monitoring of circuit breakers.

Automatic extraction of lineaments based on wavelet edge detection and aided tracking by hillshade

Lineaments refer to the linear or curvilinear textures on remote sensing image, whose general spatial distribution characteristics are often the response of deep geological sturcture at the surface. Firstly, we use wavelet modulus maximum transformation to detect the edges with 4 scales on Landsat – 8 OLI B5 image and analyze their multi-scale characteristics. As the result, it is determined that the optimal scale of edge detection is 4, and the outline that consist of the edge pixels is roughly corresponding to the geological structure of mine area. Thus the incomplete lineaments have been extracted by using the 2D otsu algorithm. Secondly, the hillshade map generated based on DEM is processed to generate binarized linear shadow. Finally, the linear shadow is superimposed on the lineaments preliminarily extracted to obtain the optimized lineaments. Experiment results show that, based on the method, there are some deformation and displacement between the lineaments extracted and the actual geological structure, and it fail to effectively extract Qilinchang Fault, but lineaments are in good correspondence with Kuangshanchang Fault, Dongtou Fault and Niulan River Fault, which are basically in accord with the geological structure framework of the mine area.

Deformation monitoring of similar material model based on point cloud feature extraction

Three-dimensional (3D) laser scanning technology can reconstruct target models and analyse structural characteristics, and extract complex geometric contents of target structures. Therefore, it is significant to apply 3D laser scanning technology to construct 3D digital models of the monitored objects and to extract their features to judge their movements. This paper presents deformation monitoring method of 3D point cloud based on image information. First, point cloud was first converted into a two-dimensional intensity image. Topological relations between each point can be determined clearly through the intensity image. Second, based on the wavelet modulus maxima technology, the characteristics of two-dimensional intensity images are extracted and the corresponding feature point cloud data are obtained. Finally, According to the feature of the point cloud extracted, the change is calculated to determine the deformation of the point cloud object. An experiment was conducted by using a FOCUS3D laser scanner of FARO. The experiment results further verify that the method yields a good effect of deformation monitoring.

Fault location in multilateral distribution network with electric vehicle charging load

AbstractIn a multilateral distribution network, faults at different locations can generate the same voltage and current at the substation; hence, the calculated fault location can correspond to several places in the network. To avoid this major problem, a two‐terminal travelling wave‐based fault location method is presented in this article, which at first identifies the faulted lateral in the distribution network and then estimates the exact fault location. The proposed fault location scheme uses wavelet energy entropy of the recorded three‐phase current signals and the magnitude of ground mode component wavelet modulus maxima (WMM) for fault detection and fault type identification in the network. After that, the faulted lateral identification followed by the exact fault location is performed using the aerial mode component WMM of the fault‐generated current transients. Since the integration of electric vehicle (EV) charging load is increasing day by day to the distribution network, the performance of the proposed algorithm is tested with EV charging load along with distributed generation integrated into the distribution network. Modeling and simulation of the distribution network and EV load are performed in MATLAB/SIMULINK. The simulation results show that the proposed method can accurately locate fault under different operating conditions.

The Half-Sine Method: A New Accurate Location Method Based on Wavelet Transform for Transmission-Line Protection from Single-Ended Measurements

In this work, a new and accurate method based on the wavelet transform is proposed for fault location in transmission-line systems. The proposed wavelet method consists of the analysis of the transient signal measured at a single end of the transmission line. Aerial current modes are used, and zero modes are included in the fault-detection scheme for low fault-inception angles. The fault distance is evaluated using the wavelet modulus maxima technique and a method based on the response to a half-sine voltage is proposed to overcome drawbacks arising from the limited sampling frequency and low fault-inception angle. The fault distance is calculated using the difference between the time when a 100 kHz half-sine signal is sent and the time when the derivative signal is received. The proposed algorithm is tested considering harmonic distortion and varying fault resistance, ground resistivity, location and inception angle. The high accuracy of the proposed algorithm is obtained even for faults close to the bus and low inception angles.

Stability analysis for SiC grinding based upon harmonic wavelet and Lipschitz exponent

In order to effectively identify the stability degree of grinding and obtain better surface integrity of machined workpiece in grinding application, in this paper, a quantitative assessment approach based on harmonic wavelet and Lipschitz exponent is proposed, using silicon carbide as an example. To begin with, the Lipschitz exponent is defined and calculated by harmonic wavelet modulus maxima (HWMM) and linear regression algorithm, respectively. Then the Kurtosis based on Lipschitz exponent (KLE) is defined as a quantitative indicator. In grinding experiment, the acceleration signals and grinding force signals of the X/Y directions are selected, and experimental parameters including wheel speed, workpiece speed and grinding depth are employed to investigate the grinding stability in grinding stage. Experimental results show that the grinding stability is relatively worse at low wheel speed, low-high workpiece speed and large grinding depth in terms of KLE index, which may provide a new guidance to explore optimized grinding parameters and thus obtain good surface integrity in engineering applications.

Joint Time–Frequency Scattering

In time series classification and regression, signals are typically mapped into some intermediate representation used for constructing models. Since the underlying task is often insensitive to time shifts, these representations are required to be time-shift invariant. We introduce the joint time-frequency scattering transform, a time-shift invariant representation which characterizes the multiscale energy distribution of a signal in time and frequency. It is computed through wavelet convolutions and modulus non-linearities and may therefore be implemented as a deep convolutional neural network whose filters are not learned but calculated from wavelets. We consider the progression from mel-spectrograms to time scattering and joint time-frequency scattering transforms, illustrating the relationship between increased discriminability and refinements of convolutional network architectures. The suitability of the joint time-frequency scattering transform for time-shift invariant characterization of time series is demonstrated through applications to chirp signals and audio synthesis experiments. The proposed transform also obtains state-of-the-art results on several audio classification tasks, outperforming time scattering transforms and achieving accuracies comparable to those of fully learned networks.

Upfc Compensated Transmission Line Fault Location Based on Travelling Wave Theory and Wavelet Modulus Maxima

Location of fault in a transmission line which includes flexible AC transmission system (FACTS) devices is a difficult task. This paper comprises of unified power flow controller (UPFC). The implementation control technique for UPFC devices set up dynamic problems in power system that must be reflecting on issues related to power system protection. The occurrence of a fault in the fault loop containing a UPFC affects transient and steady-state parameters of voltage and current in the power system. In this paper the identification of fault section before or after UPFC for a transmission line with wavelet fuzzy logic system discriminator. Wavelet technique extracts features, and the faulty location is found when the normalized features are fed to the fuzzy logic system. As soon as the fault section is located, the control shifts to that and is analyzed basing on the representation of the traveling wave theory through wavelet modulus maxima. This criterion detects the faulty section with respect to FACTS device very accuratelyin terms of distance from relay point. The above-said method is simulated for various fault situations and locations with wide changes in the conditions of operation in the presence of UPFC.

Research on adaptive modulus maxima selection of wavelet modulus maxima denoising

Microelectromechanical system (MEMS) accelerometers are small in size, low in power consumption and easily integrated. They can be used in intelligent hydraulic components to obtain the dynamic acceleration of the system and monitor the operating status of the system. In this study, based on the noise characteristics of MEMS accelerometer output signals, a wavelet modulus maxima denoising algorithm based on adaptive threshold estimation is proposed, in which SureShrink threshold estimation is used to choose the right modulus maxima. Then, the signal-to-noise ratio and mean-square-error are used as the evaluating indices of the denoising performance for the wavelet modulus maxima denoising based on SureShrink threshold estimation, the wavelet modulus maxima denoising based on BayesShrink threshold estimation and the normal wavelet modulus maxima denoising. The simulation results show that the wavelet modulus maxima denoising based on SureShrink threshold estimation has better denoising performance than the normal modulus maxima denoising and the wavelet modulus maxima method based on BayesShrink threshold estimation, and effectively eliminates the noise of MEMS accelerometer output signals.

Fault location in distribution network using travelling waves

PurposeAn electrical power distribution network is expected to deliver uninterrupted power supply to the customers. The disruption in power supply occurs whenever there is a fault in the system. Therefore, fast fault detection and its precise location are necessary to restore the power supply. Several techniques are proposed in the past for fault location in distribution network but they have limitations as their fault location accuracy depends on system conditions. The purpose of this paper is to present a travelling wave-based fault location method, which is fast, accurate and independent of system conditions.Design/methodology/approachThis paper proposes an effective method for fault detection, classification and location using wavelet analysis of travelling waves for a multilateral distribution network embedded with distributed generation (DG) and electric vehicle (EV) charging load. The wavelet energy entropy (WEE) is used for fault detection and classification purpose, and wavelet modulus maxima (WMM) of aerial mode component is used for faulted lateral identification and exact fault location.FindingsThe proposed method effectively detects and classifies the faults, and accurately determines the exact fault location in a multilateral distribution network. It is also found that the proposed method is robust and its accuracy is not affected by the presence of distributed generation and electric vehicle charging load in the system.Originality/valueTravelling wave based method for fault location is implemented for a multilateral distribution network containing distributed generation and electric vehicle load. For the first time, a fault location method is tested in the presence of EV charging load in distribution network.

Gear fault diagnosis based on a new wavelet adaptive threshold de-noising method

PurposeThis 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/approachA 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.FindingsThe 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/valueA 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.

Simulation analysis of low strain dynamic testing of pile with inhomogeneous elastic modulus

Low strain dynamic testing is an important nondestructive testing method in the engineering. However, the pile foundation material is usually assumed as having a uniform elastic modulus in low strain simulations. In this paper, we consider the elastic modulus of concrete as having an inhomogeneous elastic modulus that is described by the Weibull distribution model. An explicit algorithm was adopted in order to solve the model. The finite element method (FEM) was used to simulate the low strain dynamic test of a 3D pile. The response velocity characteristics of different shape parameters were obtained using this method, and the Daubechies wavelet transform was used to analyze the characteristics of the wavelet modulus. The result shows that simulation response velocity has a correlation with the different homogeneity of the elastic modulus.

Diagnosis of Compound Fault Using Sparsity Promoted-Based Sparse Component Analysis.

Compound faults often occur in rotating machinery, which increases the difficulty of fault diagnosis. In this case, blind source separation, which usually includes independent component analysis (ICA) and sparse component analysis (SCA), was proposed to separate mixed signals. SCA, which is based on the sparsity of target signals, was developed to sever the compound faults and effectively diagnose the fault due to its advantage over ICA in underdetermined conditions. However, there is an issue regarding the vibration signals, which are inadequately sparse, and it is difficult to represent them in a sparse way. Accordingly, to overcome the above-mentioned problem, a sparsity-promoted approach named wavelet modulus maxima is applied to obtain the sparse observation signal. Then, the potential function is utilized to estimate the number of source signals and the mixed matrix based on the sparse signal. Finally, the separation of the source signals can be achieved according to the shortest path method. To validate the effectiveness of the proposed method, the simulated signals and vibration signals measured from faulty roller bearings are used. The faults that occur in a roller bearing are the outer-race flaw, the inner-race flaw and the rolling element flaw. The results show that the fault features acquired using the proposed approach are evidently close to the theoretical values. For instance, the inner-race feature frequency 101.3 Hz is very similar to the theoretical calculation 101 Hz. Therefore, it is effective to achieve the separation of compound faults utilizing the suggest method, even in underdetermined cases. In addition, a comparison is applied to prove that the proposed method outperforms the traditional SCA method when the vibration signals are inadequate.

Power Disturbances Analysis Based on Wavelet Modulus Maxima and Lipschitz Regularity

In the practical application of power grid operation and engineering, it will be inevitable for power grid to have some disturbances, such as sudden power off signal, voltage swell signal, voltage sag signal, voltage spike, voltage transient oscillation signal and signals mixed with various disturbances occurring in the same time. This paper utilizes wavelet modulus maximum transform and Lipchitz index to extra modulus maximum at multiple scales. By calculating the effective value of the modulus maxima, we can quantitatively analyze various kinds of interference when they occur and their values.

Multicomponent decomposition by wavelet modulus maxima and synchronous detection

There are various signal decomposition methods, but none of them is satisfactory and all have their own drawbacks. It is worth exploring a new signal decomposition approach with better performance for processing the complex vibration signals. By employing the wavelet modulus maxima and synchronous detection, a novel multicomponent signal decomposition method is proposed in this paper. Firstly, the wavelet modulus maxima of a multicomponent signal are calculated by optimized complex wavelet transform, then the highest instantaneous frequency (IF) is extracted by searching the wavelet modulus maxima with the smallest scales at all the time instants. With the obtained IF, the synchronous detection method is used to calculate the phase offset and the instantaneous amplitude. It follows that the corresponding component with highest IF can be reconstructed. Then the used wavelet modulus maxima in this iteration are removed from the wavelet scalogram and the next IF is sequentially computed. By repeating this process, all components are successively separated from high frequency to low frequency. Compared with ensemble empirical mode decomposition and Hilbert vibration decomposition, it has been proved by three typical multicomponent signals with different noise intensity that the proposed signal decomposition method has higher accuracy, frequency resolution and is more robust to noises. Moreover, the application results further show that the proposed method can be more effectively applied to fault diagnosis of gearboxes, especially when the operating condition is varying or the fault feature is weak.

A Hybrid Wavelet-Based Method for the Peak Detection of Photoplethysmography Signals.

The noninvasive peripheral oxygen saturation (SpO2) and the pulse rate can be extracted from photoplethysmography (PPG) signals. However, the accuracy of the extraction is directly affected by the quality of the signal obtained and the peak of the signal identified; therefore, a hybrid wavelet-based method is proposed in this study. Firstly, we suppressed the partial motion artifacts and corrected the baseline drift by using a wavelet method based on the principle of wavelet multiresolution. And then, we designed a quadratic spline wavelet modulus maximum algorithm to identify the PPG peaks automatically. To evaluate this hybrid method, a reflective pulse oximeter was used to acquire ten subjects' PPG signals under sitting, raising hand, and gently walking postures, and the peak recognition results on the raw signal and on the corrected signal were compared, respectively. The results showed that the hybrid method not only corrected the morphologies of the signal well but also optimized the peaks identification quality, subsequently elevating the measurement accuracy of SpO2 and the pulse rate. As a result, our hybrid wavelet-based method profoundly optimized the evaluation of respiratory function and heart rate variability analysis.

Computer Aided Segmentation of Breast Ultrasound Images Using Scale Invariant Feature Transform (SIFT) and Bag Of Features

Automatic and precise segmentation of breast ultrasound (BUS) image is a challenging task. The proposed method successfully implemented a segmentation algorithm by region growing from a seed point based on texture features generated by Gray Level Co-occurrence Matrix (GLCM). The seed points generated by canny edge detection and wavelet modulus maxima methods are refined by Support Vector Machine (SVM) trained by Scale Invariant Feature Transform (SIFT). The segmented images are compared with ground truth images and True Positive Rate (TPR) of 90.1% and average SI (Similarity Index) of 0.85 demonstrates that the proposed method can segment the tumor regions efficiently and accurately.

Rahbin: A quadcopter unmanned aerial vehicle based on a systematic image processing approach toward an automated asphalt pavement inspection

Automatic inspection of pavement cracking is a critical issue in pavement management systems. In this study, a quadcopter-based digital imaging system is introduced for collecting pavement surface data over a distressed area for visual conditions interpretation. An aerial evaluation was carried out using a quadcopter unmanned aerial vehicle (QUAV), which is a device equipped with a set of automatic systems. Since QUAV flies autonomously and has high maneuverability, it is potentially useful in a variety of conditions particularly the positions dangerous for surveillance and reconnaissance. The main purpose of this work is to design a multi-stage system for QUAV image analysis consisting of image processing, threshold selection, and classification stages. The images are transformed into a new domain; then, an adaptive thresholding is applied to build the pattern of transformed cracks; and finally, the polar support vector machine (the PSVM) is applied for interpretation of crack distress. The PSVM is an automation procedure based on the support vector machine (SVM) classifier defined in the polar coordinate frame. A Mixture of Wavelet modulus and three-dimensional polar Radon transform (3DPRT) are used for feature generation. We show that the PSVM method can be successfully applied to classify the crack and is capable of providing new features about cracking distress, threshold selection and classification. In order to show the applicability and efficiency of the proposed system and method, a test was conducted applying a variety of pavement distresses. The experimental results demonstrate that the applied system provides reliable output. In addition, the comparison of the derived information with the on-site manual quantifications revealed the potentiality of the QUAV and multi-stage system for future practice.

A New Method for Appearance Quality Detection of Lens Module Based on Machine Vision

The lens module is widely used in many types of equipment, such as mobile phone, image recognition product, iPad, computer camera module, security product, car rear view module, industrial endoscope and medical endoscope product. In the process of appearance quality inspection of lens module, it is a problem that the inspection accuracy depends mainly on manual workers. To save the time and amount of this quality detection labor, it’s necessary for us to design the lens module’s appearance quality automatic inspection system. This paper proposes a subpixel-accurate edge detection algorithm based on wavelet transform with the cubic spline interpolation for the lens module’s appearance quality inspection system. Firstly, calculate the wavelet modulus maximum, and detect a pixel-accurate edge. Then apply the cubic spline interpolation to obtain subpixel-accurate edge at the side of the pixel-accurate edge. Finally, an industrial measurement method using this subpixel-accurate edge detection algorithm is studied, and some experiments are conducted to demonstrate the effectiveness of the lens module’s appearance quality inspection system. Comparing with the traditional methods, the lens module’s appearance quality inspection system has a good anti-noise performance and stable inspection accuracy.