Mathematical Morphology Filtering Research Articles

Day-Ahead Dynamic Assessment of Consumption Service Reserve Based on Morphological Filter

With the development goal of a low-cost and low-carbon reserve market, this paper proposes a dynamic assessment method for day-ahead consumption service reserve demand considering the forecast error of uncertainty power. The iterative self-organizing data analysis techniques algorithm is adopted to cluster the historical actual power into typical scenarios. In addition, the online matching between the typical scenario and the day-ahead forecast power is conducted. In order to realize the hierarchical quantification of reserve demand, the reserve resources in the whole power system are classified according to their response time. Furthermore, the mathematical morphology filter based on the structural elements that are consistent with the response time of the hierarchical reserve resources is initially applied to decompose the historical forecast error of the matched scenarios. The simulation results verify that the proposed dynamic assessment effectively reduces the reserve cost on the basis of being able to cope with multi-time-scale power fluctuations.

An adaptive feature extraction technique for ship-radiated noise based on weighted multiscale mathematical morphological filtering

Aiming at the problem of feature extraction of ship-radiated noise in complex marine environment, an adaptive weighted multiscale mathematical morphological filtering method is proposed. The weight coefficients of the structure elements of different scales in multiscale morphological filtering are determined adaptively by an improved particle swarm optimization algorithm, and the Teager energy kurtosis is used as the evaluation index of the filtered optimal signal to provide optimized weights for each scale. Finally, the optimized multiscale mathematical morphology filter with selective adaptive weights is bound and applied to the feature extraction of ship-radiated noise. The analysis results of simulated signal and measured ship-radiated noise show that this method has strong noise suppression and feature extraction ability, and the calculation is simple and fast, which provides an effective method for ship feature extraction.

State of the art on vibration signal processing towards data‐driven gear fault diagnosis

Gear fault diagnosis (GFD) based on vibration signals is a popular research topic in industry and academia. This paper provides a comprehensive summary and systematic review of vibration signal-based GFD methods in recent years, thereby providing insights for relevant researchers. The authors first introduce the common gear faults and their vibration signal characteristics. The authors overview and compare the common feature extraction methods, such as adaptive mode decomposition, deconvolution, mathematical morphological filtering, and entropy. For each method, this paper introduces its idea, analyses its advantages and disadvantages, and reviews its application in GFD. Then the authors present machine learning-based methods for gear fault recognition and emphasise deep learning-based methods. Moreover, the authors compare different fault recognition methods. Finally, the authors discuss the challenges and opportunities towards data-driven GFD.

De-noising magnetotelluric data using variational mode decomposition combined with mathematical morphology filtering and wavelet thresholding

De-noising magnetotelluric (MT) data has become a key research topic closely related to the application of the MT method itself. Variational mode decomposition (VMD), a newly developed non-recursive signal decomposition method, has been applied to the denoising of MT data. However, some high-frequency noise may remain after VMD denoising. Moreover, the useful low-frequency signal and large-scale noise cannot always be easily separated, resulting in the loss of useful low-frequency signal components during signal reconstruction. To overcome this problem, a new time series editing strategy is proposed, which combines wavelet thresholding (WT) and mathematical morphology filtering (MMF) for further processing of the VMD output. WT is used to remove the remaining high-frequency noise from the retained signal after VMD, while MMF is utilized to extract the useful low-frequency signal components from the signal rejected by VMD. Finally, the WT and MMF outputs are merged to obtain the final denoising results. The proposed method was evaluated using a simulated dataset and two real datasets collected in the Hami Basin of East Tianshan, China. The simulated data tests showed that the proposed method has superior denoising ability compared to other time-domain methods, while the real data study showed that the quality of MT responses obtained from the proposed method is superior to that from conventional methods, especially for the low-frequency band.

A Generalized Seismic Attenuation Compensation Operator Optimized by 2-D Mathematical Morphology Filtering

In this work, a Robust Worst-Case (RWC) estimation is presented for recovering sparse reflectivity series from uniformly quantized seismic signals. First, the <i>Orthogonal Matching Pursuit</i> (OMP) algorithm is applied on a quantized seismic trace. A set of conservative estimates of the reflectivity impulses and a dimension-reduced system are accordingly obtained. Second, the error induced by the quantization is modeled as a systemic uncertainty in a multiplication way. This modeling imposes a greater model uncertainty on the estimated reflectivity impulses with small values and vice versa. Finally, a RWC deconvolution scheme is designed for the dimension-reduced system. Among those roughly estimated impulses from the OMP algorithm, the ones statistically less affected by the quantization process are assigned with higher confidence weights and the ones statistically with higher magnitudes of quantization error are assigned with lower confidence weights. By rescaling the quantization error, the proposed scheme significantly increases the robustness of the solution to the quantization error and hence better improves the visual saliency of seismic signals than that of the OMP algorithm. This scheme is tested on both synthetic and real seismic data and the performance is evaluated by compared to that of the OMP algorithm. The results shows that the new scheme significantly outperforms the OMP algorithm by observing the following two aspects: first, the falsely or overly estimated impulses are significantly suppressed in the RWC estimation compared with that of the OMP algorithm, and second, the RWC estimation exhibits enhanced robustness to the change of the quantization interval.

Fault detection, location, and classification method on compressed air energy storages based inter‐connected micro‐grid clusters using traveling‐waves, current injection method, on‐line wavelet, and mathematical morphology

A new current injection technique and traveling-waves-based fast and accurate method for fault detection, location, and classification on interconnected compressed air energy storages based micro-grid clusters presented in this paper. The compressed air energy storages based micro-grid clusters increase due to some issues such as DC sources and loads expanding, energy-saving, and the power quality developing. It is necessary to recognize the fault type and location to continue service and prevent outages expansion. In this method, a circuit kit joined to the network. Fault detection is done by analyzing the coupled kits currents and the traveling-waves of the fault current and applying them to a mathematical morphology filter, in the Fault time. Determine the fault location and type done by using a mathematical morphology filter, circuit equations, and current calculations. DC pole to ground and pole to pole faults in lines, loads, and resources are considered as MTDC microgrid disturbances. The presented method was tested in a DC interconnected micro-grid clusters connected to several energy storages and renewable resources with many faults. The results represent the validity of the proposed method. The main advantages of the proposed fault location and classification technique are higher speed and accuracy than conventional approaches. This method robustly works to variations in fault resistance, sampling frequency, and operates very fine in high impedance fault conditions.

A feature extraction method of ship-radiated noise based on mathematical morphological filtering

In order to extract feature of ship signal more effectively, we propose a new approach for mathematical morphological filtering based on the morphological features. Mathematical morphological filter is a new nonlinear filter, which can effectively extract the edge contour and shape characteristics. The stimulation signal is processed by mathematical morphological filtering of different structure elements, which confirms the effect of morphological filtering on suppressing noise and preserving the nonlinear characteristics. Using flat structure element, the measured ship-radiated noise signals are processed by average filter, and the filtered signals are analyzed on the frequency spectrum. Compared with other filters, the result shows that the mathematical morphological filtering can successfully extract the effective information from the ship-radiated noise signals.

A signal analysis and identification scheme for an online multiphase micron-sized particle analyzer system

Online microparticle detection is of utmost importance for industrial production. This paper proposes a signal processing and feature identification strategy to achieve particle size statistics for online measurement in a three-phase stirred tank reactor based on the electrical sensing zone (ESZ) method. Signal denoising and de-interference are achieved using the wavelet soft threshold method combined with mathematical morphological filtering. Pulse selection is implemented using pulse width limiting conditions. The key features that distinguish the pulse waveforms are defined based on the differences in the motion characteristics of the different types of particles through the aperture. Finally, the unsupervised classification algorithm balanced iterative reducing and clustering using hierarchies clustering is employed to distinguish the pulsed features between hard particles and bubbles. The results show that the particle size distribution identified by this strategy agrees with offline measurements indicating the effectiveness of the scheme. The effects of electromagnetic noise and the interference of small bubbles that approximate the particle size in solution in the online, in-situ measurement task are solved. This study scheme has a guiding and facilitating role in applying the ESZ principle to the industrial online measurement environment.

Research on current harmonic suppression of PMSM based on mathematical morphology

Due to the existence of power converter dead time, flux harmonics and other nonlinear factors, the motor phase current contains a large number of high-order harmonics, so the electromagnetic torque generated by the interaction of current and flux also contains various orders of harmonics. In order to eliminate current harmonics, mathematical morphology filter is proposed to suppress current harmonics. However, the single-scale morphological filtering has shortcomings in preserving information details. In order to make up for the deficiency of the traditional single-scale morphological filtering in preserving information details, a weighted multi-scale mathematical morphology method is proposed to suppress the current harmonics.

Diffraction imaging using a mathematical morphological filter with a time-varying structuring element

Diffraction imaging is an important technique for high-resolution imaging because of the close relationship between diffractions and small-scale discontinuities. Therefore, we we have developed a diffraction imaging method using a mathematical morphological filter (MMF). In a common image gather, reflections have an evident energy band associated with the Fresnel zone and stationary point, whereas diffractions can be observed in a wide illumination direction and therefore have no energy band. Based on these phenomena, we analyze the amplitude distributions of the diffractions and reflections and develop a time-varying structuring element (SE) in the MMF. Based on the time-varying SE, our method can effectively suppress reflections and has the advantage of automatically preserving the diffractions energy near the stationary point. Numerical and field experiments demonstrate the efficient performance of the proposed method in imaging diffractions and obtaining high-resolution information.

Detection and Adaptive Video Processing of Hyperopia Scene in Sports Video

In the research of motion video, the existing target detection methods are susceptible to changes in the motion video scene and cannot accurately detect the motion state of the target. Moving target detection technology is an important branch of computer vision technology. Its function is to implement real-time monitoring, real-time video capture, and detection of objects in the target area and store information that users are interested in as an important basis for exercise. This article focuses on how to efficiently perform motion detection on real-time video. By introducing the mathematical model of image processing, the traditional motion detection algorithm is improved and the improved motion detection algorithm is implemented in the system. This article combines the advantages of the widely used frame difference method, target detection algorithm, and background difference method and introduces the moving object detection method combining these two algorithms. When using Gaussian mixture model for modeling, improve the parts with differences, and keep the unmatched Gaussian distribution so that the modeling effect is similar to the actual background; the binary image is obtained through the difference between frames and the threshold, and the motion change domain is extracted through mathematical morphological filtering, and finally, the moving target is detected. The experiment proved the following: when there are more motion states, the recall rate is slightly better than that of the VIBE algorithm. It decreased about 0.05 or so, but the relative accuracy rate increased by about 0.12, and the increase ratio is significantly higher than the decrease ratio. Departments need to adopt effective target extraction methods. In order to improve the accuracy of moving target detection, this paper studies the method of background model establishment and target extraction and proposes its own improvement.

Analysis and validation of mathematical morphology filters for single-ended fault localization in VSC-HVDC links

Fast and accurate fault detection and localization are important topics in voltage source converter-based high-voltage direct current transmission (VSC-HVDC) grids in order to isolate the fault after the first milliseconds of occurrence. Common signal processing methods used for detecting fault traveling wave (TW) peaks like Fourier transform, S-transform and wavelet-based techniques transform the signal into the frequency domain, requiring complicated computations. In this paper, the application of mathematical morphology (MM)-based filters for detecting and locating faults in VSC-HVDC links is studied. MM-based methods analyze the signal in the time domain and detect the wave peaks accurately. Multiple MM-based filters resulted from basic MM operators are presented and used for TW-based fault study in VSC-HVDC grids. Several fault cases are applied to the CIGRE VSC-HVDC model in PSCAD, and the MM-based scripts are written in MATLAB. The impact of different sizes and types of the structuring elements on the accuracy of peak detection is analyzed. The results show the accuracy of MM filters for detecting and locating fault transient in VSC-HVDC links. The proposed method gives accurate results for both low-impedance and high-impedance faults.

Surface Diffraction Noise Attenuation for Marine Seismic Data Processing With Mathematical Morphological Filtering

Surface diffraction noise (SDN) is a kind of coherent noise and pervasive in marine seismic data acquisition. This kind of noise is intractable during seismic data processing. We present a new method based on mesh scanning and mathematical morphological filtering (MMF) to attenuate the SDN in marine surveys. The idea of mesh scanning will be first put forward to get the energy-map of the survey. Compared with the traditional mesh scanning method, the energy-map computing formulas will be improved to protect the reflections from the recognition of SDNs, especially the shallow reflections. All the possible surface diffractors will be shown on the computed energy-map with strong energy. Then, a 2-D mathematical morphological closing operation will be innovatively used to get the upper envelope of the energy-map and reduce the number of the possible surface diffractors with a maximal value picking. After confirming all the possible surface diffractors on the energy-map, the travel-time curves of SDNs could be calculated easily according to the kinematic propagation law of seismic wave for all the related shot gathers and the MMF will be used to extract all the SDNs along the trajectories of them within appropriate time windows. Both a real 2-D and a real 3-D marine seismic data will be processed by the proposed method to show the positive effects in attenuating the SDNs.

Synchrophasor based islanding detection for microgrids using moving window principal component analysis and extended mathematical morphology

Extensive deployment of non-dispatchable renewable energy sources in microgrids (MGs) has led to frequent fault events, DG and line trips and is arduous to discriminate islanding events (IEs) from other transient conditions. For enhanced grid security, time synchronised data from phasor measurement units is utilised. However, data handling at the control centre is tedious as system variables monitored often exhibit non-linear characteristics that impose a problem to the operator in discriminating between islanding and non-IEs. To cope with this, a zero non-detection zone, reliable and precise islanding detection method for data-intensive grid-connected MG, is proposed. Voltage phasors, frequency and rate of change of frequency from various locations are processed through moving window principal component analysis (MWPCA) cascaded with extended mathematical morphological filter (EMMF). MWPCA reduces data dimensionality and Q statistics obtained are passed to EMMF, which acts as a non-linear filter. Further, the islanding detecting factor identified all IEs within the prescribed time limit with minimum false alarms. Accuracy, precision and reliability demonstrated by using a case study model using DIgSILENT are encouraging. It can be adopted by operators to run MG securely. Further, testing of the proposed method on a typical utility feeder shows promising results.

Traveling wave-based protection for SVC connected transmission lines using game theory

This paper proposes a novel single-end protection scheme for the compensated transmission line based on traveling waves and Game Theory. Initially, this study investigates the effects of Static VAR Compensator (SVC) on Traveling Waves (TW) passing through SVC location. Subsequently, it evaluates the impacts of SVC switching on conventional protections based on traveling wave theory. Following that, a single-end method to locate faults on compensated transmission lines with SVC is presented in this study. Moreover, Game theory was utilized to discriminate extracted TWs in this paper. In order to investigate the sensitivity of the proposed scheme regarding the extraction techniques of TW, two techniques, namely Mathematical Morphology Filter (MMF) and Discrete Wavelet Transform (DWT) have been used to process the signals in this study. The simulation studies using PSCAD software indicate that the proposed protection scheme is reliable and accurate for all possible types of faults on transmission lines with and without SVC. In addition, the proposed technique presents noise immunity.

Low-frequency noise attenuation in seismic and microseismic data using mathematical morphological filtering

SUMMARYLow-frequency noise is one of the most common types of noise in seismic and microseismic data. Conventional approaches, such as the high-pass filtering method, utilize the low-frequency nature and distinguish between signal and noise based on their different frequency contents. However, conventional approaches are limited or even invalid when the signal and noise shares the same frequency band. Moreover, high-pass filtering method will suppress not only low-frequency noise but also low-frequency signal when they overlap in a same frequency band, which is extremely important in the inversion process for building the subsurface velocity model. To overcome the drawbacks of conventional high-pass filtering approach, we developed a novel method based on the mathematical morphology theorem to separate signal and noise using their differences in morphological scale. We extracted empirical relation between morphological scale and frequency band so that the mathematical morphology based approach can be conveniently used in low-frequency noise attenuation. The proposed method is termed as the mathematical morphological filtering (MMF) method. We compare the MMF approach with high-pass filtering and empirical mode decomposition (EMD) approaches using synthetic, reflection seismic and microseismic examples. The various examples demonstrate that the proposed MMF method can preserve more low-frequency signal than the high-pass filtering approach, and is more efficient and causes fewer artefacts than the EMD approach.

A Between-Subject fNIRS-BCI Study on Detecting Self-Regulated Intention During Walking.

Most BCI (brain-computer interface) studies have focused on detecting motion intention from a resting state. However, the dynamic regulation of two motion states, which usually happens in real life, is rarely studied. Besides, popular within-subject methods also require an extensive and time-consuming learning stage when testing on a new subject. This paper proposed a method to discriminate dynamic gait- adjustment intention with strong adaptability for different subjects. Cerebral hemoglobin signals obtained from 30 subjects were studied to decode gait-adjustment intention. Cerebral hemoglobin information was recorded by using fNIRS (functional near infrared spectroscopy) technology. Mathematical morphology filtering was applied to remove zero drift and EWM (Entropy Weight Method) was used to calculate the average hemoglobin values over Regions of Interest (ROIs). The gradient boosting decision tree (GBDT) was utilized to detect the onset of self-regulated intention. A 2-layer-GA-SVM (Genetic Algorithm-Support Vector Machine) model based on stacking algorithm was further proposed to identify the four types of self-regulated intention (speed increase, speed reduction, step increase, and step reduction). It was found that GBDT had a good performance to detect the onset intention with an average AUC (Area Under Curve) of 0.894. The 2-layer-GA-SVM model boosted the average ACC (accuracy) of four types of intention from 70.6% to 84.4% ( p = 0.005 ) from the single GA-SVM model. Furthermore, the proposed method passed pseudo-online test with the average results as following: AUC = 0.883, TPR (True Positive Rate) = 97.5%, FPR (False Positive Rate) = 0.11%, and LAY (Detection Latency) = -0.52 ± 2.57 seconds for the recognition of gait-adjustment intention; ACC = 80% for the recognition of adjusted gait. The results indicate that it is feasible to decode dynamic gait-adjustment intentions from a motion state for different subjects based on fNIRS technology. It has a potential to realize the practical application of fNIRS-based brain-computer interface technology in controlling walking-assistive devices.

De-noising low-frequency magnetotelluric data using mathematical morphology filtering and sparse representation

De-noising the magnetotelluric (MT) data using the conventional time-series editing methods is at the risk of losing low-frequency signals, especially the signal below 1 Hz. To overcome this deficiency, we propose a combinatorial method based on sparse representation and mathematical morphology filtering. First, the effective low-frequency signal is reconstructed using the mathematical morphological filtering (MMF) method and protected. Then, the residual noisy signal of high frequency is sparsely decomposed using the subspace pursuit (SP) algorithm to obtain noise-free high-frequency MT signals. Finally, the effective low-frequency signal is added to the de-noised high-frequency signal to get the full-band MT data. We evaluate the proposed method using a synthetic data set and two real data sets collected in Qiadam Basin, the northeastern part of the Tibetan Plateau. Experimental results demonstrate that the presented approach can be used to remove different kinds of cultural noises while preserve the low-frequency signal below 1 Hz. The evaluation results also indicate that the proposed method is superior to the conventional methods in terms of the signal-to-noise ratio (SNR), reconstruction error (E) and normalized cross-correlation (NCC).

A combined mathematical morphology and extreme learning machine techniques based approach to micro-grid protection

Abstract This work introduces a smart differential protection scheme for a microgrid system using a nonlinear signal transformation named as ‘mathematical morphology (MM)’. Here, the mathematical morphology is used as a feature extraction technique. Thus, the three elementary MM filtering operators like erosion, dilation, and opening-closing-difference-filter (OCDF) are used to operate on the extracted phasor current signals and its symmetrical components for the extraction of differential feature vector. Further, the extracted feature vector is fed as an input to train and test two distinct extreme machine learning (ELM) classifiers meant for primary and backup protection. To justify and verify the better performance of the proposed method numerous fault and no-fault conditions are simulated by considering several operating conditions, such as topology of microgrid (radial/mesh) and mode of microgrid operation (islanding/grid-connecting). The experimental outcomes confirm the efficiency and reliability of the offered microgrid protection scheme (MPS) in diverse operating condition.

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

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.