AMicroseismic Signal Denoising Algorithm Combining VMD and Wavelet Threshold Denoising Optimized by BWOA

The denoising of microseismic signals is a prerequisite for subsequent analysis and research.In this research,a new microseismic signal denoising algorithm called the Black Widow Optimization Algorithm(BWOA)optimized VariationalMode Decomposition(VMD)jointWavelet Threshold Denoising(WTD)algorithm(BVW)is proposed.The BVW algorithm integrates VMD and WTD,both of which are optimized by BWOA.Specifically,this algorithm utilizes VMD to decompose the microseismic signal to be denoised into several Band-Limited IntrinsicMode Functions(BLIMFs).Subsequently,these BLIMFs whose correlation coefficients with the microseismic signal to be denoised are higher than a threshold are selected as the effective mode functions,and the effective mode functions are denoised using WTD to filter out the residual low-and intermediate-frequency noise.Finally,the denoised microseismic signal is obtained through reconstruction.The ideal values of VMD parameters and WTD parameters are acquired by searching with BWOA to achieve the best VMD decomposition performance and solve the problem of relying on experience and requiring a large workload in the application of the WTD algorithm.The outcomes of simulated experiments indicate that this algorithm is capable of achieving good denoising performance under noise of different intensities,and the denoising performance is significantly better than the commonly used VMD and Empirical Mode Decomposition(EMD)algorithms.The BVW algorithm is more efficient in filtering noise,the waveform after denoising is smoother,the amplitude of the waveform is the closest to the original signal,and the signal-to-noise ratio(SNR)and the root mean square error after denoising are more satisfying.The case based on Fankou Lead-Zinc Mine shows that for microseismic signals with different intensities of noise monitored on-site,compared with VMD and EMD,the BVW algorithm ismore efficient in filtering noise,and the SNR after denoising is higher.

Up-Sampled Cross-Correlation Based Object Tracking & Vibration Measurement in Agriculture Tractor System

This research introduces a challenge in integrating and cleaning the data,which is a crucial task in object matching.While the object is detected and then measured,the vibration at different light intensities may influence the durability and reliability of mechanical systems or structures and cause problems such as damage,abnormal stopping,and disaster.Recent research failed to improve the accuracy rate and the computation time in tracking an object and in the vibration measurement.To solve all these problems,this proposed research simplifies the scaling factor determination by assigning a known real-world dimension to a predetermined portion of the image.A novel white color sticker of the known dimensions marked with a color dot is pasted on the surface of an object for the best result in the template matching using the Improved Up-Sampled Cross-Correlation(UCC)algorithm.The vibration measurement is calculated using the Finite-Difference Algorithm(FDA),a machine vision systemfitted with a macro lens sensor that is capable of capturing the image at a closer range,which does not affect the quality of displacement measurement from the video frames.Thefield test was conducted on the TAFE(Tractors and Farm Equipment Limited)tractor parts,and the percentage of error was recorded between 30%and 50%at very low vibration values close to zero,whereas it was recorded between 5%and 10%error in most high-accelerations,the essential range for vibration analysis.Finally,the suggested system is more suitable for measuring the vibration of stationary machinery having low frequency ranges.The use of a macro lens enables to capture of image frames at very close-ups.A 30%to 50%error percentage has been reported when the vibration amplitude is very small.Therefore,this study is not suitable for Nano vibration analysis.

Multi-Level Image Segmentation Combining Chaotic Initialized Chimp Optimization Algorithm and Cauchy Mutation

To enhance the diversity and distribution uniformity of initial population,as well as to avoid local extrema in the Chimp Optimization Algorithm(CHOA),this paper improves the CHOA based on chaos initialization and Cauchy mutation.First,Sin chaos is introduced to improve the random population initialization scheme of the CHOA,which not only guarantees the diversity of the population,but also enhances the distribution uniformity of the initial population.Next,Cauchy mutation is added to optimize the global search ability of the CHOA in the process of position(threshold)updating to avoid the CHOA falling into local optima.Finally,an improved CHOA was formed through the combination of chaos initialization and Cauchy mutation(CICMCHOA),then taking fuzzy Kapur as the objective function,this paper applied CICMCHOA to natural and medical image segmentation,and compared it with four algorithms,including the improved Satin Bowerbird optimizer(ISBO),Cuckoo Search(ICS),etc.The experimental results deriving from visual and specific indicators demonstrate that CICMCHOA delivers superior segmentation effects in image segmentation.

Defect image segmentation using multilevel thresholding based on firefly algorithm with opposition-learning

To segment defects from the quad flat non-lead QFN package surface a multilevel Otsu thresholding method based on the firefly algorithm with opposition-learning is proposed. First the Otsu thresholding algorithm is expanded to a multilevel Otsu thresholding algorithm. Secondly a firefly algorithm with opposition-learning OFA is proposed.In the OFA opposite fireflies are generated to increase the diversity of the fireflies and improve the global search ability. Thirdly the OFA is applied to searching multilevel thresholds for image segmentation. Finally the proposed method is implemented to segment the QFN images with defects and the results are compared with three methods i.e. the exhaustive search method the multilevel Otsu thresholding method based on particle swarm optimization and the multilevel Otsu thresholding method based on the firefly algorithm. Experimental results show that the proposed method can segment QFN surface defects images more efficiently and at a greater speed than that of the other three methods.

Dynamic threshold for SPWVD parameter estimation based on Otsu algorithm

Time-frequency-based methods are proven to be effective for parameter estimation of linear frequency modulation （LFM） signals. The smoothed pseudo Winger-Ville distribution （SPWVD） is used for the parameter estimation of multi-LFM signals, and a method of the SPWVD binarization by a dynamic threshold based on the Otsu algorithm is proposed. The proposed method is effective in the demand for the estimation of different parameters and the unknown signal-to-noise ratio （SNR） circumstance. The performance of this method is confirmed by numerical simulation.

A Context Sensitive Multilevel Thresholding Using Swarm Based Algorithms

In this paper, a comprehensive energy function is used to formulate the three most popular objective functions:Kapur's, Otsu and Tsalli's functions for performing effective multilevel color image thresholding. These new energy based objective criterions are further combined with the proficient search capability of swarm based algorithms to improve the efficiency and robustness. The proposed multilevel thresholding approach accurately determines the optimal threshold values by using generated energy curve, and acutely distinguishes different objects within the multi-channel complex images. The performance evaluation indices and experiments on different test images illustrate that Kapur's entropy aided with differential evolution and bacterial foraging optimization algorithm generates the most accurate and visually pleasing segmented images.

An Improved Artificial Immune Algorithm with a Dynamic Threshold

An improved artificial immune algorithm with a dynamic threshold is presented. The calculation for the affinity function in the real-valued coding artificial immune algorithm is modified through considering the antibody＇s fitness and setting the dynamic threshold value. Numerical experiments show that compared with the genetic algorithm and the originally real-valued coding artificial immune algorithm, the improved algorithm possesses high speed of convergence and good performance for preventing premature convergence.

Fast recursive algorithm for two-dimensional Tsallis entropy thresholding method

Recently, a two-dimensional （2-D） Tsallis entropy thresholding method has been proposed as a new method for image segmentation. But the computation complexity of 2-D Tsallis entropy is very large and becomes an obstacle to real time image processing systems. A fast recursive algorithm for 2-D Tsallis entropy thresholding is proposed. The key variables involved in calculating 2-D Tsallis entropy are written in recursive form. Thus, many repeating calculations are avoided and the computation complexity reduces to O（L2） from O（L4）. The effectiveness of the proposed algorithm is illustrated by experimental results.

2-D mini mumfuzzy entropy method of image thresholding based on genetic algorithm

A new image thresholding method is introduced, which is based on 2-D histgram and minimizing the measures of fuzziness of an input image. A new definition of fuzzy membership function is proposed, it denotes the characteristic relationship between the gray level of each pixel and the average value of its neighborhood. When the threshold is not located at the obvious and deep valley of the histgram, genetic algorithm is devoted to the problem of selecting the appropriate threshold value. The experimental results indicate that the proposed method has good performance.

Optimized quantum singular value thresholding algorithm based on a hybrid quantum computer

Quantum singular value thresholding(QSVT) algorithm,as a core module of many mathematical models,seeks the singular values of a sparse and low rank matrix exceeding a threshold and their associated singular vectors.The existing all-qubit QSVT algorithm demands lots of ancillary qubits,remaining a huge challenge for realization on nearterm intermediate-scale quantum computers.In this paper,we propose a hybrid QSVT(HQSVT) algorithm utilizing both discrete variables(DVs) and continuous variables(CVs).In our algorithm,raw data vectors are encoded into a qubit system and the following data processing is fulfilled by hybrid quantum operations.Our algorithm requires O [log(MN)] qubits with0(1) qumodes and totally performs 0(1) operations,which significantly reduces the space and runtime consumption.

Study and Implementation of Web Mining Classification Algorithm Based on Building Tree of Detection Class Threshold

A new classification algorithm for web mining is proposed on the basis of general classification algorithm for data mining in order to implement personalized information services. The building tree method of detecting class threshold is used for construction of decision tree according to the concept of user expectation so as to find classification rules in different layers. Compared with the traditional C4.5 algorithm, the disadvantage of excessive adaptation in C4.5 has been improved so that classification results not only have much higher accuracy but also statistic meaning.

Reduction of ultrasonic echo noise based on improved wavelet threshold de-noising algorithm for friction welding

In the ultrasonic detection of defects in friction welded joints, it is difficult to exactly detect some weak bonding defects because of the noise pollution. This paper proposed an improved threshold function based on the multi-resolution analysis wavelet threshold de-noising method which was put forward by Donoho and Johnstone, and applied this method in the de-noising of the defective signals. This threshold function overcomes the discontinuous shortcoming of the hard-threshold function and the disadvantage of soft threshold function which causes an invariable deviation between the estimated wavelet coeffwients and the decomposed wavelet coefficients. The improved threshold function is of simple expression and convenient for calculation. The actual test results of defect noise signal show that this improved method can get less mean square error （ MSE ） and higher signal-to-noise ratio of reconstructed signals than those calculated from hard threshold and soft threshold methods. The improved threshold function has excellent de-noising effect.

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.

Fuzzy Hybrid Coyote Optimization Algorithm for Image Thresholding

In order to address the problems of Coyote Optimization Algorithm in image thresholding,such as easily falling into local optimum,and slow convergence speed,a Fuzzy Hybrid Coyote Optimization Algorithm(here-inafter referred to as FHCOA)based on chaotic initialization and reverse learning strategy is proposed,and its effect on image thresholding is verified.Through chaotic initialization,the random number initialization mode in the standard coyote optimization algorithm(COA)is replaced by chaotic sequence.Such sequence is nonlinear and long-term unpredictable,these characteristics can effectively improve the diversity of the population in the optimization algorithm.Therefore,in this paper we first perform chaotic initialization,using chaotic sequence to replace random number initialization in standard COA.By combining the lens imaging reverse learning strategy and the optimal worst reverse learning strategy,a hybrid reverse learning strategy is then formed.In the process of algorithm traversal,the best coyote and the worst coyote in the pack are selected for reverse learning operation respectively,which prevents the algorithm falling into local optimum to a certain extent and also solves the problem of premature convergence.Based on the above improvements,the coyote optimization algorithm has better global convergence and computational robustness.The simulation results show that the algorithmhas better thresholding effect than the five commonly used optimization algorithms in image thresholding when multiple images are selected and different threshold numbers are set.

Mean Threshold and ARNN Algorithms for Identification of Eye Commands in an EEG-Controlled Wheelchair

This paper represented Autoregressive Neural Network (ARNN) and meant threshold methods for recognizing eye movements for control of an electrical wheelchair using EEG technology. The eye movements such as eyes open, eyes blinks, glancing left and glancing right related to a few areas of human brain were investigated. A Hamming low pass filter was applied to remove noise and artifacts of the eye signals and to extract the frequency range of the measured signals. An autoregressive model was employed to produce coefficients containing features of the EEG eye signals. The coefficients obtained were inserted the input layer of a neural network model to classify the eye activities. In addition, a mean threshold algorithm was employed for classifying eye movements. Two methods were compared to find the better one for applying in the wheelchair control to follow users to reach the desired direction. Experimental results of controlling the wheelchair in the indoor environment illustrated the effectiveness of the proposed approaches.

基于POA-VMD-WT的MEMS去噪方法

针对MEMS传感器所测得的加速度和角速度输出信号噪声较大问题,提出一种基于鹈鹕优化算法(pelican optimization algorithm,POA)的变分模态分解(variational mode decomposition,VMD)结合小波阈值(wavelet threshold,WT)的去噪方法。首先利用POA对VMD的参数组合进行优化选择,然后应用POA-VMD将含噪信号自适应、非递归地分解为一系列本征模态函数(intrinsic mode function,IMF)。再通过计算每个IMF的余弦相似度对IMFs进行分类,根据计算结果将IMFs分为噪声主导分量与信号主导分量,对分类后的噪声主导分量进行改进小波阈值去噪处理,最后对处理后的噪声分量与信号主导分量进行重构,获得降噪后的MEMS传感器信号。静态和动态实验结果表明,该方法去噪处理后信号的信噪比分别提高12和10 dB,均方误差分别降低75.5%和46.6%,去噪效果显著,能够提高MEMS传感器的精度。

次同步振荡在交直流电网中传播的关键影响因素

随着“双高”电力系统的发展,次同步振荡问题日益凸出,亟需研究交直流线路次同步振荡传播的关键影响因素。从系统响应量测时序数据着手,提出了一种次同步振荡传播关键影响因素定量分析方法。首先,基于自适应噪声完全集合经验模态分解(complete ensemble empirical mode decomposition, CEEMDAN)的改进小波阈值去噪方法对量测数据进行降噪处理,减少噪声对Prony分析的影响;其次,基于次同步振荡传播各影响因素的相关系数和互信息量建立相关性评价组合模型;最后,计算交直流不同参数在综合模型中的评价指标,得出次同步振荡在交直流线路中传播的关键影响因素。通过在PSCAD搭建2区域4机系统进行分析,结果表明:影响交流线路次同步振荡传播的极强相关参数为交流线路潮流,影响直流线路次同步振荡传播的极强相关参数为次同步振荡频率下交流线路阻抗特性。

基于遗传算法优化Canny算子的织绣文物纹样抽取方法研究

织绣文物中的纹样彰显中国传统文化造型艺术的形式美感,是文创设计的重要素材源泉。针对传统Canny算子进行纹样边缘检测需人为确定阈值的问题,本文提出基于遗传算法自适应优化Canny算子阈值的方法,将边缘点、非边缘点类间梯度幅值方差设置为适应度函数,通过格雷码对阈值染色体编解码、锦标赛选择算法优选交叉变异后的阈值,求解最优适应度对应的高低阈值,用于纹样边缘检测;此外,运用形态学操作得到纹样的完整、精确轮廓;最后,添加Alpha通道抽取纹样前景。实验表明,上述方法在织绣纹样抽取精度和像素准确度(PA)上,优于传统Canny算子、混合蛙跳自适应Canny算子、Otsu法优化Canny算子等已有方法,可有效检测织绣文物图像中纹样真实边缘并完成纹样抽取。

基于ZOA优化VMD-IAWT岩石声发射信号降噪算法

针对岩石破裂过程中产生的声发射(AE)信号夹杂大量噪声的问题,提出了一种基于斑马优化算法(ZOA)改进变分模态分解(VMD)并与改进的自适应小波阈值(IAWT)联合的声发射信号降噪算法。利用ZOA算法优选出影响VMD分解效果的模态个数K和二次惩罚因子α;通过相关系数将分解出的IMFs划分为有效分量、含噪分量和剔除分量;针对小波阈值(WT)降噪算法不具备自动调整小波基以及软、硬阈值函数存在偏差大和不连续的弊端,提出了IAWT算法去除IMFs中的噪声分量,并与有效分量合并重构,得到降噪后的AE信号。通过模拟和实测AE信号验证并与现有降噪算法对比,结果表明ZOA-VMD-IAWT降噪算法适合处理AE信号,信号的时频特征得以保留。研究结果可为岩石AE信号理论及实际工程应用提供参考。

月球探测器鲁棒环形山检测及光学导航方法

针对月球探测器环形山检测方法受光照影响、鲁棒性差的问题,本文提出一种基于极大熵阈值三值化的鲁棒环形山检测算法。采用不同滤波核对图像进行去噪平滑,然后对处理后的图像进行极大熵阈值分割、将图像信息三值化,去除图像对光源的敏感性,同时最大程度保留图像信息;提出一种归一化多指标约束环形山匹配和拟合方法完成环形山提取,将环形山提取算法应用于光学导航中进行打靶实验验证算法实时性表现。仿真结果表明:与传统基于形态学或自适应边缘检测的方法相比,本文方法在较大尺度条件下提取出连续、光滑的环形山边缘,有效环形山数量提升35%以上,同时实时性更好、计算消耗降低40%;基于鲁棒环形山提取的光学导航算法实时性更好。