检索结果-南通市图书馆

AMicroseismic Signal denoising Algorithm Combining VMD and wavelet threshold denoising Optimized by BWOA

Computer Modeling in Engineering & Sciences 2024年第10期141卷 187-217页

作者： Dijun Rao Min Huang Xiuzhi Shi Zhi Yu Zhengxiang He Zijin Mining Group Co. Ltd.Xiamen361016China Zijin(Changsha)Engineering Technology Co. Ltd.Changsha410006China State Key Laboratory of Comprehensive Utilization of Low-Grade Refractory Gold Ores Shanghang364204China School of Resources and Safety Engineering Central South UniversityChangsha410083China School of Resources&Environment Engineering Jiangxi University of Science and TechnologyGanzhou341000China Zijin School of Geology and Mining Fuzhou UniversityFuzhou350116China State Key Laboratory for Fine Exploration and Intelligent Development of Coal Resources China University of Mining and TechnologyXuzhou221116China

The denoising of microseismic signals is a prerequisite for subsequent analysis and *** 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 *** BVW algorithm integrates VMD and WTD,both of which are optimized by ***,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 ***,the denoised microseismic signal is obtained through *** 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 *** 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)*** 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 *** 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.

关键词： Variational mode decomposition microseismic signal denoising wavelet threshold denoising black widow optimization algorithm

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Smooth pulse recovery based on hybrid wavelet threshold denoising and first derivative adaptive smoothing filter

引用

High Power Laser Science and Engineering 2021年第2期9卷 17-25页

作者： Xinlei Qian Wei Fan Xinghua Lu Xiaochao Wang National Laboratory on High Power Laser and Physics Shanghai Institute of Optics and Fine MechanicsChinese Academy of SciencesShanghai 201800China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of SciencesBeijing 100049China

Based on the pulse-shaping unit in the front end of high-power laser facilities,we propose a new hybrid scheme in a closed-loop control system including wavelet threshold denoising for pretreatment and a first derivative adaptive smoothing filter for smooth pulse recovery,so as to effectively restrain the influence of electrical noise and FM-to-AM modulation in the time–power curve,and enhance the calibration accuracy of the pulse shape in the feedback control *** related simulation and experiment results show that the proposed scheme can obtain a better shaping effect on the high-contrast temporal shape in comparison with the cumulative average algorithm and orthogonal matching pursuit algorithm combined with a traditional smoothing *** implementation of the hybrid scheme mechanism increased the signal-to-noise ratio of the laser pulse from about 11 dB to 30 dB,and the filtered pulse is smooth without modulation,with smoothness of about 98.8%.

关键词： first derivative adaptive smoothing filter recovery of smooth pulse signal-to-noise ratio wavelet threshold denoising

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

The Application of Entropy Analysis Based on wavelet threshold denoising in Radiation Source Feature Extraction

The Application of Entropy Analysis Based on Wavelet Thresho...

引用

2012 International Conference on Mechanical Engineering and Automation(ICMEA2012)

作者： L i Yi-bing Li Jing-chao Lin Yun Harbin Engineering University Harbin China

Feature extraction is an important step in the modulation recognition of communication signals. With advantages in denoising methods based on wavelet threshold denoising, the paper proposes a method of entropy analysis to extract the features of signals. After the treatment of wavelet threshold denoising it discoveries the Shannon entropy and Index entropy as the identification features and identifies the modulations of the signals. Characteristics extracted by traditional methods often need high signal-to-noise ratio, and are not stable sometimes. However, entropy characteristics after wavelet threshold denoising overcome it. Simulation results show that certain disparities exist between different signals’ entropy values. Signals could be well distinguished by setting thresholds after wavelet threshold denoising. Good recognition effects are found in different circumstances under two transformations. The conclusion is drawn that using the entropy analysis method to extract the signal characteristics is less affected by noise, and the entropy curves are relatively stable. It’s more conducive to distinguish the signal modulation and has a very good application value.

关键词： Feature extraction Entropy characteristics Shannon entropy Index entropy wavelet threshold denoising

来源： cnki会议评论

在线全文

cnki会议

学校读者我要写书评

暂无评论

基于EEMD、相关系数、排列熵和小波阈值去噪的新型水下声学信号去噪方法

引用

哈尔滨工程大学学报（英文版） 2024年第1期23卷 222-237页

作者：张玉燕杨志霞杜晓莉罗小元 Department of Instrument Science and Technology Yanshan UniversityQinhuangdao 066000China Department of Control Science and Engineering Yanshan UniversityQinhuangdao 066000China

The complexities of the marine environment and the unique characteristics of underwater channels pose challenges in obtaining reliable signals underwater,necessitating the filtration of underwater acoustic ***,an underwater acoustic signal denoising method based on ensemble empirical mode decomposition(EEMD),correlation coefficient(CC),permutation entropy(PE),and wavelet threshold denoising(WTD)is ***,simulation experiments are conducted using simulated and real underwater acoustic *** experimental results reveal that the proposed denoising method outperforms other previous methods in terms of signal-to-noise ratio,root mean square error,and *** proposed method eliminates noise and retains valuable information in the signal.

关键词： Ensemble empirical mode decomposition Correlation coefficient Permutation entropy wavelet threshold denoising Underwater acoustic signal denoising

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Fault diagnosis of railway point machines based on wavelet transform and artificial immune algorithm

引用

Transportation Safety and Environment 2023年第4期5卷 117-126页

作者： Xiaochun Wu Weikang Yang Jianrong Cao School of Automation and Electrical Engineering Lanzhou Jiaotong UniversityLanzhou 730070GansuChina China Railway Rolling Stock Corporation Qingdao Sifang Rolling Stock Research Institute Co. LtdQingdao 266000ShandongChina

Aiming at the current problems of high failure rate and low diagnostic efficiency of railway point machines(RPMs)in the railway industry,a short-time method of fault diagnosis is *** the effect of noise on power signals in the data acquisition process of the railway centralized signaling monitoring(CSM)system,this study utilizes wavelet threshold denoising to eliminate *** results show that the accuracy of fault diagnosis can be improved by 4.4% after denoising the power *** in order to attain a lighter weight and shorten the running time of the diagnosis model,Mallat wavelet decomposition and artificial immune algorithm are applied to RPM fault ***,voluminous experiments using veritable power signals collected from CSM are introduced,which show that combining these methods can procure higher precision of RPMs and curtail fault diagnosis *** substantiates the validity and feasibility of the presented approach.

关键词： railway point machines wavelet threshold denoising Mallat wavelet decomposition artificial immune algorithm

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Ultrasonic Signal denoising Based on a New wavelet thresholding Function

Ultrasonic Signal Denoising Based on a New Wavelet Threshold...

引用

第37届中国控制会议

作者： Yan Luo Wei Xue Yunyun Yu School of Autonation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systemsy

In the ultrasonic nondestructive testing, the echo signal is polluted by noise and the signal-to-noise ratio is low. To solve the problem, a new wavelet thresholding function is introduced in this paper based on soft and hard thresholding functions. Compared with the existing improved thresholding functions, the new thresholding function is simple in computation and continuous and easy to adjust, it is suitable for some kinds of mathematical disposals. The simulation results show that the new thresholding function can suppress the noise effectively, it is better than soft and hard thresholding functions and the existing improved thresholding functions and has high practical value.

关键词： wavelet threshold denoising Ultrasonic Detection Signal to Noise Ratio Signal Processing

来源： cnki会议评论

在线全文

cnki会议

学校读者我要写书评

暂无评论

Research on Image denoising Method Based on wavelet Transform

Research on Image Denoising Method Based on Wavelet Transfor...

引用

第37届中国控制会议

作者： JunLei Song MeiJuan Chen Chang Jiang YanXia Huang Qi Liu Yuan Meng WenQin Mo KaiFeng Dong Fang Jin School of Automation China University of Geosciences Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

In the process of image acquisition and transmission, the image always generates noise due to internal and external interference. Noise reduces the quality of the image, and makes it difficult for subsequent image processing. Therefore, image denoising is very important in image processing. wavelet denoising can effectively filter out noise and retain high-frequency information of the image, this method has the characteristics of fast operation speed and has become an important branch of image denoising. threshold functions commonly used in wavelet threshold denoising include hard threshold function and soft threshold function. The hard threshold function is not continuous as a whole. Although the soft threshold function has good continuity, there is always a constant deviation between the processed coefficient and the original coefficient when the wavelet coefficient is large. In response to these deficiencies, this paper establishes a new improved threshold function based on traditional soft and hard threshold functions. By processing the thresholds of wavelet coefficients, a reasonable balance between smoothing and edge oscillations can be achieved after image denoising. The improved threshold function not only overcomes the shortcomings of the soft and hard threshold functions, but also provides more flexibility in the processing of image *** MATLAB simulation, the denoising effects of the soft, hard threshold functions and the threshold function constructed in this paper are compared in terms of signal-to-noise ratio（SNR） and root mean square error（MSE）. The MATLAB simulation results show that compared with the traditional threshold function, the improved threshold function has a higher signal-to-noise ratio（SNR = 26.27709） and a smaller mean square error（MSE = 153.4579）, and it has a good noise reduction effect.

关键词： wavelet threshold denoising Image denoising wavelet transform threshold function

来源： cnki会议评论

在线全文

cnki会议

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：