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Landslide displacement prediction based on optimized empirical mode decomposition and deep bidirectional long short-term memory network

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Journal of Mountain Science 2023年第3期20卷 637-656页

作者： ZHANG Ming-yue HAN Yang YANG Ping WANG Cong-ling School of Mechanical and Electrical Engineering University of Electronic Science and Technology of ChinaChengdu 611731China

There are two technical challenges in predicting slope *** first one is the random displacement,which could not be decomposed and predicted by numerically resolving the observed accumulated displacement and time series of a *** second one is the dynamic evolution of a landslide,which could not be feasibly simulated simply by traditional prediction *** this paper,a dynamic model of displacement prediction is introduced for composite landslides based on a combination of empirical mode decomposition with soft screening stop criteria(SSSC-EMD)and deep bidirectional long short-term memory(DBi-LSTM)neural *** the proposed model,the time series analysis and SSSC-EMD are used to decompose the observed accumulated displacements of a slope into three components,*** displacement,periodic displacement,and random ***,by analyzing the evolution pattern of a landslide and its key factors triggering landslides,appropriate influencing factors are selected for each displacement component,and DBi-LSTM neural network to carry out multi-datadriven dynamic prediction for each displacement *** accumulated displacement prediction has been obtained by a summation of each *** accuracy verification and engineering practicability of the model,field observations from two known landslides in China,the Xintan landslide and the Bazimen landslide were collected for comparison and *** case study verified that the model proposed in this paper can better characterize the"stepwise"deformation characteristics of a *** compared with long short-term memory(LSTM)neural network,support vector machine(SVM),and autoregressive integrated moving average(ARIMA)model,DBi-LSTM neural network has higher accuracy in predicting the periodic displacement of slope deformation,with the mean absolute percentage error reduced by 3.063%,14.913%,and 13.960%respectively,and the root mean square error reduced by 1.951 mm,8.954 mm and 7.7

关键词： Landslide displacement empirical mode decomposition Soft screening stop criteria Deep bidirectional long short-term memory neural network Xintan landslide Bazimen landslide

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Feature Layer Fusion of Linear Features and empirical mode decomposition of Human EMG Signal

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Journal of Electronic Science and Technology 2022年第3期20卷 257-269页

作者： Jun-Yao Wang Yue-Hong Dai Xia-Xi Si the School of Aeronautics and Astronautics University of Electronic Science and Technology of ChinaChengdu611731China the Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province University of Electronic Science and Technology of ChinaChengdu611731China

To explore the influence of the fusion of different features on recognition,this paper took the electromyography(EMG)signals of rectus femoris under different motions(walk,step,ramp,squat,and sitting)as samples,linear features(time-domain features(variance(VAR)and root mean square(RMS)),frequency-domain features(mean frequency(MF)and mean power frequency(MPF)),and nonlinear features(empirical mode decomposition(EMD))of the samples were *** feature fusion algorithms,the series splicing method and complex vector method,were designed,which were verified by a double hidden layer(BP)error back propagation neural *** show that with the increase of the types and complexity of feature fusions,the recognition rate of the EMG signal to actions is gradually *** the EMG signal is used in the series splicing method,the recognition rate of time-domain+frequency-domain+empirical mode decomposition(TD+FD+EMD)splicing is the highest,and the average recognition rate is 92.32%.And this rate is raised to 96.1%by using the complex vector method,and the variance of the BP system is also reduced.

关键词： Complex vector method electromyography(EMG)signal empirical mode decomposition feature layer fusion series splicing method

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empirical mode decomposition using variable filtering with time scale calibrating

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Journal of Systems Engineering and Electronics 2008年第6期19卷 1076-1081页

作者： Yuan Ye Mei Wenbo Wu Siliang Yuan Qi School of Information Science and Technology Beijing Inst. of Technology Beijing 100081 P. R. China School of Industrial Design and Information Engineering Beijing Inst. of Clothing Technology Beijing 100029 P. R. China The Science and Technology Committee of China Aerospace Science and Industry Corporation Beijing 100854 P. R. China

A novel and efficient method for decomposing a signal into a set of intrinsic mode functions （IMFs） and a trend is proposed. Unlike the original empirical mode decomposition （EMD）, which uses spline fits to extract variations from the signal by separating the local mean from the fluctuations in the decomposing process, this new method being proposed takes advantage of the theory of variable finite impulse response （FIR） filtering where filter coefficients and breakpoint frequencies can be adjusted to track any peak-to-peak time scale changes. The IMFs are results of a multiple variable frequency response FIR filtering when signals pass through the filters. Numerical examples validate that in contrast with the original EMD, the proposed method can fine-tune the frequency resolution and suppress the aliasing effectively.

关键词： empirical mode decomposition variable FIR filtering time scale calibrating.

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Pores-Preserving Face Cleaning Based on Improved empirical mode decomposition

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Journal of Computer Science & Technology 2009年第3期24卷 557-567页

作者：刘艳丽徐晓刚郭延文王进段鑫陈曦彭群生 State Key Lab of CAD & CG Zhejiang University Department of Mathematics Zhejiang University State Key Lab of Novel Software Technology Nanjing University

In this paper, we propose a novel method of cleaning up facial imperfections such as bumps and blemishes that may detract from a pleasing digital portrait. Contrasting with traditional methods which tend to blur facial details, our method fully retains fine scale skin textures （pores etc.） of the subject. Our key idea is to find a quantity, namely normalized local energy, to capture different characteristics of fine scale details and distractions, based on empirical mode decomposition, and then build a quantitative measurement of facial skin appearance which characterizes both imperfections and facial details in a unified framework. Finally, we use the quantitative measurement as a guide to enhance facial skin. We also introduce a few high-level, intuitive parameters for controlling the amount of enhancement. In addition, an adaptive local mean and neighborhood limited empirical mode decomposition algorithm is also developed to improve in two respects the performance of empirical mode decomposition. It can effectively avoid the gray spots effect commonly associated with traditional empirical mode decomposition when dealing with high-nonstationary images.

关键词： image enhancement empirical mode decomposition normalized local energy

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Denoising of chaotic signal using independent component analysis and empirical mode decomposition with circulate translating

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Chinese Physics B 2016年第1期25卷 400-406页

作者：王文波张晓东常毓禅汪祥莉王钊陈希郑雷 College of Science Wuhan University of Science and TechnologyWuhan 430065China State Key Laboratory of Satellite Ocean Environment Dynamics Second Institute of OceanographyState Oceanic AdministrationHangzhou 310012China School of Finance Renmin University of ChinaBeijing 100872China School of Computer Science and Technology Wuhan University of TechnologyWuhan 430063China College of Information Science and Engineering Wuhan University of Science and TechnologyWuhan 430081China Wuhan NARI Limited Liability Company of Sate Grid Electric Power Research Institute Wuhan 430074China

In this paper, a new method to reduce noises within chaotic signals based on ICA （independent component analysis） and EMD （empirical mode decomposition） is proposed. The basic idea is decomposing chaotic signals and constructing multidimensional input vectors, firstly, on the base of EMD and its translation invariance. Secondly, it makes the indepen- dent component analysis on the input vectors, which means that a self adapting denoising is carried out for the intrinsic mode functions （IMFs） of chaotic signals. Finally, all IMFs compose the new denoised chaotic signal. Experiments on the Lorenz chaotic signal composed of different Gaussian noises and the monthly observed chaotic sequence on sunspots were put into practice. The results proved that the method proposed in this paper is effective in denoising of chaotic signals. Moreover, it can correct the center point in the phase space effectively, which makes it approach the real track of the chaotic attractor.

关键词： independent component analysis empirical mode decomposition chaotic signal denoising

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Source Separation of Diesel Engine Vibration Based on the empirical mode decomposition and Independent Component Analysis

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Chinese Journal of Mechanical Engineering 2012年第3期25卷 557-563页

作者： DU Xianfeng LI Zhijun BI Fengrong ZHANG Junhong WANG Xia SHAO Kang State Key Laboratory of Engines Tianjin UniversityTianjin 300072China

Vibration signals from diesel engine contain many different components mainly caused by combustion and mechanism operations,several blind source separation techniques are available for decomposing the signal into its components in the case of multichannel measurements,such as independent component analysis（ICA）.However,the source separation of vibration signal from single-channel is *** order to study the source separation from single-channel signal for the purpose of source extraction,the combination method of empirical mode decomposition（EMD） and ICA is proposed in diesel engine signal *** performance of the described methods of EMD-wavelet and EMD-ICA in vibration signal application is compared,and the results show that EMD-ICA method outperforms the other,and overcomes the drawback of ICA in the case of single-channel *** independent source signal components can be separated and identified effectively from one-channel measurement by ***,EMD-ICA improves the extraction and identification abilities of source signals from diesel engine vibration measurements.

关键词： empirical mode decomposition independent component analysis source separation single-channel signal

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FAST IMPLEMENTATION OF ORTHOGONAL empirical mode decomposition AND ITS APPLIGA-TION INTO HARMONIC DETECTION

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Chinese Journal of Mechanical Engineering 2008年第2期21卷 93-98页

作者： QIN Yi QIN Shuren MAO Yongfang College of Mechanical Engineering Chongqing University Chongqing 400044 China

Since the empirical mode decomposition （EMD） lacks strict orthogonality, the method of orthogonal empirical mode decomposition （OEMD） is innovationally proposed. The primary thought of this method is to obtain the intrinsic mode function （IMF） and the residual function by auto-adaptive band-pass filtering. OEMD is proved to preserve strict orthogonality and completeness theoretically, and the orthogonal basis function of OEMD is generated, then an algorithm to implement OEMD fast, IMF binary searching algorithm is built based on the point that the analytical band-pass filtering preserves perfect band-pass feature in the frequency domain. The application into harmonic detection shows that OEMD successfully conquers mode aliasing, avoids the occurrence of false mode, and is featured by fast computing speed. Furthermore, it can achieve harmonic detection accurately combined with the least square method.

关键词： empirical mode decomposition Orthogonal empirical mode decomposition Analytic band-pass filtering Binary searching Harmonic detection

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Regional features of the temperature trend in China based on empirical mode decomposition

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Journal of Geographical Sciences 2008年第2期18卷 166-176页

作者： SUN Xian LIN Zhenshan CHENG Xiaoxia JIANG Chuangye Shaanxi Climate Center Xi＇an 710015 China The School of Geographic Sciences Nanjing Normal University Nanjing 210097 China

By the empirical mode decomposition method, we analyzed the observed monthly average temperature in more than 700 stations from 1951-2001 over China. Simultaneously, the temperature variability of each station is calculated by this method, and classification chart of long term trend and temperature variability distributing chart of China are obtained, supported by GIS, 1 kmxl km resolution. The results show that： in recent 50 years, the temperature has increased by more than 0.4~C/10a in most parts of northern China, while in Southwest China and the middle and lower Yangtze Valley, the increase is not significant. The areas with a negative temperature change rate are distributed sporadically in Southwest China. Meanwhile, the temperature data from 1881 to 2001 in nine study regions in China are also analyzed, indicating that in the past 100 years, the temperature has been increasing all the way in Northeast China, North China, South China, Northwest China and Xinjiang and declining in Southwest China. An inverse ‘V-shaped’ trend is also found in Central China. But in Tibet the change is less significant.

关键词： China temperature empirical mode decomposition intrinsic mode function

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Analysis of entropies based on empirical mode decomposition in amnesic mild cognitive impairment of diabetes mellitus

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Journal of Innovative Optical Health Sciences 2015年第5期8卷 11-30页

作者： Dong Cui Jinhuan Wang Zhijie Bian Qiuli Li Lei Wang Xiaoli Li School of Information Science and Engineering Yanshan University QinhuangdaoP.R.China School of Electrical Engineering Yanshan University QinhuangdaoP.R.China Department of Neurology General Hospital of Second Artillery Corps of PLA Beijing P.R.China State Key Laboratory of Cognitive Neuroscience and Learning&IDG/McGovern Institute for Brain Research Beijing Normal University BeijingP.R.China Center for Collaboration and Innovation in Brain and Learning Sciences Beijing Normal University BeijingP.R.China

EEG characteristics that correlate with the cognitive functions are important in detecting mild cognitive impairment(MCI)in *** investigate the complexity between aMCI group and age-matched non-aMCI control group in T2DM,six entropies combining empirical mode decomposition(EMD),including Approximate entropy(ApEn),Sample entropy(SaEn),Fuzzy entropy(FEn),Permutation entropy(PEn),Power spectrum entropy(PsEn)and Wavelet entropy(WEn)were used in the study.A feature extraction technique based on maximization of the area under the curve(AUC)and a support vector machine(SVM)were subsequently used to for features selection and classi¯***,Pearson's linear correlation was employed to study associations between these entropies and cognitive *** to other entropies,FEn had a higher classification accuracy,sensitivity and specificity of 68%,67.1% and 71.9%,*** 43 salient features achieved classification accuracy,sensitivity and speci¯city of 73.8%,72.3% and 77.9%,respectively.P4,T4 and C4 were the highest ranking salient *** analysis showed that FEn based on EMD was positively correlated to memory at electrodes F7,F8 and P4,and PsEn based on EMD was positively correlated to Montreal cognitive assessment(MoCA)and memory at electrode *** sum,FEn based on EMD in righttemporal and occipital regions may be more suitable for early diagnosis of the MCI with T2DM.

关键词： Entropy empirical mode decomposition amnestic mild cognitive impairment type 2 diabetes mellitus

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Vehicle-Bridge Interaction Simulation and Damage Identification of a Bridge Using Responses Measured in a Passing Vehicle by empirical mode decomposition Method

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Open Journal of Civil Engineering 2023年第4期13卷 742-755页

作者： Shohel Rana Md. Rifat Zaman Md. Ibrahim Islam Ifty Seyedali Mirmotalebi Tahsin Tareque Department of Civil Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh Department of Civil Engineering Atish Dipankar University of Science & Technology Dhaka Bangladesh Department of Civil Architectural and Environmental Engineering North Carolina Agricultural and Technical State University Greensboro North Carolina USA Department of Civil Engineering University of Texas Rio Grande Valley Edinburg Texas USA

To prevent early bridge failures, effective Structural Health Monitoring (SHM) is vital. Vibration-based damage assessment is a powerful tool in this regard, as it relies on changes in a structure’s dynamic characteristics as it degrades. By measuring the vibration response of a bridge due to passing vehicles, this approach can identify potential structural damage. This dissertation introduces a novel technique grounded in Vehicle-Bridge Interaction (VBI) to evaluate bridge health. It aims to detect damage by analyzing the response of passing vehicles, taking into account VBI. The theoretical foundation of this method begins with representing the bridge’s superstructure using a Finite Element model and employing a half-car dynamic model to simulate the vehicle with suspension. Two sets of motion equations, one for the bridge and one for the vehicle are generated using the Finite Element Method, mode superposition, and D’Alembert’s principle. The combined dynamics are solved using the Newmark-beta method, accounting for road surface roughness. A new approach for damage identification based on the response of passing vehicles is proposed. The response is theoretically composed of vehicle frequency, bridge natural frequency, and a pseudo-frequency component related to vehicle speed. The empirical mode decomposition (EMD) method is applied to decompose the signal into its constituent parts, and damage detection relies on the Intrinsic mode Functions (IMFs) corresponding to the vehicle speed component. This technique effectively identifies various damage scenarios considered in the study.

关键词： Structural Health Monitoring Vibration-Based Damage Identification Vehicle-Bridge Interaction Finite Element model empirical mode decomposition

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