Travel time Lamb wave tomography has been shown to be an effective nondestructive evaluation （NDE） technique for plate-like structures. The methods used previously to extract arrival times of the fastest or multi La...Travel time Lamb wave tomography has been shown to be an effective nondestructive evaluation （NDE） technique for plate-like structures. The methods used previously to extract arrival times of the fastest or multi Lamb wave modes are mostly based on various timefrequency methods such as Wigner-Ville distribution, shorttime Fourier transform, and recently explored wavelet transform（WT）. Frankly speaking, uses of these signal processing methods improve the accuracy of the arrival time extraction to a great extent relative to directly extract arrival times in time-domain from Lamb waveforms. Hilbert-Huang transform（HHT） is also an efficient way for analyzing and processing non-stationary signals. The resolving power of time and frequency is restricted from Heisenberg principle in wavelet analysis, while in HHT, the time resolving power is precise and steady, and frequency resolving power is adaptive according to signal intrinsic characteristics. Conclusion can be made that the HI-IT method is more adaptive than WT anal;/sis in ~.!~M~ zing non-stationary signals. Based on the abo~, ~tiaf method is attempted to extract arrival times from Lamb waveforms in this paper. The Lamb wave tomography images generated with arrival times from HHT method were compared with those of WT. The results show that the new method improves the quality of tomography image, which demonstrates the applicability of HHT method in extracting arrival times of Lamb waves.展开更多
The Hilbert-based time-frequency analysis has promising capacity to reveal the time-variant behaviors of a sys- tem.To admit well-behaved Hilbert transforms,component decomposition of signals must be performed beforeh...The Hilbert-based time-frequency analysis has promising capacity to reveal the time-variant behaviors of a sys- tem.To admit well-behaved Hilbert transforms,component decomposition of signals must be performed beforehand.This was first systematically implemented by the empirical mode decomposition(EMD)in the Hilbert-Huang transform,which can provide a time-frequency representation of the signals.The EMD,however,has limitations in distinguishing different components in narrowband signals commonly found in free-decay vibration signals.In this study,a technique for decompo- sing components in narrowband signals based on waves' beating phenomena is proposed to improve the EMD,in which the time scale structure of the signal is unveiled by the Hilbert transform as a result of wave beating,the order of component ex- traction is reversed from that in the EMD and the end effect is confined.The proposed technique is verified by performing the component decomposition of a simulated signal and a free decay signal actually measured in an instrumented bridge structure.In addition,the adaptability of the technique to time-variant dynamic systems is demonstrated with a simulated time-variant MDOF system.展开更多
The algorithm is an image encryption algorithm based on the improved baker transformation and chaotic substitution box(S-box). It mainly uses the initial values and parameters of a one-dimensional logistic chaotic sys...The algorithm is an image encryption algorithm based on the improved baker transformation and chaotic substitution box(S-box). It mainly uses the initial values and parameters of a one-dimensional logistic chaotic system as an encryption key. Specifically, in the image scrambling stage, the algorithm primarily uses an improved baker transform method to process the image. In the image diffusion stage, the algorithm first uses the chaotic S-box method to process the encryption key. Secondly, an exclusive OR(XOR) operation is performed on the image and the encryption key to initially diffuse the image. Finally, the image is again diffused using the method of ortho XOR. Simulation analysis shows that the algorithm can achieve good encryption effect, simple and easy implementation, and good security. In the digital image communication transmission, it has good practical value.展开更多
The staggered distribution of joints and fissures in space constitutes the weak part of any rock mass.The identification of rock mass structural planes and the extraction of characteristic parameters are the basis of ...The staggered distribution of joints and fissures in space constitutes the weak part of any rock mass.The identification of rock mass structural planes and the extraction of characteristic parameters are the basis of rock-mass integrity evaluation,which is very important for analysis of slope stability.The laser scanning technique can be used to acquire the coordinate information pertaining to each point of the structural plane,but large amount of point cloud data,uneven density distribution,and noise point interference make the identification efficiency and accuracy of different types of structural planes limited by point cloud data analysis technology.A new point cloud identification and segmentation algorithm for rock mass structural surfaces is proposed.Based on the distribution states of the original point cloud in different neighborhoods in space,the point clouds are characterized by multi-dimensional eigenvalues and calculated by the robust randomized Hough transform(RRHT).The normal vector difference and the final eigenvalue are proposed for characteristic distinction,and the identification of rock mass structural surfaces is completed through regional growth,which strengthens the difference expression of point clouds.In addition,nearest Voxel downsampling is also introduced in the RRHT calculation,which further reduces the number of sources of neighborhood noises,thereby improving the accuracy and stability of the calculation.The advantages of the method have been verified by laboratory models.The results showed that the proposed method can better achieve the segmentation and statistics of structural planes with interfaces and sharp boundaries.The method works well in the identification of joints,fissures,and other structural planes on Mangshezhai slope in the Three Gorges Reservoir area,China.It can provide a stable and effective technique for the identification and segmentation of rock mass structural planes,which is beneficial in engineering practice.展开更多
The chirplet transform is the generalization form of fast Fourier transform , short-time Fourier transform, and wavelet transform. It has the most flexible time frequency window and successfully used in practices. How...The chirplet transform is the generalization form of fast Fourier transform , short-time Fourier transform, and wavelet transform. It has the most flexible time frequency window and successfully used in practices. However, the chirplet transform has not inherent inverse transform, and can not overcome the signal reconstructing problem. In this paper, we proposed the improved chirplet transform (ICT) and constructed the inverse ICT. Finally, by simulating the harmonic voltages, The power of the improved chirplet transform are illustrated for harmonic detection. The contours clearly showed the harmonic occurrence time and harmonic duration.展开更多
Carbonates present complex pore systems that strongly influence the physical properties and their interrelationships.This study proposes a new approach to establish pore-type mixing-based permeability transforms by in...Carbonates present complex pore systems that strongly influence the physical properties and their interrelationships.This study proposes a new approach to establish pore-type mixing-based permeability transforms by integrating well-log and core data.We investigate the influence of pore-structure heterogeneity on permeability and velocity through the rock-frame flexibility factors(γ and γ_(μ)),derivable using standard sonic and density logs.We derive permeability transforms,with correlation coefficients,R of 0.8 to 0.9,from core measurements and pore-structure variations-dependent physical parameters,namely the porosity exponent(m),Poisson’s ratio(σ),velocity deviation log(VDL),and velocity ratio(VR).Through extrapolation using log-data,the m-and VDL-based correlations provide significantly better permeability estimates,with the highest accuracy attained with the m-based correlation,whereas the VR-andσ-based correlations lead to permeability overes-timation for high porosities.We plotted log-derived porosity vs.permeability,obtained applying the m-based correlation,to generate consistent porosity-permeability relationships,which account for pore-structure heterogeneity,by sorting the scattering points into distinct groups/trends by considering the variations of pore-structure types and abundance of a specific porosity.For the studied oilfield,three porosity-permeability relationships are identified,with correlation coefficients approaching 0.9,thus validating the approach and supporting its application in petrophysically similar reservoirs.展开更多
The midside node sensitivity of eight-node isoparametric element in 3-D BEM is investigated. The paper points out that the suggestion, based upon which the midside nodes should be located in the middle third of distan...The midside node sensitivity of eight-node isoparametric element in 3-D BEM is investigated. The paper points out that the suggestion, based upon which the midside nodes should be located in the middle third of distance between the adjacent corners, should be followed even more strictly for the conventional isoparametric transformation (CIT) in BEM as that in FEM. A new coordinate transformation relation has been put forward to solve the singular integral problem. The computation is carried to two cases: a cubic body subjected to tensile stress and pure bending. The numerical results show that the improved isoparametric transformation (IIT) is easier and more flexible to practice.展开更多
The original fault data of oil immersed transformer often contains a large number of unnecessary attributes,which greatly increases the elapsed time of the algorithm and reduces the classification accuracy,leading to ...The original fault data of oil immersed transformer often contains a large number of unnecessary attributes,which greatly increases the elapsed time of the algorithm and reduces the classification accuracy,leading to the rise of the diagnosis error rate.Therefore,in order to obtain high quality oil immersed transformer fault attribute data sets,an improved imperialist competitive algorithm was proposed to optimize the rough set to discretize the original fault data set and the attribute reduction.The feasibility of the proposed algorithm was verified by experiments and compared with other intelligent algorithms.Results show that the algorithm was stable at the 27th iteration with a reduction rate of 56.25%and a reduction accuracy of 98%.By using BP neural network to classify the reduction results,the accuracy was 86.25%,and the overall effect was better than those of the original data and other algorithms.Hence,the proposed method is effective for fault attribute reduction of oil immersed transformer.展开更多
The damage or loss of urban road manhole covers may cause great risk to residents＇ lives and property if they cannot be discovered in time. Most existing research recommendations for solving this problem are difficul...The damage or loss of urban road manhole covers may cause great risk to residents＇ lives and property if they cannot be discovered in time. Most existing research recommendations for solving this problem are difficult to implement. This paper proposes an algorithm that combines the improved Hough transform and image comparison to identify the damage or loss of the manhole covers in complicated surface conditions by using existing urban road video images. Focusing on the pre-processed images, the edge contour tracking algorithm is applied to find all of the edges. Then with the improved Hough transformation, color recognition and image matching algorithm, the manhole cover area is found and the change rates of the manhole cover area are calculated. Based on the threshold of the change rates, it can be determined whether there is potential damage or loss in the manhole cover. Compared with the traditional Hough transform, the proposed method can effectively improve the processing speed and reduce invalid sampling and accumulation. Experimental results indicate that the proposed algorithm has the functions of effective positioning and early warning in the conditions of complex background, different perspectives, and different videoing time and conditions, such as when the target is partially covered.展开更多
Imaging the spatial precession cone-shaped targets with narrowband radar is a new technical approach in mid-course recognition problem. However, most existing time-frequency methods still have some inevitable deficien...Imaging the spatial precession cone-shaped targets with narrowband radar is a new technical approach in mid-course recognition problem. However, most existing time-frequency methods still have some inevitable deficiencies for extracting microDoppler information in practical applications, which leads to blurring of the image. A new narrowband radar imaging algorithm for the precession cone-shaped targets is proposed. The instantaneous frequency of each scattering point is gained by using the improved Hilbert-Huang transform, then the positions of scattering points in the parameter domain are reconstructed. Numerical simulation and experiment results confirm the effectiveness and high precision of the proposed algorithm.展开更多
Acoustic Emission(AE)waveforms contain information on microscopic structural features that can be related with damage of coal rock masses.In this paper,the Hilbert-Huang transform(HHT)method is used to obtain detailed...Acoustic Emission(AE)waveforms contain information on microscopic structural features that can be related with damage of coal rock masses.In this paper,the Hilbert-Huang transform(HHT)method is used to obtain detailed structural characteristics of coal rock masses associated with damage,at different loading stages,from the analyses of the characteristics of AE waveforms.The results show that the HHT method can be used to decompose the target waveform into multiple intrinsic mode function(IMF)components,with the energy mainly concentrated in the c1−c4 IMF components,where the c1 component has the highest frequency and the largest amount of energy.As the loading continues,the proportion of energy occupied by the low-frequency IMF component shows an increasing trend.In the initial compaction stage,the Hilbert marginal spectrum is mainly concentrated in the low frequency range of 0−40 kHz.The plastic deformation stage is associated to energy accumulation in the frequency range of 0−25 kHz and 200−350 kHz,while the instability damage stage is mainly concentrated in the frequency range of 0−25 kHz.At 20 kHz,the instability damage reaches its maximum value.There is a relatively clear instantaneous energy peak at each stage,albeit being more distinct at the beginning and at the end of the compaction phase.Since the effective duration of the waveform is short,its resulting energy is small,and so there is a relatively high value from the instantaneous energy peak.The waveform lasts a relatively long time after the peak that coincides with failure,which is the period where the waveform reaches its maximum energy level.The Hilbert three-dimensional energy spectrum is generally zero in the region where the real energy is zero.In addition,its energy spectrum is intermittent rather than continuous.It is therefore consistent with the characteristics of the several dynamic ranges mentioned above,and it indicates more clearly the low-frequency energy concentration in the critical stage of instability failure.This study well reflects the response law of geophysical signals in the process of coal rock instability and failure,providing a basis for monitoring coal rock dynamic disasters.展开更多
The field seismic data is disturbed by the interferential information, which has low signal to noise ratio (SNR). That is disadvantage for seismic data interpretation. So it is important to remove the noise of seismic...The field seismic data is disturbed by the interferential information, which has low signal to noise ratio (SNR). That is disadvantage for seismic data interpretation. So it is important to remove the noise of seismic data. Independent component analysis (ICA) can remove most of the noise interference. However, ICA has some defects in noise reduction, because it needs some conditions that seismic data is independent reciprocally for denoising. To solve these defects, this paper proposes an improved ICA algorithm to noise reduction. Through simulation experiments, it can be obtained that the best decomposition levels of the new algorithm is 3. At last, the proposed improved ICA is applied to deal with the actual seismic data. The results show that it can effectively eliminate most of seismic noise such as random noise, linear interference, surface waves, and so on. The improved ICA is not only easy to denoising, but also has excellent mathematical theoretical properties.展开更多
Hilbert-Huang Transform (HHT) is a newly developed powerful method for nonlinear and non-stationary time series analysis. The empirical mode decomposition is the key part of HHT, while its algorithm was protected by N...Hilbert-Huang Transform (HHT) is a newly developed powerful method for nonlinear and non-stationary time series analysis. The empirical mode decomposition is the key part of HHT, while its algorithm was protected by NASA as a US patent, which limits the wide application among the scientific community. Two approaches, mirror periodic and extrema extending methods, have been developed for handling the end effects of empirical mode decomposition. The implementation of the HHT is realized in detail to widen the application. The detailed comparison of the results from two methods with that from Huang et al. (1998, 1999), and the comparison between two methods are presented. Generally, both methods reproduce faithful results as those of Huang et al. For mirror periodic method (MPM), the data are extended once forever. Ideally, it is a way for handling the end effects of the HHT, especially for the signal that has symmetric waveform. The extrema extending method (EEM) behaves as good as MPM, and it is better than MPM for the signal that has strong asymmetric waveform. However, it has to perform extrema envelope extending in every shifting process.展开更多
In this paper we discuss the use of the Hilbert-Huang transform（HHT） to enhance the time-frequency analysis of microtremor measurements. HHT is a powerful algorithm that combines the process of empirical mode decomp...In this paper we discuss the use of the Hilbert-Huang transform（HHT） to enhance the time-frequency analysis of microtremor measurements. HHT is a powerful algorithm that combines the process of empirical mode decomposition（EMD） and the Hilbert transform to compose the HilbertHuang spectrum that contains the time-frequency-energy information of the recorded signals. HHT is an adaptive algorithm and does not require the signals to be linear or stationary. HHT is advantageous for analyzing microtremor data, since observed microtremors are commonly contaminated by nonstationary transient noises close to the recording instruments. This is especially true when microtremors are measured in an urban environment. In our data processing HHT was used to（1） eliminate the unwanted short-duration transient constituents from microtremor data and use only the coherent portion of the data to carry out the widely used horizontal to vertical spectral ratio（H/V） method;（2） identify and eliminate the continuous industrial noise in certain frequency band; and（3） enhance the H/V analysis by using the Hilbert-Huang spectrum（HHS）. The efficacy of this proposed approach is demonstrated by the examples of applying it to microtremor data acquired in the metropolitan Beijing area.展开更多
Traditional modal parameter identifi cation methods have many disadvantages,especially when used for processing nonlinear and non-stationary signals.In addition,they are usually not able to accurately identify the dam...Traditional modal parameter identifi cation methods have many disadvantages,especially when used for processing nonlinear and non-stationary signals.In addition,they are usually not able to accurately identify the damping ratio and damage.In this study,methods based on the Hilbert-Huang transform(HHT) are investigated for structural modal parameter identifi cation and damage diagnosis.First,mirror extension and prediction via a radial basis function(RBF) neural network are used to restrain the troublesome end-effect issue in empirical mode decomposition(EMD),which is a crucial part of HHT.Then,the approaches based on HHT combined with other techniques,such as the random decrement technique(RDT),natural excitation technique(NExT) and stochastic subspace identifi cation(SSI),are proposed to identify modal parameters of structures.Furthermore,a damage diagnosis method based on the HHT is also proposed.Time-varying instantaneous frequency and instantaneous energy are used to identify the damage evolution of the structure.The relative amplitude of the Hilbert marginal spectrum is used to identify the damage location of the structure.Finally,acceleration records at gauge points from shaking table testing of a 12-story reinforced concrete frame model are taken to validate the proposed approaches.The results show that the proposed approaches based on HHT for modal parameter identifi cation and damage diagnosis are reliable and practical.展开更多
Under the conditions of strong sea clutter and complex moving targets,it is extremely difficult to detect moving targets in the maritime surface.This paper proposes a new algorithm named improved tunable Q-factor wave...Under the conditions of strong sea clutter and complex moving targets,it is extremely difficult to detect moving targets in the maritime surface.This paper proposes a new algorithm named improved tunable Q-factor wavelet transform(TQWT)for moving target detection.Firstly,this paper establishes a moving target model and sparsely compensates the Doppler migration of the moving target in the fractional Fourier transform(FRFT)domain.Then,TQWT is adopted to decompose the signal based on the discrimination between the sea clutter and the target’s oscillation characteristics,using the basis pursuit denoising(BPDN)algorithm to get the wavelet coefficients.Furthermore,an energy selection method based on the optimal distribution of sub-bands energy is proposed to sparse the coefficients and reconstruct the target.Finally,experiments on the Council for Scientific and Industrial Research(CSIR)dataset indicate the performance of the proposed method and provide the basis for subsequent target detection.展开更多
Due to piping vibration, fluid pulsation and other environmental disturbances, variations of amplitude and frequency to the raw signals of vortex flowmeter are imposed. It is difficult to extract vortex frequencies wh...Due to piping vibration, fluid pulsation and other environmental disturbances, variations of amplitude and frequency to the raw signals of vortex flowmeter are imposed. It is difficult to extract vortex frequencies which indicate volumetric flowrate from noisy data, especially at low flowrates. Hilbert-Huang transform was adopted to estimate vortex frequency. The noisy raw signal was decomposed into different intrinsic modes by empirical mode decomposition, the time-frequency characteristics of each mode were analyzed, and the vortex frequency was obtained by calculating partial mode’s instantaneous frequency. Experimental results show that the proposed method can estimate the vortex frequency with less than 2% relative error; and in the low flowrate range studied, the denoising ability of Hilbert-Huang transform is markedly better than Fourier based algorithms. These findings reveal that this method is accurate for vortex signal processing and at the same time has strong anti-disturbance ability.展开更多
In fault diagnosis of rotating machinery, Hil- bert-Huang transform （HHT） is often used to extract the fault characteristic signal and analyze decomposition results in time-frequency domain. However, end effect occu...In fault diagnosis of rotating machinery, Hil- bert-Huang transform （HHT） is often used to extract the fault characteristic signal and analyze decomposition results in time-frequency domain. However, end effect occurs in HHT, which leads to a series of problems such as modal aliasing and false IMF （Intrinsic Mode Func- tion）. To counter such problems in HHT, a new method is put forward to process signal by combining the general- ized regression neural network （GRNN） with the bound- ary local characteristic-scale continuation （BLCC）. Firstly, the improved EMD （Empirical Mode Decompo- sition） method is used to inhibit the end effect problem that appeared in conventional EMD. Secondly, the gen- erated IMF components are used in HHT. Simulation and measurement experiment for the cases of time domain, frequency domain and related parameters of Hilbert- Huang spectrum show that the method described here can restrain the end effect compared with the results obtained through mirror continuation, as the absolute percentage of the maximum mean of the beginning end point offset and the terminal point offset are reduced from 30.113% and 27.603% to 0.510% and 6.039% respectively, thus reducing the modal aliasing, and eliminating the false IMF components of HHT. The proposed method caneffectively inhibit end effect, reduce modal aliasing and false IMF components, and show the real structure of signal components accuratelX.展开更多
The operating state of bearing affects the performance of rotating machinery;thus,how to accurately extract features from the original vibration signals and recognise the faulty parts as early as possible is very crit...The operating state of bearing affects the performance of rotating machinery;thus,how to accurately extract features from the original vibration signals and recognise the faulty parts as early as possible is very critical.In this study,the one‐dimensional ternary model which has been proved to be an effective statistical method in feature selection is introduced and shapelet transformation is proposed to calculate the parameter of one‐dimensional ternary model that is usually selected by trial and error.Then XGBoost is used to recognise the faults from the obtained features,and artificial bee colony algorithm(ABC)is introduced to optimise the parameters of XGBoost.Moreover,for improving the performance of intelligent algorithm,an improved strategy where the evolution is guided by the probability that the optimal solution appears in certain solution space is proposed.The experimental results based on the failure vibration signal samples show that the average accuracy of fault signal recognition can reach 97%,which is much higher than the ones corresponding to traditional extraction strategies.And with the help of improved ABC algorithm,the performance of XGBoost classifier could be optimised;the accuracy could be improved from 97.02%to 98.60%compared with the traditional classification strategy.展开更多
基金National Natural Science Foundation of China(No.10504020,10874110)Shanghai Leading Academic Discipline Project,China(No.S30108)Science and Technology Commission of Shanghai Municipality,China(No.08DZ2231100)
文摘Travel time Lamb wave tomography has been shown to be an effective nondestructive evaluation （NDE） technique for plate-like structures. The methods used previously to extract arrival times of the fastest or multi Lamb wave modes are mostly based on various timefrequency methods such as Wigner-Ville distribution, shorttime Fourier transform, and recently explored wavelet transform（WT）. Frankly speaking, uses of these signal processing methods improve the accuracy of the arrival time extraction to a great extent relative to directly extract arrival times in time-domain from Lamb waveforms. Hilbert-Huang transform（HHT） is also an efficient way for analyzing and processing non-stationary signals. The resolving power of time and frequency is restricted from Heisenberg principle in wavelet analysis, while in HHT, the time resolving power is precise and steady, and frequency resolving power is adaptive according to signal intrinsic characteristics. Conclusion can be made that the HI-IT method is more adaptive than WT anal;/sis in ~.!~M~ zing non-stationary signals. Based on the abo~, ~tiaf method is attempted to extract arrival times from Lamb waveforms in this paper. The Lamb wave tomography images generated with arrival times from HHT method were compared with those of WT. The results show that the new method improves the quality of tomography image, which demonstrates the applicability of HHT method in extracting arrival times of Lamb waves.
文摘The Hilbert-based time-frequency analysis has promising capacity to reveal the time-variant behaviors of a sys- tem.To admit well-behaved Hilbert transforms,component decomposition of signals must be performed beforehand.This was first systematically implemented by the empirical mode decomposition(EMD)in the Hilbert-Huang transform,which can provide a time-frequency representation of the signals.The EMD,however,has limitations in distinguishing different components in narrowband signals commonly found in free-decay vibration signals.In this study,a technique for decompo- sing components in narrowband signals based on waves' beating phenomena is proposed to improve the EMD,in which the time scale structure of the signal is unveiled by the Hilbert transform as a result of wave beating,the order of component ex- traction is reversed from that in the EMD and the end effect is confined.The proposed technique is verified by performing the component decomposition of a simulated signal and a free decay signal actually measured in an instrumented bridge structure.In addition,the adaptability of the technique to time-variant dynamic systems is demonstrated with a simulated time-variant MDOF system.
基金supported by the National Natural Science Foundation of China (Grant No. 61672124)the Password Theory Project of the 13th Five-Year Plan National Cryptography Development Fund,China (Grant No. MMJJ20170203)+3 种基金the Liaoning Provincial Science and Technology Innovation Leading Talents Program Project,China (Grant No. XLYC1802013)the Key Research and Development Projects of Liaoning Province,China (Grant No. 2019020105-JH2/103)the Jinan City ‘20 universities’ Funding Projects Introducing Innovation Team Program,China (Grant No. 2019GXRC031)the “Double First-rate”Construction Project (“Innovation Project”),China (Grant No. SSCXXM013)。
文摘The algorithm is an image encryption algorithm based on the improved baker transformation and chaotic substitution box(S-box). It mainly uses the initial values and parameters of a one-dimensional logistic chaotic system as an encryption key. Specifically, in the image scrambling stage, the algorithm primarily uses an improved baker transform method to process the image. In the image diffusion stage, the algorithm first uses the chaotic S-box method to process the encryption key. Secondly, an exclusive OR(XOR) operation is performed on the image and the encryption key to initially diffuse the image. Finally, the image is again diffused using the method of ortho XOR. Simulation analysis shows that the algorithm can achieve good encryption effect, simple and easy implementation, and good security. In the digital image communication transmission, it has good practical value.
基金the National Natural Science Foundation of China(51909136)the Open Research Fund of Key Laboratory of Geological Hazards on Three Gorges Reservoir Area(China Three Gorges University),Ministry of Education,Grant No.2022KDZ21Fund of National Major Water Conservancy Project Construction(0001212022CC60001)。
文摘The staggered distribution of joints and fissures in space constitutes the weak part of any rock mass.The identification of rock mass structural planes and the extraction of characteristic parameters are the basis of rock-mass integrity evaluation,which is very important for analysis of slope stability.The laser scanning technique can be used to acquire the coordinate information pertaining to each point of the structural plane,but large amount of point cloud data,uneven density distribution,and noise point interference make the identification efficiency and accuracy of different types of structural planes limited by point cloud data analysis technology.A new point cloud identification and segmentation algorithm for rock mass structural surfaces is proposed.Based on the distribution states of the original point cloud in different neighborhoods in space,the point clouds are characterized by multi-dimensional eigenvalues and calculated by the robust randomized Hough transform(RRHT).The normal vector difference and the final eigenvalue are proposed for characteristic distinction,and the identification of rock mass structural surfaces is completed through regional growth,which strengthens the difference expression of point clouds.In addition,nearest Voxel downsampling is also introduced in the RRHT calculation,which further reduces the number of sources of neighborhood noises,thereby improving the accuracy and stability of the calculation.The advantages of the method have been verified by laboratory models.The results showed that the proposed method can better achieve the segmentation and statistics of structural planes with interfaces and sharp boundaries.The method works well in the identification of joints,fissures,and other structural planes on Mangshezhai slope in the Three Gorges Reservoir area,China.It can provide a stable and effective technique for the identification and segmentation of rock mass structural planes,which is beneficial in engineering practice.
文摘The chirplet transform is the generalization form of fast Fourier transform , short-time Fourier transform, and wavelet transform. It has the most flexible time frequency window and successfully used in practices. However, the chirplet transform has not inherent inverse transform, and can not overcome the signal reconstructing problem. In this paper, we proposed the improved chirplet transform (ICT) and constructed the inverse ICT. Finally, by simulating the harmonic voltages, The power of the improved chirplet transform are illustrated for harmonic detection. The contours clearly showed the harmonic occurrence time and harmonic duration.
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(No.XDA14010302)the National Key Research and Development Program of China(No.2017YFC0603104)the Ministère de l’Enseignement Supérieur et de la Recherche,Agence Nigérienne des Allocations et des Bourses,ANAB-Niger(No.2015563007).
文摘Carbonates present complex pore systems that strongly influence the physical properties and their interrelationships.This study proposes a new approach to establish pore-type mixing-based permeability transforms by integrating well-log and core data.We investigate the influence of pore-structure heterogeneity on permeability and velocity through the rock-frame flexibility factors(γ and γ_(μ)),derivable using standard sonic and density logs.We derive permeability transforms,with correlation coefficients,R of 0.8 to 0.9,from core measurements and pore-structure variations-dependent physical parameters,namely the porosity exponent(m),Poisson’s ratio(σ),velocity deviation log(VDL),and velocity ratio(VR).Through extrapolation using log-data,the m-and VDL-based correlations provide significantly better permeability estimates,with the highest accuracy attained with the m-based correlation,whereas the VR-andσ-based correlations lead to permeability overes-timation for high porosities.We plotted log-derived porosity vs.permeability,obtained applying the m-based correlation,to generate consistent porosity-permeability relationships,which account for pore-structure heterogeneity,by sorting the scattering points into distinct groups/trends by considering the variations of pore-structure types and abundance of a specific porosity.For the studied oilfield,three porosity-permeability relationships are identified,with correlation coefficients approaching 0.9,thus validating the approach and supporting its application in petrophysically similar reservoirs.
文摘The midside node sensitivity of eight-node isoparametric element in 3-D BEM is investigated. The paper points out that the suggestion, based upon which the midside nodes should be located in the middle third of distance between the adjacent corners, should be followed even more strictly for the conventional isoparametric transformation (CIT) in BEM as that in FEM. A new coordinate transformation relation has been put forward to solve the singular integral problem. The computation is carried to two cases: a cubic body subjected to tensile stress and pure bending. The numerical results show that the improved isoparametric transformation (IIT) is easier and more flexible to practice.
基金Sponsored by the National Natural Science Foundation of China(Grant No.51504085)the Natural Science Foundation for Returness of Heilongjiang Province of China(Grant No.LC2017026).
文摘The original fault data of oil immersed transformer often contains a large number of unnecessary attributes,which greatly increases the elapsed time of the algorithm and reduces the classification accuracy,leading to the rise of the diagnosis error rate.Therefore,in order to obtain high quality oil immersed transformer fault attribute data sets,an improved imperialist competitive algorithm was proposed to optimize the rough set to discretize the original fault data set and the attribute reduction.The feasibility of the proposed algorithm was verified by experiments and compared with other intelligent algorithms.Results show that the algorithm was stable at the 27th iteration with a reduction rate of 56.25%and a reduction accuracy of 98%.By using BP neural network to classify the reduction results,the accuracy was 86.25%,and the overall effect was better than those of the original data and other algorithms.Hence,the proposed method is effective for fault attribute reduction of oil immersed transformer.
基金The Natural Science Fundation of Education Department of Anhui Province(No.KJ2012B051)
文摘The damage or loss of urban road manhole covers may cause great risk to residents＇ lives and property if they cannot be discovered in time. Most existing research recommendations for solving this problem are difficult to implement. This paper proposes an algorithm that combines the improved Hough transform and image comparison to identify the damage or loss of the manhole covers in complicated surface conditions by using existing urban road video images. Focusing on the pre-processed images, the edge contour tracking algorithm is applied to find all of the edges. Then with the improved Hough transformation, color recognition and image matching algorithm, the manhole cover area is found and the change rates of the manhole cover area are calculated. Based on the threshold of the change rates, it can be determined whether there is potential damage or loss in the manhole cover. Compared with the traditional Hough transform, the proposed method can effectively improve the processing speed and reduce invalid sampling and accumulation. Experimental results indicate that the proposed algorithm has the functions of effective positioning and early warning in the conditions of complex background, different perspectives, and different videoing time and conditions, such as when the target is partially covered.
基金supported by the China National Funds for Distinguished Young Scientists(61025006)
文摘Imaging the spatial precession cone-shaped targets with narrowband radar is a new technical approach in mid-course recognition problem. However, most existing time-frequency methods still have some inevitable deficiencies for extracting microDoppler information in practical applications, which leads to blurring of the image. A new narrowband radar imaging algorithm for the precession cone-shaped targets is proposed. The instantaneous frequency of each scattering point is gained by using the improved Hilbert-Huang transform, then the positions of scattering points in the parameter domain are reconstructed. Numerical simulation and experiment results confirm the effectiveness and high precision of the proposed algorithm.
基金Projects(51904167, 51474134, 51774194) supported by the National Natural Science Foundation of ChinaProject(SKLCRSM19KF008) supported by the Research Fund of the State Key Laboratory of Coal Resources and Safe Mining,CUMT,China+5 种基金Project(cstc2019jcyj-bsh0041) supported by the Natural Science Foundation of Chongqing,ChinaProject(2011DA105287-BH201903) supported by the Postdoctoral ScienceFunded by State Key Laboratory of Coal Mine Disaster Dynamics and Control,ChinaProject(2019SDZY034-2) supported by the Key R&D plan of Shandong Province,ChinaProject(2020M670781) supported by the China Postdoctoral Science FoundationProject supported by the Taishan Scholars ProjectProject supported by the Taishan Scholar Talent Team Support Plan for Advantaged&Unique Discipline Areas,China
文摘Acoustic Emission(AE)waveforms contain information on microscopic structural features that can be related with damage of coal rock masses.In this paper,the Hilbert-Huang transform(HHT)method is used to obtain detailed structural characteristics of coal rock masses associated with damage,at different loading stages,from the analyses of the characteristics of AE waveforms.The results show that the HHT method can be used to decompose the target waveform into multiple intrinsic mode function(IMF)components,with the energy mainly concentrated in the c1−c4 IMF components,where the c1 component has the highest frequency and the largest amount of energy.As the loading continues,the proportion of energy occupied by the low-frequency IMF component shows an increasing trend.In the initial compaction stage,the Hilbert marginal spectrum is mainly concentrated in the low frequency range of 0−40 kHz.The plastic deformation stage is associated to energy accumulation in the frequency range of 0−25 kHz and 200−350 kHz,while the instability damage stage is mainly concentrated in the frequency range of 0−25 kHz.At 20 kHz,the instability damage reaches its maximum value.There is a relatively clear instantaneous energy peak at each stage,albeit being more distinct at the beginning and at the end of the compaction phase.Since the effective duration of the waveform is short,its resulting energy is small,and so there is a relatively high value from the instantaneous energy peak.The waveform lasts a relatively long time after the peak that coincides with failure,which is the period where the waveform reaches its maximum energy level.The Hilbert three-dimensional energy spectrum is generally zero in the region where the real energy is zero.In addition,its energy spectrum is intermittent rather than continuous.It is therefore consistent with the characteristics of the several dynamic ranges mentioned above,and it indicates more clearly the low-frequency energy concentration in the critical stage of instability failure.This study well reflects the response law of geophysical signals in the process of coal rock instability and failure,providing a basis for monitoring coal rock dynamic disasters.
基金Funded by the Project of China Geological Survey (No.1212010916040)the Sichuan Science and Technology Program (No.2017JY0051)the Sichuan Science and Technology Program (No.2018GZ0200)
文摘The field seismic data is disturbed by the interferential information, which has low signal to noise ratio (SNR). That is disadvantage for seismic data interpretation. So it is important to remove the noise of seismic data. Independent component analysis (ICA) can remove most of the noise interference. However, ICA has some defects in noise reduction, because it needs some conditions that seismic data is independent reciprocally for denoising. To solve these defects, this paper proposes an improved ICA algorithm to noise reduction. Through simulation experiments, it can be obtained that the best decomposition levels of the new algorithm is 3. At last, the proposed improved ICA is applied to deal with the actual seismic data. The results show that it can effectively eliminate most of seismic noise such as random noise, linear interference, surface waves, and so on. The improved ICA is not only easy to denoising, but also has excellent mathematical theoretical properties.
基金This study is supported by the National Natural Science Foundation of China(NSFC)under contract Nos 49790010,40076010 and 49634140,National Key Basic Research and Development Plan in China under contract No.G1999043701)and the OCEAN-863 Project of China.
文摘Hilbert-Huang Transform (HHT) is a newly developed powerful method for nonlinear and non-stationary time series analysis. The empirical mode decomposition is the key part of HHT, while its algorithm was protected by NASA as a US patent, which limits the wide application among the scientific community. Two approaches, mirror periodic and extrema extending methods, have been developed for handling the end effects of empirical mode decomposition. The implementation of the HHT is realized in detail to widen the application. The detailed comparison of the results from two methods with that from Huang et al. (1998, 1999), and the comparison between two methods are presented. Generally, both methods reproduce faithful results as those of Huang et al. For mirror periodic method (MPM), the data are extended once forever. Ideally, it is a way for handling the end effects of the HHT, especially for the signal that has symmetric waveform. The extrema extending method (EEM) behaves as good as MPM, and it is better than MPM for the signal that has strong asymmetric waveform. However, it has to perform extrema envelope extending in every shifting process.
基金supported by the Ministry of Science and Technology of China (No. 2006DFA21650)the Institute of Earthquake Science, China Earthquake Administration (No. 0207690229)
文摘In this paper we discuss the use of the Hilbert-Huang transform（HHT） to enhance the time-frequency analysis of microtremor measurements. HHT is a powerful algorithm that combines the process of empirical mode decomposition（EMD） and the Hilbert transform to compose the HilbertHuang spectrum that contains the time-frequency-energy information of the recorded signals. HHT is an adaptive algorithm and does not require the signals to be linear or stationary. HHT is advantageous for analyzing microtremor data, since observed microtremors are commonly contaminated by nonstationary transient noises close to the recording instruments. This is especially true when microtremors are measured in an urban environment. In our data processing HHT was used to（1） eliminate the unwanted short-duration transient constituents from microtremor data and use only the coherent portion of the data to carry out the widely used horizontal to vertical spectral ratio（H/V） method;（2） identify and eliminate the continuous industrial noise in certain frequency band; and（3） enhance the H/V analysis by using the Hilbert-Huang spectrum（HHS）. The efficacy of this proposed approach is demonstrated by the examples of applying it to microtremor data acquired in the metropolitan Beijing area.
基金Gansu Science and Technology Key Project under Grant No.2GS057-A52-008
文摘Traditional modal parameter identifi cation methods have many disadvantages,especially when used for processing nonlinear and non-stationary signals.In addition,they are usually not able to accurately identify the damping ratio and damage.In this study,methods based on the Hilbert-Huang transform(HHT) are investigated for structural modal parameter identifi cation and damage diagnosis.First,mirror extension and prediction via a radial basis function(RBF) neural network are used to restrain the troublesome end-effect issue in empirical mode decomposition(EMD),which is a crucial part of HHT.Then,the approaches based on HHT combined with other techniques,such as the random decrement technique(RDT),natural excitation technique(NExT) and stochastic subspace identifi cation(SSI),are proposed to identify modal parameters of structures.Furthermore,a damage diagnosis method based on the HHT is also proposed.Time-varying instantaneous frequency and instantaneous energy are used to identify the damage evolution of the structure.The relative amplitude of the Hilbert marginal spectrum is used to identify the damage location of the structure.Finally,acceleration records at gauge points from shaking table testing of a 12-story reinforced concrete frame model are taken to validate the proposed approaches.The results show that the proposed approaches based on HHT for modal parameter identifi cation and damage diagnosis are reliable and practical.
基金the National Natural Science Foundation of China(U19B2031).
文摘Under the conditions of strong sea clutter and complex moving targets,it is extremely difficult to detect moving targets in the maritime surface.This paper proposes a new algorithm named improved tunable Q-factor wavelet transform(TQWT)for moving target detection.Firstly,this paper establishes a moving target model and sparsely compensates the Doppler migration of the moving target in the fractional Fourier transform(FRFT)domain.Then,TQWT is adopted to decompose the signal based on the discrimination between the sea clutter and the target’s oscillation characteristics,using the basis pursuit denoising(BPDN)algorithm to get the wavelet coefficients.Furthermore,an energy selection method based on the optimal distribution of sub-bands energy is proposed to sparse the coefficients and reconstruct the target.Finally,experiments on the Council for Scientific and Industrial Research(CSIR)dataset indicate the performance of the proposed method and provide the basis for subsequent target detection.
基金Project(20030335058) supported by the Special Research Fund for the Doctoral Programof Higher Education of China
文摘Due to piping vibration, fluid pulsation and other environmental disturbances, variations of amplitude and frequency to the raw signals of vortex flowmeter are imposed. It is difficult to extract vortex frequencies which indicate volumetric flowrate from noisy data, especially at low flowrates. Hilbert-Huang transform was adopted to estimate vortex frequency. The noisy raw signal was decomposed into different intrinsic modes by empirical mode decomposition, the time-frequency characteristics of each mode were analyzed, and the vortex frequency was obtained by calculating partial mode’s instantaneous frequency. Experimental results show that the proposed method can estimate the vortex frequency with less than 2% relative error; and in the low flowrate range studied, the denoising ability of Hilbert-Huang transform is markedly better than Fourier based algorithms. These findings reveal that this method is accurate for vortex signal processing and at the same time has strong anti-disturbance ability.
基金Supported by National Natural Science Foundation of China(Grant No.51375467)Quality Inspection of Public Welfare Industry Research Projects,China(Grant No.201410009)
文摘In fault diagnosis of rotating machinery, Hil- bert-Huang transform （HHT） is often used to extract the fault characteristic signal and analyze decomposition results in time-frequency domain. However, end effect occurs in HHT, which leads to a series of problems such as modal aliasing and false IMF （Intrinsic Mode Func- tion）. To counter such problems in HHT, a new method is put forward to process signal by combining the general- ized regression neural network （GRNN） with the bound- ary local characteristic-scale continuation （BLCC）. Firstly, the improved EMD （Empirical Mode Decompo- sition） method is used to inhibit the end effect problem that appeared in conventional EMD. Secondly, the gen- erated IMF components are used in HHT. Simulation and measurement experiment for the cases of time domain, frequency domain and related parameters of Hilbert- Huang spectrum show that the method described here can restrain the end effect compared with the results obtained through mirror continuation, as the absolute percentage of the maximum mean of the beginning end point offset and the terminal point offset are reduced from 30.113% and 27.603% to 0.510% and 6.039% respectively, thus reducing the modal aliasing, and eliminating the false IMF components of HHT. The proposed method caneffectively inhibit end effect, reduce modal aliasing and false IMF components, and show the real structure of signal components accuratelX.
基金National Nature Science Foundation of China,Grant/Award Number:U1813201the Key Scientific Research Projects of Henan Province,Grant/Award Number:22A413011+2 种基金the Training Program for Young Teachers in Universities of Henan Province,Grant/Award Number:2020GGJS137Henan Province Science and Technology R&D projects,Grant/Award Number:202102210135,212102310547 and 212102210080High‐end foreign expert program of Ministry of Science and Technology,Grant/Award Number:G2021026006L。
文摘The operating state of bearing affects the performance of rotating machinery;thus,how to accurately extract features from the original vibration signals and recognise the faulty parts as early as possible is very critical.In this study,the one‐dimensional ternary model which has been proved to be an effective statistical method in feature selection is introduced and shapelet transformation is proposed to calculate the parameter of one‐dimensional ternary model that is usually selected by trial and error.Then XGBoost is used to recognise the faults from the obtained features,and artificial bee colony algorithm(ABC)is introduced to optimise the parameters of XGBoost.Moreover,for improving the performance of intelligent algorithm,an improved strategy where the evolution is guided by the probability that the optimal solution appears in certain solution space is proposed.The experimental results based on the failure vibration signal samples show that the average accuracy of fault signal recognition can reach 97%,which is much higher than the ones corresponding to traditional extraction strategies.And with the help of improved ABC algorithm,the performance of XGBoost classifier could be optimised;the accuracy could be improved from 97.02%to 98.60%compared with the traditional classification strategy.
