The proposed robust reversible watermarking algorithm addresses the compatibility challenges between robustness and reversibility in existing video watermarking techniques by leveraging scene smoothness for frame grou...The proposed robust reversible watermarking algorithm addresses the compatibility challenges between robustness and reversibility in existing video watermarking techniques by leveraging scene smoothness for frame grouping videos.Grounded in the H.264 video coding standard,the algorithm first employs traditional robust watermark stitching technology to embed watermark information in the low-frequency coefficient domain of the U channel.Subsequently,it utilizes histogram migration techniques in the high-frequency coefficient domain of the U channel to embed auxiliary information,enabling successful watermark extraction and lossless recovery of the original video content.Experimental results demonstrate the algorithm’s strong imperceptibility,with each embedded frame in the experimental videos achieving a mean peak signal-to-noise ratio of 49.3830 dB and a mean structural similarity of 0.9996.Compared with the three comparison algorithms,the performance of the two experimental indexes is improved by 7.59%and 0.4%on average.At the same time,the proposed algorithm has strong robustness to both offline and online attacks:In the face of offline attacks,the average normalized correlation coefficient between the extracted watermark and the original watermark is 0.9989,and the average bit error rate is 0.0089.In the face of online attacks,the normalized correlation coefficient between the extracted watermark and the original watermark is 0.8840,and the mean bit error rate is 0.2269.Compared with the three comparison algorithms,the performance of the two experimental indexes is improved by 1.27%and 18.16%on average,highlighting the algorithm’s robustness.Furthermore,the algorithm exhibits low computational complexity,with the mean encoding and the mean decoding time differentials during experimental video processing being 3.934 and 2.273 s,respectively,underscoring its practical utility.展开更多
Earthquake-induced soil liquefaction poses significant risks to the stability of geotechnical structures worldwide.An understanding of the liquefaction triggering,and the post-failure large deformation behaviour is es...Earthquake-induced soil liquefaction poses significant risks to the stability of geotechnical structures worldwide.An understanding of the liquefaction triggering,and the post-failure large deformation behaviour is essential for designing resilient infrastructure.The present study develops a Smoothed Particle Hydrodynamics(SPH)framework for earthquake-induced liquefaction hazard assessment of geotechnical structures.The coupled flowdeformation behaviour of soils subjected to cyclic loading is described using the PM4Sand model implemented in a three-phase,single-layer SPH framework.A staggered discretisation scheme based on the stress particle SPH approach is adopted to minimise numerical inaccuracies caused by zero-energy modes and tensile instability.Further,non-reflecting boundary conditions for seismic analysis of semi-infinite soil domains using the SPH method are proposed.The numerical framework is employed for the analysis of cyclic direct simple shear test,seismic analysis of a level ground site,and liquefaction-induced failure of the Lower San Fernando Dam.Satisfactory agreement for liquefaction triggering and post-failure behaviour demonstrates that the SPH framework can be utilised to assess the effect of seismic loading on field-scale geotechnical structures.The present study also serves as the basis for future advancements of the SPH method for applications related to earthquake geotechnical engineering.展开更多
Fault degradation prognostic, which estimates the time before a failure occurs and process breakdowns, has been recognized as a key component in maintenance strategies nowadays. Fault degradation processes are, in gen...Fault degradation prognostic, which estimates the time before a failure occurs and process breakdowns, has been recognized as a key component in maintenance strategies nowadays. Fault degradation processes are, in general,slowly varying and can be modeled by autoregressive models. However, industrial processes always show typical nonstationary nature, which may bring two challenges: how to capture fault degradation information and how to model nonstationary processes. To address the critical issues, a novel fault degradation modeling and online fault prognostic strategy is developed in this paper. First, a fault degradation-oriented slow feature analysis(FDSFA) algorithm is proposed to extract fault degradation directions along which candidate fault degradation features are extracted. The trend ability assessment is then applied to select major fault degradation features. Second, a key fault degradation factor(KFDF) is calculated to characterize the fault degradation tendency by combining major fault degradation features and their stability weighting factors. After that, a time-varying regression model with temporal smoothness regularization is established considering nonstationary characteristics. On the basis of updating strategy, an online fault prognostic model is further developed by analyzing and modeling the prediction errors. The performance of the proposed method is illustrated with a real industrial process.展开更多
Smoothness prior approach for spectral smoothing is investigated using Fourier frequency filter analysis.We show that the regularization parameter in penalized least squares could continuously control the bandwidth of...Smoothness prior approach for spectral smoothing is investigated using Fourier frequency filter analysis.We show that the regularization parameter in penalized least squares could continuously control the bandwidth of low-pass filter.Besides,due to its property of interpolating the missing values automatically and smoothly,a spectral baseline correction algorithm based on the approach is proposed.This algorithm generally comprises spectral peak detection and baseline estimation.First,the spectral peak regions are detected and identified according to the second derivatives.Then,generalized smoothness prior approach combining identification information could estimate the baseline in peak regions.Results with both the simulated and real spectra show accurate baseline-corrected signals with this method.展开更多
Let h, be a measurable function defined on R^＋ ×R^＋. Let Ω ∈ L（log L^＋）^υq （S^n1-1 × S^n2-1） （1≤ υq ≤ 2） be homogeneous of degree zero and satisfy certain cancellation conditions. We show that...Let h, be a measurable function defined on R^＋ ×R^＋. Let Ω ∈ L（log L^＋）^υq （S^n1-1 × S^n2-1） （1≤ υq ≤ 2） be homogeneous of degree zero and satisfy certain cancellation conditions. We show that the singular integral Tf（x1,x2）=p.v.∫∫R^n1＋n2 Ω（y′1,y′2）h（｜y1｜,｜y2｜）/｜y1｜^n1｜y2｜^n2 f（x1-y1,x2-y2）dy1dy2maps from Sp,q^α1,α2F（R^n1×R^n2）boundedly to itself for 1 〈 p, q 〈 ∞, α1, α2 ∈R.展开更多
Nonlinear m-term approximation plays an important role in machine learning, signal processing and statistical estimating. In this paper by means of a nondecreasing dominated function, a greedy adaptive compression num...Nonlinear m-term approximation plays an important role in machine learning, signal processing and statistical estimating. In this paper by means of a nondecreasing dominated function, a greedy adaptive compression numerical algorithm in the best m -term approximation with regard to tensor product wavelet-type basis is pro-posed. The algorithm provides the asymptotically optimal approximation for the class of periodic functions with mixed Besov smoothness in the L q norm. Moreover, it depends only on the expansion of function f by tensor pro-duct wavelet-type basis, but neither on q nor on any special features of f.展开更多
Based on the traditional fifth-order weighted essentially non-oscillatory(WENO)scheme,a smoothness indicator is introduced to improve the capability of WENO schemes for resolving short waves.In the construction of the...Based on the traditional fifth-order weighted essentially non-oscillatory(WENO)scheme,a smoothness indicator is introduced to improve the capability of WENO schemes for resolving short waves.In the construction of the new smoothness indicator,the proportion of the first-order term in the original smoothness indicator is reduced by replacing the square of the first-order term with the product of the first-order and the third-order terms.To preserve the fifth-order of convergence rate,the smoothness indicator is combined with the method of Borges,et al.The numerical results show that the proposed schemes are more suitable for simulating turbulent flows or aeroacoustics problems than the previous fifth-order WENO schemes,thanks to its improved resolution on short waves.展开更多
Automatically assessing fabric smoothness grade is very important in the evaluation of fabric appearance.A system for objectively evaluating the fabric smoothness grade based on a grating projection unit and double co...Automatically assessing fabric smoothness grade is very important in the evaluation of fabric appearance.A system for objectively evaluating the fabric smoothness grade based on a grating projection unit and double colored CCD(short form of charge coupled device) was constructed in this paper.Two images captured by different CCD compensated each other which reduced the influence of noises.The application of the four-step phase-shifting method enabled the calculation of the exact phase in a point easy and quick.A large amount of 3D points with three coordinates X,Y and Z were obtained precisely making the definition and calculation of fabric smoothness characters easy.Then four parameters which intuitively denoted the fabric smoothness degree were obtained.Finally,a proper neural network was built,which successfully performed the fabric smoothness classification.The experimental results show that the system is applicable for all the fabric whatever pattern or color.The experimental grades provided by this grating projection system are also highly consistent with the subjective results.展开更多
Extending the results of [4] in the univariate case, in this paper we prove that the bivariate interpolation polynomials of Hermite-Fejér based on the Chebyshev nodes of the first kind, those of Lagrange based o...Extending the results of [4] in the univariate case, in this paper we prove that the bivariate interpolation polynomials of Hermite-Fejér based on the Chebyshev nodes of the first kind, those of Lagrange based on the Chebyshev nodes of second kind and ±1, and those of bivariate Shepard operators, have the property of partial preservation of global smoothness, with respect to various bivariate moduli of continuity.展开更多
For garment or fabric appearance, the cloth smoothness grade is one of the most important performance factors in textile and garment community. In this paper, on the base of Rough Set Theory,a new objective method for...For garment or fabric appearance, the cloth smoothness grade is one of the most important performance factors in textile and garment community. In this paper, on the base of Rough Set Theory,a new objective method for fabric smoothness grade evaluation was constructed. The objective smoothness grading model took the parameters of 120 AATCC replicas' point-sampled models as the conditional attributes and formed the smoothness grading decision table. Then, NS discretization method and genetic algorithm reduction method were used in the attributes discretization and feature reduction. Finally, the grading model was expressed as simple and intuitional classification rules. The simulation results show the validity of the fabric smoothness grading system which is built on the use of rough sets.展开更多
For the step-weight function , we prove that the Holder spaces ∧a,p on the interval [-1,1], defined in terms of moduli of smoothness with the step-weight function ,are linearly isomorphic to some sequence spaces, an...For the step-weight function , we prove that the Holder spaces ∧a,p on the interval [-1,1], defined in terms of moduli of smoothness with the step-weight function ,are linearly isomorphic to some sequence spaces, and the isomorphism is given by the cofficients of function with respect to a system of orthonormal splines with knots uniformly distributed according to the measure with density . In case ∧a,p is contained in the space of continuous functions, we give a discrete characterization of this space, using only values of function at the appropriate knots. Application of these results to characterize the order of polynomial approximation is presented.展开更多
基金supported in part by the National Natural Science Foundation of China under Grants 62202496,62272478the Basic Frontier Innovation Project of Engineering university of People Armed Police under Grants WJY202314,WJY202221.
文摘The proposed robust reversible watermarking algorithm addresses the compatibility challenges between robustness and reversibility in existing video watermarking techniques by leveraging scene smoothness for frame grouping videos.Grounded in the H.264 video coding standard,the algorithm first employs traditional robust watermark stitching technology to embed watermark information in the low-frequency coefficient domain of the U channel.Subsequently,it utilizes histogram migration techniques in the high-frequency coefficient domain of the U channel to embed auxiliary information,enabling successful watermark extraction and lossless recovery of the original video content.Experimental results demonstrate the algorithm’s strong imperceptibility,with each embedded frame in the experimental videos achieving a mean peak signal-to-noise ratio of 49.3830 dB and a mean structural similarity of 0.9996.Compared with the three comparison algorithms,the performance of the two experimental indexes is improved by 7.59%and 0.4%on average.At the same time,the proposed algorithm has strong robustness to both offline and online attacks:In the face of offline attacks,the average normalized correlation coefficient between the extracted watermark and the original watermark is 0.9989,and the average bit error rate is 0.0089.In the face of online attacks,the normalized correlation coefficient between the extracted watermark and the original watermark is 0.8840,and the mean bit error rate is 0.2269.Compared with the three comparison algorithms,the performance of the two experimental indexes is improved by 1.27%and 18.16%on average,highlighting the algorithm’s robustness.Furthermore,the algorithm exhibits low computational complexity,with the mean encoding and the mean decoding time differentials during experimental video processing being 3.934 and 2.273 s,respectively,underscoring its practical utility.
基金The insights gained through these works,particularly from projects IITD/IRD/CW14168,CW14469,and CW14378,have significantly contributed to the development of the analysis presented in this study。
文摘Earthquake-induced soil liquefaction poses significant risks to the stability of geotechnical structures worldwide.An understanding of the liquefaction triggering,and the post-failure large deformation behaviour is essential for designing resilient infrastructure.The present study develops a Smoothed Particle Hydrodynamics(SPH)framework for earthquake-induced liquefaction hazard assessment of geotechnical structures.The coupled flowdeformation behaviour of soils subjected to cyclic loading is described using the PM4Sand model implemented in a three-phase,single-layer SPH framework.A staggered discretisation scheme based on the stress particle SPH approach is adopted to minimise numerical inaccuracies caused by zero-energy modes and tensile instability.Further,non-reflecting boundary conditions for seismic analysis of semi-infinite soil domains using the SPH method are proposed.The numerical framework is employed for the analysis of cyclic direct simple shear test,seismic analysis of a level ground site,and liquefaction-induced failure of the Lower San Fernando Dam.Satisfactory agreement for liquefaction triggering and post-failure behaviour demonstrates that the SPH framework can be utilised to assess the effect of seismic loading on field-scale geotechnical structures.The present study also serves as the basis for future advancements of the SPH method for applications related to earthquake geotechnical engineering.
基金Project(U1709211) supported by NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization,ChinaProject(ICT2021A15) supported by the State Key Laboratory of Industrial Control Technology,Zhejiang University,ChinaProject(TPL2019C03) supported by Open Fund of Science and Technology on Thermal Energy and Power Laboratory,China。
文摘Fault degradation prognostic, which estimates the time before a failure occurs and process breakdowns, has been recognized as a key component in maintenance strategies nowadays. Fault degradation processes are, in general,slowly varying and can be modeled by autoregressive models. However, industrial processes always show typical nonstationary nature, which may bring two challenges: how to capture fault degradation information and how to model nonstationary processes. To address the critical issues, a novel fault degradation modeling and online fault prognostic strategy is developed in this paper. First, a fault degradation-oriented slow feature analysis(FDSFA) algorithm is proposed to extract fault degradation directions along which candidate fault degradation features are extracted. The trend ability assessment is then applied to select major fault degradation features. Second, a key fault degradation factor(KFDF) is calculated to characterize the fault degradation tendency by combining major fault degradation features and their stability weighting factors. After that, a time-varying regression model with temporal smoothness regularization is established considering nonstationary characteristics. On the basis of updating strategy, an online fault prognostic model is further developed by analyzing and modeling the prediction errors. The performance of the proposed method is illustrated with a real industrial process.
基金Supported by the National Basic Research Program of China(61178072)
文摘Smoothness prior approach for spectral smoothing is investigated using Fourier frequency filter analysis.We show that the regularization parameter in penalized least squares could continuously control the bandwidth of low-pass filter.Besides,due to its property of interpolating the missing values automatically and smoothly,a spectral baseline correction algorithm based on the approach is proposed.This algorithm generally comprises spectral peak detection and baseline estimation.First,the spectral peak regions are detected and identified according to the second derivatives.Then,generalized smoothness prior approach combining identification information could estimate the baseline in peak regions.Results with both the simulated and real spectra show accurate baseline-corrected signals with this method.
文摘Let h, be a measurable function defined on R^＋ ×R^＋. Let Ω ∈ L（log L^＋）^υq （S^n1-1 × S^n2-1） （1≤ υq ≤ 2） be homogeneous of degree zero and satisfy certain cancellation conditions. We show that the singular integral Tf（x1,x2）=p.v.∫∫R^n1＋n2 Ω（y′1,y′2）h（｜y1｜,｜y2｜）/｜y1｜^n1｜y2｜^n2 f（x1-y1,x2-y2）dy1dy2maps from Sp,q^α1,α2F（R^n1×R^n2）boundedly to itself for 1 〈 p, q 〈 ∞, α1, α2 ∈R.
基金Supported by National Natural Science Foundation of China (No. 60872161, 10501026, 60675010 and 10626029)Natural Science Foundation of Tianjin (No. 08JCYBJC09600)China Postdoctoral Science Foundation ( No. 20070420708).
文摘Nonlinear m-term approximation plays an important role in machine learning, signal processing and statistical estimating. In this paper by means of a nondecreasing dominated function, a greedy adaptive compression numerical algorithm in the best m -term approximation with regard to tensor product wavelet-type basis is pro-posed. The algorithm provides the asymptotically optimal approximation for the class of periodic functions with mixed Besov smoothness in the L q norm. Moreover, it depends only on the expansion of function f by tensor pro-duct wavelet-type basis, but neither on q nor on any special features of f.
基金Supported by the National Natural Science Foundation of China(50830201,11102179)the Nanjing University of Aeronautics and Astronautics Research Funding(NP 2011033)
文摘Based on the traditional fifth-order weighted essentially non-oscillatory(WENO)scheme,a smoothness indicator is introduced to improve the capability of WENO schemes for resolving short waves.In the construction of the new smoothness indicator,the proportion of the first-order term in the original smoothness indicator is reduced by replacing the square of the first-order term with the product of the first-order and the third-order terms.To preserve the fifth-order of convergence rate,the smoothness indicator is combined with the method of Borges,et al.The numerical results show that the proposed schemes are more suitable for simulating turbulent flows or aeroacoustics problems than the previous fifth-order WENO schemes,thanks to its improved resolution on short waves.
文摘Automatically assessing fabric smoothness grade is very important in the evaluation of fabric appearance.A system for objectively evaluating the fabric smoothness grade based on a grating projection unit and double colored CCD(short form of charge coupled device) was constructed in this paper.Two images captured by different CCD compensated each other which reduced the influence of noises.The application of the four-step phase-shifting method enabled the calculation of the exact phase in a point easy and quick.A large amount of 3D points with three coordinates X,Y and Z were obtained precisely making the definition and calculation of fabric smoothness characters easy.Then four parameters which intuitively denoted the fabric smoothness degree were obtained.Finally,a proper neural network was built,which successfully performed the fabric smoothness classification.The experimental results show that the system is applicable for all the fabric whatever pattern or color.The experimental grades provided by this grating projection system are also highly consistent with the subjective results.
文摘Extending the results of [4] in the univariate case, in this paper we prove that the bivariate interpolation polynomials of Hermite-Fejér based on the Chebyshev nodes of the first kind, those of Lagrange based on the Chebyshev nodes of second kind and ±1, and those of bivariate Shepard operators, have the property of partial preservation of global smoothness, with respect to various bivariate moduli of continuity.
文摘For garment or fabric appearance, the cloth smoothness grade is one of the most important performance factors in textile and garment community. In this paper, on the base of Rough Set Theory,a new objective method for fabric smoothness grade evaluation was constructed. The objective smoothness grading model took the parameters of 120 AATCC replicas' point-sampled models as the conditional attributes and formed the smoothness grading decision table. Then, NS discretization method and genetic algorithm reduction method were used in the attributes discretization and feature reduction. Finally, the grading model was expressed as simple and intuitional classification rules. The simulation results show the validity of the fabric smoothness grading system which is built on the use of rough sets.
文摘For the step-weight function , we prove that the Holder spaces ∧a,p on the interval [-1,1], defined in terms of moduli of smoothness with the step-weight function ,are linearly isomorphic to some sequence spaces, and the isomorphism is given by the cofficients of function with respect to a system of orthonormal splines with knots uniformly distributed according to the measure with density . In case ∧a,p is contained in the space of continuous functions, we give a discrete characterization of this space, using only values of function at the appropriate knots. Application of these results to characterize the order of polynomial approximation is presented.
