A new method, the dynamic reduction method(DRM) combined with the strain-softening method, was applied to evaluate the possible slip surface of a highly heterogeneous rock slope of the Dagangshan hydropower station in...A new method, the dynamic reduction method(DRM) combined with the strain-softening method, was applied to evaluate the possible slip surface of a highly heterogeneous rock slope of the Dagangshan hydropower station in Southwest China.In DRM, only the strength of the failure elements is reduced and the softening reduction factor K is adopted to calculate the strength parameters. The simulation results calculated by DRM show that the further slip surface on the right slope of the Dagangshan hydropower station is limited in the middle part of the slope, while both SRM(strength reduction method) and LEM(limit equilibrium method) predict a failure surface which extends upper and longer. The observations and analysis from the three recorded sliding events indicate that the failure mode predicted by DRM is more likely the scenario.The results in this study illustrate that for highly heterogeneous slopes with geological discontinuities in different length scales, the proposed DRM can provide a reliable prediction of the location of the slip surface.展开更多
This paper presents a deep neural network(DNN)-based speech enhancement algorithm based on the soft audible noise masking for the single-channel wind noise reduction. To reduce the low-frequency residual noise, the ps...This paper presents a deep neural network(DNN)-based speech enhancement algorithm based on the soft audible noise masking for the single-channel wind noise reduction. To reduce the low-frequency residual noise, the psychoacoustic model is adopted to calculate the masking threshold from the estimated clean speech spectrum. The gain for noise suppression is obtained based on soft audible noise masking by comparing the estimated wind noise spectrum with the masking threshold. To deal with the abruptly time-varying noisy signals, two separate DNN models are utilized to estimate the spectra of clean speech and wind noise components. Experimental results on the subjective and objective quality tests show that the proposed algorithm achieves the better performance compared with the conventional DNN-based wind noise reduction method.展开更多
The Backlund transformation（BT） of the m Kd V-s G equation is constructed by introducing a new transformation. Infinitely many nonlocal symmetries are obtained in terms of its BT. The soliton-periodic wave interacti...The Backlund transformation（BT） of the m Kd V-s G equation is constructed by introducing a new transformation. Infinitely many nonlocal symmetries are obtained in terms of its BT. The soliton-periodic wave interaction solutions are explicitly derived by the classical Lie-group reduction method. Particularly, some special concrete soliton and periodic wave interaction solutions and their behaviours are discussed both in analytical and graphical ways.展开更多
Dimensionality reduction is becoming an important problem in hyperspectral image classification. Band selection as an effective dimensionality reduction method has attracted more research interests. In this paper, a b...Dimensionality reduction is becoming an important problem in hyperspectral image classification. Band selection as an effective dimensionality reduction method has attracted more research interests. In this paper, a band selection method for hyperspectral remote sensing images based on subspace partition and particle frog leaping optimization algorithm is proposed. Three new evolution strategies are designed to form a probabilistic network extension structure to avoid local convergence. At the same time, the information entropy of the selected band subset is used as the weight of inter-class separability, and a new band selection criterion function is constructed. The simulation results show that the proposed algorithm has certain advantages over the existing similar algorithms in terms of classification accuracy and running time.展开更多
基金supported by the National Key R&D Program of China (2017YFC1501301)the National Natural Science Foundation of China (Grant Nos. 41521002, 41572283 and 41130745)+2 种基金the Funding of Science and Technology Office of Sichuan Province (Grant Nos. 2015JQ0020 and 2017TD0018)the 1000 Young Talent Program of Chinathe research fund of the State Key Laboratory of Geohazard Prevention and Geoenvironment Protection (Grant No. SKLGP2017Z012)
文摘A new method, the dynamic reduction method(DRM) combined with the strain-softening method, was applied to evaluate the possible slip surface of a highly heterogeneous rock slope of the Dagangshan hydropower station in Southwest China.In DRM, only the strength of the failure elements is reduced and the softening reduction factor K is adopted to calculate the strength parameters. The simulation results calculated by DRM show that the further slip surface on the right slope of the Dagangshan hydropower station is limited in the middle part of the slope, while both SRM(strength reduction method) and LEM(limit equilibrium method) predict a failure surface which extends upper and longer. The observations and analysis from the three recorded sliding events indicate that the failure mode predicted by DRM is more likely the scenario.The results in this study illustrate that for highly heterogeneous slopes with geological discontinuities in different length scales, the proposed DRM can provide a reliable prediction of the location of the slip surface.
基金partially supported by the National Natural Science Foundation of China (Nos.11590772, 11590770)the Pre-research Project for Equipment of General Information System (No.JZX2017-0994/Y306)
文摘This paper presents a deep neural network(DNN)-based speech enhancement algorithm based on the soft audible noise masking for the single-channel wind noise reduction. To reduce the low-frequency residual noise, the psychoacoustic model is adopted to calculate the masking threshold from the estimated clean speech spectrum. The gain for noise suppression is obtained based on soft audible noise masking by comparing the estimated wind noise spectrum with the masking threshold. To deal with the abruptly time-varying noisy signals, two separate DNN models are utilized to estimate the spectra of clean speech and wind noise components. Experimental results on the subjective and objective quality tests show that the proposed algorithm achieves the better performance compared with the conventional DNN-based wind noise reduction method.
基金Supported by the Natural Science Foundation of Zhejiang Province under Grant No.LZ15A050001the National Natural Science Foundation of China under Grant No.11675146Talent Fund and K.C.Wong Magna Fund in Ningbo University
文摘The Backlund transformation（BT） of the m Kd V-s G equation is constructed by introducing a new transformation. Infinitely many nonlocal symmetries are obtained in terms of its BT. The soliton-periodic wave interaction solutions are explicitly derived by the classical Lie-group reduction method. Particularly, some special concrete soliton and periodic wave interaction solutions and their behaviours are discussed both in analytical and graphical ways.
基金supported by the National Natural Science Foundation of China(No.61571149)
文摘Dimensionality reduction is becoming an important problem in hyperspectral image classification. Band selection as an effective dimensionality reduction method has attracted more research interests. In this paper, a band selection method for hyperspectral remote sensing images based on subspace partition and particle frog leaping optimization algorithm is proposed. Three new evolution strategies are designed to form a probabilistic network extension structure to avoid local convergence. At the same time, the information entropy of the selected band subset is used as the weight of inter-class separability, and a new band selection criterion function is constructed. The simulation results show that the proposed algorithm has certain advantages over the existing similar algorithms in terms of classification accuracy and running time.
